train_rnn_nofuture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f47fa9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-23 21:00:29.397135: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-23 21:00:30.919419: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from math import sqrt\n",
    "from tensorflow import keras\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from sklearn.metrics import mean_absolute_error as mae\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "\n",
    "\n",
    "from BaselinePrerocess import baseline_process\n",
    "from baselines.rnn import rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0092be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b6b865",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1e14ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====== preprocessing parameters ======\n",
    "n_hours = 72\n",
    "K = 24 \n",
    "masked_value = 1e-10\n",
    "split_1 = 0.8\n",
    "split_2 = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b444521c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['WS_S1', 'WS_S4', 'FLOW_S25A', 'GATE_S25A', 'HWS_S25A', 'TWS_S25A',\n",
      "       'FLOW_S25B', 'GATE_S25B', 'GATE_S25B2', 'HWS_S25B', 'TWS_S25B',\n",
      "       'PUMP_S25B', 'FLOW_S26', 'GATE_S26_1', 'GATE_S26_2', 'HWS_S26',\n",
      "       'TWS_S26', 'PUMP_S26', 'MEAN_RAIN'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "train_X_mask, val_X_mask, test_X_mask, \\\n",
    "train_ws_y, val_ws_y, test_ws_y, \\\n",
    "scaler, ws_scaler = baseline_process(n_hours, K, masked_value, split_1, split_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85e89a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77069, 96, 16) (9634, 96, 16) (19268, 96, 16) (77069, 96) (9634, 96) (19268, 96)\n"
     ]
    }
   ],
   "source": [
    "print(train_X_mask.shape, val_X_mask.shape, test_X_mask.shape, train_ws_y.shape, val_ws_y.shape, test_ws_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef511c74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77069, 72, 16) (9634, 72, 16) (19268, 72, 16) (77069, 96) (9634, 96) (19268, 96)\n"
     ]
    }
   ],
   "source": [
    "train_X_mask = train_X_mask[:, :n_hours, :]\n",
    "val_X_mask = val_X_mask[:, :n_hours, :]\n",
    "test_X_mask = test_X_mask[:, :n_hours, :]\n",
    "print(train_X_mask.shape, val_X_mask.shape, test_X_mask.shape, train_ws_y.shape, val_ws_y.shape, test_ws_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8003a2dc",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0eb668b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===== model parameters ======\n",
    "input_shape = train_X_mask.shape[1:]\n",
    "rnn_unit1 = 64\n",
    "rnn_unit2 = 32\n",
    "rnn_unit3 = 16\n",
    "l1_reg = 1e-5\n",
    "l2_reg = 1e-5\n",
    "dropout = 0.1\n",
    "masked_value = 1e-10\n",
    "\n",
    "learning_rate = 1e-4\n",
    "decay_steps = 10000\n",
    "decay_rate = 0.95\n",
    "PATIENCE = 500\n",
    "EPOCHS = 3000\n",
    "BATCH = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a7d6a860",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-23 21:00:38.970800: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10402 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:08:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 72, 16)]          0         \n",
      "                                                                 \n",
      " masking (Masking)           (None, 72, 16)            0         \n",
      "                                                                 \n",
      " simple_rnn (SimpleRNN)      (None, 72, 64)            5184      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 72, 64)            0         \n",
      "                                                                 \n",
      " simple_rnn_1 (SimpleRNN)    (None, 72, 32)            3104      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 72, 32)            0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 96)                221280    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 229568 (896.75 KB)\n",
      "Trainable params: 229568 (896.75 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = rnn(input_shape=input_shape,\n",
    "            rnn_unit1=rnn_unit1,\n",
    "            rnn_unit2=rnn_unit2,\n",
    "            rnn_unit3=rnn_unit3,\n",
    "            l1_reg=l1_reg, \n",
    "            l2_reg=l2_reg, \n",
    "            dropout=dropout,\n",
    "            masked_value=masked_value\n",
    "            )\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3316f5cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-23 21:00:47.686811: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fed8401a090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-07-23 21:00:47.686865: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1\n",
      "2023-07-23 21:00:47.696921: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-07-23 21:00:47.928333: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\n",
      "2023-07-23 21:00:48.299122: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1: val_mae improved from inf to 0.08875, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 30s - loss: 0.0439 - mae: 0.1328 - val_loss: 0.0237 - val_mae: 0.0888 - 30s/epoch - 198ms/step\n",
      "Epoch 2/3000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/aul/homes/jshi008/miniconda3/envs/tf213/lib/python3.10/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2: val_mae improved from 0.08875 to 0.06901, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0188 - mae: 0.0706 - val_loss: 0.0181 - val_mae: 0.0690 - 24s/epoch - 157ms/step\n",
      "Epoch 3/3000\n",
      "\n",
      "Epoch 3: val_mae improved from 0.06901 to 0.06278, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0155 - mae: 0.0566 - val_loss: 0.0163 - val_mae: 0.0628 - 23s/epoch - 151ms/step\n",
      "Epoch 4/3000\n",
      "\n",
      "Epoch 4: val_mae improved from 0.06278 to 0.06005, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0138 - mae: 0.0493 - val_loss: 0.0154 - val_mae: 0.0601 - 24s/epoch - 156ms/step\n",
      "Epoch 5/3000\n",
      "\n",
      "Epoch 5: val_mae improved from 0.06005 to 0.05666, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0127 - mae: 0.0448 - val_loss: 0.0144 - val_mae: 0.0567 - 23s/epoch - 151ms/step\n",
      "Epoch 6/3000\n",
      "\n",
      "Epoch 6: val_mae improved from 0.05666 to 0.05498, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0119 - mae: 0.0417 - val_loss: 0.0137 - val_mae: 0.0550 - 23s/epoch - 154ms/step\n",
      "Epoch 7/3000\n",
      "\n",
      "Epoch 7: val_mae improved from 0.05498 to 0.05314, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0113 - mae: 0.0395 - val_loss: 0.0130 - val_mae: 0.0531 - 23s/epoch - 151ms/step\n",
      "Epoch 8/3000\n",
      "\n",
      "Epoch 8: val_mae improved from 0.05314 to 0.05178, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0108 - mae: 0.0378 - val_loss: 0.0125 - val_mae: 0.0518 - 24s/epoch - 161ms/step\n",
      "Epoch 9/3000\n",
      "\n",
      "Epoch 9: val_mae improved from 0.05178 to 0.04968, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0103 - mae: 0.0366 - val_loss: 0.0118 - val_mae: 0.0497 - 24s/epoch - 160ms/step\n",
      "Epoch 10/3000\n",
      "\n",
      "Epoch 10: val_mae improved from 0.04968 to 0.04825, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0099 - mae: 0.0354 - val_loss: 0.0113 - val_mae: 0.0483 - 24s/epoch - 156ms/step\n",
      "Epoch 11/3000\n",
      "\n",
      "Epoch 11: val_mae did not improve from 0.04825\n",
      "151/151 - 23s - loss: 0.0095 - mae: 0.0344 - val_loss: 0.0112 - val_mae: 0.0498 - 23s/epoch - 151ms/step\n",
      "Epoch 12/3000\n",
      "\n",
      "Epoch 12: val_mae improved from 0.04825 to 0.04819, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0091 - mae: 0.0334 - val_loss: 0.0107 - val_mae: 0.0482 - 23s/epoch - 150ms/step\n",
      "Epoch 13/3000\n",
      "\n",
      "Epoch 13: val_mae improved from 0.04819 to 0.04633, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0088 - mae: 0.0325 - val_loss: 0.0102 - val_mae: 0.0463 - 24s/epoch - 157ms/step\n",
      "Epoch 14/3000\n",
      "\n",
      "Epoch 14: val_mae did not improve from 0.04633\n",
      "151/151 - 23s - loss: 0.0084 - mae: 0.0319 - val_loss: 0.0100 - val_mae: 0.0467 - 23s/epoch - 151ms/step\n",
      "Epoch 15/3000\n",
      "\n",
      "Epoch 15: val_mae improved from 0.04633 to 0.04536, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0081 - mae: 0.0313 - val_loss: 0.0096 - val_mae: 0.0454 - 23s/epoch - 150ms/step\n",
      "Epoch 16/3000\n",
      "\n",
      "Epoch 16: val_mae did not improve from 0.04536\n",
      "151/151 - 23s - loss: 0.0079 - mae: 0.0309 - val_loss: 0.0094 - val_mae: 0.0457 - 23s/epoch - 152ms/step\n",
      "Epoch 17/3000\n",
      "\n",
      "Epoch 17: val_mae improved from 0.04536 to 0.04490, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0076 - mae: 0.0305 - val_loss: 0.0091 - val_mae: 0.0449 - 23s/epoch - 152ms/step\n",
      "Epoch 18/3000\n",
      "\n",
      "Epoch 18: val_mae improved from 0.04490 to 0.04412, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0074 - mae: 0.0302 - val_loss: 0.0087 - val_mae: 0.0441 - 23s/epoch - 152ms/step\n",
      "Epoch 19/3000\n",
      "\n",
      "Epoch 19: val_mae improved from 0.04412 to 0.04407, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0071 - mae: 0.0299 - val_loss: 0.0085 - val_mae: 0.0441 - 23s/epoch - 153ms/step\n",
      "Epoch 20/3000\n",
      "\n",
      "Epoch 20: val_mae improved from 0.04407 to 0.04307, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0069 - mae: 0.0297 - val_loss: 0.0082 - val_mae: 0.0431 - 23s/epoch - 151ms/step\n",
      "Epoch 21/3000\n",
      "\n",
      "Epoch 21: val_mae improved from 0.04307 to 0.04261, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0067 - mae: 0.0294 - val_loss: 0.0080 - val_mae: 0.0426 - 23s/epoch - 155ms/step\n",
      "Epoch 22/3000\n",
      "\n",
      "Epoch 22: val_mae did not improve from 0.04261\n",
      "151/151 - 23s - loss: 0.0065 - mae: 0.0293 - val_loss: 0.0079 - val_mae: 0.0433 - 23s/epoch - 153ms/step\n",
      "Epoch 23/3000\n",
      "\n",
      "Epoch 23: val_mae improved from 0.04261 to 0.04188, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0062 - mae: 0.0290 - val_loss: 0.0075 - val_mae: 0.0419 - 23s/epoch - 154ms/step\n",
      "Epoch 24/3000\n",
      "\n",
      "Epoch 24: val_mae did not improve from 0.04188\n",
      "151/151 - 23s - loss: 0.0060 - mae: 0.0287 - val_loss: 0.0073 - val_mae: 0.0420 - 23s/epoch - 151ms/step\n",
      "Epoch 25/3000\n",
      "\n",
      "Epoch 25: val_mae improved from 0.04188 to 0.04144, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0058 - mae: 0.0285 - val_loss: 0.0071 - val_mae: 0.0414 - 23s/epoch - 153ms/step\n",
      "Epoch 26/3000\n",
      "\n",
      "Epoch 26: val_mae improved from 0.04144 to 0.04026, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0056 - mae: 0.0282 - val_loss: 0.0068 - val_mae: 0.0403 - 23s/epoch - 150ms/step\n",
      "Epoch 27/3000\n",
      "\n",
      "Epoch 27: val_mae did not improve from 0.04026\n",
      "151/151 - 24s - loss: 0.0054 - mae: 0.0279 - val_loss: 0.0067 - val_mae: 0.0415 - 24s/epoch - 156ms/step\n",
      "Epoch 28/3000\n",
      "\n",
      "Epoch 28: val_mae did not improve from 0.04026\n",
      "151/151 - 23s - loss: 0.0053 - mae: 0.0278 - val_loss: 0.0065 - val_mae: 0.0405 - 23s/epoch - 151ms/step\n",
      "Epoch 29/3000\n",
      "\n",
      "Epoch 29: val_mae did not improve from 0.04026\n",
      "151/151 - 23s - loss: 0.0051 - mae: 0.0275 - val_loss: 0.0064 - val_mae: 0.0414 - 23s/epoch - 150ms/step\n",
      "Epoch 30/3000\n",
      "\n",
      "Epoch 30: val_mae improved from 0.04026 to 0.04003, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0049 - mae: 0.0273 - val_loss: 0.0061 - val_mae: 0.0400 - 23s/epoch - 150ms/step\n",
      "Epoch 31/3000\n",
      "\n",
      "Epoch 31: val_mae improved from 0.04003 to 0.03965, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0047 - mae: 0.0272 - val_loss: 0.0059 - val_mae: 0.0396 - 23s/epoch - 150ms/step\n",
      "Epoch 32/3000\n",
      "\n",
      "Epoch 32: val_mae improved from 0.03965 to 0.03961, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0045 - mae: 0.0270 - val_loss: 0.0057 - val_mae: 0.0396 - 23s/epoch - 151ms/step\n",
      "Epoch 33/3000\n",
      "\n",
      "Epoch 33: val_mae did not improve from 0.03961\n",
      "151/151 - 23s - loss: 0.0044 - mae: 0.0269 - val_loss: 0.0056 - val_mae: 0.0402 - 23s/epoch - 150ms/step\n",
      "Epoch 34/3000\n",
      "\n",
      "Epoch 34: val_mae improved from 0.03961 to 0.03934, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0042 - mae: 0.0267 - val_loss: 0.0054 - val_mae: 0.0393 - 24s/epoch - 157ms/step\n",
      "Epoch 35/3000\n",
      "\n",
      "Epoch 35: val_mae did not improve from 0.03934\n",
      "151/151 - 23s - loss: 0.0041 - mae: 0.0266 - val_loss: 0.0052 - val_mae: 0.0394 - 23s/epoch - 153ms/step\n",
      "Epoch 36/3000\n",
      "\n",
      "Epoch 36: val_mae did not improve from 0.03934\n",
      "151/151 - 23s - loss: 0.0039 - mae: 0.0265 - val_loss: 0.0052 - val_mae: 0.0397 - 23s/epoch - 153ms/step\n",
      "Epoch 37/3000\n",
      "\n",
      "Epoch 37: val_mae did not improve from 0.03934\n",
      "151/151 - 23s - loss: 0.0038 - mae: 0.0263 - val_loss: 0.0050 - val_mae: 0.0395 - 23s/epoch - 154ms/step\n",
      "Epoch 38/3000\n",
      "\n",
      "Epoch 38: val_mae did not improve from 0.03934\n",
      "151/151 - 24s - loss: 0.0036 - mae: 0.0262 - val_loss: 0.0049 - val_mae: 0.0397 - 24s/epoch - 157ms/step\n",
      "Epoch 39/3000\n",
      "\n",
      "Epoch 39: val_mae did not improve from 0.03934\n",
      "151/151 - 24s - loss: 0.0035 - mae: 0.0261 - val_loss: 0.0048 - val_mae: 0.0396 - 24s/epoch - 158ms/step\n",
      "Epoch 40/3000\n",
      "\n",
      "Epoch 40: val_mae improved from 0.03934 to 0.03918, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0034 - mae: 0.0260 - val_loss: 0.0046 - val_mae: 0.0392 - 24s/epoch - 157ms/step\n",
      "Epoch 41/3000\n",
      "\n",
      "Epoch 41: val_mae improved from 0.03918 to 0.03874, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0033 - mae: 0.0259 - val_loss: 0.0044 - val_mae: 0.0387 - 23s/epoch - 154ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/3000\n",
      "\n",
      "Epoch 42: val_mae did not improve from 0.03874\n",
      "151/151 - 23s - loss: 0.0031 - mae: 0.0257 - val_loss: 0.0044 - val_mae: 0.0395 - 23s/epoch - 154ms/step\n",
      "Epoch 43/3000\n",
      "\n",
      "Epoch 43: val_mae did not improve from 0.03874\n",
      "151/151 - 23s - loss: 0.0030 - mae: 0.0256 - val_loss: 0.0042 - val_mae: 0.0389 - 23s/epoch - 154ms/step\n",
      "Epoch 44/3000\n",
      "\n",
      "Epoch 44: val_mae did not improve from 0.03874\n",
      "151/151 - 24s - loss: 0.0029 - mae: 0.0255 - val_loss: 0.0041 - val_mae: 0.0388 - 24s/epoch - 156ms/step\n",
      "Epoch 45/3000\n",
      "\n",
      "Epoch 45: val_mae did not improve from 0.03874\n",
      "151/151 - 23s - loss: 0.0028 - mae: 0.0254 - val_loss: 0.0041 - val_mae: 0.0395 - 23s/epoch - 155ms/step\n",
      "Epoch 46/3000\n",
      "\n",
      "Epoch 46: val_mae did not improve from 0.03874\n",
      "151/151 - 24s - loss: 0.0027 - mae: 0.0253 - val_loss: 0.0039 - val_mae: 0.0389 - 24s/epoch - 160ms/step\n",
      "Epoch 47/3000\n",
      "\n",
      "Epoch 47: val_mae did not improve from 0.03874\n",
      "151/151 - 24s - loss: 0.0026 - mae: 0.0251 - val_loss: 0.0039 - val_mae: 0.0392 - 24s/epoch - 158ms/step\n",
      "Epoch 48/3000\n",
      "\n",
      "Epoch 48: val_mae improved from 0.03874 to 0.03842, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0025 - mae: 0.0249 - val_loss: 0.0037 - val_mae: 0.0384 - 23s/epoch - 152ms/step\n",
      "Epoch 49/3000\n",
      "\n",
      "Epoch 49: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0024 - mae: 0.0248 - val_loss: 0.0038 - val_mae: 0.0394 - 23s/epoch - 155ms/step\n",
      "Epoch 50/3000\n",
      "\n",
      "Epoch 50: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0023 - mae: 0.0247 - val_loss: 0.0036 - val_mae: 0.0388 - 23s/epoch - 155ms/step\n",
      "Epoch 51/3000\n",
      "\n",
      "Epoch 51: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0023 - mae: 0.0245 - val_loss: 0.0035 - val_mae: 0.0388 - 23s/epoch - 151ms/step\n",
      "Epoch 52/3000\n",
      "\n",
      "Epoch 52: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0022 - mae: 0.0245 - val_loss: 0.0036 - val_mae: 0.0397 - 23s/epoch - 152ms/step\n",
      "Epoch 53/3000\n",
      "\n",
      "Epoch 53: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0021 - mae: 0.0243 - val_loss: 0.0035 - val_mae: 0.0393 - 23s/epoch - 152ms/step\n",
      "Epoch 54/3000\n",
      "\n",
      "Epoch 54: val_mae did not improve from 0.03842\n",
      "151/151 - 23s - loss: 0.0020 - mae: 0.0242 - val_loss: 0.0034 - val_mae: 0.0390 - 23s/epoch - 151ms/step\n",
      "Epoch 55/3000\n",
      "\n",
      "Epoch 55: val_mae improved from 0.03842 to 0.03838, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0020 - mae: 0.0241 - val_loss: 0.0033 - val_mae: 0.0384 - 24s/epoch - 157ms/step\n",
      "Epoch 56/3000\n",
      "\n",
      "Epoch 56: val_mae improved from 0.03838 to 0.03832, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0019 - mae: 0.0240 - val_loss: 0.0032 - val_mae: 0.0383 - 23s/epoch - 151ms/step\n",
      "Epoch 57/3000\n",
      "\n",
      "Epoch 57: val_mae did not improve from 0.03832\n",
      "151/151 - 24s - loss: 0.0019 - mae: 0.0239 - val_loss: 0.0032 - val_mae: 0.0388 - 24s/epoch - 159ms/step\n",
      "Epoch 58/3000\n",
      "\n",
      "Epoch 58: val_mae did not improve from 0.03832\n",
      "151/151 - 23s - loss: 0.0018 - mae: 0.0238 - val_loss: 0.0032 - val_mae: 0.0386 - 23s/epoch - 155ms/step\n",
      "Epoch 59/3000\n",
      "\n",
      "Epoch 59: val_mae did not improve from 0.03832\n",
      "151/151 - 23s - loss: 0.0018 - mae: 0.0237 - val_loss: 0.0031 - val_mae: 0.0383 - 23s/epoch - 154ms/step\n",
      "Epoch 60/3000\n",
      "\n",
      "Epoch 60: val_mae improved from 0.03832 to 0.03813, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0018 - mae: 0.0236 - val_loss: 0.0031 - val_mae: 0.0381 - 23s/epoch - 154ms/step\n",
      "Epoch 61/3000\n",
      "\n",
      "Epoch 61: val_mae improved from 0.03813 to 0.03763, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0017 - mae: 0.0235 - val_loss: 0.0030 - val_mae: 0.0376 - 23s/epoch - 155ms/step\n",
      "Epoch 62/3000\n",
      "\n",
      "Epoch 62: val_mae did not improve from 0.03763\n",
      "151/151 - 23s - loss: 0.0017 - mae: 0.0235 - val_loss: 0.0030 - val_mae: 0.0380 - 23s/epoch - 153ms/step\n",
      "Epoch 63/3000\n",
      "\n",
      "Epoch 63: val_mae did not improve from 0.03763\n",
      "151/151 - 24s - loss: 0.0017 - mae: 0.0234 - val_loss: 0.0029 - val_mae: 0.0379 - 24s/epoch - 156ms/step\n",
      "Epoch 64/3000\n",
      "\n",
      "Epoch 64: val_mae did not improve from 0.03763\n",
      "151/151 - 23s - loss: 0.0016 - mae: 0.0233 - val_loss: 0.0029 - val_mae: 0.0377 - 23s/epoch - 155ms/step\n",
      "Epoch 65/3000\n",
      "\n",
      "Epoch 65: val_mae did not improve from 0.03763\n",
      "151/151 - 23s - loss: 0.0016 - mae: 0.0233 - val_loss: 0.0029 - val_mae: 0.0377 - 23s/epoch - 152ms/step\n",
      "Epoch 66/3000\n",
      "\n",
      "Epoch 66: val_mae did not improve from 0.03763\n",
      "151/151 - 23s - loss: 0.0016 - mae: 0.0232 - val_loss: 0.0029 - val_mae: 0.0380 - 23s/epoch - 149ms/step\n",
      "Epoch 67/3000\n",
      "\n",
      "Epoch 67: val_mae improved from 0.03763 to 0.03745, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0016 - mae: 0.0232 - val_loss: 0.0028 - val_mae: 0.0374 - 23s/epoch - 150ms/step\n",
      "Epoch 68/3000\n",
      "\n",
      "Epoch 68: val_mae did not improve from 0.03745\n",
      "151/151 - 23s - loss: 0.0015 - mae: 0.0231 - val_loss: 0.0029 - val_mae: 0.0381 - 23s/epoch - 150ms/step\n",
      "Epoch 69/3000\n",
      "\n",
      "Epoch 69: val_mae did not improve from 0.03745\n",
      "151/151 - 24s - loss: 0.0015 - mae: 0.0231 - val_loss: 0.0028 - val_mae: 0.0375 - 24s/epoch - 156ms/step\n",
      "Epoch 70/3000\n",
      "\n",
      "Epoch 70: val_mae did not improve from 0.03745\n",
      "151/151 - 23s - loss: 0.0015 - mae: 0.0230 - val_loss: 0.0028 - val_mae: 0.0377 - 23s/epoch - 155ms/step\n",
      "Epoch 71/3000\n",
      "\n",
      "Epoch 71: val_mae did not improve from 0.03745\n",
      "151/151 - 24s - loss: 0.0015 - mae: 0.0229 - val_loss: 0.0028 - val_mae: 0.0377 - 24s/epoch - 159ms/step\n",
      "Epoch 72/3000\n",
      "\n",
      "Epoch 72: val_mae did not improve from 0.03745\n",
      "151/151 - 24s - loss: 0.0015 - mae: 0.0229 - val_loss: 0.0027 - val_mae: 0.0376 - 24s/epoch - 160ms/step\n",
      "Epoch 73/3000\n",
      "\n",
      "Epoch 73: val_mae improved from 0.03745 to 0.03705, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0014 - mae: 0.0228 - val_loss: 0.0027 - val_mae: 0.0370 - 23s/epoch - 151ms/step\n",
      "Epoch 74/3000\n",
      "\n",
      "Epoch 74: val_mae did not improve from 0.03705\n",
      "151/151 - 23s - loss: 0.0014 - mae: 0.0228 - val_loss: 0.0027 - val_mae: 0.0373 - 23s/epoch - 152ms/step\n",
      "Epoch 75/3000\n",
      "\n",
      "Epoch 75: val_mae did not improve from 0.03705\n",
      "151/151 - 23s - loss: 0.0014 - mae: 0.0228 - val_loss: 0.0027 - val_mae: 0.0371 - 23s/epoch - 151ms/step\n",
      "Epoch 76/3000\n",
      "\n",
      "Epoch 76: val_mae did not improve from 0.03705\n",
      "151/151 - 23s - loss: 0.0014 - mae: 0.0227 - val_loss: 0.0026 - val_mae: 0.0371 - 23s/epoch - 153ms/step\n",
      "Epoch 77/3000\n",
      "\n",
      "Epoch 77: val_mae did not improve from 0.03705\n",
      "151/151 - 24s - loss: 0.0014 - mae: 0.0227 - val_loss: 0.0027 - val_mae: 0.0375 - 24s/epoch - 157ms/step\n",
      "Epoch 78/3000\n",
      "\n",
      "Epoch 78: val_mae improved from 0.03705 to 0.03688, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0014 - mae: 0.0227 - val_loss: 0.0026 - val_mae: 0.0369 - 24s/epoch - 156ms/step\n",
      "Epoch 79/3000\n",
      "\n",
      "Epoch 79: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0014 - mae: 0.0226 - val_loss: 0.0026 - val_mae: 0.0370 - 23s/epoch - 150ms/step\n",
      "Epoch 80/3000\n",
      "\n",
      "Epoch 80: val_mae did not improve from 0.03688\n",
      "151/151 - 24s - loss: 0.0014 - mae: 0.0226 - val_loss: 0.0026 - val_mae: 0.0371 - 24s/epoch - 158ms/step\n",
      "Epoch 81/3000\n",
      "\n",
      "Epoch 81: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0226 - val_loss: 0.0026 - val_mae: 0.0372 - 23s/epoch - 151ms/step\n",
      "Epoch 82/3000\n",
      "\n",
      "Epoch 82: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0225 - val_loss: 0.0026 - val_mae: 0.0373 - 23s/epoch - 151ms/step\n",
      "Epoch 83/3000\n",
      "\n",
      "Epoch 83: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0225 - val_loss: 0.0026 - val_mae: 0.0371 - 23s/epoch - 152ms/step\n",
      "Epoch 84/3000\n",
      "\n",
      "Epoch 84: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0225 - val_loss: 0.0026 - val_mae: 0.0369 - 23s/epoch - 151ms/step\n",
      "Epoch 85/3000\n",
      "\n",
      "Epoch 85: val_mae did not improve from 0.03688\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0224 - val_loss: 0.0025 - val_mae: 0.0369 - 23s/epoch - 153ms/step\n",
      "Epoch 86/3000\n",
      "\n",
      "Epoch 86: val_mae improved from 0.03688 to 0.03661, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0224 - val_loss: 0.0025 - val_mae: 0.0366 - 23s/epoch - 153ms/step\n",
      "Epoch 87/3000\n",
      "\n",
      "Epoch 87: val_mae did not improve from 0.03661\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0224 - val_loss: 0.0025 - val_mae: 0.0369 - 23s/epoch - 154ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 88/3000\n",
      "\n",
      "Epoch 88: val_mae improved from 0.03661 to 0.03652, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0224 - val_loss: 0.0025 - val_mae: 0.0365 - 23s/epoch - 151ms/step\n",
      "Epoch 89/3000\n",
      "\n",
      "Epoch 89: val_mae did not improve from 0.03652\n",
      "151/151 - 22s - loss: 0.0013 - mae: 0.0223 - val_loss: 0.0025 - val_mae: 0.0368 - 22s/epoch - 148ms/step\n",
      "Epoch 90/3000\n",
      "\n",
      "Epoch 90: val_mae did not improve from 0.03652\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0224 - val_loss: 0.0025 - val_mae: 0.0369 - 23s/epoch - 150ms/step\n",
      "Epoch 91/3000\n",
      "\n",
      "Epoch 91: val_mae did not improve from 0.03652\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0223 - val_loss: 0.0025 - val_mae: 0.0367 - 23s/epoch - 153ms/step\n",
      "Epoch 92/3000\n",
      "\n",
      "Epoch 92: val_mae improved from 0.03652 to 0.03638, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0013 - mae: 0.0223 - val_loss: 0.0024 - val_mae: 0.0364 - 24s/epoch - 156ms/step\n",
      "Epoch 93/3000\n",
      "\n",
      "Epoch 93: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0223 - val_loss: 0.0025 - val_mae: 0.0369 - 23s/epoch - 151ms/step\n",
      "Epoch 94/3000\n",
      "\n",
      "Epoch 94: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0013 - mae: 0.0223 - val_loss: 0.0025 - val_mae: 0.0367 - 23s/epoch - 152ms/step\n",
      "Epoch 95/3000\n",
      "\n",
      "Epoch 95: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0025 - val_mae: 0.0368 - 23s/epoch - 153ms/step\n",
      "Epoch 96/3000\n",
      "\n",
      "Epoch 96: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 151ms/step\n",
      "Epoch 97/3000\n",
      "\n",
      "Epoch 97: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0025 - val_mae: 0.0368 - 23s/epoch - 151ms/step\n",
      "Epoch 98/3000\n",
      "\n",
      "Epoch 98: val_mae did not improve from 0.03638\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0364 - 23s/epoch - 153ms/step\n",
      "Epoch 99/3000\n",
      "\n",
      "Epoch 99: val_mae improved from 0.03638 to 0.03621, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0362 - 24s/epoch - 159ms/step\n",
      "Epoch 100/3000\n",
      "\n",
      "Epoch 100: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 150ms/step\n",
      "Epoch 101/3000\n",
      "\n",
      "Epoch 101: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 153ms/step\n",
      "Epoch 102/3000\n",
      "\n",
      "Epoch 102: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0362 - 23s/epoch - 150ms/step\n",
      "Epoch 103/3000\n",
      "\n",
      "Epoch 103: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 153ms/step\n",
      "Epoch 104/3000\n",
      "\n",
      "Epoch 104: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0363 - 23s/epoch - 150ms/step\n",
      "Epoch 105/3000\n",
      "\n",
      "Epoch 105: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 154ms/step\n",
      "Epoch 106/3000\n",
      "\n",
      "Epoch 106: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 154ms/step\n",
      "Epoch 107/3000\n",
      "\n",
      "Epoch 107: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 155ms/step\n",
      "Epoch 108/3000\n",
      "\n",
      "Epoch 108: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0363 - 23s/epoch - 151ms/step\n",
      "Epoch 109/3000\n",
      "\n",
      "Epoch 109: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0364 - 23s/epoch - 150ms/step\n",
      "Epoch 110/3000\n",
      "\n",
      "Epoch 110: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0221 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 152ms/step\n",
      "Epoch 111/3000\n",
      "\n",
      "Epoch 111: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0364 - 23s/epoch - 151ms/step\n",
      "Epoch 112/3000\n",
      "\n",
      "Epoch 112: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 151ms/step\n",
      "Epoch 113/3000\n",
      "\n",
      "Epoch 113: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0369 - 23s/epoch - 152ms/step\n",
      "Epoch 114/3000\n",
      "\n",
      "Epoch 114: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 151ms/step\n",
      "Epoch 115/3000\n",
      "\n",
      "Epoch 115: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0363 - 23s/epoch - 151ms/step\n",
      "Epoch 116/3000\n",
      "\n",
      "Epoch 116: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0012 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 153ms/step\n",
      "Epoch 117/3000\n",
      "\n",
      "Epoch 117: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 150ms/step\n",
      "Epoch 118/3000\n",
      "\n",
      "Epoch 118: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0364 - 23s/epoch - 150ms/step\n",
      "Epoch 119/3000\n",
      "\n",
      "Epoch 119: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 153ms/step\n",
      "Epoch 120/3000\n",
      "\n",
      "Epoch 120: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0366 - 23s/epoch - 150ms/step\n",
      "Epoch 121/3000\n",
      "\n",
      "Epoch 121: val_mae did not improve from 0.03621\n",
      "151/151 - 24s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0024 - val_mae: 0.0367 - 24s/epoch - 157ms/step\n",
      "Epoch 122/3000\n",
      "\n",
      "Epoch 122: val_mae did not improve from 0.03621\n",
      "151/151 - 22s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0023 - val_mae: 0.0364 - 22s/epoch - 149ms/step\n",
      "Epoch 123/3000\n",
      "\n",
      "Epoch 123: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0024 - val_mae: 0.0365 - 23s/epoch - 152ms/step\n",
      "Epoch 124/3000\n",
      "\n",
      "Epoch 124: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 154ms/step\n",
      "Epoch 125/3000\n",
      "\n",
      "Epoch 125: val_mae did not improve from 0.03621\n",
      "151/151 - 24s - loss: 0.0011 - mae: 0.0220 - val_loss: 0.0023 - val_mae: 0.0363 - 24s/epoch - 157ms/step\n",
      "Epoch 126/3000\n",
      "\n",
      "Epoch 126: val_mae did not improve from 0.03621\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0365 - 23s/epoch - 154ms/step\n",
      "Epoch 127/3000\n",
      "\n",
      "Epoch 127: val_mae improved from 0.03621 to 0.03602, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0360 - 23s/epoch - 151ms/step\n",
      "Epoch 128/3000\n",
      "\n",
      "Epoch 128: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0364 - 23s/epoch - 149ms/step\n",
      "Epoch 129/3000\n",
      "\n",
      "Epoch 129: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0361 - 23s/epoch - 153ms/step\n",
      "Epoch 130/3000\n",
      "\n",
      "Epoch 130: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0364 - 23s/epoch - 150ms/step\n",
      "Epoch 131/3000\n",
      "\n",
      "Epoch 131: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 150ms/step\n",
      "Epoch 132/3000\n",
      "\n",
      "Epoch 132: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 153ms/step\n",
      "Epoch 133/3000\n",
      "\n",
      "Epoch 133: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0366 - 23s/epoch - 154ms/step\n",
      "Epoch 134/3000\n",
      "\n",
      "Epoch 134: val_mae did not improve from 0.03602\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 152ms/step\n",
      "Epoch 135/3000\n",
      "\n",
      "Epoch 135: val_mae did not improve from 0.03602\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0364 - 23s/epoch - 153ms/step\n",
      "Epoch 136/3000\n",
      "\n",
      "Epoch 136: val_mae improved from 0.03602 to 0.03598, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0360 - 23s/epoch - 155ms/step\n",
      "Epoch 137/3000\n",
      "\n",
      "Epoch 137: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0219 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 155ms/step\n",
      "Epoch 138/3000\n",
      "\n",
      "Epoch 138: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 152ms/step\n",
      "Epoch 139/3000\n",
      "\n",
      "Epoch 139: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0365 - 23s/epoch - 150ms/step\n",
      "Epoch 140/3000\n",
      "\n",
      "Epoch 140: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 151ms/step\n",
      "Epoch 141/3000\n",
      "\n",
      "Epoch 141: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 142/3000\n",
      "\n",
      "Epoch 142: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 143/3000\n",
      "\n",
      "Epoch 143: val_mae did not improve from 0.03598\n",
      "151/151 - 24s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0364 - 24s/epoch - 160ms/step\n",
      "Epoch 144/3000\n",
      "\n",
      "Epoch 144: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0361 - 23s/epoch - 151ms/step\n",
      "Epoch 145/3000\n",
      "\n",
      "Epoch 145: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0365 - 23s/epoch - 151ms/step\n",
      "Epoch 146/3000\n",
      "\n",
      "Epoch 146: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0361 - 23s/epoch - 155ms/step\n",
      "Epoch 147/3000\n",
      "\n",
      "Epoch 147: val_mae did not improve from 0.03598\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0365 - 23s/epoch - 152ms/step\n",
      "Epoch 148/3000\n",
      "\n",
      "Epoch 148: val_mae improved from 0.03598 to 0.03591, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 149/3000\n",
      "\n",
      "Epoch 149: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0361 - 23s/epoch - 155ms/step\n",
      "Epoch 150/3000\n",
      "\n",
      "Epoch 150: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 150ms/step\n",
      "Epoch 151/3000\n",
      "\n",
      "Epoch 151: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 150ms/step\n",
      "Epoch 152/3000\n",
      "\n",
      "Epoch 152: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 151ms/step\n",
      "Epoch 153/3000\n",
      "\n",
      "Epoch 153: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 152ms/step\n",
      "Epoch 154/3000\n",
      "\n",
      "Epoch 154: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 154ms/step\n",
      "Epoch 155/3000\n",
      "\n",
      "Epoch 155: val_mae did not improve from 0.03591\n",
      "151/151 - 24s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0359 - 24s/epoch - 158ms/step\n",
      "Epoch 156/3000\n",
      "\n",
      "Epoch 156: val_mae did not improve from 0.03591\n",
      "151/151 - 24s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0023 - val_mae: 0.0365 - 24s/epoch - 158ms/step\n",
      "Epoch 157/3000\n",
      "\n",
      "Epoch 157: val_mae did not improve from 0.03591\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 153ms/step\n",
      "Epoch 158/3000\n",
      "\n",
      "Epoch 158: val_mae improved from 0.03591 to 0.03580, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 159/3000\n",
      "\n",
      "Epoch 159: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0023 - val_mae: 0.0362 - 23s/epoch - 153ms/step\n",
      "Epoch 160/3000\n",
      "\n",
      "Epoch 160: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 151ms/step\n",
      "Epoch 161/3000\n",
      "\n",
      "Epoch 161: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 162/3000\n",
      "\n",
      "Epoch 162: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 151ms/step\n",
      "Epoch 163/3000\n",
      "\n",
      "Epoch 163: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 151ms/step\n",
      "Epoch 164/3000\n",
      "\n",
      "Epoch 164: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0011 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 154ms/step\n",
      "Epoch 165/3000\n",
      "\n",
      "Epoch 165: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 166/3000\n",
      "\n",
      "Epoch 166: val_mae did not improve from 0.03580\n",
      "151/151 - 24s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0023 - val_mae: 0.0362 - 24s/epoch - 156ms/step\n",
      "Epoch 167/3000\n",
      "\n",
      "Epoch 167: val_mae did not improve from 0.03580\n",
      "151/151 - 24s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0023 - val_mae: 0.0367 - 24s/epoch - 158ms/step\n",
      "Epoch 168/3000\n",
      "\n",
      "Epoch 168: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 154ms/step\n",
      "Epoch 169/3000\n",
      "\n",
      "Epoch 169: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 151ms/step\n",
      "Epoch 170/3000\n",
      "\n",
      "Epoch 170: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 151ms/step\n",
      "Epoch 171/3000\n",
      "\n",
      "Epoch 171: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0217 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 153ms/step\n",
      "Epoch 172/3000\n",
      "\n",
      "Epoch 172: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 153ms/step\n",
      "Epoch 173/3000\n",
      "\n",
      "Epoch 173: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0023 - val_mae: 0.0363 - 23s/epoch - 153ms/step\n",
      "Epoch 174/3000\n",
      "\n",
      "Epoch 174: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 154ms/step\n",
      "Epoch 175/3000\n",
      "\n",
      "Epoch 175: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 153ms/step\n",
      "Epoch 176/3000\n",
      "\n",
      "Epoch 176: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 155ms/step\n",
      "Epoch 177/3000\n",
      "\n",
      "Epoch 177: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 154ms/step\n",
      "Epoch 178/3000\n",
      "\n",
      "Epoch 178: val_mae did not improve from 0.03580\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 154ms/step\n",
      "Epoch 179/3000\n",
      "\n",
      "Epoch 179: val_mae improved from 0.03580 to 0.03573, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 180/3000\n",
      "\n",
      "Epoch 180: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 153ms/step\n",
      "Epoch 181/3000\n",
      "\n",
      "Epoch 181: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 154ms/step\n",
      "Epoch 182/3000\n",
      "\n",
      "Epoch 182: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 183/3000\n",
      "\n",
      "Epoch 183: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 184/3000\n",
      "\n",
      "Epoch 184: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0023 - val_mae: 0.0364 - 23s/epoch - 155ms/step\n",
      "Epoch 185/3000\n",
      "\n",
      "Epoch 185: val_mae did not improve from 0.03573\n",
      "151/151 - 24s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0361 - 24s/epoch - 161ms/step\n",
      "Epoch 186/3000\n",
      "\n",
      "Epoch 186: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 154ms/step\n",
      "Epoch 187/3000\n",
      "\n",
      "Epoch 187: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 155ms/step\n",
      "Epoch 188/3000\n",
      "\n",
      "Epoch 188: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 155ms/step\n",
      "Epoch 189/3000\n",
      "\n",
      "Epoch 189: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 190/3000\n",
      "\n",
      "Epoch 190: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 153ms/step\n",
      "Epoch 191/3000\n",
      "\n",
      "Epoch 191: val_mae did not improve from 0.03573\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 152ms/step\n",
      "Epoch 192/3000\n",
      "\n",
      "Epoch 192: val_mae improved from 0.03573 to 0.03564, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 193/3000\n",
      "\n",
      "Epoch 193: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 151ms/step\n",
      "Epoch 194/3000\n",
      "\n",
      "Epoch 194: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 151ms/step\n",
      "Epoch 195/3000\n",
      "\n",
      "Epoch 195: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 196/3000\n",
      "\n",
      "Epoch 196: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 151ms/step\n",
      "Epoch 197/3000\n",
      "\n",
      "Epoch 197: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 152ms/step\n",
      "Epoch 198/3000\n",
      "\n",
      "Epoch 198: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 199/3000\n",
      "\n",
      "Epoch 199: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 200/3000\n",
      "\n",
      "Epoch 200: val_mae did not improve from 0.03564\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 150ms/step\n",
      "Epoch 201/3000\n",
      "\n",
      "Epoch 201: val_mae improved from 0.03564 to 0.03546, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 202/3000\n",
      "\n",
      "Epoch 202: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 151ms/step\n",
      "Epoch 203/3000\n",
      "\n",
      "Epoch 203: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 155ms/step\n",
      "Epoch 204/3000\n",
      "\n",
      "Epoch 204: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 154ms/step\n",
      "Epoch 205/3000\n",
      "\n",
      "Epoch 205: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 154ms/step\n",
      "Epoch 206/3000\n",
      "\n",
      "Epoch 206: val_mae did not improve from 0.03546\n",
      "151/151 - 24s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 24s/epoch - 156ms/step\n",
      "Epoch 207/3000\n",
      "\n",
      "Epoch 207: val_mae did not improve from 0.03546\n",
      "151/151 - 24s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0357 - 24s/epoch - 156ms/step\n",
      "Epoch 208/3000\n",
      "\n",
      "Epoch 208: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 209/3000\n",
      "\n",
      "Epoch 209: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 153ms/step\n",
      "Epoch 210/3000\n",
      "\n",
      "Epoch 210: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 152ms/step\n",
      "Epoch 211/3000\n",
      "\n",
      "Epoch 211: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 212/3000\n",
      "\n",
      "Epoch 212: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 213/3000\n",
      "\n",
      "Epoch 213: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 149ms/step\n",
      "Epoch 214/3000\n",
      "\n",
      "Epoch 214: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 0.0010 - mae: 0.0215 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 215/3000\n",
      "\n",
      "Epoch 215: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 9.9952e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 154ms/step\n",
      "Epoch 216/3000\n",
      "\n",
      "Epoch 216: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 9.9737e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 217/3000\n",
      "\n",
      "Epoch 217: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 9.9801e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 150ms/step\n",
      "Epoch 218/3000\n",
      "\n",
      "Epoch 218: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 9.9811e-04 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 219/3000\n",
      "\n",
      "Epoch 219: val_mae did not improve from 0.03546\n",
      "151/151 - 23s - loss: 9.9595e-04 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 220/3000\n",
      "\n",
      "Epoch 220: val_mae improved from 0.03546 to 0.03545, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.9653e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 221/3000\n",
      "\n",
      "Epoch 221: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9720e-04 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 222/3000\n",
      "\n",
      "Epoch 222: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9695e-04 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 223/3000\n",
      "\n",
      "Epoch 223: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9491e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 154ms/step\n",
      "Epoch 224/3000\n",
      "\n",
      "Epoch 224: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9292e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 155ms/step\n",
      "Epoch 225/3000\n",
      "\n",
      "Epoch 225: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.9083e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 24s/epoch - 156ms/step\n",
      "Epoch 226/3000\n",
      "\n",
      "Epoch 226: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9322e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 227/3000\n",
      "\n",
      "Epoch 227: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9148e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 228/3000\n",
      "\n",
      "Epoch 228: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9036e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 152ms/step\n",
      "Epoch 229/3000\n",
      "\n",
      "Epoch 229: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.9102e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 230/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 230: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8904e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 151ms/step\n",
      "Epoch 231/3000\n",
      "\n",
      "Epoch 231: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8723e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 150ms/step\n",
      "Epoch 232/3000\n",
      "\n",
      "Epoch 232: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.8961e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 24s/epoch - 157ms/step\n",
      "Epoch 233/3000\n",
      "\n",
      "Epoch 233: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8707e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 150ms/step\n",
      "Epoch 234/3000\n",
      "\n",
      "Epoch 234: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8658e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 152ms/step\n",
      "Epoch 235/3000\n",
      "\n",
      "Epoch 235: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8926e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 236/3000\n",
      "\n",
      "Epoch 236: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8601e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 154ms/step\n",
      "Epoch 237/3000\n",
      "\n",
      "Epoch 237: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8612e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 150ms/step\n",
      "Epoch 238/3000\n",
      "\n",
      "Epoch 238: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8600e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 151ms/step\n",
      "Epoch 239/3000\n",
      "\n",
      "Epoch 239: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8165e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 152ms/step\n",
      "Epoch 240/3000\n",
      "\n",
      "Epoch 240: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8499e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 241/3000\n",
      "\n",
      "Epoch 241: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.8280e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 158ms/step\n",
      "Epoch 242/3000\n",
      "\n",
      "Epoch 242: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8398e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 243/3000\n",
      "\n",
      "Epoch 243: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8337e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 244/3000\n",
      "\n",
      "Epoch 244: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8272e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 154ms/step\n",
      "Epoch 245/3000\n",
      "\n",
      "Epoch 245: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7934e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 246/3000\n",
      "\n",
      "Epoch 246: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8136e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0361 - 23s/epoch - 154ms/step\n",
      "Epoch 247/3000\n",
      "\n",
      "Epoch 247: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8046e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 153ms/step\n",
      "Epoch 248/3000\n",
      "\n",
      "Epoch 248: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.8196e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 249/3000\n",
      "\n",
      "Epoch 249: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7796e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 154ms/step\n",
      "Epoch 250/3000\n",
      "\n",
      "Epoch 250: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7819e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 155ms/step\n",
      "Epoch 251/3000\n",
      "\n",
      "Epoch 251: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7699e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 155ms/step\n",
      "Epoch 252/3000\n",
      "\n",
      "Epoch 252: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.7689e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 157ms/step\n",
      "Epoch 253/3000\n",
      "\n",
      "Epoch 253: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7873e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 150ms/step\n",
      "Epoch 254/3000\n",
      "\n",
