train_tcn_nofuture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f47fa9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 22:07:20.060459: 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",
      "2023-07-22 22:07:21.764155: 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.tcn import TCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0092be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"6\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b08072",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n",
    "\n",
    "\n",
    "# ===== model parameters ======\n",
    "nb_filters = 32\n",
    "kernel_size = 2\n",
    "dropout = 0.2\n",
    "dense_units1 = 128\n",
    "dense_units2 = 64\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": "markdown",
   "id": "62b6b865",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "id": "44fa97a5",
   "metadata": {},
   "outputs": [],
   "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, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3e209fbc",
   "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": [
    "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": 9,
   "id": "a7d6a860",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 22:07:53.680961: 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: 0000:88:00.0, 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",
      " tcn (TCN)                   (None, 72, 32)            25248     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 72, 32)            1056      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 72, 32)            0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 96)                221280    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 247584 (967.12 KB)\n",
      "Trainable params: 247584 (967.12 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(shape=(train_X_mask.shape[1], train_X_mask.shape[2]))\n",
    "masked_inputs = Masking(mask_value=masked_value)(inputs)\n",
    "\n",
    "tcn = TCN(nb_filters=nb_filters,\n",
    "          kernel_size=kernel_size,\n",
    "          use_batch_norm=False,\n",
    "          use_weight_norm=False,\n",
    "          use_layer_norm=True,\n",
    "          return_sequences=True,\n",
    "          dropout_rate=dropout,\n",
    "          activation='relu', \n",
    "          input_shape=(train_X_mask.shape[1], train_X_mask.shape[2])\n",
    "         )(masked_inputs)\n",
    "x = Dense(32)(tcn)\n",
    "x = Dropout(dropout)(x)\n",
    "x = Flatten()(x)\n",
    "outputs = Dense(96)(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3316f5cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 22:08:06.456253: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\n",
      "2023-07-22 22:08:07.858316: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7efac0008390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-07-22 22:08:07.858375: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1\n",
      "2023-07-22 22:08:07.868857: 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",
      "2023-07-22 22:08:08.282261: 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 0.33895, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 30s - loss: 4.8348 - mae: 1.6917 - val_loss: 0.1858 - val_mae: 0.3389 - 30s/epoch - 200ms/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 0.33895 to 0.25262, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 1.6170 - mae: 1.0020 - val_loss: 0.1005 - val_mae: 0.2526 - 9s/epoch - 60ms/step\n",
      "Epoch 3/3000\n",
      "\n",
      "Epoch 3: val_mae improved from 0.25262 to 0.22879, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.8473 - mae: 0.7260 - val_loss: 0.0821 - val_mae: 0.2288 - 9s/epoch - 61ms/step\n",
      "Epoch 4/3000\n",
      "\n",
      "Epoch 4: val_mae improved from 0.22879 to 0.22034, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.5594 - mae: 0.5890 - val_loss: 0.0743 - val_mae: 0.2203 - 9s/epoch - 61ms/step\n",
      "Epoch 5/3000\n",
      "\n",
      "Epoch 5: val_mae improved from 0.22034 to 0.21568, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.4234 - mae: 0.5119 - val_loss: 0.0695 - val_mae: 0.2157 - 9s/epoch - 61ms/step\n",
      "Epoch 6/3000\n",
      "\n",
      "Epoch 6: val_mae improved from 0.21568 to 0.20643, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.3473 - mae: 0.4631 - val_loss: 0.0634 - val_mae: 0.2064 - 9s/epoch - 61ms/step\n",
      "Epoch 7/3000\n",
      "\n",
      "Epoch 7: val_mae improved from 0.20643 to 0.19543, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.2949 - mae: 0.4264 - val_loss: 0.0566 - val_mae: 0.1954 - 9s/epoch - 61ms/step\n",
      "Epoch 8/3000\n",
      "\n",
      "Epoch 8: val_mae improved from 0.19543 to 0.18874, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.2545 - mae: 0.3960 - val_loss: 0.0524 - val_mae: 0.1887 - 9s/epoch - 61ms/step\n",
      "Epoch 9/3000\n",
      "\n",
      "Epoch 9: val_mae improved from 0.18874 to 0.18134, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.2226 - mae: 0.3700 - val_loss: 0.0483 - val_mae: 0.1813 - 9s/epoch - 61ms/step\n",
      "Epoch 10/3000\n",
      "\n",
      "Epoch 10: val_mae improved from 0.18134 to 0.17812, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1950 - mae: 0.3461 - val_loss: 0.0461 - val_mae: 0.1781 - 9s/epoch - 61ms/step\n",
      "Epoch 11/3000\n",
      "\n",
      "Epoch 11: val_mae improved from 0.17812 to 0.17440, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1726 - mae: 0.3255 - val_loss: 0.0439 - val_mae: 0.1744 - 9s/epoch - 61ms/step\n",
      "Epoch 12/3000\n",
      "\n",
      "Epoch 12: val_mae improved from 0.17440 to 0.17160, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1522 - mae: 0.3056 - val_loss: 0.0421 - val_mae: 0.1716 - 9s/epoch - 61ms/step\n",
      "Epoch 13/3000\n",
      "\n",
      "Epoch 13: val_mae improved from 0.17160 to 0.16815, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1355 - mae: 0.2880 - val_loss: 0.0401 - val_mae: 0.1682 - 9s/epoch - 60ms/step\n",
      "Epoch 14/3000\n",
      "\n",
      "Epoch 14: val_mae improved from 0.16815 to 0.16330, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1210 - mae: 0.2721 - val_loss: 0.0378 - val_mae: 0.1633 - 9s/epoch - 61ms/step\n",
      "Epoch 15/3000\n",
      "\n",
      "Epoch 15: val_mae improved from 0.16330 to 0.16242, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.1081 - mae: 0.2571 - val_loss: 0.0371 - val_mae: 0.1624 - 9s/epoch - 61ms/step\n",
      "Epoch 16/3000\n",
      "\n",
      "Epoch 16: val_mae improved from 0.16242 to 0.15877, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0974 - mae: 0.2439 - val_loss: 0.0355 - val_mae: 0.1588 - 9s/epoch - 61ms/step\n",
      "Epoch 17/3000\n",
      "\n",
      "Epoch 17: val_mae improved from 0.15877 to 0.15662, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0874 - mae: 0.2310 - val_loss: 0.0344 - val_mae: 0.1566 - 9s/epoch - 61ms/step\n",
      "Epoch 18/3000\n",
      "\n",
      "Epoch 18: val_mae improved from 0.15662 to 0.15384, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0788 - mae: 0.2192 - val_loss: 0.0332 - val_mae: 0.1538 - 9s/epoch - 61ms/step\n",
      "Epoch 19/3000\n",
      "\n",
      "Epoch 19: val_mae did not improve from 0.15384\n",
      "151/151 - 9s - loss: 0.0716 - mae: 0.2089 - val_loss: 0.0333 - val_mae: 0.1552 - 9s/epoch - 60ms/step\n",
      "Epoch 20/3000\n",
      "\n",
      "Epoch 20: val_mae improved from 0.15384 to 0.14852, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0648 - mae: 0.1985 - val_loss: 0.0309 - val_mae: 0.1485 - 9s/epoch - 61ms/step\n",
      "Epoch 21/3000\n",
      "\n",
      "Epoch 21: val_mae did not improve from 0.14852\n",
      "151/151 - 9s - loss: 0.0591 - mae: 0.1896 - val_loss: 0.0307 - val_mae: 0.1489 - 9s/epoch - 59ms/step\n",
      "Epoch 22/3000\n",
      "\n",
      "Epoch 22: val_mae did not improve from 0.14852\n",
      "151/151 - 9s - loss: 0.0539 - mae: 0.1809 - val_loss: 0.0311 - val_mae: 0.1505 - 9s/epoch - 60ms/step\n",
      "Epoch 23/3000\n",
      "\n",
      "Epoch 23: val_mae improved from 0.14852 to 0.14708, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0491 - mae: 0.1726 - val_loss: 0.0298 - val_mae: 0.1471 - 9s/epoch - 61ms/step\n",
      "Epoch 24/3000\n",
      "\n",
      "Epoch 24: val_mae did not improve from 0.14708\n",
      "151/151 - 9s - loss: 0.0451 - mae: 0.1655 - val_loss: 0.0298 - val_mae: 0.1474 - 9s/epoch - 60ms/step\n",
      "Epoch 25/3000\n",
      "\n",
      "Epoch 25: val_mae improved from 0.14708 to 0.14623, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0414 - mae: 0.1584 - val_loss: 0.0293 - val_mae: 0.1462 - 9s/epoch - 62ms/step\n",
      "Epoch 26/3000\n",
      "\n",
      "Epoch 26: val_mae improved from 0.14623 to 0.14602, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0379 - mae: 0.1515 - val_loss: 0.0291 - val_mae: 0.1460 - 9s/epoch - 61ms/step\n",
      "Epoch 27/3000\n",
      "\n",
      "Epoch 27: val_mae improved from 0.14602 to 0.14062, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0349 - mae: 0.1454 - val_loss: 0.0273 - val_mae: 0.1406 - 9s/epoch - 61ms/step\n",
      "Epoch 28/3000\n",
      "\n",
      "Epoch 28: val_mae improved from 0.14062 to 0.13982, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0321 - mae: 0.1393 - val_loss: 0.0269 - val_mae: 0.1398 - 9s/epoch - 62ms/step\n",
      "Epoch 29/3000\n",
      "\n",
      "Epoch 29: val_mae improved from 0.13982 to 0.13937, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0295 - mae: 0.1336 - val_loss: 0.0266 - val_mae: 0.1394 - 9s/epoch - 61ms/step\n",
      "Epoch 30/3000\n",
      "\n",
      "Epoch 30: val_mae improved from 0.13937 to 0.13435, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0272 - mae: 0.1282 - val_loss: 0.0250 - val_mae: 0.1344 - 9s/epoch - 61ms/step\n",
      "Epoch 31/3000\n",
      "\n",
      "Epoch 31: val_mae improved from 0.13435 to 0.13349, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0252 - mae: 0.1233 - val_loss: 0.0246 - val_mae: 0.1335 - 9s/epoch - 61ms/step\n",
      "Epoch 32/3000\n",
      "\n",
      "Epoch 32: val_mae improved from 0.13349 to 0.13306, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0232 - mae: 0.1184 - val_loss: 0.0243 - val_mae: 0.1331 - 9s/epoch - 61ms/step\n",
      "Epoch 33/3000\n",
      "\n",
      "Epoch 33: val_mae improved from 0.13306 to 0.12775, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0214 - mae: 0.1138 - val_loss: 0.0227 - val_mae: 0.1278 - 9s/epoch - 61ms/step\n",
      "Epoch 34/3000\n",
      "\n",
      "Epoch 34: val_mae improved from 0.12775 to 0.12375, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0199 - mae: 0.1096 - val_loss: 0.0214 - val_mae: 0.1238 - 9s/epoch - 61ms/step\n",
      "Epoch 35/3000\n",
      "\n",
      "Epoch 35: val_mae did not improve from 0.12375\n",
      "151/151 - 9s - loss: 0.0184 - mae: 0.1054 - val_loss: 0.0215 - val_mae: 0.1246 - 9s/epoch - 60ms/step\n",
      "Epoch 36/3000\n",
      "\n",
      "Epoch 36: val_mae improved from 0.12375 to 0.11731, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0170 - mae: 0.1013 - val_loss: 0.0194 - val_mae: 0.1173 - 9s/epoch - 61ms/step\n",
      "Epoch 37/3000\n",
      "\n",
      "Epoch 37: val_mae improved from 0.11731 to 0.11275, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0158 - mae: 0.0976 - val_loss: 0.0181 - val_mae: 0.1128 - 9s/epoch - 61ms/step\n",
      "Epoch 38/3000\n",
      "\n",
      "Epoch 38: val_mae did not improve from 0.11275\n",
      "151/151 - 9s - loss: 0.0147 - mae: 0.0940 - val_loss: 0.0180 - val_mae: 0.1133 - 9s/epoch - 60ms/step\n",
      "Epoch 39/3000\n",
      "\n",
      "Epoch 39: val_mae improved from 0.11275 to 0.10767, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0136 - mae: 0.0905 - val_loss: 0.0165 - val_mae: 0.1077 - 9s/epoch - 61ms/step\n",
      "Epoch 40/3000\n",
      "\n",
      "Epoch 40: val_mae improved from 0.10767 to 0.10261, saving model to saved_models/tcn_gate_pump_nofuture.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 0.0126 - mae: 0.0871 - val_loss: 0.0152 - val_mae: 0.1026 - 9s/epoch - 61ms/step\n",
      "Epoch 41/3000\n",
      "\n",
      "Epoch 41: val_mae improved from 0.10261 to 0.09850, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0117 - mae: 0.0840 - val_loss: 0.0142 - val_mae: 0.0985 - 9s/epoch - 61ms/step\n",
      "Epoch 42/3000\n",
      "\n",
      "Epoch 42: val_mae improved from 0.09850 to 0.09337, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0109 - mae: 0.0809 - val_loss: 0.0129 - val_mae: 0.0934 - 9s/epoch - 61ms/step\n",
      "Epoch 43/3000\n",
      "\n",
      "Epoch 43: val_mae improved from 0.09337 to 0.08993, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0101 - mae: 0.0780 - val_loss: 0.0121 - val_mae: 0.0899 - 9s/epoch - 61ms/step\n",
      "Epoch 44/3000\n",
      "\n",
      "Epoch 44: val_mae did not improve from 0.08993\n",
      "151/151 - 9s - loss: 0.0094 - mae: 0.0752 - val_loss: 0.0120 - val_mae: 0.0900 - 9s/epoch - 60ms/step\n",
      "Epoch 45/3000\n",
      "\n",
      "Epoch 45: val_mae improved from 0.08993 to 0.08388, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0088 - mae: 0.0726 - val_loss: 0.0107 - val_mae: 0.0839 - 9s/epoch - 61ms/step\n",
      "Epoch 46/3000\n",
      "\n",
      "Epoch 46: val_mae improved from 0.08388 to 0.07843, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0082 - mae: 0.0701 - val_loss: 0.0095 - val_mae: 0.0784 - 9s/epoch - 61ms/step\n",
      "Epoch 47/3000\n",
      "\n",
      "Epoch 47: val_mae did not improve from 0.07843\n",
      "151/151 - 9s - loss: 0.0076 - mae: 0.0677 - val_loss: 0.0096 - val_mae: 0.0788 - 9s/epoch - 60ms/step\n",
      "Epoch 48/3000\n",
      "\n",
      "Epoch 48: val_mae improved from 0.07843 to 0.07372, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0071 - mae: 0.0654 - val_loss: 0.0085 - val_mae: 0.0737 - 9s/epoch - 61ms/step\n",
      "Epoch 49/3000\n",
      "\n",
      "Epoch 49: val_mae did not improve from 0.07372\n",
      "151/151 - 9s - loss: 0.0067 - mae: 0.0633 - val_loss: 0.0086 - val_mae: 0.0742 - 9s/epoch - 60ms/step\n",
      "Epoch 50/3000\n",
      "\n",
      "Epoch 50: val_mae improved from 0.07372 to 0.06887, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0063 - mae: 0.0612 - val_loss: 0.0076 - val_mae: 0.0689 - 9s/epoch - 61ms/step\n",
      "Epoch 51/3000\n",
      "\n",
      "Epoch 51: val_mae improved from 0.06887 to 0.06604, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0059 - mae: 0.0592 - val_loss: 0.0070 - val_mae: 0.0660 - 9s/epoch - 61ms/step\n",
      "Epoch 52/3000\n",
      "\n",
      "Epoch 52: val_mae improved from 0.06604 to 0.06385, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0055 - mae: 0.0573 - val_loss: 0.0066 - val_mae: 0.0639 - 9s/epoch - 61ms/step\n",
      "Epoch 53/3000\n",
      "\n",
      "Epoch 53: val_mae improved from 0.06385 to 0.06355, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0052 - mae: 0.0555 - val_loss: 0.0066 - val_mae: 0.0636 - 9s/epoch - 61ms/step\n",
      "Epoch 54/3000\n",
      "\n",
      "Epoch 54: val_mae improved from 0.06355 to 0.06176, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0048 - mae: 0.0538 - val_loss: 0.0062 - val_mae: 0.0618 - 9s/epoch - 61ms/step\n",
      "Epoch 55/3000\n",
      "\n",
      "Epoch 55: val_mae improved from 0.06176 to 0.05931, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0046 - mae: 0.0522 - val_loss: 0.0058 - val_mae: 0.0593 - 9s/epoch - 61ms/step\n",
      "Epoch 56/3000\n",
      "\n",
      "Epoch 56: val_mae improved from 0.05931 to 0.05890, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0043 - mae: 0.0508 - val_loss: 0.0057 - val_mae: 0.0589 - 9s/epoch - 61ms/step\n",
      "Epoch 57/3000\n",
      "\n",
      "Epoch 57: val_mae improved from 0.05890 to 0.05878, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0041 - mae: 0.0493 - val_loss: 0.0057 - val_mae: 0.0588 - 9s/epoch - 61ms/step\n",
      "Epoch 58/3000\n",
      "\n",
      "Epoch 58: val_mae improved from 0.05878 to 0.05731, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0038 - mae: 0.0478 - val_loss: 0.0054 - val_mae: 0.0573 - 9s/epoch - 61ms/step\n",
      "Epoch 59/3000\n",
      "\n",
      "Epoch 59: val_mae improved from 0.05731 to 0.05650, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0037 - mae: 0.0467 - val_loss: 0.0053 - val_mae: 0.0565 - 9s/epoch - 62ms/step\n",
      "Epoch 60/3000\n",
      "\n",
      "Epoch 60: val_mae improved from 0.05650 to 0.05481, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0035 - mae: 0.0454 - val_loss: 0.0050 - val_mae: 0.0548 - 9s/epoch - 61ms/step\n",
      "Epoch 61/3000\n",
      "\n",
      "Epoch 61: val_mae did not improve from 0.05481\n",
      "151/151 - 9s - loss: 0.0033 - mae: 0.0442 - val_loss: 0.0051 - val_mae: 0.0552 - 9s/epoch - 60ms/step\n",
      "Epoch 62/3000\n",
      "\n",
      "Epoch 62: val_mae improved from 0.05481 to 0.05386, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0032 - mae: 0.0433 - val_loss: 0.0049 - val_mae: 0.0539 - 9s/epoch - 61ms/step\n",
      "Epoch 63/3000\n",
      "\n",
      "Epoch 63: val_mae did not improve from 0.05386\n",
      "151/151 - 9s - loss: 0.0030 - mae: 0.0423 - val_loss: 0.0049 - val_mae: 0.0543 - 9s/epoch - 60ms/step\n",
      "Epoch 64/3000\n",
      "\n",
      "Epoch 64: val_mae improved from 0.05386 to 0.05220, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0029 - mae: 0.0412 - val_loss: 0.0046 - val_mae: 0.0522 - 9s/epoch - 61ms/step\n",
      "Epoch 65/3000\n",
      "\n",
      "Epoch 65: val_mae did not improve from 0.05220\n",
      "151/151 - 9s - loss: 0.0027 - mae: 0.0403 - val_loss: 0.0046 - val_mae: 0.0525 - 9s/epoch - 59ms/step\n",
      "Epoch 66/3000\n",
      "\n",
      "Epoch 66: val_mae improved from 0.05220 to 0.05201, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0026 - mae: 0.0394 - val_loss: 0.0046 - val_mae: 0.0520 - 9s/epoch - 61ms/step\n",
      "Epoch 67/3000\n",
      "\n",
      "Epoch 67: val_mae improved from 0.05201 to 0.05108, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0025 - mae: 0.0385 - val_loss: 0.0044 - val_mae: 0.0511 - 9s/epoch - 61ms/step\n",
      "Epoch 68/3000\n",
      "\n",
      "Epoch 68: val_mae improved from 0.05108 to 0.05099, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0024 - mae: 0.0378 - val_loss: 0.0044 - val_mae: 0.0510 - 9s/epoch - 61ms/step\n",
      "Epoch 69/3000\n",
      "\n",
      "Epoch 69: val_mae improved from 0.05099 to 0.04964, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0023 - mae: 0.0370 - val_loss: 0.0042 - val_mae: 0.0496 - 9s/epoch - 61ms/step\n",
      "Epoch 70/3000\n",
      "\n",
      "Epoch 70: val_mae improved from 0.04964 to 0.04945, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0023 - mae: 0.0363 - val_loss: 0.0041 - val_mae: 0.0494 - 9s/epoch - 61ms/step\n",
      "Epoch 71/3000\n",
      "\n",
      "Epoch 71: val_mae did not improve from 0.04945\n",
      "151/151 - 9s - loss: 0.0022 - mae: 0.0356 - val_loss: 0.0042 - val_mae: 0.0497 - 9s/epoch - 60ms/step\n",
      "Epoch 72/3000\n",
      "\n",
      "Epoch 72: val_mae improved from 0.04945 to 0.04855, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0021 - mae: 0.0349 - val_loss: 0.0040 - val_mae: 0.0485 - 9s/epoch - 61ms/step\n",
      "Epoch 73/3000\n",
      "\n",
      "Epoch 73: val_mae did not improve from 0.04855\n",
      "151/151 - 9s - loss: 0.0020 - mae: 0.0343 - val_loss: 0.0041 - val_mae: 0.0494 - 9s/epoch - 60ms/step\n",
      "Epoch 74/3000\n",
      "\n",
      "Epoch 74: val_mae improved from 0.04855 to 0.04717, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0020 - mae: 0.0338 - val_loss: 0.0038 - val_mae: 0.0472 - 9s/epoch - 61ms/step\n",
      "Epoch 75/3000\n",
      "\n",
      "Epoch 75: val_mae did not improve from 0.04717\n",
      "151/151 - 9s - loss: 0.0019 - mae: 0.0332 - val_loss: 0.0038 - val_mae: 0.0475 - 9s/epoch - 60ms/step\n",
      "Epoch 76/3000\n",
      "\n",
      "Epoch 76: val_mae improved from 0.04717 to 0.04669, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0018 - mae: 0.0326 - val_loss: 0.0037 - val_mae: 0.0467 - 9s/epoch - 61ms/step\n",
      "Epoch 77/3000\n",
      "\n",
      "Epoch 77: val_mae improved from 0.04669 to 0.04661, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0018 - mae: 0.0322 - val_loss: 0.0036 - val_mae: 0.0466 - 9s/epoch - 61ms/step\n",
      "Epoch 78/3000\n",
      "\n",
      "Epoch 78: val_mae did not improve from 0.04661\n",
      "151/151 - 9s - loss: 0.0017 - mae: 0.0317 - val_loss: 0.0038 - val_mae: 0.0476 - 9s/epoch - 60ms/step\n",
      "Epoch 79/3000\n",
      "\n",
      "Epoch 79: val_mae improved from 0.04661 to 0.04498, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0017 - mae: 0.0312 - val_loss: 0.0034 - val_mae: 0.0450 - 9s/epoch - 61ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80/3000\n",
      "\n",
      "Epoch 80: val_mae did not improve from 0.04498\n",
      "151/151 - 9s - loss: 0.0016 - mae: 0.0307 - val_loss: 0.0034 - val_mae: 0.0450 - 9s/epoch - 60ms/step\n",
      "Epoch 81/3000\n",
      "\n",
      "Epoch 81: val_mae did not improve from 0.04498\n",
      "151/151 - 9s - loss: 0.0016 - mae: 0.0303 - val_loss: 0.0035 - val_mae: 0.0455 - 9s/epoch - 60ms/step\n",
      "Epoch 82/3000\n",
      "\n",
      "Epoch 82: val_mae improved from 0.04498 to 0.04474, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0015 - mae: 0.0299 - val_loss: 0.0033 - val_mae: 0.0447 - 9s/epoch - 62ms/step\n",
      "Epoch 83/3000\n",
      "\n",
      "Epoch 83: val_mae improved from 0.04474 to 0.04438, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0033 - val_mae: 0.0444 - 9s/epoch - 61ms/step\n",
      "Epoch 84/3000\n",
      "\n",
      "Epoch 84: val_mae did not improve from 0.04438\n",
      "151/151 - 9s - loss: 0.0015 - mae: 0.0291 - val_loss: 0.0034 - val_mae: 0.0452 - 9s/epoch - 60ms/step\n",
      "Epoch 85/3000\n",
      "\n",
      "Epoch 85: val_mae improved from 0.04438 to 0.04407, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0014 - mae: 0.0287 - val_loss: 0.0032 - val_mae: 0.0441 - 9s/epoch - 61ms/step\n",
      "Epoch 86/3000\n",
      "\n",
      "Epoch 86: val_mae improved from 0.04407 to 0.04279, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0014 - mae: 0.0284 - val_loss: 0.0031 - val_mae: 0.0428 - 9s/epoch - 61ms/step\n",
      "Epoch 87/3000\n",
      "\n",
      "Epoch 87: val_mae did not improve from 0.04279\n",
      "151/151 - 9s - loss: 0.0014 - mae: 0.0281 - val_loss: 0.0031 - val_mae: 0.0434 - 9s/epoch - 60ms/step\n",
      "Epoch 88/3000\n",
      "\n",
      "Epoch 88: val_mae did not improve from 0.04279\n",
      "151/151 - 9s - loss: 0.0014 - mae: 0.0278 - val_loss: 0.0032 - val_mae: 0.0441 - 9s/epoch - 60ms/step\n",
      "Epoch 89/3000\n",
      "\n",
      "Epoch 89: val_mae did not improve from 0.04279\n",
      "151/151 - 9s - loss: 0.0013 - mae: 0.0275 - val_loss: 0.0030 - val_mae: 0.0428 - 9s/epoch - 59ms/step\n",
      "Epoch 90/3000\n",
      "\n",
      "Epoch 90: val_mae did not improve from 0.04279\n",
      "151/151 - 9s - loss: 0.0013 - mae: 0.0271 - val_loss: 0.0032 - val_mae: 0.0437 - 9s/epoch - 60ms/step\n",
      "Epoch 91/3000\n",
      "\n",
      "Epoch 91: val_mae improved from 0.04279 to 0.04154, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0013 - mae: 0.0268 - val_loss: 0.0029 - val_mae: 0.0415 - 9s/epoch - 61ms/step\n",
      "Epoch 92/3000\n",
      "\n",
      "Epoch 92: val_mae did not improve from 0.04154\n",
      "151/151 - 9s - loss: 0.0013 - mae: 0.0266 - val_loss: 0.0030 - val_mae: 0.0423 - 9s/epoch - 60ms/step\n",
      "Epoch 93/3000\n",
      "\n",
      "Epoch 93: val_mae did not improve from 0.04154\n",
      "151/151 - 9s - loss: 0.0012 - mae: 0.0263 - val_loss: 0.0030 - val_mae: 0.0423 - 9s/epoch - 61ms/step\n",
      "Epoch 94/3000\n",
      "\n",
      "Epoch 94: val_mae improved from 0.04154 to 0.04127, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0012 - mae: 0.0261 - val_loss: 0.0028 - val_mae: 0.0413 - 9s/epoch - 62ms/step\n",
      "Epoch 95/3000\n",
      "\n",
      "Epoch 95: val_mae did not improve from 0.04127\n",
      "151/151 - 9s - loss: 0.0012 - mae: 0.0258 - val_loss: 0.0028 - val_mae: 0.0413 - 9s/epoch - 60ms/step\n",
      "Epoch 96/3000\n",
      "\n",
      "Epoch 96: val_mae improved from 0.04127 to 0.04092, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0012 - mae: 0.0256 - val_loss: 0.0028 - val_mae: 0.0409 - 9s/epoch - 62ms/step\n",
      "Epoch 97/3000\n",
      "\n",
      "Epoch 97: val_mae improved from 0.04092 to 0.04078, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0012 - mae: 0.0254 - val_loss: 0.0027 - val_mae: 0.0408 - 9s/epoch - 61ms/step\n",
      "Epoch 98/3000\n",
      "\n",
      "Epoch 98: val_mae did not improve from 0.04078\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0252 - val_loss: 0.0027 - val_mae: 0.0408 - 9s/epoch - 60ms/step\n",
      "Epoch 99/3000\n",
      "\n",
      "Epoch 99: val_mae improved from 0.04078 to 0.03907, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0250 - val_loss: 0.0025 - val_mae: 0.0391 - 9s/epoch - 61ms/step\n",
      "Epoch 100/3000\n",
      "\n",
      "Epoch 100: val_mae did not improve from 0.03907\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0248 - val_loss: 0.0026 - val_mae: 0.0400 - 9s/epoch - 60ms/step\n",
      "Epoch 101/3000\n",
      "\n",
      "Epoch 101: val_mae did not improve from 0.03907\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0246 - val_loss: 0.0026 - val_mae: 0.0396 - 9s/epoch - 60ms/step\n",
      "Epoch 102/3000\n",
      "\n",
      "Epoch 102: val_mae did not improve from 0.03907\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0244 - val_loss: 0.0026 - val_mae: 0.0401 - 9s/epoch - 59ms/step\n",
      "Epoch 103/3000\n",
      "\n",