      "Epoch 254: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7618e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 255/3000\n",
      "\n",
      "Epoch 255: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.7729e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0360 - 24s/epoch - 158ms/step\n",
      "Epoch 256/3000\n",
      "\n",
      "Epoch 256: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7325e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 257/3000\n",
      "\n",
      "Epoch 257: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7603e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 154ms/step\n",
      "Epoch 258/3000\n",
      "\n",
      "Epoch 258: val_mae did not improve from 0.03545\n",
      "151/151 - 24s - loss: 9.7493e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 162ms/step\n",
      "Epoch 259/3000\n",
      "\n",
      "Epoch 259: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7428e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 260/3000\n",
      "\n",
      "Epoch 260: val_mae did not improve from 0.03545\n",
      "151/151 - 23s - loss: 9.7324e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 261/3000\n",
      "\n",
      "Epoch 261: val_mae improved from 0.03545 to 0.03539, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.7375e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 149ms/step\n",
      "Epoch 262/3000\n",
      "\n",
      "Epoch 262: val_mae did not improve from 0.03539\n",
      "151/151 - 23s - loss: 9.7546e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 263/3000\n",
      "\n",
      "Epoch 263: val_mae improved from 0.03539 to 0.03532, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.7224e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 152ms/step\n",
      "Epoch 264/3000\n",
      "\n",
      "Epoch 264: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7162e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 265/3000\n",
      "\n",
      "Epoch 265: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7332e-04 - mae: 0.0214 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 266/3000\n",
      "\n",
      "Epoch 266: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7126e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 152ms/step\n",
      "Epoch 267/3000\n",
      "\n",
      "Epoch 267: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6954e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0362 - 23s/epoch - 151ms/step\n",
      "Epoch 268/3000\n",
      "\n",
      "Epoch 268: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7068e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 269/3000\n",
      "\n",
      "Epoch 269: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7076e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 270/3000\n",
      "\n",
      "Epoch 270: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.7057e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 271/3000\n",
      "\n",
      "Epoch 271: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6966e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 272/3000\n",
      "\n",
      "Epoch 272: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6902e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 273/3000\n",
      "\n",
      "Epoch 273: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6712e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 274/3000\n",
      "\n",
      "Epoch 274: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6769e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 154ms/step\n",
      "Epoch 275/3000\n",
      "\n",
      "Epoch 275: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6634e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 276/3000\n",
      "\n",
      "Epoch 276: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6663e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 277/3000\n",
      "\n",
      "Epoch 277: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6903e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 278/3000\n",
      "\n",
      "Epoch 278: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6748e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 279/3000\n",
      "\n",
      "Epoch 279: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6707e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 280/3000\n",
      "\n",
      "Epoch 280: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6584e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 281/3000\n",
      "\n",
      "Epoch 281: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6699e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 152ms/step\n",
      "Epoch 282/3000\n",
      "\n",
      "Epoch 282: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6635e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 150ms/step\n",
      "Epoch 283/3000\n",
      "\n",
      "Epoch 283: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6459e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 284/3000\n",
      "\n",
      "Epoch 284: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6490e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 150ms/step\n",
      "Epoch 285/3000\n",
      "\n",
      "Epoch 285: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6331e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 149ms/step\n",
      "Epoch 286/3000\n",
      "\n",
      "Epoch 286: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6375e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 287/3000\n",
      "\n",
      "Epoch 287: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6329e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 288/3000\n",
      "\n",
      "Epoch 288: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6241e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 289/3000\n",
      "\n",
      "Epoch 289: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6263e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 153ms/step\n",
      "Epoch 290/3000\n",
      "\n",
      "Epoch 290: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6223e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 291/3000\n",
      "\n",
      "Epoch 291: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6242e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 292/3000\n",
      "\n",
      "Epoch 292: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6174e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 293/3000\n",
      "\n",
      "Epoch 293: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5996e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 24s/epoch - 156ms/step\n",
      "Epoch 294/3000\n",
      "\n",
      "Epoch 294: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.6077e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 157ms/step\n",
      "Epoch 295/3000\n",
      "\n",
      "Epoch 295: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6051e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 155ms/step\n",
      "Epoch 296/3000\n",
      "\n",
      "Epoch 296: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5987e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 162ms/step\n",
      "Epoch 297/3000\n",
      "\n",
      "Epoch 297: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.6000e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0356 - 24s/epoch - 161ms/step\n",
      "Epoch 298/3000\n",
      "\n",
      "Epoch 298: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.6141e-04 - mae: 0.0213 - val_loss: 0.0022 - val_mae: 0.0361 - 24s/epoch - 157ms/step\n",
      "Epoch 299/3000\n",
      "\n",
      "Epoch 299: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.6000e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 152ms/step\n",
      "Epoch 300/3000\n",
      "\n",
      "Epoch 300: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5792e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 157ms/step\n",
      "Epoch 301/3000\n",
      "\n",
      "Epoch 301: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5749e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 158ms/step\n",
      "Epoch 302/3000\n",
      "\n",
      "Epoch 302: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5835e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 153ms/step\n",
      "Epoch 303/3000\n",
      "\n",
      "Epoch 303: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5893e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 304/3000\n",
      "\n",
      "Epoch 304: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5866e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 305/3000\n",
      "\n",
      "Epoch 305: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5725e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 306/3000\n",
      "\n",
      "Epoch 306: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5783e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 150ms/step\n",
      "Epoch 307/3000\n",
      "\n",
      "Epoch 307: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5504e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 308/3000\n",
      "\n",
      "Epoch 308: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5582e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 309/3000\n",
      "\n",
      "Epoch 309: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5391e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 24s/epoch - 157ms/step\n",
      "Epoch 310/3000\n",
      "\n",
      "Epoch 310: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5548e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 311/3000\n",
      "\n",
      "Epoch 311: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5417e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 312/3000\n",
      "\n",
      "Epoch 312: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5431e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 313/3000\n",
      "\n",
      "Epoch 313: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5631e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 314/3000\n",
      "\n",
      "Epoch 314: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5414e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 315/3000\n",
      "\n",
      "Epoch 315: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5395e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 152ms/step\n",
      "Epoch 316/3000\n",
      "\n",
      "Epoch 316: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5402e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 317/3000\n",
      "\n",
      "Epoch 317: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5288e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 150ms/step\n",
      "Epoch 318/3000\n",
      "\n",
      "Epoch 318: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5117e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 150ms/step\n",
      "Epoch 319/3000\n",
      "\n",
      "Epoch 319: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5358e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 320/3000\n",
      "\n",
      "Epoch 320: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5208e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 321/3000\n",
      "\n",
      "Epoch 321: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5208e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 322/3000\n",
      "\n",
      "Epoch 322: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5262e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 323/3000\n",
      "\n",
      "Epoch 323: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.4852e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 324/3000\n",
      "\n",
      "Epoch 324: val_mae did not improve from 0.03532\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 9.5008e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 325/3000\n",
      "\n",
      "Epoch 325: val_mae did not improve from 0.03532\n",
      "151/151 - 24s - loss: 9.5206e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0360 - 24s/epoch - 156ms/step\n",
      "Epoch 326/3000\n",
      "\n",
      "Epoch 326: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5072e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 153ms/step\n",
      "Epoch 327/3000\n",
      "\n",
      "Epoch 327: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5062e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 328/3000\n",
      "\n",
      "Epoch 328: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.4956e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 329/3000\n",
      "\n",
      "Epoch 329: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.5010e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 330/3000\n",
      "\n",
      "Epoch 330: val_mae did not improve from 0.03532\n",
      "151/151 - 23s - loss: 9.4896e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 331/3000\n",
      "\n",
      "Epoch 331: val_mae improved from 0.03532 to 0.03525, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.4899e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 332/3000\n",
      "\n",
      "Epoch 332: val_mae did not improve from 0.03525\n",
      "151/151 - 23s - loss: 9.5014e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 333/3000\n",
      "\n",
      "Epoch 333: val_mae did not improve from 0.03525\n",
      "151/151 - 23s - loss: 9.5158e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 334/3000\n",
      "\n",
      "Epoch 334: val_mae improved from 0.03525 to 0.03517, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.4905e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 151ms/step\n",
      "Epoch 335/3000\n",
      "\n",
      "Epoch 335: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.5018e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 150ms/step\n",
      "Epoch 336/3000\n",
      "\n",
      "Epoch 336: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4927e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 337/3000\n",
      "\n",
      "Epoch 337: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4792e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 338/3000\n",
      "\n",
      "Epoch 338: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4500e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 339/3000\n",
      "\n",
      "Epoch 339: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4855e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 150ms/step\n",
      "Epoch 340/3000\n",
      "\n",
      "Epoch 340: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4731e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 341/3000\n",
      "\n",
      "Epoch 341: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4690e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 342/3000\n",
      "\n",
      "Epoch 342: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4802e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 156ms/step\n",
      "Epoch 343/3000\n",
      "\n",
      "Epoch 343: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4489e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 155ms/step\n",
      "Epoch 344/3000\n",
      "\n",
      "Epoch 344: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4452e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 24s/epoch - 156ms/step\n",
      "Epoch 345/3000\n",
      "\n",
      "Epoch 345: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4252e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 346/3000\n",
      "\n",
      "Epoch 346: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4176e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 347/3000\n",
      "\n",
      "Epoch 347: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4356e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 150ms/step\n",
      "Epoch 348/3000\n",
      "\n",
      "Epoch 348: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4617e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 349/3000\n",
      "\n",
      "Epoch 349: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4524e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 350/3000\n",
      "\n",
      "Epoch 350: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4352e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 151ms/step\n",
      "Epoch 351/3000\n",
      "\n",
      "Epoch 351: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4481e-04 - mae: 0.0211 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 155ms/step\n",
      "Epoch 352/3000\n",
      "\n",
      "Epoch 352: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4527e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0353 - 24s/epoch - 156ms/step\n",
      "Epoch 353/3000\n",
      "\n",
      "Epoch 353: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4364e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 354/3000\n",
      "\n",
      "Epoch 354: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4386e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 150ms/step\n",
      "Epoch 355/3000\n",
      "\n",
      "Epoch 355: val_mae improved from 0.03517 to 0.03517, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.4299e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 356/3000\n",
      "\n",
      "Epoch 356: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4256e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 357/3000\n",
      "\n",
      "Epoch 357: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4168e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 156ms/step\n",
      "Epoch 358/3000\n",
      "\n",
      "Epoch 358: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4140e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 359/3000\n",
      "\n",
      "Epoch 359: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4422e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 149ms/step\n",
      "Epoch 360/3000\n",
      "\n",
      "Epoch 360: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4167e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 153ms/step\n",
      "Epoch 361/3000\n",
      "\n",
      "Epoch 361: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4473e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 362/3000\n",
      "\n",
      "Epoch 362: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4123e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 363/3000\n",
      "\n",
      "Epoch 363: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4102e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0357 - 24s/epoch - 157ms/step\n",
      "Epoch 364/3000\n",
      "\n",
      "Epoch 364: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4005e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 365/3000\n",
      "\n",
      "Epoch 365: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4086e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 24s/epoch - 156ms/step\n",
      "Epoch 366/3000\n",
      "\n",
      "Epoch 366: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4007e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 156ms/step\n",
      "Epoch 367/3000\n",
      "\n",
      "Epoch 367: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.4244e-04 - mae: 0.0212 - val_loss: 0.0021 - val_mae: 0.0352 - 24s/epoch - 156ms/step\n",
      "Epoch 368/3000\n",
      "\n",
      "Epoch 368: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4155e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 155ms/step\n",
      "Epoch 369/3000\n",
      "\n",
      "Epoch 369: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4130e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 149ms/step\n",
      "Epoch 370/3000\n",
      "\n",
      "Epoch 370: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4197e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 155ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 371/3000\n",
      "\n",
      "Epoch 371: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4066e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 155ms/step\n",
      "Epoch 372/3000\n",
      "\n",
      "Epoch 372: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4010e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 156ms/step\n",
      "Epoch 373/3000\n",
      "\n",
      "Epoch 373: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3862e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 159ms/step\n",
      "Epoch 374/3000\n",
      "\n",
      "Epoch 374: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4127e-04 - mae: 0.0212 - val_loss: 0.0022 - val_mae: 0.0360 - 23s/epoch - 154ms/step\n",
      "Epoch 375/3000\n",
      "\n",
      "Epoch 375: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3850e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 156ms/step\n",
      "Epoch 376/3000\n",
      "\n",
      "Epoch 376: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.4083e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 155ms/step\n",
      "Epoch 377/3000\n",
      "\n",
      "Epoch 377: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3779e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 378/3000\n",
      "\n",
      "Epoch 378: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3771e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 379/3000\n",
      "\n",
      "Epoch 379: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3916e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 380/3000\n",
      "\n",
      "Epoch 380: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3790e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 149ms/step\n",
      "Epoch 381/3000\n",
      "\n",
      "Epoch 381: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3912e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 382/3000\n",
      "\n",
      "Epoch 382: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3958e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 383/3000\n",
      "\n",
      "Epoch 383: val_mae did not improve from 0.03517\n",
      "151/151 - 22s - loss: 9.3748e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 22s/epoch - 149ms/step\n",
      "Epoch 384/3000\n",
      "\n",
      "Epoch 384: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3678e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 385/3000\n",
      "\n",
      "Epoch 385: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3584e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 154ms/step\n",
      "Epoch 386/3000\n",
      "\n",
      "Epoch 386: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3770e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 387/3000\n",
      "\n",
      "Epoch 387: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3400e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 151ms/step\n",
      "Epoch 388/3000\n",
      "\n",
      "Epoch 388: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3765e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 151ms/step\n",
      "Epoch 389/3000\n",
      "\n",
      "Epoch 389: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3576e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 151ms/step\n",
      "Epoch 390/3000\n",
      "\n",
      "Epoch 390: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3657e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 156ms/step\n",
      "Epoch 391/3000\n",
      "\n",
      "Epoch 391: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3555e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 392/3000\n",
      "\n",
      "Epoch 392: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3689e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 154ms/step\n",
      "Epoch 393/3000\n",
      "\n",
      "Epoch 393: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3507e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 154ms/step\n",
      "Epoch 394/3000\n",
      "\n",
      "Epoch 394: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3579e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 156ms/step\n",
      "Epoch 395/3000\n",
      "\n",
      "Epoch 395: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3530e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 156ms/step\n",
      "Epoch 396/3000\n",
      "\n",
      "Epoch 396: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3679e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 397/3000\n",
      "\n",
      "Epoch 397: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3368e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 398/3000\n",
      "\n",
      "Epoch 398: val_mae did not improve from 0.03517\n",
      "151/151 - 22s - loss: 9.3389e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 22s/epoch - 149ms/step\n",
      "Epoch 399/3000\n",
      "\n",
      "Epoch 399: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3470e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 155ms/step\n",
      "Epoch 400/3000\n",
      "\n",
      "Epoch 400: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3613e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 158ms/step\n",
      "Epoch 401/3000\n",
      "\n",
      "Epoch 401: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3392e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 149ms/step\n",
      "Epoch 402/3000\n",
      "\n",
      "Epoch 402: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3570e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 155ms/step\n",
      "Epoch 403/3000\n",
      "\n",
      "Epoch 403: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3342e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 404/3000\n",
      "\n",
      "Epoch 404: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3407e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 405/3000\n",
      "\n",
      "Epoch 405: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3330e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 406/3000\n",
      "\n",
      "Epoch 406: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3162e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 152ms/step\n",
      "Epoch 407/3000\n",
      "\n",
      "Epoch 407: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3470e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 150ms/step\n",
      "Epoch 408/3000\n",
      "\n",
      "Epoch 408: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3258e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 153ms/step\n",
      "Epoch 409/3000\n",
      "\n",
      "Epoch 409: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3293e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 153ms/step\n",
      "Epoch 410/3000\n",
      "\n",
      "Epoch 410: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3298e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 151ms/step\n",
      "Epoch 411/3000\n",
      "\n",
      "Epoch 411: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3361e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 150ms/step\n",
      "Epoch 412/3000\n",
      "\n",
      "Epoch 412: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3303e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 152ms/step\n",
      "Epoch 413/3000\n",
      "\n",
      "Epoch 413: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3160e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 414/3000\n",
      "\n",
      "Epoch 414: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3325e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 415/3000\n",
      "\n",
      "Epoch 415: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3190e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 150ms/step\n",
      "Epoch 416/3000\n",
      "\n",
      "Epoch 416: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3406e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 155ms/step\n",
      "Epoch 417/3000\n",
      "\n",
      "Epoch 417: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3117e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 152ms/step\n",
      "Epoch 418/3000\n",
      "\n",
      "Epoch 418: val_mae did not improve from 0.03517\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 9.3139e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 419/3000\n",
      "\n",
      "Epoch 419: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3058e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 420/3000\n",
      "\n",
      "Epoch 420: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3100e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 421/3000\n",
      "\n",
      "Epoch 421: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3204e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 422/3000\n",
      "\n",
      "Epoch 422: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.2986e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0356 - 24s/epoch - 157ms/step\n",
      "Epoch 423/3000\n",
      "\n",
      "Epoch 423: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3133e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 424/3000\n",
      "\n",
      "Epoch 424: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3214e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 425/3000\n",
      "\n",
      "Epoch 425: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3078e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 24s/epoch - 160ms/step\n",
      "Epoch 426/3000\n",
      "\n",
      "Epoch 426: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.3171e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 157ms/step\n",
      "Epoch 427/3000\n",
      "\n",
      "Epoch 427: val_mae did not improve from 0.03517\n",
      "151/151 - 24s - loss: 9.2865e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 156ms/step\n",
      "Epoch 428/3000\n",
      "\n",
      "Epoch 428: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.3003e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 429/3000\n",
      "\n",
      "Epoch 429: val_mae did not improve from 0.03517\n",
      "151/151 - 23s - loss: 9.2908e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 152ms/step\n",
      "Epoch 430/3000\n",
      "\n",
      "Epoch 430: val_mae improved from 0.03517 to 0.03515, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 9.3032e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 24s/epoch - 157ms/step\n",
      "Epoch 431/3000\n",
      "\n",
      "Epoch 431: val_mae improved from 0.03515 to 0.03514, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 9.2860e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 24s/epoch - 157ms/step\n",
      "Epoch 432/3000\n",
      "\n",
      "Epoch 432: val_mae did not improve from 0.03514\n",
      "151/151 - 25s - loss: 9.2922e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 25s/epoch - 165ms/step\n",
      "Epoch 433/3000\n",
      "\n",
      "Epoch 433: val_mae did not improve from 0.03514\n",
      "151/151 - 23s - loss: 9.2954e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 155ms/step\n",
      "Epoch 434/3000\n",
      "\n",
      "Epoch 434: val_mae did not improve from 0.03514\n",
      "151/151 - 23s - loss: 9.2976e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 152ms/step\n",
      "Epoch 435/3000\n",
      "\n",
      "Epoch 435: val_mae did not improve from 0.03514\n",
      "151/151 - 24s - loss: 9.2945e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 157ms/step\n",
      "Epoch 436/3000\n",
      "\n",
      "Epoch 436: val_mae did not improve from 0.03514\n",
      "151/151 - 24s - loss: 9.2749e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0356 - 24s/epoch - 157ms/step\n",
      "Epoch 437/3000\n",
      "\n",
      "Epoch 437: val_mae did not improve from 0.03514\n",
      "151/151 - 23s - loss: 9.2979e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 152ms/step\n",
      "Epoch 438/3000\n",
      "\n",
      "Epoch 438: val_mae improved from 0.03514 to 0.03509, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.2798e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 439/3000\n",
      "\n",
      "Epoch 439: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2835e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 150ms/step\n",
      "Epoch 440/3000\n",
      "\n",
      "Epoch 440: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2836e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 149ms/step\n",
      "Epoch 441/3000\n",
      "\n",
      "Epoch 441: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2741e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 149ms/step\n",
      "Epoch 442/3000\n",
      "\n",
      "Epoch 442: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2784e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 155ms/step\n",
      "Epoch 443/3000\n",
      "\n",
      "Epoch 443: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.3005e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 444/3000\n",
      "\n",
      "Epoch 444: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2755e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 152ms/step\n",
      "Epoch 445/3000\n",
      "\n",
      "Epoch 445: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2807e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 446/3000\n",
      "\n",
      "Epoch 446: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2701e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 154ms/step\n",
      "Epoch 447/3000\n",
      "\n",
      "Epoch 447: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2729e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 448/3000\n",
      "\n",
      "Epoch 448: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2772e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 449/3000\n",
      "\n",
      "Epoch 449: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2765e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 149ms/step\n",
      "Epoch 450/3000\n",
      "\n",
      "Epoch 450: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2688e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 451/3000\n",
      "\n",
      "Epoch 451: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2869e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 452/3000\n",
      "\n",
      "Epoch 452: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2649e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 453/3000\n",
      "\n",
      "Epoch 453: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2790e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 151ms/step\n",
      "Epoch 454/3000\n",
      "\n",
      "Epoch 454: val_mae did not improve from 0.03509\n",
      "151/151 - 24s - loss: 9.2686e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 24s/epoch - 160ms/step\n",
      "Epoch 455/3000\n",
      "\n",
      "Epoch 455: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2705e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 152ms/step\n",
      "Epoch 456/3000\n",
      "\n",
      "Epoch 456: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2468e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 457/3000\n",
      "\n",
      "Epoch 457: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2655e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 151ms/step\n",
      "Epoch 458/3000\n",
      "\n",
      "Epoch 458: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2762e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 459/3000\n",
      "\n",
      "Epoch 459: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2673e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 460/3000\n",
      "\n",
      "Epoch 460: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2394e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 461/3000\n",
      "\n",
      "Epoch 461: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2494e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 462/3000\n",
      "\n",
      "Epoch 462: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2509e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 150ms/step\n",
      "Epoch 463/3000\n",
      "\n",
      "Epoch 463: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2618e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 150ms/step\n",
      "Epoch 464/3000\n",
      "\n",
      "Epoch 464: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2666e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 465/3000\n",
      "\n",
      "Epoch 465: val_mae did not improve from 0.03509\n",
      "151/151 - 24s - loss: 9.2479e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 156ms/step\n",
      "Epoch 466/3000\n",
      "\n",
      "Epoch 466: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2583e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 155ms/step\n",
      "Epoch 467/3000\n",
      "\n",
      "Epoch 467: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2537e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 468/3000\n",
      "\n",
      "Epoch 468: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2562e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 154ms/step\n",
      "Epoch 469/3000\n",
      "\n",
      "Epoch 469: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2393e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 155ms/step\n",
      "Epoch 470/3000\n",
      "\n",
      "Epoch 470: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2416e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 155ms/step\n",
      "Epoch 471/3000\n",
      "\n",
      "Epoch 471: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2642e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 472/3000\n",
      "\n",
      "Epoch 472: val_mae did not improve from 0.03509\n",
      "151/151 - 23s - loss: 9.2625e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 473/3000\n",
      "\n",
      "Epoch 473: val_mae improved from 0.03509 to 0.03499, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.2560e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 151ms/step\n",
      "Epoch 474/3000\n",
      "\n",
      "Epoch 474: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2334e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 23s/epoch - 153ms/step\n",
      "Epoch 475/3000\n",
      "\n",
      "Epoch 475: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2563e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0358 - 23s/epoch - 150ms/step\n",
      "Epoch 476/3000\n",
      "\n",
      "Epoch 476: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2280e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 477/3000\n",
      "\n",
      "Epoch 477: val_mae did not improve from 0.03499\n",
      "151/151 - 24s - loss: 9.2619e-04 - mae: 0.0211 - val_loss: 0.0021 - val_mae: 0.0351 - 24s/epoch - 157ms/step\n",
      "Epoch 478/3000\n",
      "\n",
      "Epoch 478: val_mae did not improve from 0.03499\n",
      "151/151 - 24s - loss: 9.2530e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 24s/epoch - 159ms/step\n",
      "Epoch 479/3000\n",
      "\n",
      "Epoch 479: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2284e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 480/3000\n",
      "\n",
      "Epoch 480: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2374e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0357 - 23s/epoch - 153ms/step\n",
      "Epoch 481/3000\n",
      "\n",
      "Epoch 481: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2288e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 149ms/step\n",
      "Epoch 482/3000\n",
      "\n",
      "Epoch 482: val_mae did not improve from 0.03499\n",
      "151/151 - 24s - loss: 9.2343e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 156ms/step\n",
      "Epoch 483/3000\n",
      "\n",
      "Epoch 483: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2219e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 484/3000\n",
      "\n",
      "Epoch 484: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2229e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 485/3000\n",
      "\n",
      "Epoch 485: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2257e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 150ms/step\n",
      "Epoch 486/3000\n",
      "\n",
      "Epoch 486: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2352e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 487/3000\n",
      "\n",
      "Epoch 487: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2401e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 488/3000\n",
      "\n",
      "Epoch 488: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2284e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 489/3000\n",
      "\n",
      "Epoch 489: val_mae did not improve from 0.03499\n",
      "151/151 - 24s - loss: 9.2340e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 157ms/step\n",
      "Epoch 490/3000\n",
      "\n",
      "Epoch 490: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2533e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0356 - 23s/epoch - 152ms/step\n",
      "Epoch 491/3000\n",
      "\n",
      "Epoch 491: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2205e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 492/3000\n",
      "\n",
      "Epoch 492: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2023e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 493/3000\n",
      "\n",
      "Epoch 493: val_mae did not improve from 0.03499\n",
      "151/151 - 23s - loss: 9.2236e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 494/3000\n",
      "\n",
      "Epoch 494: val_mae improved from 0.03499 to 0.03498, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.2203e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 150ms/step\n",
      "Epoch 495/3000\n",
      "\n",
      "Epoch 495: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2138e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 153ms/step\n",
      "Epoch 496/3000\n",
      "\n",
      "Epoch 496: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2193e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 497/3000\n",
      "\n",
      "Epoch 497: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2299e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 149ms/step\n",
      "Epoch 498/3000\n",
      "\n",
      "Epoch 498: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2195e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 150ms/step\n",
      "Epoch 499/3000\n",
      "\n",
      "Epoch 499: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2288e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 153ms/step\n",
      "Epoch 500/3000\n",
      "\n",
      "Epoch 500: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2291e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 154ms/step\n",
      "Epoch 501/3000\n",
      "\n",
      "Epoch 501: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2022e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 502/3000\n",
      "\n",
      "Epoch 502: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.1875e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 153ms/step\n",
      "Epoch 503/3000\n",
      "\n",
      "Epoch 503: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2057e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 152ms/step\n",
      "Epoch 504/3000\n",
      "\n",
      "Epoch 504: val_mae did not improve from 0.03498\n",
      "151/151 - 23s - loss: 9.2276e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 150ms/step\n",
      "Epoch 505/3000\n",
      "\n",
      "Epoch 505: val_mae improved from 0.03498 to 0.03488, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.2332e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 152ms/step\n",
      "Epoch 506/3000\n",
      "\n",
      "Epoch 506: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2069e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 153ms/step\n",
      "Epoch 507/3000\n",
      "\n",
      "Epoch 507: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2137e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 508/3000\n",
      "\n",
      "Epoch 508: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2019e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 150ms/step\n",
      "Epoch 509/3000\n",
      "\n",
      "Epoch 509: val_mae did not improve from 0.03488\n",
      "151/151 - 24s - loss: 9.2179e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 24s/epoch - 156ms/step\n",
      "Epoch 510/3000\n",
      "\n",
      "Epoch 510: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.1923e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 154ms/step\n",
      "Epoch 511/3000\n",
      "\n",
      "Epoch 511: val_mae did not improve from 0.03488\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 9.1958e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 153ms/step\n",
      "Epoch 512/3000\n",
      "\n",
      "Epoch 512: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2017e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 153ms/step\n",
      "Epoch 513/3000\n",
      "\n",
      "Epoch 513: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.1821e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 154ms/step\n",
      "Epoch 514/3000\n",
      "\n",
      "Epoch 514: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2006e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 150ms/step\n",
      "Epoch 515/3000\n",
      "\n",
      "Epoch 515: val_mae did not improve from 0.03488\n",
      "151/151 - 24s - loss: 9.2273e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0355 - 24s/epoch - 157ms/step\n",
      "Epoch 516/3000\n",
      "\n",
      "Epoch 516: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2189e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 153ms/step\n",
      "Epoch 517/3000\n",
      "\n",
      "Epoch 517: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.1789e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 23s/epoch - 151ms/step\n",
      "Epoch 518/3000\n",
      "\n",
      "Epoch 518: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.1961e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 519/3000\n",
      "\n",
      "Epoch 519: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2006e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 152ms/step\n",
      "Epoch 520/3000\n",
      "\n",
      "Epoch 520: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2040e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 151ms/step\n",
      "Epoch 521/3000\n",
      "\n",
      "Epoch 521: val_mae did not improve from 0.03488\n",
      "151/151 - 23s - loss: 9.2018e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0359 - 23s/epoch - 153ms/step\n",
      "Epoch 522/3000\n",
      "\n",
      "Epoch 522: val_mae improved from 0.03488 to 0.03474, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1893e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 151ms/step\n",
      "Epoch 523/3000\n",
      "\n",
      "Epoch 523: val_mae did not improve from 0.03474\n",
      "151/151 - 24s - loss: 9.2155e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 158ms/step\n",
      "Epoch 524/3000\n",
      "\n",
      "Epoch 524: val_mae did not improve from 0.03474\n",
      "151/151 - 23s - loss: 9.2037e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 155ms/step\n",
      "Epoch 525/3000\n",
      "\n",
      "Epoch 525: val_mae did not improve from 0.03474\n",
      "151/151 - 23s - loss: 9.1948e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 152ms/step\n",
      "Epoch 526/3000\n",
      "\n",
      "Epoch 526: val_mae did not improve from 0.03474\n",
      "151/151 - 23s - loss: 9.1992e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 154ms/step\n",
      "Epoch 527/3000\n",
      "\n",
      "Epoch 527: val_mae did not improve from 0.03474\n",
      "151/151 - 24s - loss: 9.1930e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 159ms/step\n",
      "Epoch 528/3000\n",
      "\n",
      "Epoch 528: val_mae did not improve from 0.03474\n",
      "151/151 - 24s - loss: 9.1987e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 159ms/step\n",
      "Epoch 529/3000\n",
      "\n",
      "Epoch 529: val_mae did not improve from 0.03474\n",
      "151/151 - 23s - loss: 9.1888e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 153ms/step\n",
      "Epoch 530/3000\n",
      "\n",
      "Epoch 530: val_mae improved from 0.03474 to 0.03467, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1654e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 531/3000\n",
      "\n",
      "Epoch 531: val_mae did not improve from 0.03467\n",
      "151/151 - 23s - loss: 9.2080e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 153ms/step\n",
      "Epoch 532/3000\n",
      "\n",
      "Epoch 532: val_mae did not improve from 0.03467\n",
      "151/151 - 23s - loss: 9.1932e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 155ms/step\n",
      "Epoch 533/3000\n",
      "\n",
      "Epoch 533: val_mae did not improve from 0.03467\n",
      "151/151 - 24s - loss: 9.1814e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 158ms/step\n",
      "Epoch 534/3000\n",
      "\n",
      "Epoch 534: val_mae did not improve from 0.03467\n",