      "Epoch 103: val_mae improved from 0.03907 to 0.03888, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0011 - mae: 0.0243 - val_loss: 0.0025 - val_mae: 0.0389 - 9s/epoch - 60ms/step\n",
      "Epoch 104/3000\n",
      "\n",
      "Epoch 104: val_mae did not improve from 0.03888\n",
      "151/151 - 9s - loss: 0.0010 - mae: 0.0241 - val_loss: 0.0026 - val_mae: 0.0396 - 9s/epoch - 58ms/step\n",
      "Epoch 105/3000\n",
      "\n",
      "Epoch 105: val_mae did not improve from 0.03888\n",
      "151/151 - 9s - loss: 0.0010 - mae: 0.0240 - val_loss: 0.0025 - val_mae: 0.0392 - 9s/epoch - 58ms/step\n",
      "Epoch 106/3000\n",
      "\n",
      "Epoch 106: val_mae improved from 0.03888 to 0.03875, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0010 - mae: 0.0238 - val_loss: 0.0024 - val_mae: 0.0387 - 9s/epoch - 59ms/step\n",
      "Epoch 107/3000\n",
      "\n",
      "Epoch 107: val_mae improved from 0.03875 to 0.03852, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 0.0010 - mae: 0.0236 - val_loss: 0.0024 - val_mae: 0.0385 - 9s/epoch - 59ms/step\n",
      "Epoch 108/3000\n",
      "\n",
      "Epoch 108: val_mae did not improve from 0.03852\n",
      "151/151 - 9s - loss: 9.9433e-04 - mae: 0.0234 - val_loss: 0.0025 - val_mae: 0.0392 - 9s/epoch - 59ms/step\n",
      "Epoch 109/3000\n",
      "\n",
      "Epoch 109: val_mae improved from 0.03852 to 0.03818, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.8508e-04 - mae: 0.0233 - val_loss: 0.0024 - val_mae: 0.0382 - 9s/epoch - 60ms/step\n",
      "Epoch 110/3000\n",
      "\n",
      "Epoch 110: val_mae improved from 0.03818 to 0.03778, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.7725e-04 - mae: 0.0232 - val_loss: 0.0023 - val_mae: 0.0378 - 9s/epoch - 59ms/step\n",
      "Epoch 111/3000\n",
      "\n",
      "Epoch 111: val_mae did not improve from 0.03778\n",
      "151/151 - 9s - loss: 9.6632e-04 - mae: 0.0231 - val_loss: 0.0024 - val_mae: 0.0381 - 9s/epoch - 58ms/step\n",
      "Epoch 112/3000\n",
      "\n",
      "Epoch 112: val_mae improved from 0.03778 to 0.03745, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.5838e-04 - mae: 0.0229 - val_loss: 0.0023 - val_mae: 0.0375 - 9s/epoch - 59ms/step\n",
      "Epoch 113/3000\n",
      "\n",
      "Epoch 113: val_mae improved from 0.03745 to 0.03737, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.5012e-04 - mae: 0.0228 - val_loss: 0.0023 - val_mae: 0.0374 - 9s/epoch - 59ms/step\n",
      "Epoch 114/3000\n",
      "\n",
      "Epoch 114: val_mae did not improve from 0.03737\n",
      "151/151 - 9s - loss: 9.3985e-04 - mae: 0.0227 - val_loss: 0.0024 - val_mae: 0.0381 - 9s/epoch - 58ms/step\n",
      "Epoch 115/3000\n",
      "\n",
      "Epoch 115: val_mae improved from 0.03737 to 0.03711, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.3164e-04 - mae: 0.0226 - val_loss: 0.0022 - val_mae: 0.0371 - 9s/epoch - 59ms/step\n",
      "Epoch 116/3000\n",
      "\n",
      "Epoch 116: val_mae improved from 0.03711 to 0.03701, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.2237e-04 - mae: 0.0225 - val_loss: 0.0022 - val_mae: 0.0370 - 9s/epoch - 60ms/step\n",
      "Epoch 117/3000\n",
      "\n",
      "Epoch 117: val_mae improved from 0.03701 to 0.03687, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 9.1580e-04 - mae: 0.0224 - val_loss: 0.0022 - val_mae: 0.0369 - 9s/epoch - 59ms/step\n",
      "Epoch 118/3000\n",
      "\n",
      "Epoch 118: val_mae did not improve from 0.03687\n",
      "151/151 - 9s - loss: 9.0591e-04 - mae: 0.0222 - val_loss: 0.0022 - val_mae: 0.0371 - 9s/epoch - 58ms/step\n",
      "Epoch 119/3000\n",
      "\n",
      "Epoch 119: val_mae did not improve from 0.03687\n",
      "151/151 - 9s - loss: 8.9924e-04 - mae: 0.0222 - val_loss: 0.0024 - val_mae: 0.0384 - 9s/epoch - 58ms/step\n",
      "Epoch 120/3000\n",
      "\n",
      "Epoch 120: val_mae did not improve from 0.03687\n",
      "151/151 - 9s - loss: 8.9499e-04 - mae: 0.0221 - val_loss: 0.0022 - val_mae: 0.0370 - 9s/epoch - 58ms/step\n",
      "Epoch 121/3000\n",
      "\n",
      "Epoch 121: val_mae did not improve from 0.03687\n",
      "151/151 - 9s - loss: 8.8643e-04 - mae: 0.0220 - val_loss: 0.0022 - val_mae: 0.0372 - 9s/epoch - 58ms/step\n",
      "Epoch 122/3000\n",
      "\n",
      "Epoch 122: val_mae improved from 0.03687 to 0.03623, saving model to saved_models/tcn_gate_pump_nofuture.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 8.7908e-04 - mae: 0.0219 - val_loss: 0.0021 - val_mae: 0.0362 - 9s/epoch - 59ms/step\n",
      "Epoch 123/3000\n",
      "\n",
      "Epoch 123: val_mae did not improve from 0.03623\n",
      "151/151 - 9s - loss: 8.7399e-04 - mae: 0.0218 - val_loss: 0.0023 - val_mae: 0.0378 - 9s/epoch - 58ms/step\n",
      "Epoch 124/3000\n",
      "\n",
      "Epoch 124: val_mae improved from 0.03623 to 0.03609, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.6415e-04 - mae: 0.0217 - val_loss: 0.0021 - val_mae: 0.0361 - 9s/epoch - 59ms/step\n",
      "Epoch 125/3000\n",
      "\n",
      "Epoch 125: val_mae did not improve from 0.03609\n",
      "151/151 - 9s - loss: 8.5894e-04 - mae: 0.0216 - val_loss: 0.0022 - val_mae: 0.0369 - 9s/epoch - 58ms/step\n",
      "Epoch 126/3000\n",
      "\n",
      "Epoch 126: val_mae improved from 0.03609 to 0.03604, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.5209e-04 - mae: 0.0215 - val_loss: 0.0021 - val_mae: 0.0360 - 9s/epoch - 59ms/step\n",
      "Epoch 127/3000\n",
      "\n",
      "Epoch 127: val_mae did not improve from 0.03604\n",
      "151/151 - 9s - loss: 8.5015e-04 - mae: 0.0215 - val_loss: 0.0022 - val_mae: 0.0366 - 9s/epoch - 58ms/step\n",
      "Epoch 128/3000\n",
      "\n",
      "Epoch 128: val_mae did not improve from 0.03604\n",
      "151/151 - 9s - loss: 8.4088e-04 - mae: 0.0214 - val_loss: 0.0021 - val_mae: 0.0362 - 9s/epoch - 58ms/step\n",
      "Epoch 129/3000\n",
      "\n",
      "Epoch 129: val_mae improved from 0.03604 to 0.03601, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.3417e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0360 - 9s/epoch - 59ms/step\n",
      "Epoch 130/3000\n",
      "\n",
      "Epoch 130: val_mae did not improve from 0.03601\n",
      "151/151 - 9s - loss: 8.3135e-04 - mae: 0.0213 - val_loss: 0.0021 - val_mae: 0.0366 - 9s/epoch - 58ms/step\n",
      "Epoch 131/3000\n",
      "\n",
      "Epoch 131: val_mae improved from 0.03601 to 0.03572, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.2511e-04 - mae: 0.0212 - val_loss: 0.0020 - val_mae: 0.0357 - 9s/epoch - 59ms/step\n",
      "Epoch 132/3000\n",
      "\n",
      "Epoch 132: val_mae improved from 0.03572 to 0.03559, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.1989e-04 - mae: 0.0211 - val_loss: 0.0020 - val_mae: 0.0356 - 9s/epoch - 59ms/step\n",
      "Epoch 133/3000\n",
      "\n",
      "Epoch 133: val_mae did not improve from 0.03559\n",
      "151/151 - 9s - loss: 8.1318e-04 - mae: 0.0210 - val_loss: 0.0020 - val_mae: 0.0358 - 9s/epoch - 58ms/step\n",
      "Epoch 134/3000\n",
      "\n",
      "Epoch 134: val_mae did not improve from 0.03559\n",
      "151/151 - 9s - loss: 8.0695e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0358 - 9s/epoch - 58ms/step\n",
      "Epoch 135/3000\n",
      "\n",
      "Epoch 135: val_mae improved from 0.03559 to 0.03530, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 8.0372e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0353 - 9s/epoch - 59ms/step\n",
      "Epoch 136/3000\n",
      "\n",
      "Epoch 136: val_mae did not improve from 0.03530\n",
      "151/151 - 9s - loss: 8.0075e-04 - mae: 0.0209 - val_loss: 0.0020 - val_mae: 0.0355 - 9s/epoch - 58ms/step\n",
      "Epoch 137/3000\n",
      "\n",
      "Epoch 137: val_mae improved from 0.03530 to 0.03508, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.9519e-04 - mae: 0.0208 - val_loss: 0.0020 - val_mae: 0.0351 - 9s/epoch - 59ms/step\n",
      "Epoch 138/3000\n",
      "\n",
      "Epoch 138: val_mae did not improve from 0.03508\n",
      "151/151 - 9s - loss: 7.9226e-04 - mae: 0.0207 - val_loss: 0.0020 - val_mae: 0.0358 - 9s/epoch - 58ms/step\n",
      "Epoch 139/3000\n",
      "\n",
      "Epoch 139: val_mae did not improve from 0.03508\n",
      "151/151 - 9s - loss: 7.8606e-04 - mae: 0.0207 - val_loss: 0.0020 - val_mae: 0.0356 - 9s/epoch - 58ms/step\n",
      "Epoch 140/3000\n",
      "\n",
      "Epoch 140: val_mae did not improve from 0.03508\n",
      "151/151 - 9s - loss: 7.8584e-04 - mae: 0.0206 - val_loss: 0.0021 - val_mae: 0.0363 - 9s/epoch - 58ms/step\n",
      "Epoch 141/3000\n",
      "\n",
      "Epoch 141: val_mae improved from 0.03508 to 0.03505, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.7913e-04 - mae: 0.0205 - val_loss: 0.0020 - val_mae: 0.0351 - 9s/epoch - 59ms/step\n",
      "Epoch 142/3000\n",
      "\n",
      "Epoch 142: val_mae improved from 0.03505 to 0.03492, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.7505e-04 - mae: 0.0205 - val_loss: 0.0019 - val_mae: 0.0349 - 9s/epoch - 59ms/step\n",
      "Epoch 143/3000\n",
      "\n",
      "Epoch 143: val_mae did not improve from 0.03492\n",
      "151/151 - 9s - loss: 7.7339e-04 - mae: 0.0204 - val_loss: 0.0020 - val_mae: 0.0355 - 9s/epoch - 58ms/step\n",
      "Epoch 144/3000\n",
      "\n",
      "Epoch 144: val_mae did not improve from 0.03492\n",
      "151/151 - 9s - loss: 7.6992e-04 - mae: 0.0204 - val_loss: 0.0020 - val_mae: 0.0352 - 9s/epoch - 58ms/step\n",
      "Epoch 145/3000\n",
      "\n",
      "Epoch 145: val_mae improved from 0.03492 to 0.03462, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.6782e-04 - mae: 0.0204 - val_loss: 0.0019 - val_mae: 0.0346 - 9s/epoch - 59ms/step\n",
      "Epoch 146/3000\n",
      "\n",
      "Epoch 146: val_mae improved from 0.03462 to 0.03442, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.6399e-04 - mae: 0.0203 - val_loss: 0.0019 - val_mae: 0.0344 - 9s/epoch - 59ms/step\n",
      "Epoch 147/3000\n",
      "\n",
      "Epoch 147: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.6072e-04 - mae: 0.0203 - val_loss: 0.0019 - val_mae: 0.0349 - 9s/epoch - 58ms/step\n",
      "Epoch 148/3000\n",
      "\n",
      "Epoch 148: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.5734e-04 - mae: 0.0202 - val_loss: 0.0020 - val_mae: 0.0355 - 9s/epoch - 58ms/step\n",
      "Epoch 149/3000\n",
      "\n",
      "Epoch 149: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.5645e-04 - mae: 0.0202 - val_loss: 0.0019 - val_mae: 0.0347 - 9s/epoch - 58ms/step\n",
      "Epoch 150/3000\n",
      "\n",
      "Epoch 150: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.5130e-04 - mae: 0.0201 - val_loss: 0.0019 - val_mae: 0.0348 - 9s/epoch - 58ms/step\n",
      "Epoch 151/3000\n",
      "\n",
      "Epoch 151: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.5005e-04 - mae: 0.0201 - val_loss: 0.0020 - val_mae: 0.0353 - 9s/epoch - 58ms/step\n",
      "Epoch 152/3000\n",
      "\n",
      "Epoch 152: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.4565e-04 - mae: 0.0200 - val_loss: 0.0019 - val_mae: 0.0345 - 9s/epoch - 58ms/step\n",
      "Epoch 153/3000\n",
      "\n",
      "Epoch 153: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.4333e-04 - mae: 0.0200 - val_loss: 0.0019 - val_mae: 0.0348 - 9s/epoch - 58ms/step\n",
      "Epoch 154/3000\n",
      "\n",
      "Epoch 154: val_mae did not improve from 0.03442\n",
      "151/151 - 9s - loss: 7.4270e-04 - mae: 0.0200 - val_loss: 0.0019 - val_mae: 0.0347 - 9s/epoch - 58ms/step\n",
      "Epoch 155/3000\n",
      "\n",
      "Epoch 155: val_mae improved from 0.03442 to 0.03423, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.4043e-04 - mae: 0.0200 - val_loss: 0.0019 - val_mae: 0.0342 - 9s/epoch - 59ms/step\n",
      "Epoch 156/3000\n",
      "\n",
      "Epoch 156: val_mae did not improve from 0.03423\n",
      "151/151 - 9s - loss: 7.3898e-04 - mae: 0.0199 - val_loss: 0.0019 - val_mae: 0.0343 - 9s/epoch - 58ms/step\n",
      "Epoch 157/3000\n",
      "\n",
      "Epoch 157: val_mae improved from 0.03423 to 0.03404, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.3663e-04 - mae: 0.0199 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 59ms/step\n",
      "Epoch 158/3000\n",
      "\n",
      "Epoch 158: val_mae did not improve from 0.03404\n",
      "151/151 - 9s - loss: 7.3435e-04 - mae: 0.0199 - val_loss: 0.0020 - val_mae: 0.0350 - 9s/epoch - 58ms/step\n",
      "Epoch 159/3000\n",
      "\n",
      "Epoch 159: val_mae did not improve from 0.03404\n",
      "151/151 - 9s - loss: 7.3206e-04 - mae: 0.0198 - val_loss: 0.0019 - val_mae: 0.0347 - 9s/epoch - 58ms/step\n",
      "Epoch 160/3000\n",
      "\n",
      "Epoch 160: val_mae did not improve from 0.03404\n",
      "151/151 - 9s - loss: 7.2906e-04 - mae: 0.0198 - val_loss: 0.0019 - val_mae: 0.0342 - 9s/epoch - 58ms/step\n",
      "Epoch 161/3000\n",
      "\n",
      "Epoch 161: val_mae did not improve from 0.03404\n",
      "151/151 - 9s - loss: 7.2929e-04 - mae: 0.0198 - val_loss: 0.0019 - val_mae: 0.0348 - 9s/epoch - 58ms/step\n",
      "Epoch 162/3000\n",
      "\n",
      "Epoch 162: val_mae did not improve from 0.03404\n",
      "151/151 - 9s - loss: 7.2529e-04 - mae: 0.0198 - val_loss: 0.0018 - val_mae: 0.0342 - 9s/epoch - 58ms/step\n",
      "Epoch 163/3000\n",
      "\n",
      "Epoch 163: val_mae improved from 0.03404 to 0.03378, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.2471e-04 - mae: 0.0197 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 59ms/step\n",
      "Epoch 164/3000\n",
      "\n",
      "Epoch 164: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.2385e-04 - mae: 0.0197 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 165/3000\n",
      "\n",
      "Epoch 165: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.2158e-04 - mae: 0.0197 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 166/3000\n",
      "\n",
      "Epoch 166: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.2030e-04 - mae: 0.0197 - val_loss: 0.0019 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 167/3000\n",
      "\n",
      "Epoch 167: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.1890e-04 - mae: 0.0196 - val_loss: 0.0019 - val_mae: 0.0348 - 9s/epoch - 58ms/step\n",
      "Epoch 168/3000\n",
      "\n",
      "Epoch 168: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.1965e-04 - mae: 0.0197 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 169/3000\n",
      "\n",
      "Epoch 169: val_mae did not improve from 0.03378\n",
      "151/151 - 9s - loss: 7.1612e-04 - mae: 0.0196 - val_loss: 0.0019 - val_mae: 0.0344 - 9s/epoch - 58ms/step\n",
      "Epoch 170/3000\n",
      "\n",
      "Epoch 170: val_mae improved from 0.03378 to 0.03359, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 7.1365e-04 - mae: 0.0196 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 59ms/step\n",
      "Epoch 171/3000\n",
      "\n",
      "Epoch 171: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.1482e-04 - mae: 0.0196 - val_loss: 0.0018 - val_mae: 0.0342 - 9s/epoch - 58ms/step\n",
      "Epoch 172/3000\n",
      "\n",
      "Epoch 172: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.1293e-04 - mae: 0.0196 - val_loss: 0.0019 - val_mae: 0.0343 - 9s/epoch - 58ms/step\n",
      "Epoch 173/3000\n",
      "\n",
      "Epoch 173: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.1035e-04 - mae: 0.0195 - val_loss: 0.0019 - val_mae: 0.0347 - 9s/epoch - 58ms/step\n",
      "Epoch 174/3000\n",
      "\n",
      "Epoch 174: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0740e-04 - mae: 0.0195 - val_loss: 0.0019 - val_mae: 0.0344 - 9s/epoch - 58ms/step\n",
      "Epoch 175/3000\n",
      "\n",
      "Epoch 175: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0672e-04 - mae: 0.0195 - val_loss: 0.0019 - val_mae: 0.0348 - 9s/epoch - 59ms/step\n",
      "Epoch 176/3000\n",
      "\n",
      "Epoch 176: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0493e-04 - mae: 0.0195 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 59ms/step\n",
      "Epoch 177/3000\n",
      "\n",
      "Epoch 177: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0507e-04 - mae: 0.0195 - val_loss: 0.0019 - val_mae: 0.0342 - 9s/epoch - 58ms/step\n",
      "Epoch 178/3000\n",
      "\n",
      "Epoch 178: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0377e-04 - mae: 0.0194 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 179/3000\n",
      "\n",
      "Epoch 179: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0267e-04 - mae: 0.0194 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 180/3000\n",
      "\n",
      "Epoch 180: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0332e-04 - mae: 0.0194 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 181/3000\n",
      "\n",
      "Epoch 181: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 7.0145e-04 - mae: 0.0194 - val_loss: 0.0019 - val_mae: 0.0344 - 9s/epoch - 58ms/step\n",
      "Epoch 182/3000\n",
      "\n",
      "Epoch 182: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 6.9859e-04 - mae: 0.0194 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 183/3000\n",
      "\n",
      "Epoch 183: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 6.9982e-04 - mae: 0.0194 - val_loss: 0.0019 - val_mae: 0.0349 - 9s/epoch - 58ms/step\n",
      "Epoch 184/3000\n",
      "\n",
      "Epoch 184: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 6.9967e-04 - mae: 0.0194 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 185/3000\n",
      "\n",
      "Epoch 185: val_mae did not improve from 0.03359\n",
      "151/151 - 9s - loss: 6.9364e-04 - mae: 0.0193 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 186/3000\n",
      "\n",
      "Epoch 186: val_mae improved from 0.03359 to 0.03347, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.9623e-04 - mae: 0.0193 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 59ms/step\n",
      "Epoch 187/3000\n",
      "\n",
      "Epoch 187: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.9429e-04 - mae: 0.0193 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 188/3000\n",
      "\n",
      "Epoch 188: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.9261e-04 - mae: 0.0193 - val_loss: 0.0019 - val_mae: 0.0345 - 9s/epoch - 58ms/step\n",
      "Epoch 189/3000\n",
      "\n",
      "Epoch 189: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.9420e-04 - mae: 0.0193 - val_loss: 0.0019 - val_mae: 0.0346 - 9s/epoch - 58ms/step\n",
      "Epoch 190/3000\n",
      "\n",
      "Epoch 190: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.9268e-04 - mae: 0.0193 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 191/3000\n",
      "\n",
      "Epoch 191: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.8958e-04 - mae: 0.0192 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 192/3000\n",
      "\n",
      "Epoch 192: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.8841e-04 - mae: 0.0192 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 193/3000\n",
      "\n",
      "Epoch 193: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.8702e-04 - mae: 0.0192 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 194/3000\n",
      "\n",
      "Epoch 194: val_mae did not improve from 0.03347\n",
      "151/151 - 9s - loss: 6.8641e-04 - mae: 0.0192 - val_loss: 0.0019 - val_mae: 0.0342 - 9s/epoch - 58ms/step\n",
      "Epoch 195/3000\n",
      "\n",
      "Epoch 195: val_mae improved from 0.03347 to 0.03344, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.8759e-04 - mae: 0.0192 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 59ms/step\n",
      "Epoch 196/3000\n",
      "\n",
      "Epoch 196: val_mae did not improve from 0.03344\n",
      "151/151 - 9s - loss: 6.8879e-04 - mae: 0.0192 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 197/3000\n",
      "\n",
      "Epoch 197: val_mae did not improve from 0.03344\n",
      "151/151 - 9s - loss: 6.8516e-04 - mae: 0.0192 - val_loss: 0.0019 - val_mae: 0.0344 - 9s/epoch - 58ms/step\n",
      "Epoch 198/3000\n",
      "\n",
      "Epoch 198: val_mae improved from 0.03344 to 0.03299, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.8454e-04 - mae: 0.0192 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 59ms/step\n",
      "Epoch 199/3000\n",
      "\n",
      "Epoch 199: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.8224e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 200/3000\n",
      "\n",
      "Epoch 200: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.8356e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 201/3000\n",
      "\n",
      "Epoch 201: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.8155e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 202/3000\n",
      "\n",
      "Epoch 202: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.8130e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 203/3000\n",
      "\n",
      "Epoch 203: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7981e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 204/3000\n",
      "\n",
      "Epoch 204: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7904e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 205/3000\n",
      "\n",
      "Epoch 205: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7638e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 206/3000\n",
      "\n",
      "Epoch 206: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7736e-04 - mae: 0.0191 - val_loss: 0.0017 - val_mae: 0.0332 - 9s/epoch - 58ms/step\n",
      "Epoch 207/3000\n",
      "\n",
      "Epoch 207: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7928e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 208/3000\n",
      "\n",
      "Epoch 208: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7748e-04 - mae: 0.0191 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 209/3000\n",
      "\n",
      "Epoch 209: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7501e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0341 - 9s/epoch - 58ms/step\n",
      "Epoch 210/3000\n",
      "\n",
      "Epoch 210: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7323e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 211/3000\n",
      "\n",
      "Epoch 211: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7238e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 212/3000\n",
      "\n",
      "Epoch 212: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7249e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 59ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 213/3000\n",
      "\n",
      "Epoch 213: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7148e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 214/3000\n",
      "\n",
      "Epoch 214: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7169e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 215/3000\n",
      "\n",
      "Epoch 215: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6792e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0340 - 9s/epoch - 58ms/step\n",
      "Epoch 216/3000\n",
      "\n",
      "Epoch 216: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.7159e-04 - mae: 0.0190 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 217/3000\n",
      "\n",
      "Epoch 217: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6716e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 218/3000\n",
      "\n",
      "Epoch 218: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6733e-04 - mae: 0.0189 - val_loss: 0.0017 - val_mae: 0.0332 - 9s/epoch - 58ms/step\n",
      "Epoch 219/3000\n",
      "\n",
      "Epoch 219: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6643e-04 - mae: 0.0189 - val_loss: 0.0017 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 220/3000\n",
      "\n",
      "Epoch 220: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6802e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 221/3000\n",
      "\n",
      "Epoch 221: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6825e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 222/3000\n",
      "\n",
      "Epoch 222: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6405e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 223/3000\n",
      "\n",
      "Epoch 223: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6366e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 224/3000\n",
      "\n",
      "Epoch 224: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6210e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 225/3000\n",
      "\n",
      "Epoch 225: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6129e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 226/3000\n",
      "\n",
      "Epoch 226: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6331e-04 - mae: 0.0189 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 227/3000\n",
      "\n",
      "Epoch 227: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6080e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 57ms/step\n",
      "Epoch 228/3000\n",
      "\n",
      "Epoch 228: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6000e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 229/3000\n",
      "\n",
      "Epoch 229: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.6176e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 230/3000\n",
      "\n",
      "Epoch 230: val_mae did not improve from 0.03299\n",
      "151/151 - 9s - loss: 6.5730e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 57ms/step\n",
      "Epoch 231/3000\n",
      "\n",
      "Epoch 231: val_mae improved from 0.03299 to 0.03293, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.5774e-04 - mae: 0.0188 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 59ms/step\n",
      "Epoch 232/3000\n",
      "\n",
      "Epoch 232: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5902e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 233/3000\n",
      "\n",
      "Epoch 233: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5699e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 234/3000\n",
      "\n",
      "Epoch 234: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5665e-04 - mae: 0.0188 - val_loss: 0.0017 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 235/3000\n",
      "\n",
      "Epoch 235: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5517e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 57ms/step\n",
      "Epoch 236/3000\n",
      "\n",
      "Epoch 236: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5445e-04 - mae: 0.0187 - val_loss: 0.0017 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 237/3000\n",
      "\n",
      "Epoch 237: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5521e-04 - mae: 0.0188 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 238/3000\n",
      "\n",
      "Epoch 238: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5290e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 239/3000\n",
      "\n",
      "Epoch 239: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5472e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 240/3000\n",
      "\n",
      "Epoch 240: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5134e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0339 - 9s/epoch - 58ms/step\n",
      "Epoch 241/3000\n",
      "\n",
      "Epoch 241: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5259e-04 - mae: 0.0187 - val_loss: 0.0017 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 242/3000\n",
      "\n",
      "Epoch 242: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5128e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 243/3000\n",
      "\n",
      "Epoch 243: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4894e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 244/3000\n",
      "\n",
      "Epoch 244: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.5161e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0337 - 9s/epoch - 58ms/step\n",
      "Epoch 245/3000\n",
      "\n",
      "Epoch 245: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4879e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 246/3000\n",
      "\n",
      "Epoch 246: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4875e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 247/3000\n",
      "\n",
      "Epoch 247: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4863e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 58ms/step\n",
      "Epoch 248/3000\n",
      "\n",
      "Epoch 248: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4798e-04 - mae: 0.0187 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 249/3000\n",
      "\n",
      "Epoch 249: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4728e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 250/3000\n",
      "\n",
      "Epoch 250: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4710e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0336 - 9s/epoch - 58ms/step\n",
      "Epoch 251/3000\n",
      "\n",
      "Epoch 251: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4443e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 252/3000\n",
      "\n",
      "Epoch 252: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4404e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 57ms/step\n",
      "Epoch 253/3000\n",
      "\n",
      "Epoch 253: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4465e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 254/3000\n",
      "\n",
      "Epoch 254: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4486e-04 - mae: 0.0186 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 255/3000\n",
      "\n",
      "Epoch 255: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4393e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 256/3000\n",
      "\n",
      "Epoch 256: val_mae did not improve from 0.03293\n",
      "151/151 - 9s - loss: 6.4380e-04 - mae: 0.0186 - val_loss: 0.0017 - val_mae: 0.0332 - 9s/epoch - 58ms/step\n",
      "Epoch 257/3000\n",
      "\n",
      "Epoch 257: val_mae improved from 0.03293 to 0.03286, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.4055e-04 - mae: 0.0186 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 59ms/step\n",
      "Epoch 258/3000\n",
      "\n",
      "Epoch 258: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.4325e-04 - mae: 0.0186 - val_loss: 0.0017 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 259/3000\n",
      "\n",
      "Epoch 259: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.4209e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0335 - 9s/epoch - 57ms/step\n",
      "Epoch 260/3000\n",
      "\n",
      "Epoch 260: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.4082e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 57ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 261/3000\n",
      "\n",
      "Epoch 261: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.4020e-04 - mae: 0.0186 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 262/3000\n",
      "\n",
      "Epoch 262: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3989e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 263/3000\n",
      "\n",
      "Epoch 263: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.4130e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0332 - 9s/epoch - 58ms/step\n",
      "Epoch 264/3000\n",
      "\n",
      "Epoch 264: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3948e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 265/3000\n",
      "\n",
      "Epoch 265: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3908e-04 - mae: 0.0185 - val_loss: 0.0018 - val_mae: 0.0338 - 9s/epoch - 58ms/step\n",
      "Epoch 266/3000\n",
      "\n",
      "Epoch 266: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3664e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 267/3000\n",
      "\n",
      "Epoch 267: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3821e-04 - mae: 0.0185 - val_loss: 0.0018 - val_mae: 0.0334 - 9s/epoch - 58ms/step\n",
      "Epoch 268/3000\n",
      "\n",
      "Epoch 268: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3603e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 269/3000\n",
      "\n",
      "Epoch 269: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3407e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 270/3000\n",
      "\n",
      "Epoch 270: val_mae did not improve from 0.03286\n",
      "151/151 - 9s - loss: 6.3425e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 271/3000\n",
      "\n",
      "Epoch 271: val_mae improved from 0.03286 to 0.03278, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.3428e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 59ms/step\n",
      "Epoch 272/3000\n",
      "\n",
      "Epoch 272: val_mae did not improve from 0.03278\n",
      "151/151 - 9s - loss: 6.3544e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 273/3000\n",
      "\n",
      "Epoch 273: val_mae improved from 0.03278 to 0.03224, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.3375e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 59ms/step\n",
      "Epoch 274/3000\n",
      "\n",
      "Epoch 274: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3335e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 275/3000\n",
      "\n",
      "Epoch 275: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3418e-04 - mae: 0.0185 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 276/3000\n",
      "\n",
      "Epoch 276: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3182e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 277/3000\n",
      "\n",
      "Epoch 277: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3210e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 59ms/step\n",
      "Epoch 278/3000\n",
      "\n",
      "Epoch 278: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2991e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 59ms/step\n",
      "Epoch 279/3000\n",
      "\n",
      "Epoch 279: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3201e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 59ms/step\n",
      "Epoch 280/3000\n",
      "\n",
      "Epoch 280: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3103e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 281/3000\n",
      "\n",
      "Epoch 281: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3013e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 282/3000\n",
      "\n",
      "Epoch 282: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2967e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 283/3000\n",
      "\n",
      "Epoch 283: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3090e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 284/3000\n",
      "\n",
      "Epoch 284: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3138e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 285/3000\n",
      "\n",
      "Epoch 285: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.3064e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 286/3000\n",
      "\n",
      "Epoch 286: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2821e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0332 - 9s/epoch - 58ms/step\n",
      "Epoch 287/3000\n",
      "\n",
      "Epoch 287: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2619e-04 - mae: 0.0184 - val_loss: 0.0018 - val_mae: 0.0333 - 9s/epoch - 58ms/step\n",
      "Epoch 288/3000\n",
      "\n",
      "Epoch 288: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2716e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 289/3000\n",
      "\n",
      "Epoch 289: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2708e-04 - mae: 0.0184 - val_loss: 0.0017 - val_mae: 0.0327 - 9s/epoch - 58ms/step\n",
      "Epoch 290/3000\n",
      "\n",
      "Epoch 290: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2520e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 291/3000\n",
      "\n",
      "Epoch 291: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2678e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0327 - 9s/epoch - 58ms/step\n",
      "Epoch 292/3000\n",
      "\n",
      "Epoch 292: val_mae did not improve from 0.03224\n",
      "151/151 - 9s - loss: 6.2549e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 293/3000\n",
      "\n",
      "Epoch 293: val_mae improved from 0.03224 to 0.03210, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.2483e-04 - mae: 0.0183 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 59ms/step\n",
      "Epoch 294/3000\n",
      "\n",
      "Epoch 294: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2493e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 295/3000\n",
      "\n",
      "Epoch 295: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2483e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 296/3000\n",
      "\n",
      "Epoch 296: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2440e-04 - mae: 0.0183 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 57ms/step\n",
      "Epoch 297/3000\n",
      "\n",
      "Epoch 297: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2385e-04 - mae: 0.0183 - val_loss: 0.0018 - val_mae: 0.0331 - 9s/epoch - 58ms/step\n",
      "Epoch 298/3000\n",
      "\n",
      "Epoch 298: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2341e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 299/3000\n",
      "\n",
      "Epoch 299: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2335e-04 - mae: 0.0183 - val_loss: 0.0016 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 300/3000\n",
      "\n",
      "Epoch 300: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2310e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 301/3000\n",
      "\n",
      "Epoch 301: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2379e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 302/3000\n",
      "\n",
      "Epoch 302: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2164e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 303/3000\n",
      "\n",
      "Epoch 303: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2191e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 304/3000\n",
      "\n",
      "Epoch 304: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2101e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 305/3000\n",
      "\n",
      "Epoch 305: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2040e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 306/3000\n",
      "\n",
      "Epoch 306: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.2058e-04 - mae: 0.0183 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 307/3000\n",
      "\n",
      "Epoch 307: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1970e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 308/3000\n",
      "\n",
      "Epoch 308: val_mae did not improve from 0.03210\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 6.1966e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 309/3000\n",
      "\n",
      "Epoch 309: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1771e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0330 - 9s/epoch - 58ms/step\n",
      "Epoch 310/3000\n",
      "\n",
      "Epoch 310: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1800e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 311/3000\n",
      "\n",
      "Epoch 311: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1855e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 312/3000\n",
      "\n",
      "Epoch 312: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1653e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 313/3000\n",
      "\n",
      "Epoch 313: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1491e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 314/3000\n",
      "\n",
      "Epoch 314: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1735e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0327 - 9s/epoch - 58ms/step\n",
      "Epoch 315/3000\n",
      "\n",
      "Epoch 315: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1524e-04 - mae: 0.0182 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 316/3000\n",
      "\n",
      "Epoch 316: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1582e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 317/3000\n",
      "\n",
      "Epoch 317: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1562e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 57ms/step\n",
      "Epoch 318/3000\n",
      "\n",
      "Epoch 318: val_mae did not improve from 0.03210\n",
      "151/151 - 9s - loss: 6.1598e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 57ms/step\n",
      "Epoch 319/3000\n",
      "\n",
      "Epoch 319: val_mae improved from 0.03210 to 0.03205, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.1336e-04 - mae: 0.0182 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 59ms/step\n",
      "Epoch 320/3000\n",
      "\n",
      "Epoch 320: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1575e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 321/3000\n",
      "\n",
      "Epoch 321: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1513e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 322/3000\n",
      "\n",
      "Epoch 322: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1541e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 323/3000\n",
      "\n",
      "Epoch 323: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1552e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 324/3000\n",
      "\n",
      "Epoch 324: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1529e-04 - mae: 0.0182 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 325/3000\n",
      "\n",
      "Epoch 325: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1121e-04 - mae: 0.0181 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 326/3000\n",
      "\n",
      "Epoch 326: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1114e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 327/3000\n",
      "\n",
      "Epoch 327: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1205e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 58ms/step\n",
      "Epoch 328/3000\n",
      "\n",
      "Epoch 328: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1153e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 57ms/step\n",
      "Epoch 329/3000\n",
      "\n",
      "Epoch 329: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1210e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0326 - 9s/epoch - 57ms/step\n",
      "Epoch 330/3000\n",
      "\n",
      "Epoch 330: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1009e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 57ms/step\n",
      "Epoch 331/3000\n",
      "\n",
      "Epoch 331: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1106e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 332/3000\n",
      "\n",
      "Epoch 332: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0955e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 333/3000\n",
      "\n",
      "Epoch 333: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1084e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 57ms/step\n",
      "Epoch 334/3000\n",
      "\n",
      "Epoch 334: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0991e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0328 - 9s/epoch - 58ms/step\n",
      "Epoch 335/3000\n",
      "\n",
      "Epoch 335: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0842e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 336/3000\n",
      "\n",
      "Epoch 336: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0867e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 57ms/step\n",
      "Epoch 337/3000\n",
      "\n",
      "Epoch 337: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0985e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 57ms/step\n",
      "Epoch 338/3000\n",
      "\n",
      "Epoch 338: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0804e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 339/3000\n",
      "\n",
      "Epoch 339: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.1068e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0327 - 9s/epoch - 58ms/step\n",
      "Epoch 340/3000\n",
      "\n",
      "Epoch 340: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0907e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 57ms/step\n",
      "Epoch 341/3000\n",
      "\n",
      "Epoch 341: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0719e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 342/3000\n",
      "\n",
      "Epoch 342: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0718e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 343/3000\n",
      "\n",
      "Epoch 343: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0681e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 344/3000\n",
      "\n",
      "Epoch 344: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0746e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 345/3000\n",
      "\n",
      "Epoch 345: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0696e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 57ms/step\n",
      "Epoch 346/3000\n",
      "\n",
      "Epoch 346: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0694e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0329 - 9s/epoch - 58ms/step\n",
      "Epoch 347/3000\n",
      "\n",
      "Epoch 347: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0754e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 348/3000\n",
      "\n",
      "Epoch 348: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0628e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 349/3000\n",
      "\n",
      "Epoch 349: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0838e-04 - mae: 0.0181 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 350/3000\n",
      "\n",
      "Epoch 350: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0493e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 351/3000\n",
      "\n",
      "Epoch 351: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0310e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 352/3000\n",
      "\n",
      "Epoch 352: val_mae did not improve from 0.03205\n",
      "151/151 - 9s - loss: 6.0432e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 353/3000\n",
      "\n",
      "Epoch 353: val_mae improved from 0.03205 to 0.03187, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.0431e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 59ms/step\n",
      "Epoch 354/3000\n",
      "\n",
      "Epoch 354: val_mae did not improve from 0.03187\n",
      "151/151 - 9s - loss: 6.0543e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 57ms/step\n",
      "Epoch 355/3000\n",
      "\n",
      "Epoch 355: val_mae did not improve from 0.03187\n",
      "151/151 - 9s - loss: 6.0496e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 356/3000\n",
      "\n",
      "Epoch 356: val_mae did not improve from 0.03187\n",
      "151/151 - 9s - loss: 6.0388e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 357/3000\n",
      "\n",
      "Epoch 357: val_mae did not improve from 0.03187\n",
      "151/151 - 9s - loss: 6.0246e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0323 - 9s/epoch - 58ms/step\n",
      "Epoch 358/3000\n",
      "\n",
      "Epoch 358: val_mae did not improve from 0.03187\n",
      "151/151 - 9s - loss: 6.0453e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 57ms/step\n",
      "Epoch 359/3000\n",
      "\n",
      "Epoch 359: val_mae improved from 0.03187 to 0.03175, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.0184e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 360/3000\n",
      "\n",
      "Epoch 360: val_mae did not improve from 0.03175\n",
      "151/151 - 9s - loss: 6.0232e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 361/3000\n",
      "\n",
      "Epoch 361: val_mae did not improve from 0.03175\n",
      "151/151 - 9s - loss: 6.0206e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 362/3000\n",
      "\n",
      "Epoch 362: val_mae did not improve from 0.03175\n",
      "151/151 - 9s - loss: 6.0181e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 363/3000\n",
      "\n",
      "Epoch 363: val_mae did not improve from 0.03175\n",
      "151/151 - 9s - loss: 6.0221e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 57ms/step\n",
      "Epoch 364/3000\n",
      "\n",
      "Epoch 364: val_mae improved from 0.03175 to 0.03171, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 6.0175e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 59ms/step\n",
      "Epoch 365/3000\n",
      "\n",
      "Epoch 365: val_mae did not improve from 0.03171\n",
      "151/151 - 9s - loss: 6.0114e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 366/3000\n",
      "\n",
      "Epoch 366: val_mae did not improve from 0.03171\n",
      "151/151 - 9s - loss: 6.0154e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 367/3000\n",
      "\n",
      "Epoch 367: val_mae did not improve from 0.03171\n",
      "151/151 - 9s - loss: 6.0051e-04 - mae: 0.0180 - val_loss: 0.0017 - val_mae: 0.0325 - 9s/epoch - 58ms/step\n",
      "Epoch 368/3000\n",
      "\n",
      "Epoch 368: val_mae did not improve from 0.03171\n",
      "151/151 - 9s - loss: 6.0076e-04 - mae: 0.0180 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 369/3000\n",
      "\n",
      "Epoch 369: val_mae improved from 0.03171 to 0.03166, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.9903e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 59ms/step\n",
      "Epoch 370/3000\n",
      "\n",
      "Epoch 370: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9970e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 371/3000\n",
      "\n",
      "Epoch 371: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9828e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 372/3000\n",
      "\n",
      "Epoch 372: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9710e-04 - mae: 0.0179 - val_loss: 0.0017 - val_mae: 0.0327 - 9s/epoch - 58ms/step\n",
      "Epoch 373/3000\n",
      "\n",
      "Epoch 373: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9723e-04 - mae: 0.0179 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 57ms/step\n",
      "Epoch 374/3000\n",
      "\n",
      "Epoch 374: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9731e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 57ms/step\n",
      "Epoch 375/3000\n",
      "\n",
      "Epoch 375: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9565e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 376/3000\n",
      "\n",
      "Epoch 376: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9839e-04 - mae: 0.0179 - val_loss: 0.0017 - val_mae: 0.0324 - 9s/epoch - 58ms/step\n",
      "Epoch 377/3000\n",
      "\n",
      "Epoch 377: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9857e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 378/3000\n",
      "\n",
      "Epoch 378: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9631e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 379/3000\n",
      "\n",
      "Epoch 379: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9706e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 380/3000\n",
      "\n",
      "Epoch 380: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9646e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 381/3000\n",
      "\n",
      "Epoch 381: val_mae did not improve from 0.03166\n",
      "151/151 - 9s - loss: 5.9601e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 382/3000\n",
      "\n",
      "Epoch 382: val_mae improved from 0.03166 to 0.03149, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.9450e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 59ms/step\n",