      "151/151 - 23s - loss: 9.1846e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 154ms/step\n",
      "Epoch 535/3000\n",
      "\n",
      "Epoch 535: val_mae did not improve from 0.03467\n",
      "151/151 - 23s - loss: 9.1928e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 155ms/step\n",
      "Epoch 536/3000\n",
      "\n",
      "Epoch 536: val_mae improved from 0.03467 to 0.03461, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 9.1865e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 156ms/step\n",
      "Epoch 537/3000\n",
      "\n",
      "Epoch 537: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1745e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 154ms/step\n",
      "Epoch 538/3000\n",
      "\n",
      "Epoch 538: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1634e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0353 - 23s/epoch - 151ms/step\n",
      "Epoch 539/3000\n",
      "\n",
      "Epoch 539: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1958e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 540/3000\n",
      "\n",
      "Epoch 540: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1634e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 541/3000\n",
      "\n",
      "Epoch 541: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1716e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 151ms/step\n",
      "Epoch 542/3000\n",
      "\n",
      "Epoch 542: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1723e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 152ms/step\n",
      "Epoch 543/3000\n",
      "\n",
      "Epoch 543: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1763e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 544/3000\n",
      "\n",
      "Epoch 544: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1802e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 545/3000\n",
      "\n",
      "Epoch 545: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1699e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 149ms/step\n",
      "Epoch 546/3000\n",
      "\n",
      "Epoch 546: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1777e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 154ms/step\n",
      "Epoch 547/3000\n",
      "\n",
      "Epoch 547: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1774e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 153ms/step\n",
      "Epoch 548/3000\n",
      "\n",
      "Epoch 548: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1800e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 549/3000\n",
      "\n",
      "Epoch 549: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1805e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 153ms/step\n",
      "Epoch 550/3000\n",
      "\n",
      "Epoch 550: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1680e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 157ms/step\n",
      "Epoch 551/3000\n",
      "\n",
      "Epoch 551: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1783e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 158ms/step\n",
      "Epoch 552/3000\n",
      "\n",
      "Epoch 552: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1573e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 153ms/step\n",
      "Epoch 553/3000\n",
      "\n",
      "Epoch 553: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1726e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 554/3000\n",
      "\n",
      "Epoch 554: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1542e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 24s/epoch - 156ms/step\n",
      "Epoch 555/3000\n",
      "\n",
      "Epoch 555: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1513e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 159ms/step\n",
      "Epoch 556/3000\n",
      "\n",
      "Epoch 556: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1647e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 158ms/step\n",
      "Epoch 557/3000\n",
      "\n",
      "Epoch 557: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1621e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0350 - 24s/epoch - 156ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 558/3000\n",
      "\n",
      "Epoch 558: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1633e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 24s/epoch - 156ms/step\n",
      "Epoch 559/3000\n",
      "\n",
      "Epoch 559: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1417e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 151ms/step\n",
      "Epoch 560/3000\n",
      "\n",
      "Epoch 560: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1658e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 156ms/step\n",
      "Epoch 561/3000\n",
      "\n",
      "Epoch 561: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1436e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 160ms/step\n",
      "Epoch 562/3000\n",
      "\n",
      "Epoch 562: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1630e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 153ms/step\n",
      "Epoch 563/3000\n",
      "\n",
      "Epoch 563: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1613e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 24s/epoch - 161ms/step\n",
      "Epoch 564/3000\n",
      "\n",
      "Epoch 564: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1483e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 152ms/step\n",
      "Epoch 565/3000\n",
      "\n",
      "Epoch 565: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1587e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 154ms/step\n",
      "Epoch 566/3000\n",
      "\n",
      "Epoch 566: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1334e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 567/3000\n",
      "\n",
      "Epoch 567: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1539e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 156ms/step\n",
      "Epoch 568/3000\n",
      "\n",
      "Epoch 568: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1341e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 24s/epoch - 156ms/step\n",
      "Epoch 569/3000\n",
      "\n",
      "Epoch 569: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1678e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 570/3000\n",
      "\n",
      "Epoch 570: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1725e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 151ms/step\n",
      "Epoch 571/3000\n",
      "\n",
      "Epoch 571: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1541e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0354 - 24s/epoch - 159ms/step\n",
      "Epoch 572/3000\n",
      "\n",
      "Epoch 572: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1570e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 573/3000\n",
      "\n",
      "Epoch 573: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1524e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 150ms/step\n",
      "Epoch 574/3000\n",
      "\n",
      "Epoch 574: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1329e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 575/3000\n",
      "\n",
      "Epoch 575: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1592e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 576/3000\n",
      "\n",
      "Epoch 576: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1338e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 577/3000\n",
      "\n",
      "Epoch 577: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1152e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 578/3000\n",
      "\n",
      "Epoch 578: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1175e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 157ms/step\n",
      "Epoch 579/3000\n",
      "\n",
      "Epoch 579: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1407e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 157ms/step\n",
      "Epoch 580/3000\n",
      "\n",
      "Epoch 580: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1450e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0352 - 23s/epoch - 150ms/step\n",
      "Epoch 581/3000\n",
      "\n",
      "Epoch 581: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1374e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 24s/epoch - 156ms/step\n",
      "Epoch 582/3000\n",
      "\n",
      "Epoch 582: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1505e-04 - mae: 0.0210 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 155ms/step\n",
      "Epoch 583/3000\n",
      "\n",
      "Epoch 583: val_mae improved from 0.03461 to 0.03461, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1261e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 155ms/step\n",
      "Epoch 584/3000\n",
      "\n",
      "Epoch 584: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1560e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 155ms/step\n",
      "Epoch 585/3000\n",
      "\n",
      "Epoch 585: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1275e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 157ms/step\n",
      "Epoch 586/3000\n",
      "\n",
      "Epoch 586: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1267e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 150ms/step\n",
      "Epoch 587/3000\n",
      "\n",
      "Epoch 587: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1415e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 156ms/step\n",
      "Epoch 588/3000\n",
      "\n",
      "Epoch 588: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1402e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 589/3000\n",
      "\n",
      "Epoch 589: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1528e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 153ms/step\n",
      "Epoch 590/3000\n",
      "\n",
      "Epoch 590: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1252e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 153ms/step\n",
      "Epoch 591/3000\n",
      "\n",
      "Epoch 591: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1298e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 153ms/step\n",
      "Epoch 592/3000\n",
      "\n",
      "Epoch 592: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1326e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 151ms/step\n",
      "Epoch 593/3000\n",
      "\n",
      "Epoch 593: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1381e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 150ms/step\n",
      "Epoch 594/3000\n",
      "\n",
      "Epoch 594: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1306e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 153ms/step\n",
      "Epoch 595/3000\n",
      "\n",
      "Epoch 595: val_mae did not improve from 0.03461\n",
      "151/151 - 23s - loss: 9.1395e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 155ms/step\n",
      "Epoch 596/3000\n",
      "\n",
      "Epoch 596: val_mae did not improve from 0.03461\n",
      "151/151 - 24s - loss: 9.1372e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 24s/epoch - 157ms/step\n",
      "Epoch 597/3000\n",
      "\n",
      "Epoch 597: val_mae improved from 0.03461 to 0.03459, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1307e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 598/3000\n",
      "\n",
      "Epoch 598: val_mae did not improve from 0.03459\n",
      "151/151 - 23s - loss: 9.1277e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0351 - 23s/epoch - 152ms/step\n",
      "Epoch 599/3000\n",
      "\n",
      "Epoch 599: val_mae improved from 0.03459 to 0.03448, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1319e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 153ms/step\n",
      "Epoch 600/3000\n",
      "\n",
      "Epoch 600: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1272e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 601/3000\n",
      "\n",
      "Epoch 601: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1039e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 150ms/step\n",
      "Epoch 602/3000\n",
      "\n",
      "Epoch 602: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1278e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 154ms/step\n",
      "Epoch 603/3000\n",
      "\n",
      "Epoch 603: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1419e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 154ms/step\n",
      "Epoch 604/3000\n",
      "\n",
      "Epoch 604: val_mae did not improve from 0.03448\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 9.1172e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 153ms/step\n",
      "Epoch 605/3000\n",
      "\n",
      "Epoch 605: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1310e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 154ms/step\n",
      "Epoch 606/3000\n",
      "\n",
      "Epoch 606: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1328e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 150ms/step\n",
      "Epoch 607/3000\n",
      "\n",
      "Epoch 607: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1137e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 608/3000\n",
      "\n",
      "Epoch 608: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1108e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 609/3000\n",
      "\n",
      "Epoch 609: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1155e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 153ms/step\n",
      "Epoch 610/3000\n",
      "\n",
      "Epoch 610: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1277e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 151ms/step\n",
      "Epoch 611/3000\n",
      "\n",
      "Epoch 611: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1238e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 150ms/step\n",
      "Epoch 612/3000\n",
      "\n",
      "Epoch 612: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1182e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 613/3000\n",
      "\n",
      "Epoch 613: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1094e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 152ms/step\n",
      "Epoch 614/3000\n",
      "\n",
      "Epoch 614: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1152e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 615/3000\n",
      "\n",
      "Epoch 615: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.0967e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 151ms/step\n",
      "Epoch 616/3000\n",
      "\n",
      "Epoch 616: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1068e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 617/3000\n",
      "\n",
      "Epoch 617: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1223e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 618/3000\n",
      "\n",
      "Epoch 618: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1122e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 619/3000\n",
      "\n",
      "Epoch 619: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1171e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 154ms/step\n",
      "Epoch 620/3000\n",
      "\n",
      "Epoch 620: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.1274e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 621/3000\n",
      "\n",
      "Epoch 621: val_mae did not improve from 0.03448\n",
      "151/151 - 23s - loss: 9.0918e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 622/3000\n",
      "\n",
      "Epoch 622: val_mae improved from 0.03448 to 0.03445, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.1027e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 153ms/step\n",
      "Epoch 623/3000\n",
      "\n",
      "Epoch 623: val_mae did not improve from 0.03445\n",
      "151/151 - 24s - loss: 9.1045e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 159ms/step\n",
      "Epoch 624/3000\n",
      "\n",
      "Epoch 624: val_mae did not improve from 0.03445\n",
      "151/151 - 24s - loss: 9.1268e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 24s/epoch - 156ms/step\n",
      "Epoch 625/3000\n",
      "\n",
      "Epoch 625: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1133e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 153ms/step\n",
      "Epoch 626/3000\n",
      "\n",
      "Epoch 626: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1179e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 627/3000\n",
      "\n",
      "Epoch 627: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1041e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 628/3000\n",
      "\n",
      "Epoch 628: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1099e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 154ms/step\n",
      "Epoch 629/3000\n",
      "\n",
      "Epoch 629: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1012e-04 - mae: 0.0209 - val_loss: 0.0021 - val_mae: 0.0351 - 23s/epoch - 151ms/step\n",
      "Epoch 630/3000\n",
      "\n",
      "Epoch 630: val_mae did not improve from 0.03445\n",
      "151/151 - 24s - loss: 9.1044e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 160ms/step\n",
      "Epoch 631/3000\n",
      "\n",
      "Epoch 631: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1079e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 154ms/step\n",
      "Epoch 632/3000\n",
      "\n",
      "Epoch 632: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1062e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n",
      "Epoch 633/3000\n",
      "\n",
      "Epoch 633: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0883e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 153ms/step\n",
      "Epoch 634/3000\n",
      "\n",
      "Epoch 634: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0940e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 155ms/step\n",
      "Epoch 635/3000\n",
      "\n",
      "Epoch 635: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1006e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n",
      "Epoch 636/3000\n",
      "\n",
      "Epoch 636: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1075e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 151ms/step\n",
      "Epoch 637/3000\n",
      "\n",
      "Epoch 637: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0958e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 638/3000\n",
      "\n",
      "Epoch 638: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1027e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 639/3000\n",
      "\n",
      "Epoch 639: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0995e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 152ms/step\n",
      "Epoch 640/3000\n",
      "\n",
      "Epoch 640: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1076e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 641/3000\n",
      "\n",
      "Epoch 641: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0982e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 149ms/step\n",
      "Epoch 642/3000\n",
      "\n",
      "Epoch 642: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0826e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n",
      "Epoch 643/3000\n",
      "\n",
      "Epoch 643: val_mae did not improve from 0.03445\n",
      "151/151 - 22s - loss: 9.0988e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 22s/epoch - 149ms/step\n",
      "Epoch 644/3000\n",
      "\n",
      "Epoch 644: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0994e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 645/3000\n",
      "\n",
      "Epoch 645: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0808e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 149ms/step\n",
      "Epoch 646/3000\n",
      "\n",
      "Epoch 646: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0821e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 149ms/step\n",
      "Epoch 647/3000\n",
      "\n",
      "Epoch 647: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1007e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 155ms/step\n",
      "Epoch 648/3000\n",
      "\n",
      "Epoch 648: val_mae did not improve from 0.03445\n",
      "151/151 - 24s - loss: 9.1062e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 158ms/step\n",
      "Epoch 649/3000\n",
      "\n",
      "Epoch 649: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0924e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 650/3000\n",
      "\n",
      "Epoch 650: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0983e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 651/3000\n",
      "\n",
      "Epoch 651: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.0921e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 652/3000\n",
      "\n",
      "Epoch 652: val_mae did not improve from 0.03445\n",
      "151/151 - 23s - loss: 9.1293e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 154ms/step\n",
      "Epoch 653/3000\n",
      "\n",
      "Epoch 653: val_mae improved from 0.03445 to 0.03431, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0842e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 654/3000\n",
      "\n",
      "Epoch 654: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0950e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 155ms/step\n",
      "Epoch 655/3000\n",
      "\n",
      "Epoch 655: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0803e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 158ms/step\n",
      "Epoch 656/3000\n",
      "\n",
      "Epoch 656: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0910e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 157ms/step\n",
      "Epoch 657/3000\n",
      "\n",
      "Epoch 657: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.1005e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 658/3000\n",
      "\n",
      "Epoch 658: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.1005e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 659/3000\n",
      "\n",
      "Epoch 659: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0857e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 154ms/step\n",
      "Epoch 660/3000\n",
      "\n",
      "Epoch 660: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.1027e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 661/3000\n",
      "\n",
      "Epoch 661: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0793e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 155ms/step\n",
      "Epoch 662/3000\n",
      "\n",
      "Epoch 662: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.1001e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 156ms/step\n",
      "Epoch 663/3000\n",
      "\n",
      "Epoch 663: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0955e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 150ms/step\n",
      "Epoch 664/3000\n",
      "\n",
      "Epoch 664: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.1010e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 150ms/step\n",
      "Epoch 665/3000\n",
      "\n",
      "Epoch 665: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0794e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 666/3000\n",
      "\n",
      "Epoch 666: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0639e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 667/3000\n",
      "\n",
      "Epoch 667: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0808e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 152ms/step\n",
      "Epoch 668/3000\n",
      "\n",
      "Epoch 668: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0940e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 669/3000\n",
      "\n",
      "Epoch 669: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0660e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 670/3000\n",
      "\n",
      "Epoch 670: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0798e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 671/3000\n",
      "\n",
      "Epoch 671: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0721e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 672/3000\n",
      "\n",
      "Epoch 672: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0704e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 673/3000\n",
      "\n",
      "Epoch 673: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0550e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 152ms/step\n",
      "Epoch 674/3000\n",
      "\n",
      "Epoch 674: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0892e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 675/3000\n",
      "\n",
      "Epoch 675: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0822e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 157ms/step\n",
      "Epoch 676/3000\n",
      "\n",
      "Epoch 676: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0987e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 154ms/step\n",
      "Epoch 677/3000\n",
      "\n",
      "Epoch 677: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0619e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 151ms/step\n",
      "Epoch 678/3000\n",
      "\n",
      "Epoch 678: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0902e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 679/3000\n",
      "\n",
      "Epoch 679: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0617e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 680/3000\n",
      "\n",
      "Epoch 680: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0736e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 151ms/step\n",
      "Epoch 681/3000\n",
      "\n",
      "Epoch 681: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0680e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n",
      "Epoch 682/3000\n",
      "\n",
      "Epoch 682: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0678e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 154ms/step\n",
      "Epoch 683/3000\n",
      "\n",
      "Epoch 683: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0765e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 155ms/step\n",
      "Epoch 684/3000\n",
      "\n",
      "Epoch 684: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0739e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 155ms/step\n",
      "Epoch 685/3000\n",
      "\n",
      "Epoch 685: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0660e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 154ms/step\n",
      "Epoch 686/3000\n",
      "\n",
      "Epoch 686: val_mae did not improve from 0.03431\n",
      "151/151 - 25s - loss: 9.0851e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 25s/epoch - 162ms/step\n",
      "Epoch 687/3000\n",
      "\n",
      "Epoch 687: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0611e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 156ms/step\n",
      "Epoch 688/3000\n",
      "\n",
      "Epoch 688: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0526e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 149ms/step\n",
      "Epoch 689/3000\n",
      "\n",
      "Epoch 689: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0427e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 690/3000\n",
      "\n",
      "Epoch 690: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0648e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 155ms/step\n",
      "Epoch 691/3000\n",
      "\n",
      "Epoch 691: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0721e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 158ms/step\n",
      "Epoch 692/3000\n",
      "\n",
      "Epoch 692: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0803e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 161ms/step\n",
      "Epoch 693/3000\n",
      "\n",
      "Epoch 693: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0532e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 156ms/step\n",
      "Epoch 694/3000\n",
      "\n",
      "Epoch 694: val_mae did not improve from 0.03431\n",
      "151/151 - 24s - loss: 9.0908e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 24s/epoch - 157ms/step\n",
      "Epoch 695/3000\n",
      "\n",
      "Epoch 695: val_mae did not improve from 0.03431\n",
      "151/151 - 25s - loss: 9.0671e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 25s/epoch - 163ms/step\n",
      "Epoch 696/3000\n",
      "\n",
      "Epoch 696: val_mae did not improve from 0.03431\n",
      "151/151 - 23s - loss: 9.0603e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 153ms/step\n",
      "Epoch 697/3000\n",
      "\n",
      "Epoch 697: val_mae improved from 0.03431 to 0.03427, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0610e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 154ms/step\n",
      "Epoch 698/3000\n",
      "\n",
      "Epoch 698: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0551e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 699/3000\n",
      "\n",
      "Epoch 699: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0561e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 154ms/step\n",
      "Epoch 700/3000\n",
      "\n",
      "Epoch 700: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0642e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 701/3000\n",
      "\n",
      "Epoch 701: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0637e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 702/3000\n",
      "\n",
      "Epoch 702: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0630e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 154ms/step\n",
      "Epoch 703/3000\n",
      "\n",
      "Epoch 703: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0692e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 149ms/step\n",
      "Epoch 704/3000\n",
      "\n",
      "Epoch 704: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0684e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 152ms/step\n",
      "Epoch 705/3000\n",
      "\n",
      "Epoch 705: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0673e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 706/3000\n",
      "\n",
      "Epoch 706: val_mae did not improve from 0.03427\n",
      "151/151 - 24s - loss: 9.0606e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 156ms/step\n",
      "Epoch 707/3000\n",
      "\n",
      "Epoch 707: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0882e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 153ms/step\n",
      "Epoch 708/3000\n",
      "\n",
      "Epoch 708: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0692e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n",
      "Epoch 709/3000\n",
      "\n",
      "Epoch 709: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0683e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 710/3000\n",
      "\n",
      "Epoch 710: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0559e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 711/3000\n",
      "\n",
      "Epoch 711: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0527e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 712/3000\n",
      "\n",
      "Epoch 712: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0603e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 713/3000\n",
      "\n",
      "Epoch 713: val_mae did not improve from 0.03427\n",
      "151/151 - 23s - loss: 9.0365e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 714/3000\n",
      "\n",
      "Epoch 714: val_mae improved from 0.03427 to 0.03418, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0428e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 715/3000\n",
      "\n",
      "Epoch 715: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0624e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0350 - 23s/epoch - 152ms/step\n",
      "Epoch 716/3000\n",
      "\n",
      "Epoch 716: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0408e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 156ms/step\n",
      "Epoch 717/3000\n",
      "\n",
      "Epoch 717: val_mae did not improve from 0.03418\n",
      "151/151 - 24s - loss: 9.0332e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 156ms/step\n",
      "Epoch 718/3000\n",
      "\n",
      "Epoch 718: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0504e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 719/3000\n",
      "\n",
      "Epoch 719: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0407e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 720/3000\n",
      "\n",
      "Epoch 720: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0663e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 721/3000\n",
      "\n",
      "Epoch 721: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0393e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 722/3000\n",
      "\n",
      "Epoch 722: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0423e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 723/3000\n",
      "\n",
      "Epoch 723: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0495e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 724/3000\n",
      "\n",
      "Epoch 724: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0327e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 725/3000\n",
      "\n",
      "Epoch 725: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0465e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 726/3000\n",
      "\n",
      "Epoch 726: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0542e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 727/3000\n",
      "\n",
      "Epoch 727: val_mae did not improve from 0.03418\n",
      "151/151 - 22s - loss: 9.0499e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 22s/epoch - 149ms/step\n",
      "Epoch 728/3000\n",
      "\n",
      "Epoch 728: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0515e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 729/3000\n",
      "\n",
      "Epoch 729: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0446e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 730/3000\n",
      "\n",
      "Epoch 730: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0520e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 731/3000\n",
      "\n",
      "Epoch 731: val_mae did not improve from 0.03418\n",
      "151/151 - 24s - loss: 9.0277e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 158ms/step\n",
      "Epoch 732/3000\n",
      "\n",
      "Epoch 732: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0342e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 733/3000\n",
      "\n",
      "Epoch 733: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0475e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 734/3000\n",
      "\n",
      "Epoch 734: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0330e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 735/3000\n",
      "\n",
      "Epoch 735: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0445e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 736/3000\n",
      "\n",
      "Epoch 736: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0491e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 737/3000\n",
      "\n",
      "Epoch 737: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0481e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 738/3000\n",
      "\n",
      "Epoch 738: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0270e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 152ms/step\n",
      "Epoch 739/3000\n",
      "\n",
      "Epoch 739: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0737e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 152ms/step\n",
      "Epoch 740/3000\n",
      "\n",
      "Epoch 740: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0333e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0349 - 23s/epoch - 154ms/step\n",
      "Epoch 741/3000\n",
      "\n",
      "Epoch 741: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0402e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0348 - 23s/epoch - 150ms/step\n",
      "Epoch 742/3000\n",
      "\n",
      "Epoch 742: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0477e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 743/3000\n",
      "\n",
      "Epoch 743: val_mae did not improve from 0.03418\n",
      "151/151 - 23s - loss: 9.0350e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 153ms/step\n",
      "Epoch 744/3000\n",
      "\n",
      "Epoch 744: val_mae improved from 0.03418 to 0.03416, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0429e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 745/3000\n",
      "\n",
      "Epoch 745: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0460e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 746/3000\n",
      "\n",
      "Epoch 746: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0526e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 747/3000\n",
      "\n",
      "Epoch 747: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0301e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 155ms/step\n",
      "Epoch 748/3000\n",
      "\n",
      "Epoch 748: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0300e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 749/3000\n",
      "\n",
      "Epoch 749: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0512e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 750/3000\n",
      "\n",
      "Epoch 750: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0240e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 751/3000\n",
      "\n",
      "Epoch 751: val_mae did not improve from 0.03416\n",
      "151/151 - 22s - loss: 9.0269e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 22s/epoch - 149ms/step\n",
      "Epoch 752/3000\n",
      "\n",
      "Epoch 752: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0349e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 753/3000\n",
      "\n",
      "Epoch 753: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0098e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 754/3000\n",
      "\n",
      "Epoch 754: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0376e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 755/3000\n",
      "\n",
      "Epoch 755: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0321e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 756/3000\n",
      "\n",
      "Epoch 756: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0175e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 153ms/step\n",
      "Epoch 757/3000\n",
      "\n",
      "Epoch 757: val_mae did not improve from 0.03416\n",
      "151/151 - 23s - loss: 9.0408e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 758/3000\n",
      "\n",
      "Epoch 758: val_mae improved from 0.03416 to 0.03412, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0641e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 759/3000\n",
      "\n",
      "Epoch 759: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0383e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 760/3000\n",
      "\n",
      "Epoch 760: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0217e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 761/3000\n",
      "\n",
      "Epoch 761: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0181e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 762/3000\n",
      "\n",
      "Epoch 762: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0398e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 763/3000\n",
      "\n",
      "Epoch 763: val_mae did not improve from 0.03412\n",
      "151/151 - 24s - loss: 9.0238e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 24s/epoch - 159ms/step\n",
      "Epoch 764/3000\n",
      "\n",
      "Epoch 764: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0447e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 765/3000\n",
      "\n",
      "Epoch 765: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0146e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 766/3000\n",
      "\n",
      "Epoch 766: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0287e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 152ms/step\n",
      "Epoch 767/3000\n",
      "\n",
      "Epoch 767: val_mae did not improve from 0.03412\n",
      "151/151 - 24s - loss: 9.0266e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 24s/epoch - 157ms/step\n",
      "Epoch 768/3000\n",
      "\n",
      "Epoch 768: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0001e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 150ms/step\n",
      "Epoch 769/3000\n",
      "\n",
      "Epoch 769: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0318e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 153ms/step\n",
      "Epoch 770/3000\n",
      "\n",
      "Epoch 770: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0405e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 771/3000\n",
      "\n",
      "Epoch 771: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0381e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 772/3000\n",
      "\n",
      "Epoch 772: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0095e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 773/3000\n",
      "\n",
      "Epoch 773: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0254e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 152ms/step\n",
      "Epoch 774/3000\n",
      "\n",
      "Epoch 774: val_mae did not improve from 0.03412\n",
      "151/151 - 23s - loss: 9.0153e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 775/3000\n",
      "\n",
      "Epoch 775: val_mae improved from 0.03412 to 0.03411, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0092e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 776/3000\n",
      "\n",
      "Epoch 776: val_mae did not improve from 0.03411\n",
      "151/151 - 23s - loss: 9.0173e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 777/3000\n",
      "\n",
      "Epoch 777: val_mae did not improve from 0.03411\n",
      "151/151 - 23s - loss: 9.0137e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 778/3000\n",
      "\n",
      "Epoch 778: val_mae did not improve from 0.03411\n",
      "151/151 - 23s - loss: 9.0314e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 779/3000\n",
      "\n",
      "Epoch 779: val_mae did not improve from 0.03411\n",
      "151/151 - 23s - loss: 9.0409e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 780/3000\n",
      "\n",
      "Epoch 780: val_mae did not improve from 0.03411\n",
      "151/151 - 23s - loss: 9.0164e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 781/3000\n",
      "\n",
      "Epoch 781: val_mae improved from 0.03411 to 0.03410, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 9.0240e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 156ms/step\n",
      "Epoch 782/3000\n",
      "\n",
      "Epoch 782: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0111e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 783/3000\n",
      "\n",
      "Epoch 783: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0425e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 784/3000\n",
      "\n",
      "Epoch 784: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0236e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 785/3000\n",
      "\n",
      "Epoch 785: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0122e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 154ms/step\n",
      "Epoch 786/3000\n",
      "\n",
      "Epoch 786: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0222e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 787/3000\n",
      "\n",
      "Epoch 787: val_mae did not improve from 0.03410\n",
      "151/151 - 23s - loss: 9.0244e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 788/3000\n",
      "\n",
      "Epoch 788: val_mae improved from 0.03410 to 0.03402, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 9.0146e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 789/3000\n",
      "\n",
      "Epoch 789: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0234e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 151ms/step\n",
      "Epoch 790/3000\n",
      "\n",
      "Epoch 790: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 9.0093e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 158ms/step\n",
      "Epoch 791/3000\n",
      "\n",
      "Epoch 791: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0008e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 792/3000\n",
      "\n",
      "Epoch 792: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0071e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 793/3000\n",
      "\n",
      "Epoch 793: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0191e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 794/3000\n",
      "\n",
      "Epoch 794: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 9.0043e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 24s/epoch - 159ms/step\n",
      "Epoch 795/3000\n",
      "\n",
      "Epoch 795: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0184e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 796/3000\n",
      "\n",
      "Epoch 796: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 9.0210e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 24s/epoch - 158ms/step\n",
      "Epoch 797/3000\n",
      "\n",
      "Epoch 797: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0004e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 156ms/step\n",
      "Epoch 798/3000\n",
      "\n",
      "Epoch 798: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0262e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 799/3000\n",
      "\n",
      "Epoch 799: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 9.0203e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 24s/epoch - 157ms/step\n",
      "Epoch 800/3000\n",
      "\n",
      "Epoch 800: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0095e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 154ms/step\n",
      "Epoch 801/3000\n",
      "\n",
      "Epoch 801: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0200e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 802/3000\n",
      "\n",
      "Epoch 802: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 8.9990e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 157ms/step\n",
      "Epoch 803/3000\n",
      "\n",
      "Epoch 803: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0006e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 156ms/step\n",
      "Epoch 804/3000\n",
      "\n",
      "Epoch 804: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0056e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 153ms/step\n",
      "Epoch 805/3000\n",
      "\n",
      "Epoch 805: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0118e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 806/3000\n",
      "\n",
      "Epoch 806: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0074e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 153ms/step\n",
      "Epoch 807/3000\n",
      "\n",
      "Epoch 807: val_mae did not improve from 0.03402\n",
      "151/151 - 24s - loss: 8.9808e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 156ms/step\n",
      "Epoch 808/3000\n",
      "\n",
      "Epoch 808: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0100e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 154ms/step\n",
      "Epoch 809/3000\n",
      "\n",
      "Epoch 809: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9948e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 810/3000\n",
      "\n",
      "Epoch 810: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0112e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 811/3000\n",
      "\n",
      "Epoch 811: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0295e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 812/3000\n",
      "\n",
      "Epoch 812: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0040e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 813/3000\n",
      "\n",
      "Epoch 813: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0156e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 152ms/step\n",
      "Epoch 814/3000\n",
      "\n",
      "Epoch 814: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0028e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 815/3000\n",
      "\n",
      "Epoch 815: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0064e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 816/3000\n",
      "\n",
      "Epoch 816: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9981e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 817/3000\n",
      "\n",