      "Epoch 383/3000\n",
      "\n",
      "Epoch 383: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9558e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 384/3000\n",
      "\n",
      "Epoch 384: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9722e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 385/3000\n",
      "\n",
      "Epoch 385: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9434e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 386/3000\n",
      "\n",
      "Epoch 386: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9627e-04 - mae: 0.0179 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 387/3000\n",
      "\n",
      "Epoch 387: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9459e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 388/3000\n",
      "\n",
      "Epoch 388: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9444e-04 - mae: 0.0179 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 58ms/step\n",
      "Epoch 389/3000\n",
      "\n",
      "Epoch 389: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9385e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 390/3000\n",
      "\n",
      "Epoch 390: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9437e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 391/3000\n",
      "\n",
      "Epoch 391: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9481e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0321 - 9s/epoch - 57ms/step\n",
      "Epoch 392/3000\n",
      "\n",
      "Epoch 392: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9206e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 393/3000\n",
      "\n",
      "Epoch 393: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9314e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 394/3000\n",
      "\n",
      "Epoch 394: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9436e-04 - mae: 0.0179 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 395/3000\n",
      "\n",
      "Epoch 395: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9180e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 396/3000\n",
      "\n",
      "Epoch 396: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9222e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 57ms/step\n",
      "Epoch 397/3000\n",
      "\n",
      "Epoch 397: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8952e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 398/3000\n",
      "\n",
      "Epoch 398: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9324e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 399/3000\n",
      "\n",
      "Epoch 399: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9383e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 400/3000\n",
      "\n",
      "Epoch 400: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9198e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 401/3000\n",
      "\n",
      "Epoch 401: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9173e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 402/3000\n",
      "\n",
      "Epoch 402: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9177e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 403/3000\n",
      "\n",
      "Epoch 403: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9219e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 404/3000\n",
      "\n",
      "Epoch 404: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9062e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 405/3000\n",
      "\n",
      "Epoch 405: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8946e-04 - mae: 0.0178 - val_loss: 0.0017 - val_mae: 0.0321 - 9s/epoch - 58ms/step\n",
      "Epoch 406/3000\n",
      "\n",
      "Epoch 406: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8712e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 407/3000\n",
      "\n",
      "Epoch 407: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9115e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 408/3000\n",
      "\n",
      "Epoch 408: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.9032e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 409/3000\n",
      "\n",
      "Epoch 409: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8795e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 410/3000\n",
      "\n",
      "Epoch 410: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8917e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 57ms/step\n",
      "Epoch 411/3000\n",
      "\n",
      "Epoch 411: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8859e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 412/3000\n",
      "\n",
      "Epoch 412: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8932e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 413/3000\n",
      "\n",
      "Epoch 413: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8838e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 414/3000\n",
      "\n",
      "Epoch 414: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8786e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 415/3000\n",
      "\n",
      "Epoch 415: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8788e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 416/3000\n",
      "\n",
      "Epoch 416: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8918e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 417/3000\n",
      "\n",
      "Epoch 417: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8812e-04 - mae: 0.0178 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 418/3000\n",
      "\n",
      "Epoch 418: val_mae did not improve from 0.03149\n",
      "151/151 - 9s - loss: 5.8660e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 419/3000\n",
      "\n",
      "Epoch 419: val_mae improved from 0.03149 to 0.03142, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.8555e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 59ms/step\n",
      "Epoch 420/3000\n",
      "\n",
      "Epoch 420: val_mae improved from 0.03142 to 0.03136, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.8574e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 59ms/step\n",
      "Epoch 421/3000\n",
      "\n",
      "Epoch 421: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8519e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 422/3000\n",
      "\n",
      "Epoch 422: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8592e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 423/3000\n",
      "\n",
      "Epoch 423: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8577e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 424/3000\n",
      "\n",
      "Epoch 424: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8641e-04 - mae: 0.0177 - val_loss: 0.0017 - val_mae: 0.0322 - 9s/epoch - 57ms/step\n",
      "Epoch 425/3000\n",
      "\n",
      "Epoch 425: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8474e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 426/3000\n",
      "\n",
      "Epoch 426: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8477e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 427/3000\n",
      "\n",
      "Epoch 427: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8359e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 428/3000\n",
      "\n",
      "Epoch 428: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8648e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 429/3000\n",
      "\n",
      "Epoch 429: val_mae did not improve from 0.03136\n",
      "151/151 - 9s - loss: 5.8329e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 430/3000\n",
      "\n",
      "Epoch 430: val_mae improved from 0.03136 to 0.03117, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.8229e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 59ms/step\n",
      "Epoch 431/3000\n",
      "\n",
      "Epoch 431: val_mae did not improve from 0.03117\n",
      "151/151 - 9s - loss: 5.8443e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 432/3000\n",
      "\n",
      "Epoch 432: val_mae improved from 0.03117 to 0.03113, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.8302e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 59ms/step\n",
      "Epoch 433/3000\n",
      "\n",
      "Epoch 433: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8469e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 434/3000\n",
      "\n",
      "Epoch 434: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8446e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 57ms/step\n",
      "Epoch 435/3000\n",
      "\n",
      "Epoch 435: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8189e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 436/3000\n",
      "\n",
      "Epoch 436: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8358e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 437/3000\n",
      "\n",
      "Epoch 437: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8396e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 438/3000\n",
      "\n",
      "Epoch 438: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8284e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 439/3000\n",
      "\n",
      "Epoch 439: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8296e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 440/3000\n",
      "\n",
      "Epoch 440: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8093e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 441/3000\n",
      "\n",
      "Epoch 441: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8234e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 442/3000\n",
      "\n",
      "Epoch 442: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8299e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 443/3000\n",
      "\n",
      "Epoch 443: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8136e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 444/3000\n",
      "\n",
      "Epoch 444: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8139e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 445/3000\n",
      "\n",
      "Epoch 445: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7925e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 446/3000\n",
      "\n",
      "Epoch 446: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7975e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 447/3000\n",
      "\n",
      "Epoch 447: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8194e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 448/3000\n",
      "\n",
      "Epoch 448: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7896e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 449/3000\n",
      "\n",
      "Epoch 449: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8097e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 450/3000\n",
      "\n",
      "Epoch 450: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8040e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 451/3000\n",
      "\n",
      "Epoch 451: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8151e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 452/3000\n",
      "\n",
      "Epoch 452: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7998e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 57ms/step\n",
      "Epoch 453/3000\n",
      "\n",
      "Epoch 453: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8137e-04 - mae: 0.0177 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 454/3000\n",
      "\n",
      "Epoch 454: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7926e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 455/3000\n",
      "\n",
      "Epoch 455: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7924e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 456/3000\n",
      "\n",
      "Epoch 456: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.8091e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 457/3000\n",
      "\n",
      "Epoch 457: val_mae did not improve from 0.03113\n",
      "151/151 - 9s - loss: 5.7825e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 458/3000\n",
      "\n",
      "Epoch 458: val_mae improved from 0.03113 to 0.03110, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.7925e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 59ms/step\n",
      "Epoch 459/3000\n",
      "\n",
      "Epoch 459: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.7811e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 460/3000\n",
      "\n",
      "Epoch 460: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.7791e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 461/3000\n",
      "\n",
      "Epoch 461: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.8020e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 462/3000\n",
      "\n",
      "Epoch 462: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.7761e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 463/3000\n",
      "\n",
      "Epoch 463: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.7850e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 464/3000\n",
      "\n",
      "Epoch 464: val_mae did not improve from 0.03110\n",
      "151/151 - 9s - loss: 5.7628e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 465/3000\n",
      "\n",
      "Epoch 465: val_mae improved from 0.03110 to 0.03103, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.7984e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 59ms/step\n",
      "Epoch 466/3000\n",
      "\n",
      "Epoch 466: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7733e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 467/3000\n",
      "\n",
      "Epoch 467: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7894e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 468/3000\n",
      "\n",
      "Epoch 468: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7585e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 469/3000\n",
      "\n",
      "Epoch 469: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7699e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 470/3000\n",
      "\n",
      "Epoch 470: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7492e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 471/3000\n",
      "\n",
      "Epoch 471: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7678e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 472/3000\n",
      "\n",
      "Epoch 472: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7741e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 473/3000\n",
      "\n",
      "Epoch 473: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7643e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 474/3000\n",
      "\n",
      "Epoch 474: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7700e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 475/3000\n",
      "\n",
      "Epoch 475: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7387e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 476/3000\n",
      "\n",
      "Epoch 476: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7666e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 477/3000\n",
      "\n",
      "Epoch 477: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7515e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 478/3000\n",
      "\n",
      "Epoch 478: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7588e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 479/3000\n",
      "\n",
      "Epoch 479: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7395e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 480/3000\n",
      "\n",
      "Epoch 480: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7550e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 481/3000\n",
      "\n",
      "Epoch 481: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7602e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 482/3000\n",
      "\n",
      "Epoch 482: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7405e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 483/3000\n",
      "\n",
      "Epoch 483: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7607e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 484/3000\n",
      "\n",
      "Epoch 484: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7609e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 57ms/step\n",
      "Epoch 485/3000\n",
      "\n",
      "Epoch 485: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7381e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 486/3000\n",
      "\n",
      "Epoch 486: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7381e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 487/3000\n",
      "\n",
      "Epoch 487: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7497e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 488/3000\n",
      "\n",
      "Epoch 488: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7473e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 489/3000\n",
      "\n",
      "Epoch 489: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7270e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 57ms/step\n",
      "Epoch 490/3000\n",
      "\n",
      "Epoch 490: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7349e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 491/3000\n",
      "\n",
      "Epoch 491: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7335e-04 - mae: 0.0176 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 57ms/step\n",
      "Epoch 492/3000\n",
      "\n",
      "Epoch 492: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7236e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 57ms/step\n",
      "Epoch 493/3000\n",
      "\n",
      "Epoch 493: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7256e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 494/3000\n",
      "\n",
      "Epoch 494: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7240e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 495/3000\n",
      "\n",
      "Epoch 495: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7302e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 496/3000\n",
      "\n",
      "Epoch 496: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7381e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 497/3000\n",
      "\n",
      "Epoch 497: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7136e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 498/3000\n",
      "\n",
      "Epoch 498: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7166e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 57ms/step\n",
      "Epoch 499/3000\n",
      "\n",
      "Epoch 499: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7133e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 500/3000\n",
      "\n",
      "Epoch 500: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7067e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 501/3000\n",
      "\n",
      "Epoch 501: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7191e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 502/3000\n",
      "\n",
      "Epoch 502: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7002e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 503/3000\n",
      "\n",
      "Epoch 503: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7207e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 504/3000\n",
      "\n",
      "Epoch 504: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7208e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 505/3000\n",
      "\n",
      "Epoch 505: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7111e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 506/3000\n",
      "\n",
      "Epoch 506: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7053e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 507/3000\n",
      "\n",
      "Epoch 507: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7087e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 508/3000\n",
      "\n",
      "Epoch 508: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7083e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 509/3000\n",
      "\n",
      "Epoch 509: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7016e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 510/3000\n",
      "\n",
      "Epoch 510: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6868e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 57ms/step\n",
      "Epoch 511/3000\n",
      "\n",
      "Epoch 511: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6926e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 512/3000\n",
      "\n",
      "Epoch 512: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7075e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 513/3000\n",
      "\n",
      "Epoch 513: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6942e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 514/3000\n",
      "\n",
      "Epoch 514: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7002e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 515/3000\n",
      "\n",
      "Epoch 515: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6981e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 516/3000\n",
      "\n",
      "Epoch 516: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6896e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 517/3000\n",
      "\n",
      "Epoch 517: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6892e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 518/3000\n",
      "\n",
      "Epoch 518: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6925e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 519/3000\n",
      "\n",
      "Epoch 519: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6759e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 520/3000\n",
      "\n",
      "Epoch 520: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6806e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 521/3000\n",
      "\n",
      "Epoch 521: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.7067e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 522/3000\n",
      "\n",
      "Epoch 522: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6707e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 523/3000\n",
      "\n",
      "Epoch 523: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6778e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 524/3000\n",
      "\n",
      "Epoch 524: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6779e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 525/3000\n",
      "\n",
      "Epoch 525: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6855e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 526/3000\n",
      "\n",
      "Epoch 526: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6748e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 527/3000\n",
      "\n",
      "Epoch 527: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6667e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 528/3000\n",
      "\n",
      "Epoch 528: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6865e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 529/3000\n",
      "\n",
      "Epoch 529: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6769e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 530/3000\n",
      "\n",
      "Epoch 530: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6742e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 531/3000\n",
      "\n",
      "Epoch 531: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6570e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 532/3000\n",
      "\n",
      "Epoch 532: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6648e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 533/3000\n",
      "\n",
      "Epoch 533: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6713e-04 - mae: 0.0175 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 534/3000\n",
      "\n",
      "Epoch 534: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6692e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 535/3000\n",
      "\n",
      "Epoch 535: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6527e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 536/3000\n",
      "\n",
      "Epoch 536: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6658e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 537/3000\n",
      "\n",
      "Epoch 537: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6607e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 538/3000\n",
      "\n",
      "Epoch 538: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6477e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 539/3000\n",
      "\n",
      "Epoch 539: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6665e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 540/3000\n",
      "\n",
      "Epoch 540: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6521e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 541/3000\n",
      "\n",
      "Epoch 541: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6518e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 542/3000\n",
      "\n",
      "Epoch 542: val_mae did not improve from 0.03103\n",
      "151/151 - 9s - loss: 5.6614e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 543/3000\n",
      "\n",
      "Epoch 543: val_mae improved from 0.03103 to 0.03091, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.6439e-04 - mae: 0.0174 - val_loss: 0.0015 - val_mae: 0.0309 - 9s/epoch - 59ms/step\n",
      "Epoch 544/3000\n",
      "\n",
      "Epoch 544: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6414e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 545/3000\n",
      "\n",
      "Epoch 545: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6531e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 546/3000\n",
      "\n",
      "Epoch 546: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6575e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 547/3000\n",
      "\n",
      "Epoch 547: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6455e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 548/3000\n",
      "\n",
      "Epoch 548: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6428e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 549/3000\n",
      "\n",
      "Epoch 549: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6410e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 550/3000\n",
      "\n",
      "Epoch 550: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6465e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 551/3000\n",
      "\n",
      "Epoch 551: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6447e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 552/3000\n",
      "\n",
      "Epoch 552: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6377e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 553/3000\n",
      "\n",
      "Epoch 553: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6357e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 554/3000\n",
      "\n",
      "Epoch 554: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6280e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 555/3000\n",
      "\n",
      "Epoch 555: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6211e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 556/3000\n",
      "\n",
      "Epoch 556: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6348e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 557/3000\n",
      "\n",
      "Epoch 557: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6315e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 558/3000\n",
      "\n",
      "Epoch 558: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6319e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 559/3000\n",
      "\n",
      "Epoch 559: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6381e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 560/3000\n",
      "\n",
      "Epoch 560: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6122e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 561/3000\n",
      "\n",
      "Epoch 561: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6512e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 562/3000\n",
      "\n",
      "Epoch 562: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6203e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 563/3000\n",
      "\n",
      "Epoch 563: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6189e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 564/3000\n",
      "\n",
      "Epoch 564: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6154e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 565/3000\n",
      "\n",
      "Epoch 565: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6050e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 566/3000\n",
      "\n",
      "Epoch 566: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6219e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 567/3000\n",
      "\n",
      "Epoch 567: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6222e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 568/3000\n",
      "\n",
      "Epoch 568: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6196e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 569/3000\n",
      "\n",
      "Epoch 569: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6106e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 570/3000\n",
      "\n",
      "Epoch 570: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6270e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 571/3000\n",
      "\n",
      "Epoch 571: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6149e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 572/3000\n",
      "\n",
      "Epoch 572: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6163e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 573/3000\n",
      "\n",
      "Epoch 573: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6166e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 574/3000\n",
      "\n",
      "Epoch 574: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6145e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 575/3000\n",
      "\n",
      "Epoch 575: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6165e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 576/3000\n",
      "\n",
      "Epoch 576: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6029e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 577/3000\n",
      "\n",
      "Epoch 577: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6025e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 578/3000\n",
      "\n",
      "Epoch 578: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5954e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 579/3000\n",
      "\n",
      "Epoch 579: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6171e-04 - mae: 0.0174 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 580/3000\n",
      "\n",
      "Epoch 580: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5936e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 581/3000\n",
      "\n",
      "Epoch 581: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5949e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 582/3000\n",
      "\n",
      "Epoch 582: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5895e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 583/3000\n",
      "\n",
      "Epoch 583: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5966e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 584/3000\n",
      "\n",
      "Epoch 584: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5993e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 585/3000\n",
      "\n",
      "Epoch 585: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.6025e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 586/3000\n",
      "\n",
      "Epoch 586: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5835e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 587/3000\n",
      "\n",
      "Epoch 587: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5920e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 588/3000\n",
      "\n",
      "Epoch 588: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5923e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 589/3000\n",
      "\n",
      "Epoch 589: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5976e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 590/3000\n",
      "\n",
      "Epoch 590: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5823e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 591/3000\n",
      "\n",
      "Epoch 591: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5954e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 592/3000\n",
      "\n",
      "Epoch 592: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5856e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 593/3000\n",
      "\n",
      "Epoch 593: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5808e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 594/3000\n",
      "\n",
      "Epoch 594: val_mae did not improve from 0.03091\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.5677e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 595/3000\n",
      "\n",
      "Epoch 595: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5975e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 596/3000\n",
      "\n",
      "Epoch 596: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5776e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 597/3000\n",
      "\n",
      "Epoch 597: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5925e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 598/3000\n",
      "\n",
      "Epoch 598: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5915e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 599/3000\n",
      "\n",
      "Epoch 599: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5823e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 600/3000\n",
      "\n",
      "Epoch 600: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5782e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 601/3000\n",
      "\n",
      "Epoch 601: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5650e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 602/3000\n",
      "\n",
      "Epoch 602: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5755e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 603/3000\n",
      "\n",
      "Epoch 603: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5863e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 604/3000\n",
      "\n",
      "Epoch 604: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5825e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 605/3000\n",
      "\n",
      "Epoch 605: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5520e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 606/3000\n",
      "\n",
      "Epoch 606: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5702e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 607/3000\n",
      "\n",
      "Epoch 607: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5795e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 608/3000\n",
      "\n",
      "Epoch 608: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5757e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 609/3000\n",
      "\n",
      "Epoch 609: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5642e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 610/3000\n",
      "\n",
      "Epoch 610: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5605e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 611/3000\n",
      "\n",
      "Epoch 611: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5684e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 612/3000\n",
      "\n",
      "Epoch 612: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5696e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 613/3000\n",
      "\n",
      "Epoch 613: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5733e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 614/3000\n",
      "\n",
      "Epoch 614: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5429e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 615/3000\n",
      "\n",
      "Epoch 615: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5726e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 616/3000\n",
      "\n",
      "Epoch 616: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5626e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 617/3000\n",
      "\n",
      "Epoch 617: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5682e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 618/3000\n",
      "\n",
      "Epoch 618: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5427e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 619/3000\n",
      "\n",
      "Epoch 619: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5566e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 620/3000\n",
      "\n",
      "Epoch 620: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5409e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 621/3000\n",
      "\n",
      "Epoch 621: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5360e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 622/3000\n",
      "\n",
      "Epoch 622: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5425e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 623/3000\n",
      "\n",
      "Epoch 623: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5465e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 624/3000\n",
      "\n",
      "Epoch 624: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5473e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 625/3000\n",
      "\n",
      "Epoch 625: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5410e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 626/3000\n",
      "\n",
      "Epoch 626: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5322e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 627/3000\n",
      "\n",
      "Epoch 627: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5497e-04 - mae: 0.0173 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 628/3000\n",
      "\n",
      "Epoch 628: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5296e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 629/3000\n",
      "\n",
      "Epoch 629: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5354e-04 - mae: 0.0172 - val_loss: 0.0015 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 630/3000\n",
      "\n",
      "Epoch 630: val_mae did not improve from 0.03091\n",
      "151/151 - 9s - loss: 5.5469e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 631/3000\n",
      "\n",
      "Epoch 631: val_mae improved from 0.03091 to 0.03090, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.5453e-04 - mae: 0.0172 - val_loss: 0.0015 - val_mae: 0.0309 - 9s/epoch - 59ms/step\n",
      "Epoch 632/3000\n",
      "\n",
      "Epoch 632: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5511e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 633/3000\n",
      "\n",
      "Epoch 633: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5233e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 634/3000\n",
      "\n",
      "Epoch 634: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5313e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 59ms/step\n",
      "Epoch 635/3000\n",
      "\n",
      "Epoch 635: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5340e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 59ms/step\n",
      "Epoch 636/3000\n",
      "\n",
      "Epoch 636: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5323e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 637/3000\n",
      "\n",
      "Epoch 637: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5419e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 638/3000\n",
      "\n",
      "Epoch 638: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5345e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 639/3000\n",
      "\n",
      "Epoch 639: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5240e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 640/3000\n",
      "\n",
      "Epoch 640: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5359e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 641/3000\n",
      "\n",
      "Epoch 641: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5279e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 642/3000\n",
      "\n",
      "Epoch 642: val_mae did not improve from 0.03090\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.5223e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 643/3000\n",
      "\n",
      "Epoch 643: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5265e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 644/3000\n",
      "\n",
      "Epoch 644: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5213e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 645/3000\n",
      "\n",
      "Epoch 645: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5284e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 646/3000\n",
      "\n",
      "Epoch 646: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5308e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 647/3000\n",
      "\n",
      "Epoch 647: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5209e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 648/3000\n",
      "\n",
      "Epoch 648: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5353e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 649/3000\n",
      "\n",
      "Epoch 649: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5136e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 650/3000\n",
      "\n",
      "Epoch 650: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5142e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 651/3000\n",
      "\n",
      "Epoch 651: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5128e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 652/3000\n",
      "\n",
      "Epoch 652: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5188e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 653/3000\n",
      "\n",
      "Epoch 653: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5025e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 654/3000\n",
      "\n",
      "Epoch 654: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5122e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 655/3000\n",
      "\n",
      "Epoch 655: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5203e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 656/3000\n",
      "\n",
      "Epoch 656: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5027e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 657/3000\n",
      "\n",
      "Epoch 657: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5185e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 658/3000\n",
      "\n",
      "Epoch 658: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5076e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 659/3000\n",
      "\n",
      "Epoch 659: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5111e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 660/3000\n",
      "\n",
      "Epoch 660: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5131e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 661/3000\n",
      "\n",
      "Epoch 661: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5080e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 662/3000\n",
      "\n",
      "Epoch 662: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.4988e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 663/3000\n",
      "\n",
      "Epoch 663: val_mae did not improve from 0.03090\n",
      "151/151 - 9s - loss: 5.5109e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 664/3000\n",
      "\n",
      "Epoch 664: val_mae improved from 0.03090 to 0.03089, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.4976e-04 - mae: 0.0172 - val_loss: 0.0015 - val_mae: 0.0309 - 9s/epoch - 59ms/step\n",
      "Epoch 665/3000\n",
      "\n",
      "Epoch 665: val_mae did not improve from 0.03089\n",
      "151/151 - 9s - loss: 5.4943e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 666/3000\n",
      "\n",
      "Epoch 666: val_mae did not improve from 0.03089\n",
      "151/151 - 9s - loss: 5.5051e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 667/3000\n",
      "\n",
      "Epoch 667: val_mae did not improve from 0.03089\n",
      "151/151 - 9s - loss: 5.4910e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 668/3000\n",
      "\n",
      "Epoch 668: val_mae improved from 0.03089 to 0.03081, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.5012e-04 - mae: 0.0172 - val_loss: 0.0015 - val_mae: 0.0308 - 9s/epoch - 59ms/step\n",
      "Epoch 669/3000\n",
      "\n",
      "Epoch 669: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4968e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 670/3000\n",
      "\n",
      "Epoch 670: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.5000e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 671/3000\n",
      "\n",
      "Epoch 671: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4897e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 672/3000\n",
      "\n",
      "Epoch 672: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.5035e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 673/3000\n",
      "\n",
      "Epoch 673: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.5000e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 674/3000\n",
      "\n",
      "Epoch 674: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4877e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 675/3000\n",
      "\n",
      "Epoch 675: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4985e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 676/3000\n",
      "\n",
      "Epoch 676: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4834e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 677/3000\n",
      "\n",
      "Epoch 677: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4824e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 678/3000\n",
      "\n",
      "Epoch 678: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4983e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 679/3000\n",
      "\n",
      "Epoch 679: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4864e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 680/3000\n",
      "\n",
      "Epoch 680: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4951e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 681/3000\n",
      "\n",
      "Epoch 681: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4920e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 682/3000\n",
      "\n",
      "Epoch 682: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4808e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 683/3000\n",
      "\n",
      "Epoch 683: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4872e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 684/3000\n",
      "\n",
      "Epoch 684: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4805e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 685/3000\n",
      "\n",
      "Epoch 685: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4782e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 686/3000\n",
      "\n",
      "Epoch 686: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4823e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 687/3000\n",
      "\n",
      "Epoch 687: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4874e-04 - mae: 0.0172 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 57ms/step\n",
      "Epoch 688/3000\n",
      "\n",
      "Epoch 688: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4733e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 689/3000\n",
      "\n",
      "Epoch 689: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4584e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 690/3000\n",
      "\n",
      "Epoch 690: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4650e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 691/3000\n",
      "\n",
      "Epoch 691: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4692e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 692/3000\n",
      "\n",
      "Epoch 692: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4665e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 693/3000\n",
      "\n",
      "Epoch 693: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4710e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 694/3000\n",
      "\n",
      "Epoch 694: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4697e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 695/3000\n",
      "\n",
      "Epoch 695: val_mae did not improve from 0.03081\n",
      "151/151 - 9s - loss: 5.4533e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 696/3000\n",
      "\n",
      "Epoch 696: val_mae improved from 0.03081 to 0.03074, saving model to saved_models/tcn_gate_pump_nofuture.h5\n",
      "151/151 - 9s - loss: 5.4793e-04 - mae: 0.0171 - val_loss: 0.0015 - val_mae: 0.0307 - 9s/epoch - 59ms/step\n",
      "Epoch 697/3000\n",
      "\n",
      "Epoch 697: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4697e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 698/3000\n",
      "\n",
      "Epoch 698: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4586e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 699/3000\n",
      "\n",
      "Epoch 699: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4508e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 700/3000\n",
      "\n",
      "Epoch 700: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4750e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 701/3000\n",
      "\n",
      "Epoch 701: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4585e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 702/3000\n",
      "\n",
      "Epoch 702: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4501e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 703/3000\n",
      "\n",
      "Epoch 703: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4629e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 704/3000\n",
      "\n",
      "Epoch 704: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4640e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 705/3000\n",
      "\n",
      "Epoch 705: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4619e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 706/3000\n",
      "\n",
      "Epoch 706: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4556e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 57ms/step\n",
      "Epoch 707/3000\n",
      "\n",
      "Epoch 707: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4730e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 708/3000\n",
      "\n",
      "Epoch 708: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4587e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 709/3000\n",
      "\n",