      "Epoch 817: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9970e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 818/3000\n",
      "\n",
      "Epoch 818: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0125e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 819/3000\n",
      "\n",
      "Epoch 819: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0010e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 820/3000\n",
      "\n",
      "Epoch 820: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9782e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 821/3000\n",
      "\n",
      "Epoch 821: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0190e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 822/3000\n",
      "\n",
      "Epoch 822: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9850e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 823/3000\n",
      "\n",
      "Epoch 823: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9994e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 824/3000\n",
      "\n",
      "Epoch 824: val_mae did not improve from 0.03402\n",
      "151/151 - 22s - loss: 8.9985e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 22s/epoch - 149ms/step\n",
      "Epoch 825/3000\n",
      "\n",
      "Epoch 825: val_mae did not improve from 0.03402\n",
      "151/151 - 22s - loss: 9.0050e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 22s/epoch - 148ms/step\n",
      "Epoch 826/3000\n",
      "\n",
      "Epoch 826: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0071e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 827/3000\n",
      "\n",
      "Epoch 827: val_mae did not improve from 0.03402\n",
      "151/151 - 22s - loss: 8.9873e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 22s/epoch - 148ms/step\n",
      "Epoch 828/3000\n",
      "\n",
      "Epoch 828: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0029e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 829/3000\n",
      "\n",
      "Epoch 829: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0164e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 830/3000\n",
      "\n",
      "Epoch 830: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9878e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 150ms/step\n",
      "Epoch 831/3000\n",
      "\n",
      "Epoch 831: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9842e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 832/3000\n",
      "\n",
      "Epoch 832: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9907e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 833/3000\n",
      "\n",
      "Epoch 833: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0021e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 834/3000\n",
      "\n",
      "Epoch 834: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0025e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 835/3000\n",
      "\n",
      "Epoch 835: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 9.0046e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 836/3000\n",
      "\n",
      "Epoch 836: val_mae did not improve from 0.03402\n",
      "151/151 - 23s - loss: 8.9867e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 149ms/step\n",
      "Epoch 837/3000\n",
      "\n",
      "Epoch 837: val_mae improved from 0.03402 to 0.03397, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9841e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 838/3000\n",
      "\n",
      "Epoch 838: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 9.0017e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 839/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 839: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9866e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 840/3000\n",
      "\n",
      "Epoch 840: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9865e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 841/3000\n",
      "\n",
      "Epoch 841: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 9.0074e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 842/3000\n",
      "\n",
      "Epoch 842: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 9.0098e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 843/3000\n",
      "\n",
      "Epoch 843: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9888e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 844/3000\n",
      "\n",
      "Epoch 844: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9918e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 845/3000\n",
      "\n",
      "Epoch 845: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9854e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 846/3000\n",
      "\n",
      "Epoch 846: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9700e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 847/3000\n",
      "\n",
      "Epoch 847: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9796e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 848/3000\n",
      "\n",
      "Epoch 848: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 8.9804e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 849/3000\n",
      "\n",
      "Epoch 849: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 9.0025e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 850/3000\n",
      "\n",
      "Epoch 850: val_mae did not improve from 0.03397\n",
      "151/151 - 24s - loss: 9.0043e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 161ms/step\n",
      "Epoch 851/3000\n",
      "\n",
      "Epoch 851: val_mae did not improve from 0.03397\n",
      "151/151 - 23s - loss: 9.0053e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 154ms/step\n",
      "Epoch 852/3000\n",
      "\n",
      "Epoch 852: val_mae improved from 0.03397 to 0.03386, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9971e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 853/3000\n",
      "\n",
      "Epoch 853: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9866e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 160ms/step\n",
      "Epoch 854/3000\n",
      "\n",
      "Epoch 854: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9837e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 156ms/step\n",
      "Epoch 855/3000\n",
      "\n",
      "Epoch 855: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9848e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 856/3000\n",
      "\n",
      "Epoch 856: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 9.0066e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 857/3000\n",
      "\n",
      "Epoch 857: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9791e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 858/3000\n",
      "\n",
      "Epoch 858: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9896e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 151ms/step\n",
      "Epoch 859/3000\n",
      "\n",
      "Epoch 859: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9830e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 154ms/step\n",
      "Epoch 860/3000\n",
      "\n",
      "Epoch 860: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9849e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 861/3000\n",
      "\n",
      "Epoch 861: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9917e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 862/3000\n",
      "\n",
      "Epoch 862: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9722e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 863/3000\n",
      "\n",
      "Epoch 863: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9886e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 864/3000\n",
      "\n",
      "Epoch 864: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9878e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 865/3000\n",
      "\n",
      "Epoch 865: val_mae did not improve from 0.03386\n",
      "151/151 - 22s - loss: 8.9805e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 22s/epoch - 149ms/step\n",
      "Epoch 866/3000\n",
      "\n",
      "Epoch 866: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9748e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 867/3000\n",
      "\n",
      "Epoch 867: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9691e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 868/3000\n",
      "\n",
      "Epoch 868: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9845e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0347 - 23s/epoch - 150ms/step\n",
      "Epoch 869/3000\n",
      "\n",
      "Epoch 869: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9764e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 870/3000\n",
      "\n",
      "Epoch 870: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9711e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 871/3000\n",
      "\n",
      "Epoch 871: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9704e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 872/3000\n",
      "\n",
      "Epoch 872: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9746e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 873/3000\n",
      "\n",
      "Epoch 873: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9831e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 874/3000\n",
      "\n",
      "Epoch 874: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9879e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 149ms/step\n",
      "Epoch 875/3000\n",
      "\n",
      "Epoch 875: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9641e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 876/3000\n",
      "\n",
      "Epoch 876: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9817e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 877/3000\n",
      "\n",
      "Epoch 877: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9902e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 878/3000\n",
      "\n",
      "Epoch 878: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9686e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 156ms/step\n",
      "Epoch 879/3000\n",
      "\n",
      "Epoch 879: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9798e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 24s/epoch - 157ms/step\n",
      "Epoch 880/3000\n",
      "\n",
      "Epoch 880: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9664e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0346 - 23s/epoch - 151ms/step\n",
      "Epoch 881/3000\n",
      "\n",
      "Epoch 881: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9788e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 882/3000\n",
      "\n",
      "Epoch 882: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9831e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 159ms/step\n",
      "Epoch 883/3000\n",
      "\n",
      "Epoch 883: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9865e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 24s/epoch - 157ms/step\n",
      "Epoch 884/3000\n",
      "\n",
      "Epoch 884: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9688e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 885/3000\n",
      "\n",
      "Epoch 885: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9570e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 886/3000\n",
      "\n",
      "Epoch 886: val_mae did not improve from 0.03386\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.9546e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 149ms/step\n",
      "Epoch 887/3000\n",
      "\n",
      "Epoch 887: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9576e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 888/3000\n",
      "\n",
      "Epoch 888: val_mae did not improve from 0.03386\n",
      "151/151 - 22s - loss: 8.9754e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 22s/epoch - 148ms/step\n",
      "Epoch 889/3000\n",
      "\n",
      "Epoch 889: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9628e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 890/3000\n",
      "\n",
      "Epoch 890: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9774e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 24s/epoch - 158ms/step\n",
      "Epoch 891/3000\n",
      "\n",
      "Epoch 891: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9584e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 892/3000\n",
      "\n",
      "Epoch 892: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9595e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 24s/epoch - 157ms/step\n",
      "Epoch 893/3000\n",
      "\n",
      "Epoch 893: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9798e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 149ms/step\n",
      "Epoch 894/3000\n",
      "\n",
      "Epoch 894: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9794e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 895/3000\n",
      "\n",
      "Epoch 895: val_mae did not improve from 0.03386\n",
      "151/151 - 24s - loss: 8.9684e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 24s/epoch - 158ms/step\n",
      "Epoch 896/3000\n",
      "\n",
      "Epoch 896: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9697e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 154ms/step\n",
      "Epoch 897/3000\n",
      "\n",
      "Epoch 897: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9757e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 154ms/step\n",
      "Epoch 898/3000\n",
      "\n",
      "Epoch 898: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9688e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 899/3000\n",
      "\n",
      "Epoch 899: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9632e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 154ms/step\n",
      "Epoch 900/3000\n",
      "\n",
      "Epoch 900: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9644e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 901/3000\n",
      "\n",
      "Epoch 901: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9741e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 149ms/step\n",
      "Epoch 902/3000\n",
      "\n",
      "Epoch 902: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9408e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 903/3000\n",
      "\n",
      "Epoch 903: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9607e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 149ms/step\n",
      "Epoch 904/3000\n",
      "\n",
      "Epoch 904: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9913e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 905/3000\n",
      "\n",
      "Epoch 905: val_mae did not improve from 0.03386\n",
      "151/151 - 23s - loss: 8.9643e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 906/3000\n",
      "\n",
      "Epoch 906: val_mae did not improve from 0.03386\n",
      "151/151 - 22s - loss: 8.9593e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 22s/epoch - 149ms/step\n",
      "Epoch 907/3000\n",
      "\n",
      "Epoch 907: val_mae improved from 0.03386 to 0.03382, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9605e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n",
      "Epoch 908/3000\n",
      "\n",
      "Epoch 908: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9852e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 155ms/step\n",
      "Epoch 909/3000\n",
      "\n",
      "Epoch 909: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9609e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 910/3000\n",
      "\n",
      "Epoch 910: val_mae did not improve from 0.03382\n",
      "151/151 - 24s - loss: 8.9709e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 158ms/step\n",
      "Epoch 911/3000\n",
      "\n",
      "Epoch 911: val_mae did not improve from 0.03382\n",
      "151/151 - 24s - loss: 8.9609e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 156ms/step\n",
      "Epoch 912/3000\n",
      "\n",
      "Epoch 912: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9772e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 913/3000\n",
      "\n",
      "Epoch 913: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9566e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 154ms/step\n",
      "Epoch 914/3000\n",
      "\n",
      "Epoch 914: val_mae did not improve from 0.03382\n",
      "151/151 - 24s - loss: 8.9745e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 24s/epoch - 157ms/step\n",
      "Epoch 915/3000\n",
      "\n",
      "Epoch 915: val_mae did not improve from 0.03382\n",
      "151/151 - 24s - loss: 8.9642e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 159ms/step\n",
      "Epoch 916/3000\n",
      "\n",
      "Epoch 916: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9703e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 917/3000\n",
      "\n",
      "Epoch 917: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9823e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 154ms/step\n",
      "Epoch 918/3000\n",
      "\n",
      "Epoch 918: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9825e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 919/3000\n",
      "\n",
      "Epoch 919: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9694e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 920/3000\n",
      "\n",
      "Epoch 920: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9462e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 921/3000\n",
      "\n",
      "Epoch 921: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9708e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 922/3000\n",
      "\n",
      "Epoch 922: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9768e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 923/3000\n",
      "\n",
      "Epoch 923: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9739e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 155ms/step\n",
      "Epoch 924/3000\n",
      "\n",
      "Epoch 924: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9566e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 925/3000\n",
      "\n",
      "Epoch 925: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9705e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 155ms/step\n",
      "Epoch 926/3000\n",
      "\n",
      "Epoch 926: val_mae did not improve from 0.03382\n",
      "151/151 - 24s - loss: 8.9424e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 156ms/step\n",
      "Epoch 927/3000\n",
      "\n",
      "Epoch 927: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9531e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 928/3000\n",
      "\n",
      "Epoch 928: val_mae did not improve from 0.03382\n",
      "151/151 - 23s - loss: 8.9482e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 154ms/step\n",
      "Epoch 929/3000\n",
      "\n",
      "Epoch 929: val_mae improved from 0.03382 to 0.03378, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9645e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n",
      "Epoch 930/3000\n",
      "\n",
      "Epoch 930: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9710e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 931/3000\n",
      "\n",
      "Epoch 931: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9552e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 932/3000\n",
      "\n",
      "Epoch 932: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9549e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 933/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 933: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9478e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 934/3000\n",
      "\n",
      "Epoch 934: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9469e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 935/3000\n",
      "\n",
      "Epoch 935: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9922e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 24s/epoch - 158ms/step\n",
      "Epoch 936/3000\n",
      "\n",
      "Epoch 936: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9590e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 155ms/step\n",
      "Epoch 937/3000\n",
      "\n",
      "Epoch 937: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9629e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 938/3000\n",
      "\n",
      "Epoch 938: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9525e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 939/3000\n",
      "\n",
      "Epoch 939: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9688e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 940/3000\n",
      "\n",
      "Epoch 940: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9542e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 152ms/step\n",
      "Epoch 941/3000\n",
      "\n",
      "Epoch 941: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9629e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 942/3000\n",
      "\n",
      "Epoch 942: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9487e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 24s/epoch - 156ms/step\n",
      "Epoch 943/3000\n",
      "\n",
      "Epoch 943: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9595e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0339 - 24s/epoch - 160ms/step\n",
      "Epoch 944/3000\n",
      "\n",
      "Epoch 944: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9405e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 24s/epoch - 161ms/step\n",
      "Epoch 945/3000\n",
      "\n",
      "Epoch 945: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9613e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 157ms/step\n",
      "Epoch 946/3000\n",
      "\n",
      "Epoch 946: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9594e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 947/3000\n",
      "\n",
      "Epoch 947: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9396e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 157ms/step\n",
      "Epoch 948/3000\n",
      "\n",
      "Epoch 948: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9736e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 153ms/step\n",
      "Epoch 949/3000\n",
      "\n",
      "Epoch 949: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9645e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 950/3000\n",
      "\n",
      "Epoch 950: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9290e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 153ms/step\n",
      "Epoch 951/3000\n",
      "\n",
      "Epoch 951: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9648e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 153ms/step\n",
      "Epoch 952/3000\n",
      "\n",
      "Epoch 952: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9580e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 953/3000\n",
      "\n",
      "Epoch 953: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9487e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 954/3000\n",
      "\n",
      "Epoch 954: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9532e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 155ms/step\n",
      "Epoch 955/3000\n",
      "\n",
      "Epoch 955: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9526e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 956/3000\n",
      "\n",
      "Epoch 956: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9402e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 957/3000\n",
      "\n",
      "Epoch 957: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9428e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 958/3000\n",
      "\n",
      "Epoch 958: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9462e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 22s/epoch - 149ms/step\n",
      "Epoch 959/3000\n",
      "\n",
      "Epoch 959: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9578e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 960/3000\n",
      "\n",
      "Epoch 960: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9295e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 961/3000\n",
      "\n",
      "Epoch 961: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9394e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 151ms/step\n",
      "Epoch 962/3000\n",
      "\n",
      "Epoch 962: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9590e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 963/3000\n",
      "\n",
      "Epoch 963: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9396e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 964/3000\n",
      "\n",
      "Epoch 964: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9462e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 965/3000\n",
      "\n",
      "Epoch 965: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9381e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 966/3000\n",
      "\n",
      "Epoch 966: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9498e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 967/3000\n",
      "\n",
      "Epoch 967: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9407e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 154ms/step\n",
      "Epoch 968/3000\n",
      "\n",
      "Epoch 968: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9426e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 969/3000\n",
      "\n",
      "Epoch 969: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9540e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 970/3000\n",
      "\n",
      "Epoch 970: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9523e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 971/3000\n",
      "\n",
      "Epoch 971: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9204e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 972/3000\n",
      "\n",
      "Epoch 972: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9470e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 22s/epoch - 148ms/step\n",
      "Epoch 973/3000\n",
      "\n",
      "Epoch 973: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9460e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 974/3000\n",
      "\n",
      "Epoch 974: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9437e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 153ms/step\n",
      "Epoch 975/3000\n",
      "\n",
      "Epoch 975: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9365e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 976/3000\n",
      "\n",
      "Epoch 976: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9480e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 977/3000\n",
      "\n",
      "Epoch 977: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9322e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 978/3000\n",
      "\n",
      "Epoch 978: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9493e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 979/3000\n",
      "\n",
      "Epoch 979: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9352e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 156ms/step\n",
      "Epoch 980/3000\n",
      "\n",
      "Epoch 980: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9305e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 981/3000\n",
      "\n",
      "Epoch 981: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9047e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 982/3000\n",
      "\n",
      "Epoch 982: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9398e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 983/3000\n",
      "\n",
      "Epoch 983: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9421e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 157ms/step\n",
      "Epoch 984/3000\n",
      "\n",
      "Epoch 984: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9381e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 156ms/step\n",
      "Epoch 985/3000\n",
      "\n",
      "Epoch 985: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9431e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 153ms/step\n",
      "Epoch 986/3000\n",
      "\n",
      "Epoch 986: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9267e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 22s/epoch - 149ms/step\n",
      "Epoch 987/3000\n",
      "\n",
      "Epoch 987: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9418e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0345 - 23s/epoch - 150ms/step\n",
      "Epoch 988/3000\n",
      "\n",
      "Epoch 988: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9393e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 989/3000\n",
      "\n",
      "Epoch 989: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9421e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 990/3000\n",
      "\n",
      "Epoch 990: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9562e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 22s/epoch - 148ms/step\n",
      "Epoch 991/3000\n",
      "\n",
      "Epoch 991: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9228e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 992/3000\n",
      "\n",
      "Epoch 992: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9209e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 155ms/step\n",
      "Epoch 993/3000\n",
      "\n",
      "Epoch 993: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9348e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 157ms/step\n",
      "Epoch 994/3000\n",
      "\n",
      "Epoch 994: val_mae did not improve from 0.03378\n",
      "151/151 - 24s - loss: 8.9198e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 158ms/step\n",
      "Epoch 995/3000\n",
      "\n",
      "Epoch 995: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9319e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 996/3000\n",
      "\n",
      "Epoch 996: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9360e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 997/3000\n",
      "\n",
      "Epoch 997: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9427e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 998/3000\n",
      "\n",
      "Epoch 998: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9295e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 999/3000\n",
      "\n",
      "Epoch 999: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9245e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1000/3000\n",
      "\n",
      "Epoch 1000: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9385e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 1001/3000\n",
      "\n",
      "Epoch 1001: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9328e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 149ms/step\n",
      "Epoch 1002/3000\n",
      "\n",
      "Epoch 1002: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9323e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 22s/epoch - 149ms/step\n",
      "Epoch 1003/3000\n",
      "\n",
      "Epoch 1003: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9340e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 152ms/step\n",
      "Epoch 1004/3000\n",
      "\n",
      "Epoch 1004: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9057e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 149ms/step\n",
      "Epoch 1005/3000\n",
      "\n",
      "Epoch 1005: val_mae did not improve from 0.03378\n",
      "151/151 - 23s - loss: 8.9265e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 1006/3000\n",
      "\n",
      "Epoch 1006: val_mae did not improve from 0.03378\n",
      "151/151 - 22s - loss: 8.9630e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0338 - 22s/epoch - 149ms/step\n",
      "Epoch 1007/3000\n",
      "\n",
      "Epoch 1007: val_mae improved from 0.03378 to 0.03371, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9279e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1008/3000\n",
      "\n",
      "Epoch 1008: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9259e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 154ms/step\n",
      "Epoch 1009/3000\n",
      "\n",
      "Epoch 1009: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9281e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1010/3000\n",
      "\n",
      "Epoch 1010: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9368e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 1011/3000\n",
      "\n",
      "Epoch 1011: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9570e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1012/3000\n",
      "\n",
      "Epoch 1012: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9549e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 149ms/step\n",
      "Epoch 1013/3000\n",
      "\n",
      "Epoch 1013: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9408e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1014/3000\n",
      "\n",
      "Epoch 1014: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9171e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 1015/3000\n",
      "\n",
      "Epoch 1015: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9078e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1016/3000\n",
      "\n",
      "Epoch 1016: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9049e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 153ms/step\n",
      "Epoch 1017/3000\n",
      "\n",
      "Epoch 1017: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9322e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1018/3000\n",
      "\n",
      "Epoch 1018: val_mae did not improve from 0.03371\n",
      "151/151 - 22s - loss: 8.9506e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 22s/epoch - 149ms/step\n",
      "Epoch 1019/3000\n",
      "\n",
      "Epoch 1019: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9247e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 149ms/step\n",
      "Epoch 1020/3000\n",
      "\n",
      "Epoch 1020: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9291e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1021/3000\n",
      "\n",
      "Epoch 1021: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9138e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1022/3000\n",
      "\n",
      "Epoch 1022: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9221e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 1023/3000\n",
      "\n",
      "Epoch 1023: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9142e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1024/3000\n",
      "\n",
      "Epoch 1024: val_mae did not improve from 0.03371\n",
      "151/151 - 24s - loss: 8.9232e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 159ms/step\n",
      "Epoch 1025/3000\n",
      "\n",
      "Epoch 1025: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9304e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n",
      "Epoch 1026/3000\n",
      "\n",
      "Epoch 1026: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9297e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 1027/3000\n",
      "\n",
      "Epoch 1027: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9209e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1028/3000\n",
      "\n",
      "Epoch 1028: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9198e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 1029/3000\n",
      "\n",
      "Epoch 1029: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9069e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 156ms/step\n",
      "Epoch 1030/3000\n",
      "\n",
      "Epoch 1030: val_mae did not improve from 0.03371\n",
      "151/151 - 24s - loss: 8.9289e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 24s/epoch - 158ms/step\n",
      "Epoch 1031/3000\n",
      "\n",
      "Epoch 1031: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9264e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 156ms/step\n",
      "Epoch 1032/3000\n",
      "\n",
      "Epoch 1032: val_mae did not improve from 0.03371\n",
      "151/151 - 24s - loss: 8.9203e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 159ms/step\n",
      "Epoch 1033/3000\n",
      "\n",
      "Epoch 1033: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9244e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 155ms/step\n",
      "Epoch 1034/3000\n",
      "\n",
      "Epoch 1034: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9203e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1035/3000\n",
      "\n",
      "Epoch 1035: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9167e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1036/3000\n",
      "\n",
      "Epoch 1036: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9288e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 154ms/step\n",
      "Epoch 1037/3000\n",
      "\n",
      "Epoch 1037: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9274e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1038/3000\n",
      "\n",
      "Epoch 1038: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9287e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 1039/3000\n",
      "\n",
      "Epoch 1039: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9021e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 1040/3000\n",
      "\n",
      "Epoch 1040: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9273e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1041/3000\n",
      "\n",
      "Epoch 1041: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9339e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 1042/3000\n",
      "\n",
      "Epoch 1042: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9315e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1043/3000\n",
      "\n",
      "Epoch 1043: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9195e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 149ms/step\n",
      "Epoch 1044/3000\n",
      "\n",
      "Epoch 1044: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9377e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1045/3000\n",
      "\n",
      "Epoch 1045: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.8983e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 1046/3000\n",
      "\n",
      "Epoch 1046: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9335e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1047/3000\n",
      "\n",
      "Epoch 1047: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9239e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1048/3000\n",
      "\n",
      "Epoch 1048: val_mae did not improve from 0.03371\n",
      "151/151 - 22s - loss: 8.9079e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 22s/epoch - 148ms/step\n",
      "Epoch 1049/3000\n",
      "\n",
      "Epoch 1049: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9004e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 149ms/step\n",
      "Epoch 1050/3000\n",
      "\n",
      "Epoch 1050: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9015e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1051/3000\n",
      "\n",
      "Epoch 1051: val_mae did not improve from 0.03371\n",
      "151/151 - 22s - loss: 8.9136e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 22s/epoch - 149ms/step\n",
      "Epoch 1052/3000\n",
      "\n",
      "Epoch 1052: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9268e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1053/3000\n",
      "\n",
      "Epoch 1053: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9275e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 1054/3000\n",
      "\n",
      "Epoch 1054: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9176e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 149ms/step\n",
      "Epoch 1055/3000\n",
      "\n",
      "Epoch 1055: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9093e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1056/3000\n",
      "\n",
      "Epoch 1056: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9261e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 155ms/step\n",
      "Epoch 1057/3000\n",
      "\n",
      "Epoch 1057: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9003e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1058/3000\n",
      "\n",
      "Epoch 1058: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9138e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1059/3000\n",
      "\n",
      "Epoch 1059: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.8997e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1060/3000\n",
      "\n",
      "Epoch 1060: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9047e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1061/3000\n",
      "\n",
      "Epoch 1061: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9195e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 1062/3000\n",
      "\n",
      "Epoch 1062: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9068e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1063/3000\n",
      "\n",
      "Epoch 1063: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9159e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1064/3000\n",
      "\n",
      "Epoch 1064: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.8942e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 150ms/step\n",
      "Epoch 1065/3000\n",
      "\n",
      "Epoch 1065: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9154e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n",
      "Epoch 1066/3000\n",
      "\n",
      "Epoch 1066: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9169e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1067/3000\n",
      "\n",
      "Epoch 1067: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9093e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1068/3000\n",
      "\n",
      "Epoch 1068: val_mae did not improve from 0.03371\n",
      "151/151 - 23s - loss: 8.9103e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1069/3000\n",
      "\n",
      "Epoch 1069: val_mae improved from 0.03371 to 0.03366, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8972e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1070/3000\n",
      "\n",
      "Epoch 1070: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.8941e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1071/3000\n",
      "\n",
      "Epoch 1071: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9173e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1072/3000\n",
      "\n",
      "Epoch 1072: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9015e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 1073/3000\n",
      "\n",
      "Epoch 1073: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9125e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 149ms/step\n",
      "Epoch 1074/3000\n",
      "\n",
      "Epoch 1074: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9115e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1075/3000\n",
      "\n",
      "Epoch 1075: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.8923e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1076/3000\n",
      "\n",
      "Epoch 1076: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9147e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1077/3000\n",
      "\n",
      "Epoch 1077: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.9269e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 153ms/step\n",
      "Epoch 1078/3000\n",
      "\n",
      "Epoch 1078: val_mae did not improve from 0.03366\n",
      "151/151 - 23s - loss: 8.8871e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1079/3000\n",
      "\n",
      "Epoch 1079: val_mae improved from 0.03366 to 0.03360, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8938e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1080/3000\n",
      "\n",
      "Epoch 1080: val_mae did not improve from 0.03360\n",
      "151/151 - 23s - loss: 8.9167e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1081/3000\n",
      "\n",
      "Epoch 1081: val_mae did not improve from 0.03360\n",
      "151/151 - 23s - loss: 8.9114e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1082/3000\n",
      "\n",
      "Epoch 1082: val_mae improved from 0.03360 to 0.03340, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.9003e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1083/3000\n",
      "\n",
      "Epoch 1083: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8898e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1084/3000\n",
      "\n",
      "Epoch 1084: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8960e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 154ms/step\n",
      "Epoch 1085/3000\n",
      "\n",
      "Epoch 1085: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9083e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1086/3000\n",
      "\n",
      "Epoch 1086: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8877e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 154ms/step\n",
      "Epoch 1087/3000\n",
      "\n",
      "Epoch 1087: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.9106e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 156ms/step\n",
      "Epoch 1088/3000\n",
      "\n",
      "Epoch 1088: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8870e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 1089/3000\n",
      "\n",
      "Epoch 1089: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9004e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1090/3000\n",
      "\n",
      "Epoch 1090: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8957e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1091/3000\n",
      "\n",
      "Epoch 1091: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9199e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1092/3000\n",
      "\n",
      "Epoch 1092: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9024e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1093/3000\n",
      "\n",
      "Epoch 1093: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9130e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0344 - 23s/epoch - 150ms/step\n",
      "Epoch 1094/3000\n",
      "\n",
      "Epoch 1094: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9068e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 1095/3000\n",
      "\n",
      "Epoch 1095: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8975e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 1096/3000\n",
      "\n",
      "Epoch 1096: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8983e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1097/3000\n",
      "\n",
      "Epoch 1097: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.9025e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 22s/epoch - 148ms/step\n",
      "Epoch 1098/3000\n",
      "\n",
      "Epoch 1098: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8961e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 22s/epoch - 149ms/step\n",
      "Epoch 1099/3000\n",
      "\n",
      "Epoch 1099: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9109e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1100/3000\n",
      "\n",
      "Epoch 1100: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8756e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1101/3000\n",
      "\n",
      "Epoch 1101: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8961e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1102/3000\n",
      "\n",
      "Epoch 1102: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9025e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 156ms/step\n",
      "Epoch 1103/3000\n",
      "\n",
      "Epoch 1103: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9089e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 155ms/step\n",
      "Epoch 1104/3000\n",
      "\n",
      "Epoch 1104: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9106e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 153ms/step\n",
      "Epoch 1105/3000\n",
      "\n",
      "Epoch 1105: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9000e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1106/3000\n",
      "\n",
      "Epoch 1106: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8946e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 22s/epoch - 149ms/step\n",
      "Epoch 1107/3000\n",
      "\n",
      "Epoch 1107: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9196e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 150ms/step\n",
      "Epoch 1108/3000\n",
      "\n",
      "Epoch 1108: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9373e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1109/3000\n",