      "Epoch 709: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4563e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 710/3000\n",
      "\n",
      "Epoch 710: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4484e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 711/3000\n",
      "\n",
      "Epoch 711: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4444e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 712/3000\n",
      "\n",
      "Epoch 712: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4533e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 713/3000\n",
      "\n",
      "Epoch 713: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4659e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 714/3000\n",
      "\n",
      "Epoch 714: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4505e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 715/3000\n",
      "\n",
      "Epoch 715: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4529e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 716/3000\n",
      "\n",
      "Epoch 716: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4485e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 717/3000\n",
      "\n",
      "Epoch 717: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4465e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 718/3000\n",
      "\n",
      "Epoch 718: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4440e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 719/3000\n",
      "\n",
      "Epoch 719: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4414e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 720/3000\n",
      "\n",
      "Epoch 720: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4520e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 721/3000\n",
      "\n",
      "Epoch 721: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4532e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 722/3000\n",
      "\n",
      "Epoch 722: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4486e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 723/3000\n",
      "\n",
      "Epoch 723: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4372e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 724/3000\n",
      "\n",
      "Epoch 724: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4321e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 725/3000\n",
      "\n",
      "Epoch 725: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4556e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 726/3000\n",
      "\n",
      "Epoch 726: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4524e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 727/3000\n",
      "\n",
      "Epoch 727: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4577e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 728/3000\n",
      "\n",
      "Epoch 728: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4502e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 729/3000\n",
      "\n",
      "Epoch 729: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4347e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 730/3000\n",
      "\n",
      "Epoch 730: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4425e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 731/3000\n",
      "\n",
      "Epoch 731: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4347e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 732/3000\n",
      "\n",
      "Epoch 732: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4344e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 733/3000\n",
      "\n",
      "Epoch 733: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4329e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 734/3000\n",
      "\n",
      "Epoch 734: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4436e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 735/3000\n",
      "\n",
      "Epoch 735: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4150e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 736/3000\n",
      "\n",
      "Epoch 736: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4404e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 737/3000\n",
      "\n",
      "Epoch 737: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4531e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 738/3000\n",
      "\n",
      "Epoch 738: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4327e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 739/3000\n",
      "\n",
      "Epoch 739: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4191e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 740/3000\n",
      "\n",
      "Epoch 740: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4302e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 741/3000\n",
      "\n",
      "Epoch 741: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4320e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 742/3000\n",
      "\n",
      "Epoch 742: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4262e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 743/3000\n",
      "\n",
      "Epoch 743: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4188e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 57ms/step\n",
      "Epoch 744/3000\n",
      "\n",
      "Epoch 744: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4162e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 745/3000\n",
      "\n",
      "Epoch 745: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4278e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 746/3000\n",
      "\n",
      "Epoch 746: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4050e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 747/3000\n",
      "\n",
      "Epoch 747: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4316e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 748/3000\n",
      "\n",
      "Epoch 748: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4236e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 749/3000\n",
      "\n",
      "Epoch 749: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4073e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 750/3000\n",
      "\n",
      "Epoch 750: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4311e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 751/3000\n",
      "\n",
      "Epoch 751: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4208e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 752/3000\n",
      "\n",
      "Epoch 752: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4104e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 753/3000\n",
      "\n",
      "Epoch 753: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4222e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 754/3000\n",
      "\n",
      "Epoch 754: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4056e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 755/3000\n",
      "\n",
      "Epoch 755: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4111e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 756/3000\n",
      "\n",
      "Epoch 756: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3942e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 757/3000\n",
      "\n",
      "Epoch 757: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4111e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 758/3000\n",
      "\n",
      "Epoch 758: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4250e-04 - mae: 0.0171 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 759/3000\n",
      "\n",
      "Epoch 759: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4135e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 760/3000\n",
      "\n",
      "Epoch 760: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4117e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 761/3000\n",
      "\n",
      "Epoch 761: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3978e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 57ms/step\n",
      "Epoch 762/3000\n",
      "\n",
      "Epoch 762: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4055e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 57ms/step\n",
      "Epoch 763/3000\n",
      "\n",
      "Epoch 763: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4058e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 764/3000\n",
      "\n",
      "Epoch 764: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4049e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 765/3000\n",
      "\n",
      "Epoch 765: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4092e-04 - mae: 0.0170 - val_loss: 0.0015 - val_mae: 0.0308 - 9s/epoch - 58ms/step\n",
      "Epoch 766/3000\n",
      "\n",
      "Epoch 766: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3967e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 767/3000\n",
      "\n",
      "Epoch 767: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4026e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 768/3000\n",
      "\n",
      "Epoch 768: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3986e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 769/3000\n",
      "\n",
      "Epoch 769: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3989e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 770/3000\n",
      "\n",
      "Epoch 770: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4062e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 771/3000\n",
      "\n",
      "Epoch 771: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3890e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 772/3000\n",
      "\n",
      "Epoch 772: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3935e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 773/3000\n",
      "\n",
      "Epoch 773: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3921e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 774/3000\n",
      "\n",
      "Epoch 774: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4031e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 57ms/step\n",
      "Epoch 775/3000\n",
      "\n",
      "Epoch 775: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3907e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 776/3000\n",
      "\n",
      "Epoch 776: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3986e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 777/3000\n",
      "\n",
      "Epoch 777: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3932e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 778/3000\n",
      "\n",
      "Epoch 778: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3856e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 779/3000\n",
      "\n",
      "Epoch 779: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3887e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 780/3000\n",
      "\n",
      "Epoch 780: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4006e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 781/3000\n",
      "\n",
      "Epoch 781: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3887e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0309 - 9s/epoch - 58ms/step\n",
      "Epoch 782/3000\n",
      "\n",
      "Epoch 782: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3749e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 783/3000\n",
      "\n",
      "Epoch 783: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.4085e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 784/3000\n",
      "\n",
      "Epoch 784: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3918e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 785/3000\n",
      "\n",
      "Epoch 785: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3890e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 786/3000\n",
      "\n",
      "Epoch 786: val_mae did not improve from 0.03074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.3757e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 787/3000\n",
      "\n",
      "Epoch 787: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3851e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 788/3000\n",
      "\n",
      "Epoch 788: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3860e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 789/3000\n",
      "\n",
      "Epoch 789: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3717e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 790/3000\n",
      "\n",
      "Epoch 790: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3767e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 791/3000\n",
      "\n",
      "Epoch 791: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3926e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 792/3000\n",
      "\n",
      "Epoch 792: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3896e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 793/3000\n",
      "\n",
      "Epoch 793: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3929e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 794/3000\n",
      "\n",
      "Epoch 794: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3862e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 795/3000\n",
      "\n",
      "Epoch 795: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3855e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 796/3000\n",
      "\n",
      "Epoch 796: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3805e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 797/3000\n",
      "\n",
      "Epoch 797: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3777e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 798/3000\n",
      "\n",
      "Epoch 798: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3704e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 799/3000\n",
      "\n",
      "Epoch 799: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3634e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 800/3000\n",
      "\n",
      "Epoch 800: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3844e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 801/3000\n",
      "\n",
      "Epoch 801: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3659e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 802/3000\n",
      "\n",
      "Epoch 802: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3673e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 803/3000\n",
      "\n",
      "Epoch 803: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3799e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 804/3000\n",
      "\n",
      "Epoch 804: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3670e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0310 - 9s/epoch - 58ms/step\n",
      "Epoch 805/3000\n",
      "\n",
      "Epoch 805: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3687e-04 - mae: 0.0170 - val_loss: 0.0015 - val_mae: 0.0308 - 9s/epoch - 58ms/step\n",
      "Epoch 806/3000\n",
      "\n",
      "Epoch 806: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3639e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 807/3000\n",
      "\n",
      "Epoch 807: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3641e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 808/3000\n",
      "\n",
      "Epoch 808: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3691e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 809/3000\n",
      "\n",
      "Epoch 809: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3852e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 810/3000\n",
      "\n",
      "Epoch 810: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3680e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 811/3000\n",
      "\n",
      "Epoch 811: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3624e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 812/3000\n",
      "\n",
      "Epoch 812: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3576e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 813/3000\n",
      "\n",
      "Epoch 813: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3623e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 814/3000\n",
      "\n",
      "Epoch 814: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3660e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 815/3000\n",
      "\n",
      "Epoch 815: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3675e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 816/3000\n",
      "\n",
      "Epoch 816: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3609e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 817/3000\n",
      "\n",
      "Epoch 817: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3544e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 818/3000\n",
      "\n",
      "Epoch 818: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3586e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 819/3000\n",
      "\n",
      "Epoch 819: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3461e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 820/3000\n",
      "\n",
      "Epoch 820: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3596e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 821/3000\n",
      "\n",
      "Epoch 821: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3543e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 822/3000\n",
      "\n",
      "Epoch 822: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3571e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 823/3000\n",
      "\n",
      "Epoch 823: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3630e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 824/3000\n",
      "\n",
      "Epoch 824: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3547e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 825/3000\n",
      "\n",
      "Epoch 825: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3480e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 826/3000\n",
      "\n",
      "Epoch 826: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3559e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 827/3000\n",
      "\n",
      "Epoch 827: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3518e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 828/3000\n",
      "\n",
      "Epoch 828: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3596e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 829/3000\n",
      "\n",
      "Epoch 829: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3512e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 830/3000\n",
      "\n",
      "Epoch 830: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3472e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 831/3000\n",
      "\n",
      "Epoch 831: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3452e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 832/3000\n",
      "\n",
      "Epoch 832: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3526e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 833/3000\n",
      "\n",
      "Epoch 833: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3476e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 834/3000\n",
      "\n",
      "Epoch 834: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3397e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 835/3000\n",
      "\n",
      "Epoch 835: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3631e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 836/3000\n",
      "\n",
      "Epoch 836: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3374e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 837/3000\n",
      "\n",
      "Epoch 837: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3404e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 838/3000\n",
      "\n",
      "Epoch 838: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3351e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 57ms/step\n",
      "Epoch 839/3000\n",
      "\n",
      "Epoch 839: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3554e-04 - mae: 0.0170 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 840/3000\n",
      "\n",
      "Epoch 840: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3537e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 841/3000\n",
      "\n",
      "Epoch 841: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3478e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 842/3000\n",
      "\n",
      "Epoch 842: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3422e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 843/3000\n",
      "\n",
      "Epoch 843: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3363e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 844/3000\n",
      "\n",
      "Epoch 844: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3480e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 845/3000\n",
      "\n",
      "Epoch 845: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3390e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 846/3000\n",
      "\n",
      "Epoch 846: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3385e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 847/3000\n",
      "\n",
      "Epoch 847: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3322e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 848/3000\n",
      "\n",
      "Epoch 848: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3283e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 849/3000\n",
      "\n",
      "Epoch 849: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3388e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 850/3000\n",
      "\n",
      "Epoch 850: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3353e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 851/3000\n",
      "\n",
      "Epoch 851: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3359e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 852/3000\n",
      "\n",
      "Epoch 852: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3239e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 853/3000\n",
      "\n",
      "Epoch 853: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3267e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 854/3000\n",
      "\n",
      "Epoch 854: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3307e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 855/3000\n",
      "\n",
      "Epoch 855: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3330e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 856/3000\n",
      "\n",
      "Epoch 856: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3484e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 857/3000\n",
      "\n",
      "Epoch 857: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3324e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 858/3000\n",
      "\n",
      "Epoch 858: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3240e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 859/3000\n",
      "\n",
      "Epoch 859: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3215e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 860/3000\n",
      "\n",
      "Epoch 860: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3326e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 861/3000\n",
      "\n",
      "Epoch 861: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3208e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 862/3000\n",
      "\n",
      "Epoch 862: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3177e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 863/3000\n",
      "\n",
      "Epoch 863: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3151e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 864/3000\n",
      "\n",
      "Epoch 864: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3252e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 865/3000\n",
      "\n",
      "Epoch 865: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3301e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 866/3000\n",
      "\n",
      "Epoch 866: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3152e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 867/3000\n",
      "\n",
      "Epoch 867: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3185e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 868/3000\n",
      "\n",
      "Epoch 868: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3060e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 869/3000\n",
      "\n",
      "Epoch 869: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3181e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 870/3000\n",
      "\n",
      "Epoch 870: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3179e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 871/3000\n",
      "\n",
      "Epoch 871: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3080e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 872/3000\n",
      "\n",
      "Epoch 872: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3221e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 873/3000\n",
      "\n",
      "Epoch 873: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3230e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0311 - 9s/epoch - 58ms/step\n",
      "Epoch 874/3000\n",
      "\n",
      "Epoch 874: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3168e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 875/3000\n",
      "\n",
      "Epoch 875: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3051e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 876/3000\n",
      "\n",
      "Epoch 876: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3225e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 877/3000\n",
      "\n",
      "Epoch 877: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3166e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 878/3000\n",
      "\n",
      "Epoch 878: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3208e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 879/3000\n",
      "\n",
      "Epoch 879: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3287e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 880/3000\n",
      "\n",
      "Epoch 880: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3125e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 881/3000\n",
      "\n",
      "Epoch 881: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3227e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 882/3000\n",
      "\n",
      "Epoch 882: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3103e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 883/3000\n",
      "\n",
      "Epoch 883: val_mae did not improve from 0.03074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.3229e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 884/3000\n",
      "\n",
      "Epoch 884: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3084e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 885/3000\n",
      "\n",
      "Epoch 885: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3267e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 886/3000\n",
      "\n",
      "Epoch 886: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3021e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 887/3000\n",
      "\n",
      "Epoch 887: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3185e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 888/3000\n",
      "\n",
      "Epoch 888: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3100e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 889/3000\n",
      "\n",
      "Epoch 889: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3051e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 890/3000\n",
      "\n",
      "Epoch 890: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3141e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 891/3000\n",
      "\n",
      "Epoch 891: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3129e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 892/3000\n",
      "\n",
      "Epoch 892: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3131e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 893/3000\n",
      "\n",
      "Epoch 893: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3042e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 894/3000\n",
      "\n",
      "Epoch 894: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3038e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 895/3000\n",
      "\n",
      "Epoch 895: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3090e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 896/3000\n",
      "\n",
      "Epoch 896: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3060e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 897/3000\n",
      "\n",
      "Epoch 897: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3023e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 898/3000\n",
      "\n",
      "Epoch 898: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3029e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 899/3000\n",
      "\n",
      "Epoch 899: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3041e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 900/3000\n",
      "\n",
      "Epoch 900: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3045e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 901/3000\n",
      "\n",
      "Epoch 901: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3007e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 902/3000\n",
      "\n",
      "Epoch 902: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3000e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 903/3000\n",
      "\n",
      "Epoch 903: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2997e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 904/3000\n",
      "\n",
      "Epoch 904: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2930e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 905/3000\n",
      "\n",
      "Epoch 905: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2754e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 906/3000\n",
      "\n",
      "Epoch 906: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3067e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 907/3000\n",
      "\n",
      "Epoch 907: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2813e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 908/3000\n",
      "\n",
      "Epoch 908: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3097e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 909/3000\n",
      "\n",
      "Epoch 909: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2906e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 910/3000\n",
      "\n",
      "Epoch 910: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2989e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 911/3000\n",
      "\n",
      "Epoch 911: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2706e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 912/3000\n",
      "\n",
      "Epoch 912: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.3028e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 913/3000\n",
      "\n",
      "Epoch 913: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2921e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 914/3000\n",
      "\n",
      "Epoch 914: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2962e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 915/3000\n",
      "\n",
      "Epoch 915: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2998e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 916/3000\n",
      "\n",
      "Epoch 916: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2945e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 917/3000\n",
      "\n",
      "Epoch 917: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2952e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 918/3000\n",
      "\n",
      "Epoch 918: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2970e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 919/3000\n",
      "\n",
      "Epoch 919: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2797e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 920/3000\n",
      "\n",
      "Epoch 920: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2799e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 921/3000\n",
      "\n",
      "Epoch 921: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2864e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 922/3000\n",
      "\n",
      "Epoch 922: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2895e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 923/3000\n",
      "\n",
      "Epoch 923: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2891e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 924/3000\n",
      "\n",
      "Epoch 924: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2698e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 925/3000\n",
      "\n",
      "Epoch 925: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2900e-04 - mae: 0.0169 - val_loss: 0.0016 - val_mae: 0.0312 - 9s/epoch - 58ms/step\n",
      "Epoch 926/3000\n",
      "\n",
      "Epoch 926: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2863e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 927/3000\n",
      "\n",
      "Epoch 927: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2928e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 928/3000\n",
      "\n",
      "Epoch 928: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2771e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 929/3000\n",
      "\n",
      "Epoch 929: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2754e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 930/3000\n",
      "\n",
      "Epoch 930: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2852e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 60ms/step\n",
      "Epoch 931/3000\n",
      "\n",
      "Epoch 931: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2708e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 932/3000\n",
      "\n",
      "Epoch 932: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2785e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 933/3000\n",
      "\n",
      "Epoch 933: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2755e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 934/3000\n",
      "\n",
      "Epoch 934: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2710e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 935/3000\n",
      "\n",
      "Epoch 935: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2719e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 936/3000\n",
      "\n",
      "Epoch 936: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2857e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 937/3000\n",
      "\n",
      "Epoch 937: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2757e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 938/3000\n",
      "\n",
      "Epoch 938: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2704e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 939/3000\n",
      "\n",
      "Epoch 939: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2711e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 940/3000\n",
      "\n",
      "Epoch 940: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2799e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 941/3000\n",
      "\n",
      "Epoch 941: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2767e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 942/3000\n",
      "\n",
      "Epoch 942: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2843e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 943/3000\n",
      "\n",
      "Epoch 943: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2779e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 944/3000\n",
      "\n",
      "Epoch 944: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2668e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 945/3000\n",
      "\n",
      "Epoch 945: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2558e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 946/3000\n",
      "\n",
      "Epoch 946: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2845e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 947/3000\n",
      "\n",
      "Epoch 947: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2643e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 948/3000\n",
      "\n",
      "Epoch 948: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2623e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 949/3000\n",
      "\n",
      "Epoch 949: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2591e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 950/3000\n",
      "\n",
      "Epoch 950: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2821e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 951/3000\n",
      "\n",
      "Epoch 951: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2775e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 952/3000\n",
      "\n",
      "Epoch 952: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2618e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 953/3000\n",
      "\n",
      "Epoch 953: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2694e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 954/3000\n",
      "\n",
      "Epoch 954: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2859e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 955/3000\n",
      "\n",
      "Epoch 955: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2733e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 956/3000\n",
      "\n",
      "Epoch 956: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2633e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 957/3000\n",
      "\n",
      "Epoch 957: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2794e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 958/3000\n",
      "\n",
      "Epoch 958: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2731e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 959/3000\n",
      "\n",
      "Epoch 959: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2621e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 960/3000\n",
      "\n",
      "Epoch 960: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2608e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 961/3000\n",
      "\n",
      "Epoch 961: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2629e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 962/3000\n",
      "\n",
      "Epoch 962: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2573e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 963/3000\n",
      "\n",
      "Epoch 963: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2600e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 964/3000\n",
      "\n",
      "Epoch 964: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2554e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 965/3000\n",
      "\n",
      "Epoch 965: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2593e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 966/3000\n",
      "\n",
      "Epoch 966: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2589e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 967/3000\n",
      "\n",
      "Epoch 967: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2565e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 968/3000\n",
      "\n",
      "Epoch 968: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2550e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 969/3000\n",
      "\n",
      "Epoch 969: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2505e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 970/3000\n",
      "\n",
      "Epoch 970: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2589e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 971/3000\n",
      "\n",
      "Epoch 971: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2510e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 972/3000\n",
      "\n",
      "Epoch 972: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2513e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 973/3000\n",
      "\n",
      "Epoch 973: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2529e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 57ms/step\n",
      "Epoch 974/3000\n",
      "\n",
      "Epoch 974: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2518e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 975/3000\n",
      "\n",
      "Epoch 975: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2574e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 976/3000\n",
      "\n",
      "Epoch 976: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2499e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 977/3000\n",
      "\n",
      "Epoch 977: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2478e-04 - mae: 0.0168 - val_loss: 0.0017 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 978/3000\n",
      "\n",
      "Epoch 978: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2419e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 979/3000\n",
      "\n",
      "Epoch 979: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2427e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 980/3000\n",
      "\n",
      "Epoch 980: val_mae did not improve from 0.03074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.2500e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 981/3000\n",
      "\n",
      "Epoch 981: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2436e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 982/3000\n",
      "\n",
      "Epoch 982: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2480e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 983/3000\n",
      "\n",
      "Epoch 983: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2387e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 984/3000\n",
      "\n",
      "Epoch 984: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2619e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 985/3000\n",
      "\n",
      "Epoch 985: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2483e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 986/3000\n",
      "\n",
      "Epoch 986: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2508e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 987/3000\n",
      "\n",
      "Epoch 987: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2399e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 988/3000\n",
      "\n",
      "Epoch 988: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2364e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 989/3000\n",
      "\n",
      "Epoch 989: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2378e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 990/3000\n",
      "\n",
      "Epoch 990: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2467e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 991/3000\n",
      "\n",
      "Epoch 991: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2400e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 992/3000\n",
      "\n",
      "Epoch 992: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2400e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 993/3000\n",
      "\n",
      "Epoch 993: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2461e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 994/3000\n",
      "\n",
      "Epoch 994: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2478e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 995/3000\n",
      "\n",
      "Epoch 995: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2359e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 996/3000\n",
      "\n",
      "Epoch 996: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2411e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 997/3000\n",
      "\n",
      "Epoch 997: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2472e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 998/3000\n",
      "\n",
      "Epoch 998: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2502e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 999/3000\n",
      "\n",
      "Epoch 999: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2449e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1000/3000\n",
      "\n",
      "Epoch 1000: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2267e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1001/3000\n",
      "\n",
      "Epoch 1001: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2426e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1002/3000\n",
      "\n",
      "Epoch 1002: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2385e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1003/3000\n",
      "\n",
      "Epoch 1003: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2409e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1004/3000\n",
      "\n",
      "Epoch 1004: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2385e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1005/3000\n",
      "\n",
      "Epoch 1005: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2524e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1006/3000\n",
      "\n",
      "Epoch 1006: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2381e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1007/3000\n",
      "\n",
      "Epoch 1007: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2333e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 1008/3000\n",
      "\n",
      "Epoch 1008: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2301e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1009/3000\n",
      "\n",
      "Epoch 1009: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2448e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1010/3000\n",
      "\n",
      "Epoch 1010: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2407e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1011/3000\n",
      "\n",
      "Epoch 1011: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2282e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1012/3000\n",
      "\n",
      "Epoch 1012: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2358e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1013/3000\n",
      "\n",
      "Epoch 1013: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2323e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1014/3000\n",
      "\n",
      "Epoch 1014: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2283e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1015/3000\n",
      "\n",
      "Epoch 1015: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2236e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1016/3000\n",
      "\n",
      "Epoch 1016: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2261e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1017/3000\n",
      "\n",
      "Epoch 1017: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2243e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1018/3000\n",
      "\n",
      "Epoch 1018: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2301e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1019/3000\n",
      "\n",
      "Epoch 1019: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2195e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1020/3000\n",
      "\n",
      "Epoch 1020: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2107e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1021/3000\n",
      "\n",
      "Epoch 1021: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2357e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1022/3000\n",
      "\n",
      "Epoch 1022: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2231e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1023/3000\n",
      "\n",
      "Epoch 1023: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2304e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1024/3000\n",
      "\n",
      "Epoch 1024: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2071e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1025/3000\n",
      "\n",
      "Epoch 1025: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2296e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1026/3000\n",
      "\n",
      "Epoch 1026: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2244e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1027/3000\n",
      "\n",
      "Epoch 1027: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2174e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 1028/3000\n",
      "\n",
      "Epoch 1028: val_mae did not improve from 0.03074\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 9s - loss: 5.2388e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1029/3000\n",
      "\n",
      "Epoch 1029: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2226e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1030/3000\n",
      "\n",
      "Epoch 1030: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2319e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1031/3000\n",
      "\n",
      "Epoch 1031: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2278e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1032/3000\n",
      "\n",
      "Epoch 1032: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2252e-04 - mae: 0.0168 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1033/3000\n",
      "\n",
      "Epoch 1033: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2132e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1034/3000\n",
      "\n",
      "Epoch 1034: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2170e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1035/3000\n",
      "\n",
      "Epoch 1035: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2334e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1036/3000\n",
      "\n",
      "Epoch 1036: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2225e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1037/3000\n",
      "\n",
      "Epoch 1037: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2273e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1038/3000\n",
      "\n",
      "Epoch 1038: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2106e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1039/3000\n",
      "\n",
      "Epoch 1039: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2118e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1040/3000\n",
      "\n",
      "Epoch 1040: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2140e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1041/3000\n",
      "\n",
      "Epoch 1041: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2162e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 1042/3000\n",
      "\n",
      "Epoch 1042: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2153e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1043/3000\n",
      "\n",