      "\n",
      "Epoch 1109: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8882e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1110/3000\n",
      "\n",
      "Epoch 1110: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8895e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1111/3000\n",
      "\n",
      "Epoch 1111: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8935e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1112/3000\n",
      "\n",
      "Epoch 1112: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.9099e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 158ms/step\n",
      "Epoch 1113/3000\n",
      "\n",
      "Epoch 1113: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8888e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1114/3000\n",
      "\n",
      "Epoch 1114: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8910e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 155ms/step\n",
      "Epoch 1115/3000\n",
      "\n",
      "Epoch 1115: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8807e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1116/3000\n",
      "\n",
      "Epoch 1116: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9137e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1117/3000\n",
      "\n",
      "Epoch 1117: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8955e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 149ms/step\n",
      "Epoch 1118/3000\n",
      "\n",
      "Epoch 1118: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8905e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1119/3000\n",
      "\n",
      "Epoch 1119: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9113e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 149ms/step\n",
      "Epoch 1120/3000\n",
      "\n",
      "Epoch 1120: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8922e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 22s/epoch - 149ms/step\n",
      "Epoch 1121/3000\n",
      "\n",
      "Epoch 1121: val_mae did not improve from 0.03340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8966e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1122/3000\n",
      "\n",
      "Epoch 1122: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8899e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 149ms/step\n",
      "Epoch 1123/3000\n",
      "\n",
      "Epoch 1123: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8935e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1124/3000\n",
      "\n",
      "Epoch 1124: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8924e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 156ms/step\n",
      "Epoch 1125/3000\n",
      "\n",
      "Epoch 1125: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8767e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1126/3000\n",
      "\n",
      "Epoch 1126: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8947e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1127/3000\n",
      "\n",
      "Epoch 1127: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9059e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 155ms/step\n",
      "Epoch 1128/3000\n",
      "\n",
      "Epoch 1128: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8724e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 156ms/step\n",
      "Epoch 1129/3000\n",
      "\n",
      "Epoch 1129: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8745e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 24s/epoch - 157ms/step\n",
      "Epoch 1130/3000\n",
      "\n",
      "Epoch 1130: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8863e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 1131/3000\n",
      "\n",
      "Epoch 1131: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8946e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1132/3000\n",
      "\n",
      "Epoch 1132: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8953e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 154ms/step\n",
      "Epoch 1133/3000\n",
      "\n",
      "Epoch 1133: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9200e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1134/3000\n",
      "\n",
      "Epoch 1134: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8882e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 24s/epoch - 159ms/step\n",
      "Epoch 1135/3000\n",
      "\n",
      "Epoch 1135: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.9047e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 158ms/step\n",
      "Epoch 1136/3000\n",
      "\n",
      "Epoch 1136: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9145e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1137/3000\n",
      "\n",
      "Epoch 1137: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8845e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1138/3000\n",
      "\n",
      "Epoch 1138: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8988e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1139/3000\n",
      "\n",
      "Epoch 1139: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8840e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 156ms/step\n",
      "Epoch 1140/3000\n",
      "\n",
      "Epoch 1140: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8885e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 154ms/step\n",
      "Epoch 1141/3000\n",
      "\n",
      "Epoch 1141: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8943e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 154ms/step\n",
      "Epoch 1142/3000\n",
      "\n",
      "Epoch 1142: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8861e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1143/3000\n",
      "\n",
      "Epoch 1143: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8981e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1144/3000\n",
      "\n",
      "Epoch 1144: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8993e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 150ms/step\n",
      "Epoch 1145/3000\n",
      "\n",
      "Epoch 1145: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8824e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1146/3000\n",
      "\n",
      "Epoch 1146: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8884e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1147/3000\n",
      "\n",
      "Epoch 1147: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8864e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1148/3000\n",
      "\n",
      "Epoch 1148: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8971e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1149/3000\n",
      "\n",
      "Epoch 1149: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8707e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 154ms/step\n",
      "Epoch 1150/3000\n",
      "\n",
      "Epoch 1150: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8735e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1151/3000\n",
      "\n",
      "Epoch 1151: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8917e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1152/3000\n",
      "\n",
      "Epoch 1152: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8732e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1153/3000\n",
      "\n",
      "Epoch 1153: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8709e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1154/3000\n",
      "\n",
      "Epoch 1154: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9088e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1155/3000\n",
      "\n",
      "Epoch 1155: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8863e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1156/3000\n",
      "\n",
      "Epoch 1156: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8764e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1157/3000\n",
      "\n",
      "Epoch 1157: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8840e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 22s/epoch - 148ms/step\n",
      "Epoch 1158/3000\n",
      "\n",
      "Epoch 1158: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8776e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1159/3000\n",
      "\n",
      "Epoch 1159: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8707e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1160/3000\n",
      "\n",
      "Epoch 1160: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8909e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1161/3000\n",
      "\n",
      "Epoch 1161: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9103e-04 - mae: 0.0209 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1162/3000\n",
      "\n",
      "Epoch 1162: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.9059e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1163/3000\n",
      "\n",
      "Epoch 1163: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8863e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1164/3000\n",
      "\n",
      "Epoch 1164: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8803e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1165/3000\n",
      "\n",
      "Epoch 1165: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8836e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 24s/epoch - 160ms/step\n",
      "Epoch 1166/3000\n",
      "\n",
      "Epoch 1166: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8823e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 24s/epoch - 162ms/step\n",
      "Epoch 1167/3000\n",
      "\n",
      "Epoch 1167: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8972e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1168/3000\n",
      "\n",
      "Epoch 1168: val_mae did not improve from 0.03340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8744e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1169/3000\n",
      "\n",
      "Epoch 1169: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8728e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1170/3000\n",
      "\n",
      "Epoch 1170: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8721e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1171/3000\n",
      "\n",
      "Epoch 1171: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8821e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 154ms/step\n",
      "Epoch 1172/3000\n",
      "\n",
      "Epoch 1172: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8662e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1173/3000\n",
      "\n",
      "Epoch 1173: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8723e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1174/3000\n",
      "\n",
      "Epoch 1174: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8919e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1175/3000\n",
      "\n",
      "Epoch 1175: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8704e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1176/3000\n",
      "\n",
      "Epoch 1176: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8892e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1177/3000\n",
      "\n",
      "Epoch 1177: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8749e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1178/3000\n",
      "\n",
      "Epoch 1178: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8716e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 1179/3000\n",
      "\n",
      "Epoch 1179: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8773e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1180/3000\n",
      "\n",
      "Epoch 1180: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8879e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1181/3000\n",
      "\n",
      "Epoch 1181: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8923e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1182/3000\n",
      "\n",
      "Epoch 1182: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8779e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 151ms/step\n",
      "Epoch 1183/3000\n",
      "\n",
      "Epoch 1183: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8642e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1184/3000\n",
      "\n",
      "Epoch 1184: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8803e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1185/3000\n",
      "\n",
      "Epoch 1185: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8965e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 157ms/step\n",
      "Epoch 1186/3000\n",
      "\n",
      "Epoch 1186: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8832e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1187/3000\n",
      "\n",
      "Epoch 1187: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8800e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1188/3000\n",
      "\n",
      "Epoch 1188: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8735e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0343 - 23s/epoch - 151ms/step\n",
      "Epoch 1189/3000\n",
      "\n",
      "Epoch 1189: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8740e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0342 - 23s/epoch - 152ms/step\n",
      "Epoch 1190/3000\n",
      "\n",
      "Epoch 1190: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8835e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1191/3000\n",
      "\n",
      "Epoch 1191: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8875e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1192/3000\n",
      "\n",
      "Epoch 1192: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8798e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 150ms/step\n",
      "Epoch 1193/3000\n",
      "\n",
      "Epoch 1193: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8884e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1194/3000\n",
      "\n",
      "Epoch 1194: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8633e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1195/3000\n",
      "\n",
      "Epoch 1195: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8706e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 1196/3000\n",
      "\n",
      "Epoch 1196: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8569e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1197/3000\n",
      "\n",
      "Epoch 1197: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8793e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1198/3000\n",
      "\n",
      "Epoch 1198: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8834e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1199/3000\n",
      "\n",
      "Epoch 1199: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8735e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 153ms/step\n",
      "Epoch 1200/3000\n",
      "\n",
      "Epoch 1200: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8752e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 152ms/step\n",
      "Epoch 1201/3000\n",
      "\n",
      "Epoch 1201: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8712e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 152ms/step\n",
      "Epoch 1202/3000\n",
      "\n",
      "Epoch 1202: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8715e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1203/3000\n",
      "\n",
      "Epoch 1203: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8739e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1204/3000\n",
      "\n",
      "Epoch 1204: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8791e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1205/3000\n",
      "\n",
      "Epoch 1205: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8677e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1206/3000\n",
      "\n",
      "Epoch 1206: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8841e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1207/3000\n",
      "\n",
      "Epoch 1207: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8579e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1208/3000\n",
      "\n",
      "Epoch 1208: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8842e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1209/3000\n",
      "\n",
      "Epoch 1209: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8691e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1210/3000\n",
      "\n",
      "Epoch 1210: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8792e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1211/3000\n",
      "\n",
      "Epoch 1211: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8831e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 152ms/step\n",
      "Epoch 1212/3000\n",
      "\n",
      "Epoch 1212: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8741e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1213/3000\n",
      "\n",
      "Epoch 1213: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8498e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1214/3000\n",
      "\n",
      "Epoch 1214: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8762e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1215/3000\n",
      "\n",
      "Epoch 1215: val_mae did not improve from 0.03340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8730e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 154ms/step\n",
      "Epoch 1216/3000\n",
      "\n",
      "Epoch 1216: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8652e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1217/3000\n",
      "\n",
      "Epoch 1217: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8682e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1218/3000\n",
      "\n",
      "Epoch 1218: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8742e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1219/3000\n",
      "\n",
      "Epoch 1219: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8536e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1220/3000\n",
      "\n",
      "Epoch 1220: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8702e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1221/3000\n",
      "\n",
      "Epoch 1221: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8902e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1222/3000\n",
      "\n",
      "Epoch 1222: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8567e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 156ms/step\n",
      "Epoch 1223/3000\n",
      "\n",
      "Epoch 1223: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8666e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 22s/epoch - 148ms/step\n",
      "Epoch 1224/3000\n",
      "\n",
      "Epoch 1224: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8773e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1225/3000\n",
      "\n",
      "Epoch 1225: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8767e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1226/3000\n",
      "\n",
      "Epoch 1226: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8475e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1227/3000\n",
      "\n",
      "Epoch 1227: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8680e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1228/3000\n",
      "\n",
      "Epoch 1228: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8684e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 22s/epoch - 148ms/step\n",
      "Epoch 1229/3000\n",
      "\n",
      "Epoch 1229: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8638e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1230/3000\n",
      "\n",
      "Epoch 1230: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8901e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1231/3000\n",
      "\n",
      "Epoch 1231: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8733e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1232/3000\n",
      "\n",
      "Epoch 1232: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8506e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1233/3000\n",
      "\n",
      "Epoch 1233: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8695e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1234/3000\n",
      "\n",
      "Epoch 1234: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8395e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1235/3000\n",
      "\n",
      "Epoch 1235: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8669e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 154ms/step\n",
      "Epoch 1236/3000\n",
      "\n",
      "Epoch 1236: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8689e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1237/3000\n",
      "\n",
      "Epoch 1237: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8478e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1238/3000\n",
      "\n",
      "Epoch 1238: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8725e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1239/3000\n",
      "\n",
      "Epoch 1239: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8789e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1240/3000\n",
      "\n",
      "Epoch 1240: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8391e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1241/3000\n",
      "\n",
      "Epoch 1241: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8664e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1242/3000\n",
      "\n",
      "Epoch 1242: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8608e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 24s/epoch - 157ms/step\n",
      "Epoch 1243/3000\n",
      "\n",
      "Epoch 1243: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8687e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1244/3000\n",
      "\n",
      "Epoch 1244: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8618e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1245/3000\n",
      "\n",
      "Epoch 1245: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8643e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1246/3000\n",
      "\n",
      "Epoch 1246: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8541e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1247/3000\n",
      "\n",
      "Epoch 1247: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8632e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1248/3000\n",
      "\n",
      "Epoch 1248: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8780e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1249/3000\n",
      "\n",
      "Epoch 1249: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8607e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1250/3000\n",
      "\n",
      "Epoch 1250: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8637e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1251/3000\n",
      "\n",
      "Epoch 1251: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8619e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 150ms/step\n",
      "Epoch 1252/3000\n",
      "\n",
      "Epoch 1252: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8616e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1253/3000\n",
      "\n",
      "Epoch 1253: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8586e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1254/3000\n",
      "\n",
      "Epoch 1254: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8628e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 153ms/step\n",
      "Epoch 1255/3000\n",
      "\n",
      "Epoch 1255: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8357e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1256/3000\n",
      "\n",
      "Epoch 1256: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8654e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 156ms/step\n",
      "Epoch 1257/3000\n",
      "\n",
      "Epoch 1257: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8719e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 157ms/step\n",
      "Epoch 1258/3000\n",
      "\n",
      "Epoch 1258: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8624e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1259/3000\n",
      "\n",
      "Epoch 1259: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8640e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1260/3000\n",
      "\n",
      "Epoch 1260: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8678e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1261/3000\n",
      "\n",
      "Epoch 1261: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8680e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1262/3000\n",
      "\n",
      "Epoch 1262: val_mae did not improve from 0.03340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8490e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 149ms/step\n",
      "Epoch 1263/3000\n",
      "\n",
      "Epoch 1263: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8574e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1264/3000\n",
      "\n",
      "Epoch 1264: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8325e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1265/3000\n",
      "\n",
      "Epoch 1265: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8509e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1266/3000\n",
      "\n",
      "Epoch 1266: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8449e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 151ms/step\n",
      "Epoch 1267/3000\n",
      "\n",
      "Epoch 1267: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8447e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1268/3000\n",
      "\n",
      "Epoch 1268: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8819e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1269/3000\n",
      "\n",
      "Epoch 1269: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8698e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1270/3000\n",
      "\n",
      "Epoch 1270: val_mae did not improve from 0.03340\n",
      "151/151 - 22s - loss: 8.8511e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 22s/epoch - 149ms/step\n",
      "Epoch 1271/3000\n",
      "\n",
      "Epoch 1271: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8617e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1272/3000\n",
      "\n",
      "Epoch 1272: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8746e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 149ms/step\n",
      "Epoch 1273/3000\n",
      "\n",
      "Epoch 1273: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8652e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1274/3000\n",
      "\n",
      "Epoch 1274: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8587e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1275/3000\n",
      "\n",
      "Epoch 1275: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8622e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 159ms/step\n",
      "Epoch 1276/3000\n",
      "\n",
      "Epoch 1276: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8616e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 1277/3000\n",
      "\n",
      "Epoch 1277: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8617e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 152ms/step\n",
      "Epoch 1278/3000\n",
      "\n",
      "Epoch 1278: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8533e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1279/3000\n",
      "\n",
      "Epoch 1279: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8648e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1280/3000\n",
      "\n",
      "Epoch 1280: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8575e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1281/3000\n",
      "\n",
      "Epoch 1281: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8345e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1282/3000\n",
      "\n",
      "Epoch 1282: val_mae did not improve from 0.03340\n",
      "151/151 - 23s - loss: 8.8455e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1283/3000\n",
      "\n",
      "Epoch 1283: val_mae did not improve from 0.03340\n",
      "151/151 - 24s - loss: 8.8482e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 158ms/step\n",
      "Epoch 1284/3000\n",
      "\n",
      "Epoch 1284: val_mae improved from 0.03340 to 0.03339, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8503e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1285/3000\n",
      "\n",
      "Epoch 1285: val_mae did not improve from 0.03339\n",
      "151/151 - 23s - loss: 8.8452e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1286/3000\n",
      "\n",
      "Epoch 1286: val_mae did not improve from 0.03339\n",
      "151/151 - 24s - loss: 8.8538e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 158ms/step\n",
      "Epoch 1287/3000\n",
      "\n",
      "Epoch 1287: val_mae did not improve from 0.03339\n",
      "151/151 - 23s - loss: 8.8546e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1288/3000\n",
      "\n",
      "Epoch 1288: val_mae did not improve from 0.03339\n",
      "151/151 - 22s - loss: 8.8649e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 22s/epoch - 149ms/step\n",
      "Epoch 1289/3000\n",
      "\n",
      "Epoch 1289: val_mae improved from 0.03339 to 0.03333, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8587e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1290/3000\n",
      "\n",
      "Epoch 1290: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8669e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 155ms/step\n",
      "Epoch 1291/3000\n",
      "\n",
      "Epoch 1291: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8360e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1292/3000\n",
      "\n",
      "Epoch 1292: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8498e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 24s/epoch - 159ms/step\n",
      "Epoch 1293/3000\n",
      "\n",
      "Epoch 1293: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8486e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 157ms/step\n",
      "Epoch 1294/3000\n",
      "\n",
      "Epoch 1294: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8459e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 24s/epoch - 157ms/step\n",
      "Epoch 1295/3000\n",
      "\n",
      "Epoch 1295: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8515e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 159ms/step\n",
      "Epoch 1296/3000\n",
      "\n",
      "Epoch 1296: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8450e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 156ms/step\n",
      "Epoch 1297/3000\n",
      "\n",
      "Epoch 1297: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8575e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 151ms/step\n",
      "Epoch 1298/3000\n",
      "\n",
      "Epoch 1298: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8482e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1299/3000\n",
      "\n",
      "Epoch 1299: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8663e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 149ms/step\n",
      "Epoch 1300/3000\n",
      "\n",
      "Epoch 1300: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8439e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0341 - 23s/epoch - 151ms/step\n",
      "Epoch 1301/3000\n",
      "\n",
      "Epoch 1301: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8569e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1302/3000\n",
      "\n",
      "Epoch 1302: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8556e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1303/3000\n",
      "\n",
      "Epoch 1303: val_mae did not improve from 0.03333\n",
      "151/151 - 23s - loss: 8.8549e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1304/3000\n",
      "\n",
      "Epoch 1304: val_mae did not improve from 0.03333\n",
      "151/151 - 24s - loss: 8.8611e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 157ms/step\n",
      "Epoch 1305/3000\n",
      "\n",
      "Epoch 1305: val_mae improved from 0.03333 to 0.03331, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 8.8517e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 157ms/step\n",
      "Epoch 1306/3000\n",
      "\n",
      "Epoch 1306: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8272e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 153ms/step\n",
      "Epoch 1307/3000\n",
      "\n",
      "Epoch 1307: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8415e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0339 - 23s/epoch - 153ms/step\n",
      "Epoch 1308/3000\n",
      "\n",
      "Epoch 1308: val_mae did not improve from 0.03331\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8394e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 155ms/step\n",
      "Epoch 1309/3000\n",
      "\n",
      "Epoch 1309: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8636e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 155ms/step\n",
      "Epoch 1310/3000\n",
      "\n",
      "Epoch 1310: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8668e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1311/3000\n",
      "\n",
      "Epoch 1311: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8550e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1312/3000\n",
      "\n",
      "Epoch 1312: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8471e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1313/3000\n",
      "\n",
      "Epoch 1313: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8389e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 155ms/step\n",
      "Epoch 1314/3000\n",
      "\n",
      "Epoch 1314: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8570e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1315/3000\n",
      "\n",
      "Epoch 1315: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8232e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1316/3000\n",
      "\n",
      "Epoch 1316: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8439e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0340 - 23s/epoch - 155ms/step\n",
      "Epoch 1317/3000\n",
      "\n",
      "Epoch 1317: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8680e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1318/3000\n",
      "\n",
      "Epoch 1318: val_mae did not improve from 0.03331\n",
      "151/151 - 24s - loss: 8.8281e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 157ms/step\n",
      "Epoch 1319/3000\n",
      "\n",
      "Epoch 1319: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8483e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1320/3000\n",
      "\n",
      "Epoch 1320: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8405e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 155ms/step\n",
      "Epoch 1321/3000\n",
      "\n",
      "Epoch 1321: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8546e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1322/3000\n",
      "\n",
      "Epoch 1322: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8337e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1323/3000\n",
      "\n",
      "Epoch 1323: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8325e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1324/3000\n",
      "\n",
      "Epoch 1324: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8593e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1325/3000\n",
      "\n",
      "Epoch 1325: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8563e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1326/3000\n",
      "\n",
      "Epoch 1326: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8348e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1327/3000\n",
      "\n",
      "Epoch 1327: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8223e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1328/3000\n",
      "\n",
      "Epoch 1328: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8376e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1329/3000\n",
      "\n",
      "Epoch 1329: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8346e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1330/3000\n",
      "\n",
      "Epoch 1330: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8357e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1331/3000\n",
      "\n",
      "Epoch 1331: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8198e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1332/3000\n",
      "\n",
      "Epoch 1332: val_mae did not improve from 0.03331\n",
      "151/151 - 24s - loss: 8.8213e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 159ms/step\n",
      "Epoch 1333/3000\n",
      "\n",
      "Epoch 1333: val_mae did not improve from 0.03331\n",
      "151/151 - 23s - loss: 8.8371e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 156ms/step\n",
      "Epoch 1334/3000\n",
      "\n",
      "Epoch 1334: val_mae improved from 0.03331 to 0.03330, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8168e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1335/3000\n",
      "\n",
      "Epoch 1335: val_mae improved from 0.03330 to 0.03312, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8205e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1336/3000\n",
      "\n",
      "Epoch 1336: val_mae did not improve from 0.03312\n",
      "151/151 - 23s - loss: 8.8612e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1337/3000\n",
      "\n",
      "Epoch 1337: val_mae did not improve from 0.03312\n",
      "151/151 - 23s - loss: 8.8267e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1338/3000\n",
      "\n",
      "Epoch 1338: val_mae did not improve from 0.03312\n",
      "151/151 - 23s - loss: 8.8546e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1339/3000\n",
      "\n",
      "Epoch 1339: val_mae improved from 0.03312 to 0.03303, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8289e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1340/3000\n",
      "\n",
      "Epoch 1340: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8391e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1341/3000\n",
      "\n",
      "Epoch 1341: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8568e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1342/3000\n",
      "\n",
      "Epoch 1342: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8460e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1343/3000\n",
      "\n",
      "Epoch 1343: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8103e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1344/3000\n",
      "\n",
      "Epoch 1344: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8583e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1345/3000\n",
      "\n",
      "Epoch 1345: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8304e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1346/3000\n",
      "\n",
      "Epoch 1346: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8378e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1347/3000\n",
      "\n",
      "Epoch 1347: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8386e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1348/3000\n",
      "\n",
      "Epoch 1348: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8516e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1349/3000\n",
      "\n",
      "Epoch 1349: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8526e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1350/3000\n",
      "\n",
      "Epoch 1350: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8286e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 153ms/step\n",
      "Epoch 1351/3000\n",
      "\n",
      "Epoch 1351: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8404e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1352/3000\n",
      "\n",
      "Epoch 1352: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8362e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1353/3000\n",
      "\n",
      "Epoch 1353: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8414e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1354/3000\n",
      "\n",
      "Epoch 1354: val_mae did not improve from 0.03303\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 23s - loss: 8.8504e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1355/3000\n",
      "\n",
      "Epoch 1355: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8357e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1356/3000\n",
      "\n",
      "Epoch 1356: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8462e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1357/3000\n",
      "\n",
      "Epoch 1357: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8244e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1358/3000\n",
      "\n",
      "Epoch 1358: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8410e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1359/3000\n",
      "\n",
      "Epoch 1359: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8231e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1360/3000\n",
      "\n",
      "Epoch 1360: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8157e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1361/3000\n",
      "\n",
      "Epoch 1361: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8303e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1362/3000\n",
      "\n",
      "Epoch 1362: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8356e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1363/3000\n",
      "\n",
      "Epoch 1363: val_mae did not improve from 0.03303\n",
      "151/151 - 24s - loss: 8.8281e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 156ms/step\n",
      "Epoch 1364/3000\n",
      "\n",
      "Epoch 1364: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8415e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 155ms/step\n",
      "Epoch 1365/3000\n",
      "\n",
      "Epoch 1365: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8140e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1366/3000\n",
      "\n",
      "Epoch 1366: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8375e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1367/3000\n",
      "\n",
      "Epoch 1367: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8546e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1368/3000\n",
      "\n",
      "Epoch 1368: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8364e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1369/3000\n",
      "\n",
      "Epoch 1369: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8171e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1370/3000\n",
      "\n",
      "Epoch 1370: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8210e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1371/3000\n",
      "\n",
      "Epoch 1371: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8443e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1372/3000\n",
      "\n",
      "Epoch 1372: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8340e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1373/3000\n",
      "\n",
      "Epoch 1373: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8369e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 156ms/step\n",
      "Epoch 1374/3000\n",
      "\n",
      "Epoch 1374: val_mae did not improve from 0.03303\n",
      "151/151 - 24s - loss: 8.8487e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 24s/epoch - 159ms/step\n",
      "Epoch 1375/3000\n",
      "\n",
      "Epoch 1375: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8249e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1376/3000\n",
      "\n",
      "Epoch 1376: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8468e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1377/3000\n",
      "\n",
      "Epoch 1377: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8433e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1378/3000\n",
      "\n",
      "Epoch 1378: val_mae did not improve from 0.03303\n",
      "151/151 - 22s - loss: 8.8217e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 22s/epoch - 149ms/step\n",
      "Epoch 1379/3000\n",
      "\n",
      "Epoch 1379: val_mae did not improve from 0.03303\n",
      "151/151 - 22s - loss: 8.8377e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 22s/epoch - 149ms/step\n",
      "Epoch 1380/3000\n",
      "\n",
      "Epoch 1380: val_mae did not improve from 0.03303\n",
      "151/151 - 23s - loss: 8.8304e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1381/3000\n",
      "\n",
      "Epoch 1381: val_mae improved from 0.03303 to 0.03292, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8544e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1382/3000\n",
      "\n",
      "Epoch 1382: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8387e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1383/3000\n",
      "\n",
      "Epoch 1383: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8232e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1384/3000\n",
      "\n",
      "Epoch 1384: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8372e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 155ms/step\n",
      "Epoch 1385/3000\n",
      "\n",
      "Epoch 1385: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8297e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1386/3000\n",
      "\n",
      "Epoch 1386: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8298e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 149ms/step\n",
      "Epoch 1387/3000\n",
      "\n",
      "Epoch 1387: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8583e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1388/3000\n",
      "\n",
      "Epoch 1388: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8295e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 151ms/step\n",
      "Epoch 1389/3000\n",
      "\n",
      "Epoch 1389: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8330e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 150ms/step\n",
      "Epoch 1390/3000\n",
      "\n",
      "Epoch 1390: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8246e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 151ms/step\n",
      "Epoch 1391/3000\n",
      "\n",
      "Epoch 1391: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8079e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1392/3000\n",
      "\n",
      "Epoch 1392: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8442e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1393/3000\n",
      "\n",
      "Epoch 1393: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8491e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 23s/epoch - 153ms/step\n",
      "Epoch 1394/3000\n",
      "\n",
      "Epoch 1394: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8275e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1395/3000\n",
      "\n",
      "Epoch 1395: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8228e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 155ms/step\n",
      "Epoch 1396/3000\n",
      "\n",
      "Epoch 1396: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8262e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1397/3000\n",
      "\n",
      "Epoch 1397: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8130e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1398/3000\n",
      "\n",
      "Epoch 1398: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8321e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0338 - 24s/epoch - 156ms/step\n",
      "Epoch 1399/3000\n",
      "\n",
      "Epoch 1399: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8295e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1400/3000\n",
      "\n",
      "Epoch 1400: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8133e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1401/3000\n",
      "\n",
      "Epoch 1401: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8244e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1402/3000\n",
      "\n",
      "Epoch 1402: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8310e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 151ms/step\n",
      "Epoch 1403/3000\n",
      "\n",
      "Epoch 1403: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8454e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 156ms/step\n",
      "Epoch 1404/3000\n",
      "\n",
      "Epoch 1404: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8270e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1405/3000\n",
      "\n",
      "Epoch 1405: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8391e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1406/3000\n",
      "\n",
      "Epoch 1406: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8279e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n",
      "Epoch 1407/3000\n",
      "\n",
      "Epoch 1407: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8250e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1408/3000\n",
      "\n",
      "Epoch 1408: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8206e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1409/3000\n",
      "\n",
      "Epoch 1409: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8317e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1410/3000\n",
      "\n",
      "Epoch 1410: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8236e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 158ms/step\n",
      "Epoch 1411/3000\n",
      "\n",
      "Epoch 1411: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8286e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 24s/epoch - 159ms/step\n",
      "Epoch 1412/3000\n",
      "\n",
      "Epoch 1412: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8291e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 161ms/step\n",
      "Epoch 1413/3000\n",
      "\n",
      "Epoch 1413: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8143e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 158ms/step\n",
      "Epoch 1414/3000\n",
      "\n",
      "Epoch 1414: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8110e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1415/3000\n",
      "\n",
      "Epoch 1415: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8245e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 153ms/step\n",
      "Epoch 1416/3000\n",
      "\n",
      "Epoch 1416: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8135e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1417/3000\n",
      "\n",
      "Epoch 1417: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8118e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1418/3000\n",
      "\n",
      "Epoch 1418: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8196e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1419/3000\n",
      "\n",
      "Epoch 1419: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8155e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1420/3000\n",
      "\n",
      "Epoch 1420: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8212e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 158ms/step\n",
      "Epoch 1421/3000\n",
      "\n",
      "Epoch 1421: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8342e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 156ms/step\n",
      "Epoch 1422/3000\n",
      "\n",
      "Epoch 1422: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8131e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1423/3000\n",
      "\n",
      "Epoch 1423: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8081e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1424/3000\n",
      "\n",
      "Epoch 1424: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8216e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1425/3000\n",
      "\n",
      "Epoch 1425: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8025e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1426/3000\n",
      "\n",
      "Epoch 1426: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8127e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1427/3000\n",
      "\n",
      "Epoch 1427: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7973e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1428/3000\n",
      "\n",
      "Epoch 1428: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8096e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1429/3000\n",
      "\n",
      "Epoch 1429: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8315e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1430/3000\n",
      "\n",
      "Epoch 1430: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8098e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1431/3000\n",
      "\n",
      "Epoch 1431: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8108e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1432/3000\n",
      "\n",
      "Epoch 1432: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8182e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1433/3000\n",
      "\n",
      "Epoch 1433: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8271e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 156ms/step\n",
      "Epoch 1434/3000\n",
      "\n",
      "Epoch 1434: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8111e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1435/3000\n",
      "\n",
      "Epoch 1435: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8027e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1436/3000\n",
      "\n",
      "Epoch 1436: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8329e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1437/3000\n",
      "\n",
      "Epoch 1437: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8302e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1438/3000\n",
      "\n",
      "Epoch 1438: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8221e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 150ms/step\n",
      "Epoch 1439/3000\n",
      "\n",
      "Epoch 1439: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8110e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1440/3000\n",
      "\n",
      "Epoch 1440: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8275e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1441/3000\n",
      "\n",
      "Epoch 1441: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8209e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1442/3000\n",
      "\n",
      "Epoch 1442: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8073e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1443/3000\n",
      "\n",
      "Epoch 1443: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8305e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 154ms/step\n",
      "Epoch 1444/3000\n",
      "\n",
      "Epoch 1444: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8411e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1445/3000\n",
      "\n",
      "Epoch 1445: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8387e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 158ms/step\n",
      "Epoch 1446/3000\n",
      "\n",
      "Epoch 1446: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8043e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 153ms/step\n",
      "Epoch 1447/3000\n",
      "\n",
      "Epoch 1447: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8004e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1448/3000\n",
      "\n",
      "Epoch 1448: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8137e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1449/3000\n",
      "\n",
      "Epoch 1449: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8082e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 1450/3000\n",
      "\n",
      "Epoch 1450: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8174e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 159ms/step\n",
      "Epoch 1451/3000\n",
      "\n",
      "Epoch 1451: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8243e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n",
      "Epoch 1452/3000\n",
      "\n",
      "Epoch 1452: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8269e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 156ms/step\n",
      "Epoch 1453/3000\n",
      "\n",
      "Epoch 1453: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8222e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 157ms/step\n",
      "Epoch 1454/3000\n",
      "\n",
      "Epoch 1454: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8218e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1455/3000\n",
      "\n",
      "Epoch 1455: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8249e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 155ms/step\n",
      "Epoch 1456/3000\n",
      "\n",
      "Epoch 1456: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8198e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1457/3000\n",
      "\n",
      "Epoch 1457: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8173e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1458/3000\n",
      "\n",
      "Epoch 1458: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8176e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1459/3000\n",
      "\n",
      "Epoch 1459: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8176e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 160ms/step\n",
      "Epoch 1460/3000\n",
      "\n",
      "Epoch 1460: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8059e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1461/3000\n",
      "\n",
      "Epoch 1461: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7976e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1462/3000\n",
      "\n",
      "Epoch 1462: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8174e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 158ms/step\n",
      "Epoch 1463/3000\n",
      "\n",
      "Epoch 1463: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7997e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1464/3000\n",
      "\n",
      "Epoch 1464: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8096e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1465/3000\n",
      "\n",
      "Epoch 1465: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8083e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1466/3000\n",
      "\n",
      "Epoch 1466: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8329e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1467/3000\n",
      "\n",
      "Epoch 1467: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8184e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1468/3000\n",
      "\n",
      "Epoch 1468: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8226e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1469/3000\n",
      "\n",
      "Epoch 1469: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8236e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1470/3000\n",
      "\n",
      "Epoch 1470: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7931e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1471/3000\n",
      "\n",
      "Epoch 1471: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8172e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1472/3000\n",
      "\n",
      "Epoch 1472: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8148e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 155ms/step\n",
      "Epoch 1473/3000\n",
      "\n",
      "Epoch 1473: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8115e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1474/3000\n",
      "\n",
      "Epoch 1474: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8089e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1475/3000\n",
      "\n",
      "Epoch 1475: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8015e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1476/3000\n",
      "\n",
      "Epoch 1476: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8085e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1477/3000\n",
      "\n",
      "Epoch 1477: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8152e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1478/3000\n",
      "\n",
      "Epoch 1478: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8123e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1479/3000\n",
      "\n",
      "Epoch 1479: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7973e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1480/3000\n",
      "\n",
      "Epoch 1480: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8145e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 155ms/step\n",
      "Epoch 1481/3000\n",
      "\n",
      "Epoch 1481: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7937e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 156ms/step\n",
      "Epoch 1482/3000\n",
      "\n",
      "Epoch 1482: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7954e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1483/3000\n",
      "\n",
      "Epoch 1483: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8121e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1484/3000\n",
      "\n",
      "Epoch 1484: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8084e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1485/3000\n",
      "\n",
      "Epoch 1485: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7997e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1486/3000\n",
      "\n",
      "Epoch 1486: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8148e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1487/3000\n",
      "\n",
      "Epoch 1487: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8204e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1488/3000\n",
      "\n",
      "Epoch 1488: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8213e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1489/3000\n",
      "\n",
      "Epoch 1489: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8001e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1490/3000\n",
      "\n",
      "Epoch 1490: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8149e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1491/3000\n",
      "\n",
      "Epoch 1491: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8182e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1492/3000\n",
      "\n",
      "Epoch 1492: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7945e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1493/3000\n",
      "\n",
      "Epoch 1493: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7998e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1494/3000\n",
      "\n",
      "Epoch 1494: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8078e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1495/3000\n",
      "\n",
      "Epoch 1495: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8171e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1496/3000\n",
      "\n",
      "Epoch 1496: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8275e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 152ms/step\n",
      "Epoch 1497/3000\n",
      "\n",
      "Epoch 1497: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8400e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 155ms/step\n",
      "Epoch 1498/3000\n",
      "\n",
      "Epoch 1498: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7987e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1499/3000\n",
      "\n",
      "Epoch 1499: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8280e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1500/3000\n",
      "\n",
      "Epoch 1500: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8023e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 159ms/step\n",
      "Epoch 1501/3000\n",
      "\n",
      "Epoch 1501: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8050e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 24s/epoch - 158ms/step\n",
      "Epoch 1502/3000\n",
      "\n",
      "Epoch 1502: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8102e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1503/3000\n",
      "\n",
      "Epoch 1503: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8022e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1504/3000\n",
      "\n",
      "Epoch 1504: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8195e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1505/3000\n",
      "\n",
      "Epoch 1505: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8164e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1506/3000\n",
      "\n",
      "Epoch 1506: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8001e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n",
      "Epoch 1507/3000\n",
      "\n",
      "Epoch 1507: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8006e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1508/3000\n",
      "\n",
      "Epoch 1508: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7981e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1509/3000\n",
      "\n",
      "Epoch 1509: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8063e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1510/3000\n",
      "\n",
      "Epoch 1510: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8211e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1511/3000\n",
      "\n",
      "Epoch 1511: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8171e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1512/3000\n",
      "\n",
      "Epoch 1512: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8192e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1513/3000\n",
      "\n",
      "Epoch 1513: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8181e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1514/3000\n",
      "\n",
      "Epoch 1514: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8231e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1515/3000\n",
      "\n",
      "Epoch 1515: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8109e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1516/3000\n",
      "\n",
      "Epoch 1516: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8229e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1517/3000\n",
      "\n",
      "Epoch 1517: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7904e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1518/3000\n",
      "\n",
      "Epoch 1518: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7941e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1519/3000\n",
      "\n",
      "Epoch 1519: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7909e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1520/3000\n",
      "\n",
      "Epoch 1520: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7955e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1521/3000\n",
      "\n",
      "Epoch 1521: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7967e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1522/3000\n",
      "\n",
      "Epoch 1522: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8119e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1523/3000\n",
      "\n",
      "Epoch 1523: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8051e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1524/3000\n",
      "\n",
      "Epoch 1524: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8144e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1525/3000\n",
      "\n",
      "Epoch 1525: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7944e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1526/3000\n",
      "\n",
      "Epoch 1526: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8060e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 1527/3000\n",
      "\n",
      "Epoch 1527: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8335e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 158ms/step\n",
      "Epoch 1528/3000\n",
      "\n",
      "Epoch 1528: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7944e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 157ms/step\n",
      "Epoch 1529/3000\n",
      "\n",
      "Epoch 1529: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8150e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 158ms/step\n",
      "Epoch 1530/3000\n",
      "\n",
      "Epoch 1530: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8068e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1531/3000\n",
      "\n",
      "Epoch 1531: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7904e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1532/3000\n",
      "\n",
      "Epoch 1532: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7928e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1533/3000\n",
      "\n",
      "Epoch 1533: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7957e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1534/3000\n",
      "\n",
      "Epoch 1534: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8063e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1535/3000\n",
      "\n",
      "Epoch 1535: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8017e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1536/3000\n",
      "\n",
      "Epoch 1536: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8026e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 158ms/step\n",
      "Epoch 1537/3000\n",
      "\n",
      "Epoch 1537: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8068e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1538/3000\n",
      "\n",
      "Epoch 1538: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8028e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 158ms/step\n",
      "Epoch 1539/3000\n",
      "\n",
      "Epoch 1539: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8106e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 1540/3000\n",
      "\n",
      "Epoch 1540: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7971e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1541/3000\n",
      "\n",
      "Epoch 1541: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7953e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1542/3000\n",
      "\n",
      "Epoch 1542: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8072e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1543/3000\n",
      "\n",
      "Epoch 1543: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8128e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1544/3000\n",
      "\n",
      "Epoch 1544: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7847e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1545/3000\n",
      "\n",
      "Epoch 1545: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7992e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1546/3000\n",
      "\n",
      "Epoch 1546: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8012e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1547/3000\n",
      "\n",
      "Epoch 1547: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8073e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1548/3000\n",
      "\n",
      "Epoch 1548: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8116e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1549/3000\n",
      "\n",
      "Epoch 1549: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8105e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1550/3000\n",
      "\n",
      "Epoch 1550: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7785e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1551/3000\n",
      "\n",
      "Epoch 1551: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7963e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1552/3000\n",
      "\n",
      "Epoch 1552: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7896e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1553/3000\n",
      "\n",
      "Epoch 1553: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7939e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1554/3000\n",
      "\n",
      "Epoch 1554: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8023e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 153ms/step\n",
      "Epoch 1555/3000\n",
      "\n",
      "Epoch 1555: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8199e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1556/3000\n",
      "\n",
      "Epoch 1556: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8077e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1557/3000\n",
      "\n",
      "Epoch 1557: val_mae did not improve from 0.03292\n",
      "151/151 - 22s - loss: 8.7838e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 22s/epoch - 149ms/step\n",
      "Epoch 1558/3000\n",
      "\n",
      "Epoch 1558: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8073e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1559/3000\n",
      "\n",
      "Epoch 1559: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7852e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1560/3000\n",
      "\n",
      "Epoch 1560: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8034e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1561/3000\n",
      "\n",
      "Epoch 1561: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8020e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1562/3000\n",
      "\n",
      "Epoch 1562: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8055e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 157ms/step\n",
      "Epoch 1563/3000\n",
      "\n",
      "Epoch 1563: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8072e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1564/3000\n",
      "\n",
      "Epoch 1564: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8044e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1565/3000\n",
      "\n",
      "Epoch 1565: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7935e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1566/3000\n",
      "\n",
      "Epoch 1566: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8189e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1567/3000\n",
      "\n",
      "Epoch 1567: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7846e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1568/3000\n",
      "\n",
      "Epoch 1568: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7807e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1569/3000\n",
      "\n",
      "Epoch 1569: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7962e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1570/3000\n",
      "\n",
      "Epoch 1570: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8023e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1571/3000\n",
      "\n",
      "Epoch 1571: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7863e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1572/3000\n",
      "\n",
      "Epoch 1572: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7991e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1573/3000\n",
      "\n",
      "Epoch 1573: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8028e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1574/3000\n",
      "\n",
      "Epoch 1574: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7829e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1575/3000\n",
      "\n",
      "Epoch 1575: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7833e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1576/3000\n",
      "\n",
      "Epoch 1576: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7801e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1577/3000\n",
      "\n",
      "Epoch 1577: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7940e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1578/3000\n",
      "\n",
      "Epoch 1578: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8079e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1579/3000\n",
      "\n",
      "Epoch 1579: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8066e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1580/3000\n",
      "\n",
      "Epoch 1580: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8065e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 155ms/step\n",
      "Epoch 1581/3000\n",
      "\n",
      "Epoch 1581: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7997e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1582/3000\n",
      "\n",
      "Epoch 1582: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7992e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1583/3000\n",
      "\n",
      "Epoch 1583: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.8048e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1584/3000\n",
      "\n",
      "Epoch 1584: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7836e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1585/3000\n",
      "\n",
      "Epoch 1585: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7881e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1586/3000\n",
      "\n",
      "Epoch 1586: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.7964e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1587/3000\n",
      "\n",
      "Epoch 1587: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7969e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0337 - 23s/epoch - 152ms/step\n",
      "Epoch 1588/3000\n",
      "\n",
      "Epoch 1588: val_mae did not improve from 0.03292\n",
      "151/151 - 23s - loss: 8.7955e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1589/3000\n",
      "\n",
      "Epoch 1589: val_mae did not improve from 0.03292\n",
      "151/151 - 24s - loss: 8.8004e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 24s/epoch - 161ms/step\n",
      "Epoch 1590/3000\n",
      "\n",
      "Epoch 1590: val_mae improved from 0.03292 to 0.03290, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 8.7987e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1591/3000\n",
      "\n",
      "Epoch 1591: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7909e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1592/3000\n",
      "\n",
      "Epoch 1592: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7969e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1593/3000\n",
      "\n",
      "Epoch 1593: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7923e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 155ms/step\n",
      "Epoch 1594/3000\n",
      "\n",
      "Epoch 1594: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.8079e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1595/3000\n",
      "\n",
      "Epoch 1595: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7974e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1596/3000\n",
      "\n",
      "Epoch 1596: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7952e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1597/3000\n",
      "\n",
      "Epoch 1597: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7825e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1598/3000\n",
      "\n",
      "Epoch 1598: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7836e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1599/3000\n",
      "\n",
      "Epoch 1599: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.8057e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 154ms/step\n",
      "Epoch 1600/3000\n",
      "\n",
      "Epoch 1600: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.8051e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1601/3000\n",
      "\n",
      "Epoch 1601: val_mae did not improve from 0.03290\n",
      "151/151 - 24s - loss: 8.7999e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 162ms/step\n",
      "Epoch 1602/3000\n",
      "\n",
      "Epoch 1602: val_mae did not improve from 0.03290\n",
      "151/151 - 24s - loss: 8.7989e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 159ms/step\n",
      "Epoch 1603/3000\n",
      "\n",
      "Epoch 1603: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7877e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1604/3000\n",
      "\n",
      "Epoch 1604: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.8122e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1605/3000\n",
      "\n",
      "Epoch 1605: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7872e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1606/3000\n",
      "\n",
      "Epoch 1606: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7987e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1607/3000\n",
      "\n",
      "Epoch 1607: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7860e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1608/3000\n",
      "\n",
      "Epoch 1608: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7900e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1609/3000\n",
      "\n",
      "Epoch 1609: val_mae improved from 0.03290 to 0.03290, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7964e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1610/3000\n",
      "\n",
      "Epoch 1610: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7975e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 154ms/step\n",
      "Epoch 1611/3000\n",
      "\n",
      "Epoch 1611: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.8027e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1612/3000\n",
      "\n",
      "Epoch 1612: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7843e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1613/3000\n",
      "\n",
      "Epoch 1613: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7875e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1614/3000\n",
      "\n",
      "Epoch 1614: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7873e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1615/3000\n",
      "\n",
      "Epoch 1615: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7875e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1616/3000\n",
      "\n",
      "Epoch 1616: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7855e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1617/3000\n",
      "\n",
      "Epoch 1617: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7850e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1618/3000\n",
      "\n",
      "Epoch 1618: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7819e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1619/3000\n",
      "\n",
      "Epoch 1619: val_mae did not improve from 0.03290\n",
      "151/151 - 24s - loss: 8.7782e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 157ms/step\n",
      "Epoch 1620/3000\n",
      "\n",
      "Epoch 1620: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7866e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1621/3000\n",
      "\n",
      "Epoch 1621: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7849e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1622/3000\n",
      "\n",
      "Epoch 1622: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7678e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1623/3000\n",
      "\n",
      "Epoch 1623: val_mae did not improve from 0.03290\n",
      "151/151 - 23s - loss: 8.7849e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1624/3000\n",
      "\n",
      "Epoch 1624: val_mae improved from 0.03290 to 0.03284, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.8004e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 1625/3000\n",
      "\n",
      "Epoch 1625: val_mae did not improve from 0.03284\n",
      "151/151 - 24s - loss: 8.8004e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 1626/3000\n",
      "\n",
      "Epoch 1626: val_mae did not improve from 0.03284\n",
      "151/151 - 24s - loss: 8.7904e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1627/3000\n",
      "\n",
      "Epoch 1627: val_mae did not improve from 0.03284\n",
      "151/151 - 24s - loss: 8.7905e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1628/3000\n",
      "\n",
      "Epoch 1628: val_mae did not improve from 0.03284\n",
      "151/151 - 24s - loss: 8.7933e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1629/3000\n",
      "\n",
      "Epoch 1629: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7887e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1630/3000\n",
      "\n",
      "Epoch 1630: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7765e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1631/3000\n",
      "\n",
      "Epoch 1631: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7785e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1632/3000\n",
      "\n",
      "Epoch 1632: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7734e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1633/3000\n",
      "\n",
      "Epoch 1633: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7746e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1634/3000\n",
      "\n",
      "Epoch 1634: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7693e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1635/3000\n",
      "\n",
      "Epoch 1635: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7795e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1636/3000\n",
      "\n",
      "Epoch 1636: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7825e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1637/3000\n",
      "\n",
      "Epoch 1637: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7980e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1638/3000\n",
      "\n",
      "Epoch 1638: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7763e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1639/3000\n",
      "\n",
      "Epoch 1639: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7899e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1640/3000\n",
      "\n",
      "Epoch 1640: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7824e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1641/3000\n",
      "\n",
      "Epoch 1641: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7852e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1642/3000\n",
      "\n",
      "Epoch 1642: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7612e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1643/3000\n",
      "\n",
      "Epoch 1643: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7777e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 150ms/step\n",
      "Epoch 1644/3000\n",
      "\n",
      "Epoch 1644: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7990e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1645/3000\n",
      "\n",
      "Epoch 1645: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7885e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0336 - 23s/epoch - 150ms/step\n",
      "Epoch 1646/3000\n",
      "\n",
      "Epoch 1646: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7957e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1647/3000\n",
      "\n",
      "Epoch 1647: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7930e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 150ms/step\n",
      "Epoch 1648/3000\n",
      "\n",
      "Epoch 1648: val_mae did not improve from 0.03284\n",
      "151/151 - 22s - loss: 8.7897e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 22s/epoch - 149ms/step\n",
      "Epoch 1649/3000\n",
      "\n",
      "Epoch 1649: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7732e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 149ms/step\n",
      "Epoch 1650/3000\n",
      "\n",
      "Epoch 1650: val_mae did not improve from 0.03284\n",
      "151/151 - 24s - loss: 8.7692e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1651/3000\n",
      "\n",
      "Epoch 1651: val_mae did not improve from 0.03284\n",
      "151/151 - 23s - loss: 8.7872e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1652/3000\n",
      "\n",
      "Epoch 1652: val_mae improved from 0.03284 to 0.03281, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 8.7850e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 157ms/step\n",
      "Epoch 1653/3000\n",
      "\n",
      "Epoch 1653: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7738e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1654/3000\n",
      "\n",
      "Epoch 1654: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7721e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1655/3000\n",
      "\n",
      "Epoch 1655: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7808e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1656/3000\n",
      "\n",
      "Epoch 1656: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7785e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 1657/3000\n",
      "\n",
      "Epoch 1657: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7748e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1658/3000\n",
      "\n",
      "Epoch 1658: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7695e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1659/3000\n",
      "\n",
      "Epoch 1659: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7825e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1660/3000\n",
      "\n",
      "Epoch 1660: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7951e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1661/3000\n",
      "\n",
      "Epoch 1661: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7827e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1662/3000\n",
      "\n",
      "Epoch 1662: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7867e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1663/3000\n",
      "\n",
      "Epoch 1663: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.8053e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 158ms/step\n",
      "Epoch 1664/3000\n",
      "\n",
      "Epoch 1664: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7776e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1665/3000\n",
      "\n",
      "Epoch 1665: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7880e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1666/3000\n",
      "\n",
      "Epoch 1666: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7880e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 156ms/step\n",
      "Epoch 1667/3000\n",
      "\n",
      "Epoch 1667: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7590e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1668/3000\n",
      "\n",
      "Epoch 1668: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7929e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1669/3000\n",
      "\n",
      "Epoch 1669: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7840e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 155ms/step\n",
      "Epoch 1670/3000\n",
      "\n",
      "Epoch 1670: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7664e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 1671/3000\n",
      "\n",
      "Epoch 1671: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.8166e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1672/3000\n",
      "\n",
      "Epoch 1672: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7957e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 149ms/step\n",
      "Epoch 1673/3000\n",
      "\n",
      "Epoch 1673: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7846e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1674/3000\n",
      "\n",
      "Epoch 1674: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7822e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1675/3000\n",
      "\n",
      "Epoch 1675: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7822e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 156ms/step\n",
      "Epoch 1676/3000\n",
      "\n",
      "Epoch 1676: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7831e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1677/3000\n",
      "\n",
      "Epoch 1677: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7671e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 1678/3000\n",
      "\n",
      "Epoch 1678: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7934e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1679/3000\n",
      "\n",
      "Epoch 1679: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7739e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1680/3000\n",
      "\n",
      "Epoch 1680: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7881e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1681/3000\n",
      "\n",
      "Epoch 1681: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7836e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1682/3000\n",
      "\n",
      "Epoch 1682: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7739e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1683/3000\n",
      "\n",
      "Epoch 1683: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7808e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1684/3000\n",
      "\n",
      "Epoch 1684: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7701e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1685/3000\n",
      "\n",
      "Epoch 1685: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7699e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1686/3000\n",
      "\n",
      "Epoch 1686: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7692e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1687/3000\n",
      "\n",
      "Epoch 1687: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7653e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1688/3000\n",
      "\n",
      "Epoch 1688: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7891e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1689/3000\n",
      "\n",
      "Epoch 1689: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7779e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 1690/3000\n",
      "\n",
      "Epoch 1690: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7724e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1691/3000\n",
      "\n",
      "Epoch 1691: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7716e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1692/3000\n",
      "\n",
      "Epoch 1692: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7700e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1693/3000\n",
      "\n",
      "Epoch 1693: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7826e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1694/3000\n",
      "\n",
      "Epoch 1694: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7833e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1695/3000\n",
      "\n",
      "Epoch 1695: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7997e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 1696/3000\n",
      "\n",
      "Epoch 1696: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7764e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1697/3000\n",
      "\n",
      "Epoch 1697: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7742e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1698/3000\n",
      "\n",
      "Epoch 1698: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7649e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1699/3000\n",
      "\n",
      "Epoch 1699: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7740e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1700/3000\n",
      "\n",
      "Epoch 1700: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7762e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1701/3000\n",
      "\n",
      "Epoch 1701: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7857e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1702/3000\n",
      "\n",
      "Epoch 1702: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7834e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1703/3000\n",
      "\n",
      "Epoch 1703: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7966e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1704/3000\n",
      "\n",
      "Epoch 1704: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7612e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1705/3000\n",
      "\n",
      "Epoch 1705: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7768e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1706/3000\n",
      "\n",
      "Epoch 1706: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7655e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1707/3000\n",
      "\n",
      "Epoch 1707: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7743e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1708/3000\n",
      "\n",
      "Epoch 1708: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7906e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1709/3000\n",
      "\n",
      "Epoch 1709: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7613e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1710/3000\n",
      "\n",