      "Epoch 1043: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2149e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1044/3000\n",
      "\n",
      "Epoch 1044: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2080e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1045/3000\n",
      "\n",
      "Epoch 1045: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2156e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1046/3000\n",
      "\n",
      "Epoch 1046: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2124e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1047/3000\n",
      "\n",
      "Epoch 1047: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2124e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1048/3000\n",
      "\n",
      "Epoch 1048: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2094e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1049/3000\n",
      "\n",
      "Epoch 1049: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2088e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1050/3000\n",
      "\n",
      "Epoch 1050: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2048e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1051/3000\n",
      "\n",
      "Epoch 1051: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2144e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1052/3000\n",
      "\n",
      "Epoch 1052: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2146e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1053/3000\n",
      "\n",
      "Epoch 1053: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2022e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1054/3000\n",
      "\n",
      "Epoch 1054: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1969e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1055/3000\n",
      "\n",
      "Epoch 1055: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2087e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 1056/3000\n",
      "\n",
      "Epoch 1056: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2053e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1057/3000\n",
      "\n",
      "Epoch 1057: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2120e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1058/3000\n",
      "\n",
      "Epoch 1058: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2145e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1059/3000\n",
      "\n",
      "Epoch 1059: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2200e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1060/3000\n",
      "\n",
      "Epoch 1060: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2176e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1061/3000\n",
      "\n",
      "Epoch 1061: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2063e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1062/3000\n",
      "\n",
      "Epoch 1062: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2044e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1063/3000\n",
      "\n",
      "Epoch 1063: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2163e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1064/3000\n",
      "\n",
      "Epoch 1064: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2034e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1065/3000\n",
      "\n",
      "Epoch 1065: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2068e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1066/3000\n",
      "\n",
      "Epoch 1066: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1910e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 57ms/step\n",
      "Epoch 1067/3000\n",
      "\n",
      "Epoch 1067: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1936e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1068/3000\n",
      "\n",
      "Epoch 1068: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2049e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1069/3000\n",
      "\n",
      "Epoch 1069: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2048e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1070/3000\n",
      "\n",
      "Epoch 1070: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2014e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1071/3000\n",
      "\n",
      "Epoch 1071: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2053e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 1072/3000\n",
      "\n",
      "Epoch 1072: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2028e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 1073/3000\n",
      "\n",
      "Epoch 1073: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1998e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 1074/3000\n",
      "\n",
      "Epoch 1074: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2047e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1075/3000\n",
      "\n",
      "Epoch 1075: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2010e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1076/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1076: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1968e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1077/3000\n",
      "\n",
      "Epoch 1077: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2090e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1078/3000\n",
      "\n",
      "Epoch 1078: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2097e-04 - mae: 0.0167 - val_loss: 0.0017 - val_mae: 0.0320 - 9s/epoch - 58ms/step\n",
      "Epoch 1079/3000\n",
      "\n",
      "Epoch 1079: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1934e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1080/3000\n",
      "\n",
      "Epoch 1080: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2084e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 1081/3000\n",
      "\n",
      "Epoch 1081: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1990e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1082/3000\n",
      "\n",
      "Epoch 1082: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1885e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1083/3000\n",
      "\n",
      "Epoch 1083: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2033e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1084/3000\n",
      "\n",
      "Epoch 1084: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1983e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1085/3000\n",
      "\n",
      "Epoch 1085: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1848e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1086/3000\n",
      "\n",
      "Epoch 1086: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1914e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1087/3000\n",
      "\n",
      "Epoch 1087: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.2009e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1088/3000\n",
      "\n",
      "Epoch 1088: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1944e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0313 - 9s/epoch - 58ms/step\n",
      "Epoch 1089/3000\n",
      "\n",
      "Epoch 1089: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1939e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1090/3000\n",
      "\n",
      "Epoch 1090: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1848e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1091/3000\n",
      "\n",
      "Epoch 1091: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1786e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1092/3000\n",
      "\n",
      "Epoch 1092: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1915e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 57ms/step\n",
      "Epoch 1093/3000\n",
      "\n",
      "Epoch 1093: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1838e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1094/3000\n",
      "\n",
      "Epoch 1094: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1883e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1095/3000\n",
      "\n",
      "Epoch 1095: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1880e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 57ms/step\n",
      "Epoch 1096/3000\n",
      "\n",
      "Epoch 1096: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1953e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1097/3000\n",
      "\n",
      "Epoch 1097: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1967e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1098/3000\n",
      "\n",
      "Epoch 1098: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1855e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1099/3000\n",
      "\n",
      "Epoch 1099: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1914e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1100/3000\n",
      "\n",
      "Epoch 1100: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1826e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1101/3000\n",
      "\n",
      "Epoch 1101: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1792e-04 - mae: 0.0167 - val_loss: 0.0017 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1102/3000\n",
      "\n",
      "Epoch 1102: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1837e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1103/3000\n",
      "\n",
      "Epoch 1103: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1684e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1104/3000\n",
      "\n",
      "Epoch 1104: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1827e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1105/3000\n",
      "\n",
      "Epoch 1105: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1810e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1106/3000\n",
      "\n",
      "Epoch 1106: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1955e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1107/3000\n",
      "\n",
      "Epoch 1107: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1778e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1108/3000\n",
      "\n",
      "Epoch 1108: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1752e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1109/3000\n",
      "\n",
      "Epoch 1109: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1876e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1110/3000\n",
      "\n",
      "Epoch 1110: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1878e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1111/3000\n",
      "\n",
      "Epoch 1111: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1951e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1112/3000\n",
      "\n",
      "Epoch 1112: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1839e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1113/3000\n",
      "\n",
      "Epoch 1113: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1866e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1114/3000\n",
      "\n",
      "Epoch 1114: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1822e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1115/3000\n",
      "\n",
      "Epoch 1115: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1737e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1116/3000\n",
      "\n",
      "Epoch 1116: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1815e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1117/3000\n",
      "\n",
      "Epoch 1117: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1746e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1118/3000\n",
      "\n",
      "Epoch 1118: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1764e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1119/3000\n",
      "\n",
      "Epoch 1119: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1751e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1120/3000\n",
      "\n",
      "Epoch 1120: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1609e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1121/3000\n",
      "\n",
      "Epoch 1121: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1717e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1122/3000\n",
      "\n",
      "Epoch 1122: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1865e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1123/3000\n",
      "\n",
      "Epoch 1123: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1923e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1124/3000\n",
      "\n",
      "Epoch 1124: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1780e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1125/3000\n",
      "\n",
      "Epoch 1125: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1958e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1126/3000\n",
      "\n",
      "Epoch 1126: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1694e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1127/3000\n",
      "\n",
      "Epoch 1127: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1814e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1128/3000\n",
      "\n",
      "Epoch 1128: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1608e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1129/3000\n",
      "\n",
      "Epoch 1129: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1716e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1130/3000\n",
      "\n",
      "Epoch 1130: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1761e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1131/3000\n",
      "\n",
      "Epoch 1131: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1710e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1132/3000\n",
      "\n",
      "Epoch 1132: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1736e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1133/3000\n",
      "\n",
      "Epoch 1133: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1769e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1134/3000\n",
      "\n",
      "Epoch 1134: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1692e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1135/3000\n",
      "\n",
      "Epoch 1135: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1600e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1136/3000\n",
      "\n",
      "Epoch 1136: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1937e-04 - mae: 0.0167 - val_loss: 0.0017 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1137/3000\n",
      "\n",
      "Epoch 1137: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1811e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1138/3000\n",
      "\n",
      "Epoch 1138: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1554e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1139/3000\n",
      "\n",
      "Epoch 1139: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1432e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1140/3000\n",
      "\n",
      "Epoch 1140: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1757e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1141/3000\n",
      "\n",
      "Epoch 1141: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1612e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1142/3000\n",
      "\n",
      "Epoch 1142: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1598e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1143/3000\n",
      "\n",
      "Epoch 1143: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1627e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1144/3000\n",
      "\n",
      "Epoch 1144: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1691e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1145/3000\n",
      "\n",
      "Epoch 1145: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1623e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1146/3000\n",
      "\n",
      "Epoch 1146: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1566e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1147/3000\n",
      "\n",
      "Epoch 1147: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1574e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1148/3000\n",
      "\n",
      "Epoch 1148: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1758e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1149/3000\n",
      "\n",
      "Epoch 1149: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1683e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1150/3000\n",
      "\n",
      "Epoch 1150: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1648e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1151/3000\n",
      "\n",
      "Epoch 1151: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1605e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1152/3000\n",
      "\n",
      "Epoch 1152: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1678e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1153/3000\n",
      "\n",
      "Epoch 1153: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1624e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1154/3000\n",
      "\n",
      "Epoch 1154: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1785e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1155/3000\n",
      "\n",
      "Epoch 1155: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1725e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1156/3000\n",
      "\n",
      "Epoch 1156: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1567e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1157/3000\n",
      "\n",
      "Epoch 1157: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1704e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0314 - 9s/epoch - 58ms/step\n",
      "Epoch 1158/3000\n",
      "\n",
      "Epoch 1158: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1509e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1159/3000\n",
      "\n",
      "Epoch 1159: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1552e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1160/3000\n",
      "\n",
      "Epoch 1160: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1853e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1161/3000\n",
      "\n",
      "Epoch 1161: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1545e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1162/3000\n",
      "\n",
      "Epoch 1162: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1580e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1163/3000\n",
      "\n",
      "Epoch 1163: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1575e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1164/3000\n",
      "\n",
      "Epoch 1164: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1627e-04 - mae: 0.0167 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1165/3000\n",
      "\n",
      "Epoch 1165: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1537e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1166/3000\n",
      "\n",
      "Epoch 1166: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1480e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1167/3000\n",
      "\n",
      "Epoch 1167: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1511e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1168/3000\n",
      "\n",
      "Epoch 1168: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1500e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1169/3000\n",
      "\n",
      "Epoch 1169: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1512e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1170/3000\n",
      "\n",
      "Epoch 1170: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1557e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1171/3000\n",
      "\n",
      "Epoch 1171: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1468e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1172/3000\n",
      "\n",
      "Epoch 1172: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1550e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1173/3000\n",
      "\n",
      "Epoch 1173: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1564e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1174/3000\n",
      "\n",
      "Epoch 1174: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1564e-04 - mae: 0.0166 - val_loss: 0.0017 - val_mae: 0.0319 - 9s/epoch - 58ms/step\n",
      "Epoch 1175/3000\n",
      "\n",
      "Epoch 1175: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1551e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1176/3000\n",
      "\n",
      "Epoch 1176: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1533e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1177/3000\n",
      "\n",
      "Epoch 1177: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1560e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1178/3000\n",
      "\n",
      "Epoch 1178: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1412e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1179/3000\n",
      "\n",
      "Epoch 1179: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1618e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1180/3000\n",
      "\n",
      "Epoch 1180: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1514e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1181/3000\n",
      "\n",
      "Epoch 1181: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1533e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1182/3000\n",
      "\n",
      "Epoch 1182: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1527e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1183/3000\n",
      "\n",
      "Epoch 1183: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1405e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1184/3000\n",
      "\n",
      "Epoch 1184: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1447e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1185/3000\n",
      "\n",
      "Epoch 1185: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1450e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1186/3000\n",
      "\n",
      "Epoch 1186: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1406e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1187/3000\n",
      "\n",
      "Epoch 1187: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1591e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1188/3000\n",
      "\n",
      "Epoch 1188: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1444e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1189/3000\n",
      "\n",
      "Epoch 1189: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1534e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0316 - 9s/epoch - 58ms/step\n",
      "Epoch 1190/3000\n",
      "\n",
      "Epoch 1190: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1540e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1191/3000\n",
      "\n",
      "Epoch 1191: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1540e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1192/3000\n",
      "\n",
      "Epoch 1192: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1453e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0315 - 9s/epoch - 58ms/step\n",
      "Epoch 1193/3000\n",
      "\n",
      "Epoch 1193: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1509e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1194/3000\n",
      "\n",
      "Epoch 1194: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1361e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1195/3000\n",
      "\n",
      "Epoch 1195: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1325e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0318 - 9s/epoch - 58ms/step\n",
      "Epoch 1196/3000\n",
      "\n",
      "Epoch 1196: val_mae did not improve from 0.03074\n",
      "151/151 - 9s - loss: 5.1387e-04 - mae: 0.0166 - val_loss: 0.0016 - val_mae: 0.0317 - 9s/epoch - 58ms/step\n",
      "Epoch 1196: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7efd85ffa9b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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/tcn_gate_pump_nofuture.h5', \n",
    "                     monitor='val_mae', \n",
    "                     mode='min', \n",
    "                     verbose=2, \n",
    "                     custom_objects={'TCN': TCN},\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": 11,
   "id": "c60c3dba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 4s 5ms/step\n",
      "inv_y.shape, inv_yhat.shape (19268, 96) (19268, 96)\n",
      "MAE = 0.1625\n",
      "RMSE = 0.2076\n"
     ]
    }
   ],
   "source": [
    "saved_model = load_model('saved_models/tcn_gate_pump_nofuture.h5', custom_objects={'TCN': TCN})\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": 12,
   "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": 13,
   "id": "d0f5ae72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "579\n",
      "(19268,)\n",
      "421\n",
      "(19268,)\n",
      "730\n",
      "(19268,)\n",
      "763\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": 14,
   "id": "88da85fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "35\n",
      "(19268,)\n",
      "117\n",
      "(19268,)\n",
      "116\n",
      "(19268,)\n",
      "674\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": 15,
   "id": "43865dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "544\n",
      "(19268,)\n",
      "304\n",
      "(19268,)\n",
      "614\n",
      "(19268,)\n",
      "89\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": "2fc5c9da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b67db63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}