      "Epoch 1710: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7870e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1711/3000\n",
      "\n",
      "Epoch 1711: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7652e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1712/3000\n",
      "\n",
      "Epoch 1712: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7516e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1713/3000\n",
      "\n",
      "Epoch 1713: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7838e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1714/3000\n",
      "\n",
      "Epoch 1714: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7655e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1715/3000\n",
      "\n",
      "Epoch 1715: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7882e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1716/3000\n",
      "\n",
      "Epoch 1716: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7726e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 1717/3000\n",
      "\n",
      "Epoch 1717: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7811e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1718/3000\n",
      "\n",
      "Epoch 1718: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7858e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1719/3000\n",
      "\n",
      "Epoch 1719: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7773e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1720/3000\n",
      "\n",
      "Epoch 1720: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7581e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1721/3000\n",
      "\n",
      "Epoch 1721: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7753e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1722/3000\n",
      "\n",
      "Epoch 1722: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7662e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1723/3000\n",
      "\n",
      "Epoch 1723: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7651e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1724/3000\n",
      "\n",
      "Epoch 1724: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7646e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1725/3000\n",
      "\n",
      "Epoch 1725: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.8003e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1726/3000\n",
      "\n",
      "Epoch 1726: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7796e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1727/3000\n",
      "\n",
      "Epoch 1727: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7784e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1728/3000\n",
      "\n",
      "Epoch 1728: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7850e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1729/3000\n",
      "\n",
      "Epoch 1729: val_mae did not improve from 0.03281\n",
      "151/151 - 24s - loss: 8.7727e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 160ms/step\n",
      "Epoch 1730/3000\n",
      "\n",
      "Epoch 1730: val_mae did not improve from 0.03281\n",
      "151/151 - 23s - loss: 8.7671e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1731/3000\n",
      "\n",
      "Epoch 1731: val_mae improved from 0.03281 to 0.03279, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7618e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 1732/3000\n",
      "\n",
      "Epoch 1732: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7762e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 156ms/step\n",
      "Epoch 1733/3000\n",
      "\n",
      "Epoch 1733: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7872e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1734/3000\n",
      "\n",
      "Epoch 1734: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7835e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1735/3000\n",
      "\n",
      "Epoch 1735: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7795e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1736/3000\n",
      "\n",
      "Epoch 1736: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7875e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 1737/3000\n",
      "\n",
      "Epoch 1737: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.8029e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 1738/3000\n",
      "\n",
      "Epoch 1738: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7774e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1739/3000\n",
      "\n",
      "Epoch 1739: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7770e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1740/3000\n",
      "\n",
      "Epoch 1740: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7849e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1741/3000\n",
      "\n",
      "Epoch 1741: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7540e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1742/3000\n",
      "\n",
      "Epoch 1742: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7652e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1743/3000\n",
      "\n",
      "Epoch 1743: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7777e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1744/3000\n",
      "\n",
      "Epoch 1744: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7778e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1745/3000\n",
      "\n",
      "Epoch 1745: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7736e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 1746/3000\n",
      "\n",
      "Epoch 1746: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7704e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 1747/3000\n",
      "\n",
      "Epoch 1747: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7725e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1748/3000\n",
      "\n",
      "Epoch 1748: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7615e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1749/3000\n",
      "\n",
      "Epoch 1749: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7845e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1750/3000\n",
      "\n",
      "Epoch 1750: val_mae did not improve from 0.03279\n",
      "151/151 - 23s - loss: 8.7769e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1751/3000\n",
      "\n",
      "Epoch 1751: val_mae improved from 0.03279 to 0.03270, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7747e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 152ms/step\n",
      "Epoch 1752/3000\n",
      "\n",
      "Epoch 1752: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7796e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1753/3000\n",
      "\n",
      "Epoch 1753: val_mae did not improve from 0.03270\n",
      "151/151 - 24s - loss: 8.7626e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 1754/3000\n",
      "\n",
      "Epoch 1754: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7739e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1755/3000\n",
      "\n",
      "Epoch 1755: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7613e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1756/3000\n",
      "\n",
      "Epoch 1756: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7562e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1757/3000\n",
      "\n",
      "Epoch 1757: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7669e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1758/3000\n",
      "\n",
      "Epoch 1758: val_mae did not improve from 0.03270\n",
      "151/151 - 24s - loss: 8.7619e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1759/3000\n",
      "\n",
      "Epoch 1759: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7420e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1760/3000\n",
      "\n",
      "Epoch 1760: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7757e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1761/3000\n",
      "\n",
      "Epoch 1761: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7683e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 1762/3000\n",
      "\n",
      "Epoch 1762: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7686e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 151ms/step\n",
      "Epoch 1763/3000\n",
      "\n",
      "Epoch 1763: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7625e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1764/3000\n",
      "\n",
      "Epoch 1764: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7751e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1765/3000\n",
      "\n",
      "Epoch 1765: val_mae did not improve from 0.03270\n",
      "151/151 - 23s - loss: 8.7712e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1766/3000\n",
      "\n",
      "Epoch 1766: val_mae improved from 0.03270 to 0.03268, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7469e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 154ms/step\n",
      "Epoch 1767/3000\n",
      "\n",
      "Epoch 1767: val_mae did not improve from 0.03268\n",
      "151/151 - 22s - loss: 8.7641e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 22s/epoch - 148ms/step\n",
      "Epoch 1768/3000\n",
      "\n",
      "Epoch 1768: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7499e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1769/3000\n",
      "\n",
      "Epoch 1769: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7815e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1770/3000\n",
      "\n",
      "Epoch 1770: val_mae did not improve from 0.03268\n",
      "151/151 - 22s - loss: 8.7790e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 22s/epoch - 149ms/step\n",
      "Epoch 1771/3000\n",
      "\n",
      "Epoch 1771: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7686e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1772/3000\n",
      "\n",
      "Epoch 1772: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7476e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1773/3000\n",
      "\n",
      "Epoch 1773: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7676e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1774/3000\n",
      "\n",
      "Epoch 1774: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7885e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1775/3000\n",
      "\n",
      "Epoch 1775: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7564e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1776/3000\n",
      "\n",
      "Epoch 1776: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7694e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 159ms/step\n",
      "Epoch 1777/3000\n",
      "\n",
      "Epoch 1777: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7691e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 158ms/step\n",
      "Epoch 1778/3000\n",
      "\n",
      "Epoch 1778: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7747e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 1779/3000\n",
      "\n",
      "Epoch 1779: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7729e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 154ms/step\n",
      "Epoch 1780/3000\n",
      "\n",
      "Epoch 1780: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7678e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1781/3000\n",
      "\n",
      "Epoch 1781: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7681e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1782/3000\n",
      "\n",
      "Epoch 1782: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7533e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1783/3000\n",
      "\n",
      "Epoch 1783: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7802e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1784/3000\n",
      "\n",
      "Epoch 1784: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7710e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1785/3000\n",
      "\n",
      "Epoch 1785: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7743e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 157ms/step\n",
      "Epoch 1786/3000\n",
      "\n",
      "Epoch 1786: val_mae did not improve from 0.03268\n",
      "151/151 - 22s - loss: 8.7561e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 22s/epoch - 148ms/step\n",
      "Epoch 1787/3000\n",
      "\n",
      "Epoch 1787: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7568e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1788/3000\n",
      "\n",
      "Epoch 1788: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7803e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1789/3000\n",
      "\n",
      "Epoch 1789: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7858e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1790/3000\n",
      "\n",
      "Epoch 1790: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7711e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1791/3000\n",
      "\n",
      "Epoch 1791: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7494e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1792/3000\n",
      "\n",
      "Epoch 1792: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7745e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 149ms/step\n",
      "Epoch 1793/3000\n",
      "\n",
      "Epoch 1793: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7590e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 1794/3000\n",
      "\n",
      "Epoch 1794: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7697e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1795/3000\n",
      "\n",
      "Epoch 1795: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7599e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 1796/3000\n",
      "\n",
      "Epoch 1796: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7572e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1797/3000\n",
      "\n",
      "Epoch 1797: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7690e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1798/3000\n",
      "\n",
      "Epoch 1798: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7726e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1799/3000\n",
      "\n",
      "Epoch 1799: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7428e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 149ms/step\n",
      "Epoch 1800/3000\n",
      "\n",
      "Epoch 1800: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7658e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1801/3000\n",
      "\n",
      "Epoch 1801: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7661e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1802/3000\n",
      "\n",
      "Epoch 1802: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7525e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n",
      "Epoch 1803/3000\n",
      "\n",
      "Epoch 1803: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7568e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n",
      "Epoch 1804/3000\n",
      "\n",
      "Epoch 1804: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7818e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1805/3000\n",
      "\n",
      "Epoch 1805: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7459e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1806/3000\n",
      "\n",
      "Epoch 1806: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7707e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1807/3000\n",
      "\n",
      "Epoch 1807: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7564e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1808/3000\n",
      "\n",
      "Epoch 1808: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7767e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 156ms/step\n",
      "Epoch 1809/3000\n",
      "\n",
      "Epoch 1809: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7496e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 161ms/step\n",
      "Epoch 1810/3000\n",
      "\n",
      "Epoch 1810: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7792e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1811/3000\n",
      "\n",
      "Epoch 1811: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7600e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 158ms/step\n",
      "Epoch 1812/3000\n",
      "\n",
      "Epoch 1812: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7750e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 159ms/step\n",
      "Epoch 1813/3000\n",
      "\n",
      "Epoch 1813: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7669e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 1814/3000\n",
      "\n",
      "Epoch 1814: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7601e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1815/3000\n",
      "\n",
      "Epoch 1815: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7852e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 1816/3000\n",
      "\n",
      "Epoch 1816: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7449e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1817/3000\n",
      "\n",
      "Epoch 1817: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7566e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 1818/3000\n",
      "\n",
      "Epoch 1818: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7600e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1819/3000\n",
      "\n",
      "Epoch 1819: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7666e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1820/3000\n",
      "\n",
      "Epoch 1820: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7743e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 24s/epoch - 158ms/step\n",
      "Epoch 1821/3000\n",
      "\n",
      "Epoch 1821: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7613e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 157ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1822/3000\n",
      "\n",
      "Epoch 1822: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7733e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 1823/3000\n",
      "\n",
      "Epoch 1823: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7548e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1824/3000\n",
      "\n",
      "Epoch 1824: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7807e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 158ms/step\n",
      "Epoch 1825/3000\n",
      "\n",
      "Epoch 1825: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7538e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 160ms/step\n",
      "Epoch 1826/3000\n",
      "\n",
      "Epoch 1826: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7687e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 160ms/step\n",
      "Epoch 1827/3000\n",
      "\n",
      "Epoch 1827: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7488e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 157ms/step\n",
      "Epoch 1828/3000\n",
      "\n",
      "Epoch 1828: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7552e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 157ms/step\n",
      "Epoch 1829/3000\n",
      "\n",
      "Epoch 1829: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7553e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1830/3000\n",
      "\n",
      "Epoch 1830: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7580e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 155ms/step\n",
      "Epoch 1831/3000\n",
      "\n",
      "Epoch 1831: val_mae did not improve from 0.03268\n",
      "151/151 - 24s - loss: 8.7570e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1832/3000\n",
      "\n",
      "Epoch 1832: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7760e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1833/3000\n",
      "\n",
      "Epoch 1833: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7641e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 1834/3000\n",
      "\n",
      "Epoch 1834: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7625e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 1835/3000\n",
      "\n",
      "Epoch 1835: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7447e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1836/3000\n",
      "\n",
      "Epoch 1836: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7722e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1837/3000\n",
      "\n",
      "Epoch 1837: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7640e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1838/3000\n",
      "\n",
      "Epoch 1838: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7552e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1839/3000\n",
      "\n",
      "Epoch 1839: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7548e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 1840/3000\n",
      "\n",
      "Epoch 1840: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7729e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 155ms/step\n",
      "Epoch 1841/3000\n",
      "\n",
      "Epoch 1841: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7682e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1842/3000\n",
      "\n",
      "Epoch 1842: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7561e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1843/3000\n",
      "\n",
      "Epoch 1843: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7537e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1844/3000\n",
      "\n",
      "Epoch 1844: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7682e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1845/3000\n",
      "\n",
      "Epoch 1845: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7494e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1846/3000\n",
      "\n",
      "Epoch 1846: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7663e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1847/3000\n",
      "\n",
      "Epoch 1847: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7922e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1848/3000\n",
      "\n",
      "Epoch 1848: val_mae did not improve from 0.03268\n",
      "151/151 - 22s - loss: 8.7549e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 22s/epoch - 149ms/step\n",
      "Epoch 1849/3000\n",
      "\n",
      "Epoch 1849: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7640e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 1850/3000\n",
      "\n",
      "Epoch 1850: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7577e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 1851/3000\n",
      "\n",
      "Epoch 1851: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7796e-04 - mae: 0.0208 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1852/3000\n",
      "\n",
      "Epoch 1852: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7577e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1853/3000\n",
      "\n",
      "Epoch 1853: val_mae did not improve from 0.03268\n",
      "151/151 - 23s - loss: 8.7583e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 149ms/step\n",
      "Epoch 1854/3000\n",
      "\n",
      "Epoch 1854: val_mae improved from 0.03268 to 0.03266, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7748e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 152ms/step\n",
      "Epoch 1855/3000\n",
      "\n",
      "Epoch 1855: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7545e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 1856/3000\n",
      "\n",
      "Epoch 1856: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7586e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1857/3000\n",
      "\n",
      "Epoch 1857: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7503e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1858/3000\n",
      "\n",
      "Epoch 1858: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7547e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1859/3000\n",
      "\n",
      "Epoch 1859: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7644e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1860/3000\n",
      "\n",
      "Epoch 1860: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7489e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1861/3000\n",
      "\n",
      "Epoch 1861: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7643e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1862/3000\n",
      "\n",
      "Epoch 1862: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7487e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1863/3000\n",
      "\n",
      "Epoch 1863: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7415e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 153ms/step\n",
      "Epoch 1864/3000\n",
      "\n",
      "Epoch 1864: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7690e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 1865/3000\n",
      "\n",
      "Epoch 1865: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7272e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 1866/3000\n",
      "\n",
      "Epoch 1866: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7830e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1867/3000\n",
      "\n",
      "Epoch 1867: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7474e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1868/3000\n",
      "\n",
      "Epoch 1868: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7575e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 149ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1869/3000\n",
      "\n",
      "Epoch 1869: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7528e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1870/3000\n",
      "\n",
      "Epoch 1870: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7422e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1871/3000\n",
      "\n",
      "Epoch 1871: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7660e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 1872/3000\n",
      "\n",
      "Epoch 1872: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7522e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1873/3000\n",
      "\n",
      "Epoch 1873: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7389e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1874/3000\n",
      "\n",
      "Epoch 1874: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7533e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 1875/3000\n",
      "\n",
      "Epoch 1875: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7581e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1876/3000\n",
      "\n",
      "Epoch 1876: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7695e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1877/3000\n",
      "\n",
      "Epoch 1877: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7726e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1878/3000\n",
      "\n",
      "Epoch 1878: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7439e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 156ms/step\n",
      "Epoch 1879/3000\n",
      "\n",
      "Epoch 1879: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7837e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 1880/3000\n",
      "\n",
      "Epoch 1880: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7536e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1881/3000\n",
      "\n",
      "Epoch 1881: val_mae did not improve from 0.03266\n",
      "151/151 - 22s - loss: 8.7765e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 22s/epoch - 149ms/step\n",
      "Epoch 1882/3000\n",
      "\n",
      "Epoch 1882: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7481e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1883/3000\n",
      "\n",
      "Epoch 1883: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7694e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 1884/3000\n",
      "\n",
      "Epoch 1884: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7489e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1885/3000\n",
      "\n",
      "Epoch 1885: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7609e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1886/3000\n",
      "\n",
      "Epoch 1886: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7581e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1887/3000\n",
      "\n",
      "Epoch 1887: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7504e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1888/3000\n",
      "\n",
      "Epoch 1888: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7498e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1889/3000\n",
      "\n",
      "Epoch 1889: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7748e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1890/3000\n",
      "\n",
      "Epoch 1890: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7575e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 153ms/step\n",
      "Epoch 1891/3000\n",
      "\n",
      "Epoch 1891: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7734e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 1892/3000\n",
      "\n",
      "Epoch 1892: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7359e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1893/3000\n",
      "\n",
      "Epoch 1893: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7570e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1894/3000\n",
      "\n",
      "Epoch 1894: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7599e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1895/3000\n",
      "\n",
      "Epoch 1895: val_mae did not improve from 0.03266\n",
      "151/151 - 22s - loss: 8.7599e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 22s/epoch - 149ms/step\n",
      "Epoch 1896/3000\n",
      "\n",
      "Epoch 1896: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7628e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 152ms/step\n",
      "Epoch 1897/3000\n",
      "\n",
      "Epoch 1897: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7684e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1898/3000\n",
      "\n",
      "Epoch 1898: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7596e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1899/3000\n",
      "\n",
      "Epoch 1899: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7386e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1900/3000\n",
      "\n",
      "Epoch 1900: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7690e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 156ms/step\n",
      "Epoch 1901/3000\n",
      "\n",
      "Epoch 1901: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7464e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1902/3000\n",
      "\n",
      "Epoch 1902: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7520e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 1903/3000\n",
      "\n",
      "Epoch 1903: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7487e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 150ms/step\n",
      "Epoch 1904/3000\n",
      "\n",
      "Epoch 1904: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7420e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 156ms/step\n",
      "Epoch 1905/3000\n",
      "\n",
      "Epoch 1905: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7420e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1906/3000\n",
      "\n",
      "Epoch 1906: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7279e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1907/3000\n",
      "\n",
      "Epoch 1907: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7493e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1908/3000\n",
      "\n",
      "Epoch 1908: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7405e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1909/3000\n",
      "\n",
      "Epoch 1909: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7437e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1910/3000\n",
      "\n",
      "Epoch 1910: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7390e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1911/3000\n",
      "\n",
      "Epoch 1911: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7330e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1912/3000\n",
      "\n",
      "Epoch 1912: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7751e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 155ms/step\n",
      "Epoch 1913/3000\n",
      "\n",
      "Epoch 1913: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7615e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1914/3000\n",
      "\n",
      "Epoch 1914: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7510e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1915/3000\n",
      "\n",
      "Epoch 1915: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7583e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1916/3000\n",
      "\n",
      "Epoch 1916: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7585e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1917/3000\n",
      "\n",
      "Epoch 1917: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7567e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1918/3000\n",
      "\n",
      "Epoch 1918: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7627e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 153ms/step\n",
      "Epoch 1919/3000\n",
      "\n",
      "Epoch 1919: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7610e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1920/3000\n",
      "\n",
      "Epoch 1920: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7547e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1921/3000\n",
      "\n",
      "Epoch 1921: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7381e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 1922/3000\n",
      "\n",
      "Epoch 1922: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7601e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1923/3000\n",
      "\n",
      "Epoch 1923: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7590e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1924/3000\n",
      "\n",
      "Epoch 1924: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7652e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1925/3000\n",
      "\n",
      "Epoch 1925: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7406e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1926/3000\n",
      "\n",
      "Epoch 1926: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7512e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1927/3000\n",
      "\n",
      "Epoch 1927: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7517e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 1928/3000\n",
      "\n",
      "Epoch 1928: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7451e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 1929/3000\n",
      "\n",
      "Epoch 1929: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7396e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1930/3000\n",
      "\n",
      "Epoch 1930: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7496e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1931/3000\n",
      "\n",
      "Epoch 1931: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7468e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 1932/3000\n",
      "\n",
      "Epoch 1932: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7562e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1933/3000\n",
      "\n",
      "Epoch 1933: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7575e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1934/3000\n",
      "\n",
      "Epoch 1934: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7468e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1935/3000\n",
      "\n",
      "Epoch 1935: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7610e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 1936/3000\n",
      "\n",
      "Epoch 1936: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7330e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1937/3000\n",
      "\n",
      "Epoch 1937: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7363e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 156ms/step\n",
      "Epoch 1938/3000\n",
      "\n",
      "Epoch 1938: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7313e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1939/3000\n",
      "\n",
      "Epoch 1939: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7450e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 1940/3000\n",
      "\n",
      "Epoch 1940: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7470e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1941/3000\n",
      "\n",
      "Epoch 1941: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7478e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1942/3000\n",
      "\n",
      "Epoch 1942: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7364e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1943/3000\n",
      "\n",
      "Epoch 1943: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7279e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1944/3000\n",
      "\n",
      "Epoch 1944: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7502e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 152ms/step\n",
      "Epoch 1945/3000\n",
      "\n",
      "Epoch 1945: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7567e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1946/3000\n",
      "\n",
      "Epoch 1946: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7490e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 1947/3000\n",
      "\n",
      "Epoch 1947: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7516e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1948/3000\n",
      "\n",
      "Epoch 1948: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7572e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1949/3000\n",
      "\n",
      "Epoch 1949: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7504e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1950/3000\n",
      "\n",
      "Epoch 1950: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7411e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1951/3000\n",
      "\n",
      "Epoch 1951: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7446e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1952/3000\n",
      "\n",
      "Epoch 1952: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7659e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1953/3000\n",
      "\n",
      "Epoch 1953: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7573e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 1954/3000\n",
      "\n",
      "Epoch 1954: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7400e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 1955/3000\n",
      "\n",
      "Epoch 1955: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7345e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1956/3000\n",
      "\n",
      "Epoch 1956: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7580e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 1957/3000\n",
      "\n",
      "Epoch 1957: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7612e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1958/3000\n",
      "\n",
      "Epoch 1958: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7450e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1959/3000\n",
      "\n",
      "Epoch 1959: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7449e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 153ms/step\n",
      "Epoch 1960/3000\n",
      "\n",
      "Epoch 1960: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7488e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 1961/3000\n",
      "\n",
      "Epoch 1961: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7472e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1962/3000\n",
      "\n",
      "Epoch 1962: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7374e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1963/3000\n",
      "\n",
      "Epoch 1963: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7478e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1964/3000\n",
      "\n",
      "Epoch 1964: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7702e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1965/3000\n",
      "\n",
      "Epoch 1965: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7506e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 1966/3000\n",
      "\n",
      "Epoch 1966: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7437e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 1967/3000\n",
      "\n",
      "Epoch 1967: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7547e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 1968/3000\n",
      "\n",
      "Epoch 1968: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7421e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 1969/3000\n",
      "\n",
      "Epoch 1969: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7370e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 1970/3000\n",
      "\n",
      "Epoch 1970: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7485e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 1971/3000\n",
      "\n",
      "Epoch 1971: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7311e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 1972/3000\n",
      "\n",
      "Epoch 1972: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7368e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 1973/3000\n",
      "\n",
      "Epoch 1973: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7579e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 156ms/step\n",
      "Epoch 1974/3000\n",
      "\n",
      "Epoch 1974: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7360e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1975/3000\n",
      "\n",
      "Epoch 1975: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7322e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1976/3000\n",
      "\n",
      "Epoch 1976: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7461e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 149ms/step\n",
      "Epoch 1977/3000\n",
      "\n",
      "Epoch 1977: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7334e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0335 - 23s/epoch - 152ms/step\n",
      "Epoch 1978/3000\n",
      "\n",
      "Epoch 1978: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7501e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 1979/3000\n",
      "\n",
      "Epoch 1979: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7407e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 1980/3000\n",
      "\n",
      "Epoch 1980: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7461e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 1981/3000\n",
      "\n",
      "Epoch 1981: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7345e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 1982/3000\n",
      "\n",
      "Epoch 1982: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7400e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1983/3000\n",
      "\n",
      "Epoch 1983: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7449e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1984/3000\n",
      "\n",
      "Epoch 1984: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7377e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 1985/3000\n",
      "\n",
      "Epoch 1985: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7437e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 149ms/step\n",
      "Epoch 1986/3000\n",
      "\n",
      "Epoch 1986: val_mae did not improve from 0.03266\n",
      "151/151 - 22s - loss: 8.7526e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 22s/epoch - 149ms/step\n",
      "Epoch 1987/3000\n",
      "\n",
      "Epoch 1987: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7552e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 1988/3000\n",
      "\n",
      "Epoch 1988: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7419e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1989/3000\n",
      "\n",
      "Epoch 1989: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7387e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 1990/3000\n",
      "\n",
      "Epoch 1990: val_mae did not improve from 0.03266\n",
      "151/151 - 22s - loss: 8.7337e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 22s/epoch - 148ms/step\n",
      "Epoch 1991/3000\n",
      "\n",
      "Epoch 1991: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7475e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 24s/epoch - 156ms/step\n",
      "Epoch 1992/3000\n",
      "\n",
      "Epoch 1992: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7544e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 157ms/step\n",
      "Epoch 1993/3000\n",
      "\n",
      "Epoch 1993: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7454e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 1994/3000\n",
      "\n",
      "Epoch 1994: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7450e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 1995/3000\n",
      "\n",
      "Epoch 1995: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7475e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 1996/3000\n",
      "\n",
      "Epoch 1996: val_mae did not improve from 0.03266\n",
      "151/151 - 23s - loss: 8.7589e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 1997/3000\n",
      "\n",
      "Epoch 1997: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7487e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 158ms/step\n",
      "Epoch 1998/3000\n",
      "\n",
      "Epoch 1998: val_mae did not improve from 0.03266\n",
      "151/151 - 24s - loss: 8.7585e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 156ms/step\n",
      "Epoch 1999/3000\n",
      "\n",
      "Epoch 1999: val_mae improved from 0.03266 to 0.03257, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 24s - loss: 8.7536e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 24s/epoch - 156ms/step\n",
      "Epoch 2000/3000\n",
      "\n",
      "Epoch 2000: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7386e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2001/3000\n",
      "\n",
      "Epoch 2001: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7409e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2002/3000\n",
      "\n",
      "Epoch 2002: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7369e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2003/3000\n",
      "\n",
      "Epoch 2003: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7465e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 2004/3000\n",
      "\n",
      "Epoch 2004: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7455e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2005/3000\n",
      "\n",
      "Epoch 2005: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7262e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 22s/epoch - 149ms/step\n",
      "Epoch 2006/3000\n",
      "\n",
      "Epoch 2006: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7411e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 149ms/step\n",
      "Epoch 2007/3000\n",
      "\n",
      "Epoch 2007: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7477e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 149ms/step\n",
      "Epoch 2008/3000\n",
      "\n",
      "Epoch 2008: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7343e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 2009/3000\n",
      "\n",
      "Epoch 2009: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7392e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2010/3000\n",
      "\n",
      "Epoch 2010: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7547e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 24s/epoch - 156ms/step\n",
      "Epoch 2011/3000\n",
      "\n",
      "Epoch 2011: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7437e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2012/3000\n",
      "\n",
      "Epoch 2012: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7455e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2013/3000\n",
      "\n",
      "Epoch 2013: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7459e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2014/3000\n",
      "\n",
      "Epoch 2014: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7405e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2015/3000\n",
      "\n",
      "Epoch 2015: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7344e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2016/3000\n",
      "\n",
      "Epoch 2016: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7232e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2017/3000\n",
      "\n",
      "Epoch 2017: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7540e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 150ms/step\n",
      "Epoch 2018/3000\n",
      "\n",
      "Epoch 2018: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7439e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2019/3000\n",
      "\n",
      "Epoch 2019: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7241e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2020/3000\n",
      "\n",
      "Epoch 2020: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7254e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 158ms/step\n",
      "Epoch 2021/3000\n",
      "\n",
      "Epoch 2021: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7281e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2022/3000\n",
      "\n",
      "Epoch 2022: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7447e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2023/3000\n",
      "\n",
      "Epoch 2023: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7405e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2024/3000\n",
      "\n",
      "Epoch 2024: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7354e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2025/3000\n",
      "\n",
      "Epoch 2025: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7453e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 2026/3000\n",
      "\n",
      "Epoch 2026: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7426e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 2027/3000\n",
      "\n",
      "Epoch 2027: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7577e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 2028/3000\n",
      "\n",
      "Epoch 2028: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7330e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 2029/3000\n",
      "\n",
      "Epoch 2029: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7205e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2030/3000\n",
      "\n",
      "Epoch 2030: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7532e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 2031/3000\n",
      "\n",
      "Epoch 2031: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7384e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2032/3000\n",
      "\n",
      "Epoch 2032: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7469e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2033/3000\n",
      "\n",
      "Epoch 2033: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7244e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 2034/3000\n",
      "\n",
      "Epoch 2034: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7326e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2035/3000\n",
      "\n",
      "Epoch 2035: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7608e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 152ms/step\n",
      "Epoch 2036/3000\n",
      "\n",
      "Epoch 2036: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7667e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2037/3000\n",
      "\n",
      "Epoch 2037: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7371e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2038/3000\n",
      "\n",
      "Epoch 2038: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7423e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 2039/3000\n",
      "\n",
      "Epoch 2039: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7304e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 2040/3000\n",
      "\n",
      "Epoch 2040: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7312e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 2041/3000\n",
      "\n",
      "Epoch 2041: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7395e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2042/3000\n",
      "\n",
      "Epoch 2042: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7289e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2043/3000\n",
      "\n",
      "Epoch 2043: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7554e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2044/3000\n",
      "\n",
      "Epoch 2044: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7209e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2045/3000\n",
      "\n",
      "Epoch 2045: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7280e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2046/3000\n",
      "\n",
      "Epoch 2046: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7504e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 2047/3000\n",
      "\n",
      "Epoch 2047: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7487e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 155ms/step\n",
      "Epoch 2048/3000\n",
      "\n",
      "Epoch 2048: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7365e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2049/3000\n",
      "\n",
      "Epoch 2049: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7538e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 2050/3000\n",
      "\n",
      "Epoch 2050: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7463e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2051/3000\n",
      "\n",
      "Epoch 2051: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7554e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2052/3000\n",
      "\n",
      "Epoch 2052: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7195e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 22s/epoch - 149ms/step\n",
      "Epoch 2053/3000\n",
      "\n",
      "Epoch 2053: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7617e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 2054/3000\n",
      "\n",
      "Epoch 2054: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7303e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2055/3000\n",
      "\n",
      "Epoch 2055: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7170e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2056/3000\n",
      "\n",
      "Epoch 2056: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7478e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2057/3000\n",
      "\n",
      "Epoch 2057: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7486e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2058/3000\n",
      "\n",
      "Epoch 2058: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7394e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2059/3000\n",
      "\n",
      "Epoch 2059: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7257e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 2060/3000\n",
      "\n",
      "Epoch 2060: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7332e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2061/3000\n",
      "\n",
      "Epoch 2061: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7408e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n",
      "Epoch 2062/3000\n",
      "\n",
      "Epoch 2062: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7334e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2063/3000\n",
      "\n",
      "Epoch 2063: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7259e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 2064/3000\n",
      "\n",
      "Epoch 2064: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7425e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 24s/epoch - 158ms/step\n",
      "Epoch 2065/3000\n",
      "\n",
      "Epoch 2065: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7312e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2066/3000\n",
      "\n",
      "Epoch 2066: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7399e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 2067/3000\n",
      "\n",
      "Epoch 2067: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7463e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 2068/3000\n",
      "\n",
      "Epoch 2068: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7506e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2069/3000\n",
      "\n",
      "Epoch 2069: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7275e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2070/3000\n",
      "\n",
      "Epoch 2070: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7101e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2071/3000\n",
      "\n",
      "Epoch 2071: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7248e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 156ms/step\n",
      "Epoch 2072/3000\n",
      "\n",
      "Epoch 2072: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7230e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2073/3000\n",
      "\n",
      "Epoch 2073: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7312e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2074/3000\n",
      "\n",
      "Epoch 2074: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7377e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2075/3000\n",
      "\n",
      "Epoch 2075: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7440e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 159ms/step\n",
      "Epoch 2076/3000\n",
      "\n",
      "Epoch 2076: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7304e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2077/3000\n",
      "\n",
      "Epoch 2077: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7415e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 2078/3000\n",
      "\n",
      "Epoch 2078: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7205e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2079/3000\n",
      "\n",
      "Epoch 2079: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7318e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2080/3000\n",
      "\n",
      "Epoch 2080: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7388e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2081/3000\n",
      "\n",
      "Epoch 2081: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7450e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2082/3000\n",
      "\n",
      "Epoch 2082: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7275e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2083/3000\n",
      "\n",
      "Epoch 2083: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7180e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2084/3000\n",
      "\n",
      "Epoch 2084: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7521e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2085/3000\n",
      "\n",
      "Epoch 2085: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7243e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2086/3000\n",
      "\n",
      "Epoch 2086: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7536e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 2087/3000\n",
      "\n",
      "Epoch 2087: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7258e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 149ms/step\n",
      "Epoch 2088/3000\n",
      "\n",
      "Epoch 2088: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7299e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2089/3000\n",
      "\n",
      "Epoch 2089: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7345e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 157ms/step\n",
      "Epoch 2090/3000\n",
      "\n",
      "Epoch 2090: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7382e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 157ms/step\n",
      "Epoch 2091/3000\n",
      "\n",
      "Epoch 2091: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7357e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2092/3000\n",
      "\n",
      "Epoch 2092: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7367e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 2093/3000\n",
      "\n",
      "Epoch 2093: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7175e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2094/3000\n",
      "\n",
      "Epoch 2094: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7401e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 2095/3000\n",
      "\n",
      "Epoch 2095: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7575e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 2096/3000\n",
      "\n",
      "Epoch 2096: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7198e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2097/3000\n",
      "\n",
      "Epoch 2097: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.6935e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 2098/3000\n",
      "\n",
      "Epoch 2098: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7349e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 156ms/step\n",
      "Epoch 2099/3000\n",
      "\n",
      "Epoch 2099: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7325e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 2100/3000\n",
      "\n",
      "Epoch 2100: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7252e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2101/3000\n",
      "\n",
      "Epoch 2101: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7242e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2102/3000\n",
      "\n",
      "Epoch 2102: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7093e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 157ms/step\n",
      "Epoch 2103/3000\n",
      "\n",
      "Epoch 2103: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7366e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2104/3000\n",
      "\n",
      "Epoch 2104: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7221e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 2105/3000\n",
      "\n",
      "Epoch 2105: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7283e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2106/3000\n",
      "\n",
      "Epoch 2106: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7430e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2107/3000\n",
      "\n",
      "Epoch 2107: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7547e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 156ms/step\n",
      "Epoch 2108/3000\n",
      "\n",
      "Epoch 2108: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7309e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2109/3000\n",
      "\n",
      "Epoch 2109: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7239e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 2110/3000\n",
      "\n",
      "Epoch 2110: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7271e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 2111/3000\n",
      "\n",
      "Epoch 2111: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7078e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 151ms/step\n",
      "Epoch 2112/3000\n",
      "\n",
      "Epoch 2112: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7430e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2113/3000\n",
      "\n",
      "Epoch 2113: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7277e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2114/3000\n",
      "\n",
      "Epoch 2114: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7381e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 153ms/step\n",
      "Epoch 2115/3000\n",
      "\n",
      "Epoch 2115: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7064e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 155ms/step\n",
      "Epoch 2116/3000\n",
      "\n",
      "Epoch 2116: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7393e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2117/3000\n",
      "\n",
      "Epoch 2117: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7233e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 149ms/step\n",
      "Epoch 2118/3000\n",
      "\n",
      "Epoch 2118: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7386e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2119/3000\n",
      "\n",
      "Epoch 2119: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7379e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2120/3000\n",
      "\n",
      "Epoch 2120: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7136e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 162ms/step\n",
      "Epoch 2121/3000\n",
      "\n",
      "Epoch 2121: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7466e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 160ms/step\n",
      "Epoch 2122/3000\n",
      "\n",
      "Epoch 2122: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7299e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 156ms/step\n",
      "Epoch 2123/3000\n",
      "\n",
      "Epoch 2123: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7298e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 155ms/step\n",
      "Epoch 2124/3000\n",
      "\n",
      "Epoch 2124: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7495e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2125/3000\n",
      "\n",
      "Epoch 2125: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7433e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2126/3000\n",
      "\n",
      "Epoch 2126: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7269e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 24s/epoch - 157ms/step\n",
      "Epoch 2127/3000\n",
      "\n",
      "Epoch 2127: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7218e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 155ms/step\n",
      "Epoch 2128/3000\n",
      "\n",
      "Epoch 2128: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7194e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 155ms/step\n",
      "Epoch 2129/3000\n",
      "\n",
      "Epoch 2129: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7200e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 155ms/step\n",
      "Epoch 2130/3000\n",
      "\n",
      "Epoch 2130: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7187e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 24s/epoch - 156ms/step\n",
      "Epoch 2131/3000\n",
      "\n",
      "Epoch 2131: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7372e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 156ms/step\n",
      "Epoch 2132/3000\n",
      "\n",
      "Epoch 2132: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7265e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 156ms/step\n",
      "Epoch 2133/3000\n",
      "\n",
      "Epoch 2133: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7138e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2134/3000\n",
      "\n",
      "Epoch 2134: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7269e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 154ms/step\n",
      "Epoch 2135/3000\n",
      "\n",
      "Epoch 2135: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7356e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2136/3000\n",
      "\n",
      "Epoch 2136: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7242e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2137/3000\n",
      "\n",
      "Epoch 2137: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7184e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2138/3000\n",
      "\n",
      "Epoch 2138: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.6989e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 149ms/step\n",
      "Epoch 2139/3000\n",
      "\n",
      "Epoch 2139: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7335e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 155ms/step\n",
      "Epoch 2140/3000\n",
      "\n",
      "Epoch 2140: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7233e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 149ms/step\n",
      "Epoch 2141/3000\n",
      "\n",
      "Epoch 2141: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7256e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2142/3000\n",
      "\n",
      "Epoch 2142: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7257e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2143/3000\n",
      "\n",
      "Epoch 2143: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7160e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 24s/epoch - 157ms/step\n",
      "Epoch 2144/3000\n",
      "\n",
      "Epoch 2144: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7396e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 2145/3000\n",
      "\n",
      "Epoch 2145: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7280e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 22s/epoch - 149ms/step\n",
      "Epoch 2146/3000\n",
      "\n",
      "Epoch 2146: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7324e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 153ms/step\n",
      "Epoch 2147/3000\n",
      "\n",
      "Epoch 2147: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7163e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 2148/3000\n",
      "\n",
      "Epoch 2148: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7179e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2149/3000\n",
      "\n",
      "Epoch 2149: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7103e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2150/3000\n",
      "\n",
      "Epoch 2150: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7246e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 155ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2151/3000\n",
      "\n",
      "Epoch 2151: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7285e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 2152/3000\n",
      "\n",
      "Epoch 2152: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7295e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2153/3000\n",
      "\n",
      "Epoch 2153: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7384e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2154/3000\n",
      "\n",
      "Epoch 2154: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7101e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 149ms/step\n",
      "Epoch 2155/3000\n",
      "\n",
      "Epoch 2155: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7167e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 2156/3000\n",
      "\n",
      "Epoch 2156: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7257e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2157/3000\n",
      "\n",
      "Epoch 2157: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7196e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 22s/epoch - 149ms/step\n",
      "Epoch 2158/3000\n",
      "\n",
      "Epoch 2158: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7018e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2159/3000\n",
      "\n",
      "Epoch 2159: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7232e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 152ms/step\n",
      "Epoch 2160/3000\n",
      "\n",
      "Epoch 2160: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7291e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 2161/3000\n",
      "\n",
      "Epoch 2161: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7222e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 153ms/step\n",
      "Epoch 2162/3000\n",
      "\n",
      "Epoch 2162: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.6988e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 2163/3000\n",
      "\n",
      "Epoch 2163: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7256e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2164/3000\n",
      "\n",
      "Epoch 2164: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7251e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2165/3000\n",
      "\n",
      "Epoch 2165: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7235e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2166/3000\n",
      "\n",
      "Epoch 2166: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7210e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 2167/3000\n",
      "\n",
      "Epoch 2167: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7325e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2168/3000\n",
      "\n",
      "Epoch 2168: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7236e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2169/3000\n",
      "\n",
      "Epoch 2169: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7257e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2170/3000\n",
      "\n",
      "Epoch 2170: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7335e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2171/3000\n",
      "\n",
      "Epoch 2171: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7120e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2172/3000\n",
      "\n",
      "Epoch 2172: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7319e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 22s/epoch - 149ms/step\n",
      "Epoch 2173/3000\n",
      "\n",
      "Epoch 2173: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7224e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 2174/3000\n",
      "\n",
      "Epoch 2174: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7149e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 154ms/step\n",
      "Epoch 2175/3000\n",
      "\n",
      "Epoch 2175: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7267e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 157ms/step\n",
      "Epoch 2176/3000\n",
      "\n",
      "Epoch 2176: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7207e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 160ms/step\n",
      "Epoch 2177/3000\n",
      "\n",
      "Epoch 2177: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7290e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2178/3000\n",
      "\n",
      "Epoch 2178: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7162e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2179/3000\n",
      "\n",
      "Epoch 2179: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7346e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 153ms/step\n",
      "Epoch 2180/3000\n",
      "\n",
      "Epoch 2180: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7199e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2181/3000\n",
      "\n",
      "Epoch 2181: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7076e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2182/3000\n",
      "\n",
      "Epoch 2182: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7226e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2183/3000\n",
      "\n",
      "Epoch 2183: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7184e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2184/3000\n",
      "\n",
      "Epoch 2184: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7353e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2185/3000\n",
      "\n",
      "Epoch 2185: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7282e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2186/3000\n",
      "\n",
      "Epoch 2186: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7193e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 153ms/step\n",
      "Epoch 2187/3000\n",
      "\n",
      "Epoch 2187: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7266e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2188/3000\n",
      "\n",
      "Epoch 2188: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.6947e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2189/3000\n",
      "\n",
      "Epoch 2189: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7161e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 154ms/step\n",
      "Epoch 2190/3000\n",
      "\n",
      "Epoch 2190: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7165e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2191/3000\n",
      "\n",
      "Epoch 2191: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7412e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2192/3000\n",
      "\n",
      "Epoch 2192: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7234e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2193/3000\n",
      "\n",
      "Epoch 2193: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7246e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2194/3000\n",
      "\n",
      "Epoch 2194: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7365e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0336 - 24s/epoch - 156ms/step\n",
      "Epoch 2195/3000\n",
      "\n",
      "Epoch 2195: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7367e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2196/3000\n",
      "\n",
      "Epoch 2196: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7381e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2197/3000\n",
      "\n",
      "Epoch 2197: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7022e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2198/3000\n",
      "\n",
      "Epoch 2198: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7337e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2199/3000\n",
      "\n",
      "Epoch 2199: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7308e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2200/3000\n",
      "\n",
      "Epoch 2200: val_mae did not improve from 0.03257\n",
      "151/151 - 24s - loss: 8.7310e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 158ms/step\n",
      "Epoch 2201/3000\n",
      "\n",
      "Epoch 2201: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7093e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 154ms/step\n",
      "Epoch 2202/3000\n",
      "\n",
      "Epoch 2202: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7165e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 151ms/step\n",
      "Epoch 2203/3000\n",
      "\n",
      "Epoch 2203: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7443e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 152ms/step\n",
      "Epoch 2204/3000\n",
      "\n",
      "Epoch 2204: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7151e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 150ms/step\n",
      "Epoch 2205/3000\n",
      "\n",
      "Epoch 2205: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7134e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 22s/epoch - 148ms/step\n",
      "Epoch 2206/3000\n",
      "\n",
      "Epoch 2206: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7111e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2207/3000\n",
      "\n",
      "Epoch 2207: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7178e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2208/3000\n",
      "\n",
      "Epoch 2208: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7404e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 149ms/step\n",
      "Epoch 2209/3000\n",
      "\n",
      "Epoch 2209: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7264e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 2210/3000\n",
      "\n",
      "Epoch 2210: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7298e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2211/3000\n",
      "\n",
      "Epoch 2211: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7011e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2212/3000\n",
      "\n",
      "Epoch 2212: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7260e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2213/3000\n",
      "\n",
      "Epoch 2213: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7302e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 155ms/step\n",
      "Epoch 2214/3000\n",
      "\n",
      "Epoch 2214: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7222e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n",
      "Epoch 2215/3000\n",
      "\n",
      "Epoch 2215: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7291e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0334 - 23s/epoch - 151ms/step\n",
      "Epoch 2216/3000\n",
      "\n",
      "Epoch 2216: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7192e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2217/3000\n",
      "\n",
      "Epoch 2217: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7309e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2218/3000\n",
      "\n",
      "Epoch 2218: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7213e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 156ms/step\n",
      "Epoch 2219/3000\n",
      "\n",
      "Epoch 2219: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7310e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 149ms/step\n",
      "Epoch 2220/3000\n",
      "\n",
      "Epoch 2220: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7116e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 2221/3000\n",
      "\n",
      "Epoch 2221: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7200e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 150ms/step\n",
      "Epoch 2222/3000\n",
      "\n",
      "Epoch 2222: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7534e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 151ms/step\n",
      "Epoch 2223/3000\n",
      "\n",
      "Epoch 2223: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7192e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 2224/3000\n",
      "\n",
      "Epoch 2224: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7233e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 22s/epoch - 148ms/step\n",
      "Epoch 2225/3000\n",
      "\n",
      "Epoch 2225: val_mae did not improve from 0.03257\n",
      "151/151 - 22s - loss: 8.7139e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 22s/epoch - 149ms/step\n",
      "Epoch 2226/3000\n",
      "\n",
      "Epoch 2226: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7152e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 149ms/step\n",
      "Epoch 2227/3000\n",
      "\n",
      "Epoch 2227: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7412e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 151ms/step\n",
      "Epoch 2228/3000\n",
      "\n",
      "Epoch 2228: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7518e-04 - mae: 0.0208 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 152ms/step\n",
      "Epoch 2229/3000\n",
      "\n",
      "Epoch 2229: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7344e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2230/3000\n",
      "\n",
      "Epoch 2230: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7197e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2231/3000\n",
      "\n",
      "Epoch 2231: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7167e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 150ms/step\n",
      "Epoch 2232/3000\n",
      "\n",
      "Epoch 2232: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7307e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 151ms/step\n",
      "Epoch 2233/3000\n",
      "\n",
      "Epoch 2233: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7388e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 2234/3000\n",
      "\n",
      "Epoch 2234: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7101e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 149ms/step\n",
      "Epoch 2235/3000\n",
      "\n",
      "Epoch 2235: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7112e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 153ms/step\n",
      "Epoch 2236/3000\n",
      "\n",
      "Epoch 2236: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7303e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 151ms/step\n",
      "Epoch 2237/3000\n",
      "\n",
      "Epoch 2237: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7099e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 150ms/step\n",
      "Epoch 2238/3000\n",
      "\n",
      "Epoch 2238: val_mae did not improve from 0.03257\n",
      "151/151 - 23s - loss: 8.7273e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 150ms/step\n",
      "Epoch 2239/3000\n",
      "\n",
      "Epoch 2239: val_mae improved from 0.03257 to 0.03255, saving model to saved_models/rnn_gate_pump_nofuture.h5\n",
      "151/151 - 23s - loss: 8.7096e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 150ms/step\n",
      "Epoch 2240/3000\n",
      "\n",
      "Epoch 2240: val_mae did not improve from 0.03255\n",
      "151/151 - 24s - loss: 8.7264e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 24s/epoch - 156ms/step\n",
      "Epoch 2241/3000\n",
      "\n",
      "Epoch 2241: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7337e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 152ms/step\n",
      "Epoch 2242/3000\n",
      "\n",
      "Epoch 2242: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.6884e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2243/3000\n",
      "\n",
      "Epoch 2243: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7532e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 23s/epoch - 154ms/step\n",
      "Epoch 2244/3000\n",
      "\n",
      "Epoch 2244: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7240e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 154ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2245/3000\n",
      "\n",
      "Epoch 2245: val_mae did not improve from 0.03255\n",
      "151/151 - 24s - loss: 8.7392e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 24s/epoch - 158ms/step\n",
      "Epoch 2246/3000\n",
      "\n",
      "Epoch 2246: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7219e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 155ms/step\n",
      "Epoch 2247/3000\n",
      "\n",
      "Epoch 2247: val_mae did not improve from 0.03255\n",
      "151/151 - 24s - loss: 8.7187e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 24s/epoch - 159ms/step\n",
      "Epoch 2248/3000\n",
      "\n",
      "Epoch 2248: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7293e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0332 - 23s/epoch - 155ms/step\n",
      "Epoch 2249/3000\n",
      "\n",
      "Epoch 2249: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7301e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0331 - 23s/epoch - 151ms/step\n",
      "Epoch 2250/3000\n",
      "\n",
      "Epoch 2250: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7210e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2251/3000\n",
      "\n",
      "Epoch 2251: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7171e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0326 - 23s/epoch - 155ms/step\n",
      "Epoch 2252/3000\n",
      "\n",
      "Epoch 2252: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7103e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 152ms/step\n",
      "Epoch 2253/3000\n",
      "\n",
      "Epoch 2253: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7195e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0331 - 23s/epoch - 150ms/step\n",
      "Epoch 2254/3000\n",
      "\n",
      "Epoch 2254: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7184e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2255/3000\n",
      "\n",
      "Epoch 2255: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7208e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0327 - 23s/epoch - 150ms/step\n",
      "Epoch 2256/3000\n",
      "\n",
      "Epoch 2256: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7260e-04 - mae: 0.0207 - val_loss: 0.0019 - val_mae: 0.0333 - 23s/epoch - 154ms/step\n",
      "Epoch 2257/3000\n",
      "\n",
      "Epoch 2257: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7365e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 155ms/step\n",
      "Epoch 2258/3000\n",
      "\n",
      "Epoch 2258: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7231e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0330 - 23s/epoch - 153ms/step\n",
      "Epoch 2259/3000\n",
      "\n",
      "Epoch 2259: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7287e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 151ms/step\n",
      "Epoch 2260/3000\n",
      "\n",
      "Epoch 2260: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7066e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2261/3000\n",
      "\n",
      "Epoch 2261: val_mae did not improve from 0.03255\n",
      "151/151 - 23s - loss: 8.7133e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0329 - 23s/epoch - 152ms/step\n",
      "Epoch 2262/3000\n",
      "\n",
      "Epoch 2262: val_mae did not improve from 0.03255\n",
      "151/151 - 24s - loss: 8.7227e-04 - mae: 0.0207 - val_loss: 0.0018 - val_mae: 0.0328 - 24s/epoch - 160ms/step\n",
      "Epoch 2263/3000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 20\u001b[0m\n\u001b[1;32m     11\u001b[0m es \u001b[38;5;241m=\u001b[39m EarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_mae\u001b[39m\u001b[38;5;124m'\u001b[39m, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, patience\u001b[38;5;241m=\u001b[39mPATIENCE)\n\u001b[1;32m     12\u001b[0m mc \u001b[38;5;241m=\u001b[39m ModelCheckpoint(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaved_models/rnn_gate_pump_nofuture.h5\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m     13\u001b[0m                      monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_mae\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m     14\u001b[0m                      mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m     15\u001b[0m                      verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, \n\u001b[1;32m     16\u001b[0m                      save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     17\u001b[0m                     )\n\u001b[0;32m---> 20\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_X_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_ws_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m          \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtest_X_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_ws_y\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m          \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mEPOCHS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m          \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mBATCH\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     24\u001b[0m \u001b[43m          \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     25\u001b[0m \u001b[43m          \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m          \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmc\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m         \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/keras/src/engine/training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1735\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1736\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1739\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1740\u001b[0m ):\n\u001b[1;32m   1741\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1742\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1744\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    854\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    855\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    856\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    859\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    860\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    146\u001b[0m   (concrete_function,\n\u001b[1;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1351\u001b[0m     args,\n\u001b[1;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1353\u001b[0m     executing_eagerly)\n\u001b[1;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1472\u001b[0m   )\n",
      "File \u001b[0;32m~/miniconda3/envs/tf213/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "lr_schedule = keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=learning_rate, \n",
    "                                                          decay_steps=decay_steps,\n",
    "                                                          decay_rate=decay_rate)\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=lr_schedule),\n",
    "              loss='mse',\n",
    "              metrics=['mae']\n",
    "             )\n",
    "\n",
    "\n",
    "es = EarlyStopping(monitor='val_mae', mode='min', verbose=2, patience=PATIENCE)\n",
    "mc = ModelCheckpoint('saved_models/rnn_gate_pump_nofuture.h5', \n",
    "                     monitor='val_mae', \n",
    "                     mode='min', \n",
    "                     verbose=2, \n",
    "                     save_best_only=True\n",
    "                    )\n",
    "\n",
    "\n",
    "model.fit(train_X_mask, train_ws_y,\n",
    "          validation_data=(test_X_mask, test_ws_y),\n",
    "          epochs=EPOCHS,\n",
    "          batch_size=BATCH,\n",
    "          verbose=2,\n",
    "          shuffle=True,\n",
    "          callbacks=[es, mc]\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d04c0b",
   "metadata": {},
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c60c3dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 11s 18ms/step\n",
      "inv_y.shape, inv_yhat.shape (19268, 96) (19268, 96)\n",
      "MAE = 0.1716\n",
      "RMSE = 0.2175\n"
     ]
    }
   ],
   "source": [
    "saved_model = load_model('saved_models/rnn_gate_pump_nofuture.h5')\n",
    "\n",
    "yhat = saved_model.predict(test_X_mask)\n",
    "\n",
    "inv_yhat = ws_scaler.inverse_transform(yhat)\n",
    "inv_y = ws_scaler.inverse_transform(test_ws_y)\n",
    "\n",
    "print(\"inv_y.shape, inv_yhat.shape\", inv_y.shape, inv_yhat.shape)\n",
    "print('MAE = {}'.format(float(\"{:.4f}\".format(mae(inv_y, inv_yhat)))))\n",
    "print('RMSE = {}'.format(float(\"{:.4f}\".format(sqrt(mse(inv_y, inv_yhat))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10108ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wss = ['WS_S1', 'TWS_S25A', 'TWS_S25B', 'TWS_S26']\n",
    "inv_yhat_reshape = inv_yhat.reshape((-1, 24, 4))\n",
    "inv_y_reshape = inv_y.reshape((-1, 24, 4))\n",
    "\n",
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    plt.plot(error[:, -1, i], linewidth=1)\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    plt.xlabel('Time steps', fontsize=18)\n",
    "    plt.ylabel('Actual Error (ft)', fontsize=18)\n",
    "    plt.title('{}'.format(wss[i]), fontsize=18)\n",
    "    plt.text(9000, -0.6, 'MAE = {}'.format(float(\"{:.3f}\".format(sum(abs(error[:, -1, i]))/len(error)))), fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0f5ae72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "1352\n",
      "(19268,)\n",
      "434\n",
      "(19268,)\n",
      "1106\n",
      "(19268,)\n",
      "503\n"
     ]
    }
   ],
   "source": [
    "up_thre = 0.5\n",
    "low_thre = -0.5\n",
    "\n",
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] > up_thre) + np.sum(error[:, -1, i] < low_thre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88da85fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "22\n",
      "(19268,)\n",
      "99\n",
      "(19268,)\n",
      "103\n",
      "(19268,)\n",
      "363\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] > up_thre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43865dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "1330\n",
      "(19268,)\n",
      "335\n",
      "(19268,)\n",
      "1003\n",
      "(19268,)\n",
      "140\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(wss)):\n",
    "    plt.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "    error = inv_yhat_reshape - inv_y_reshape\n",
    "    error_24h = error[:, -1, i]\n",
    "    print(error_24h.shape)\n",
    "    print(np.sum(error[:, -1, i] < low_thre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6040167e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf5784c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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