train_cnn_nofuture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f47fa9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 21:59:53.794848: 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 21:59:55.375372: 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.cnn import cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0092be29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b6b865",
   "metadata": {},
   "source": [
    "### Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1e14ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ====== preprocessing parameters ======\n",
    "n_hours = 72\n",
    "K = 24 \n",
    "masked_value = 1e-10\n",
    "split_1 = 0.8\n",
    "split_2 = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b444521c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['WS_S1', 'WS_S4', 'FLOW_S25A', 'GATE_S25A', 'HWS_S25A', 'TWS_S25A',\n",
      "       'FLOW_S25B', 'GATE_S25B', 'GATE_S25B2', 'HWS_S25B', 'TWS_S25B',\n",
      "       'PUMP_S25B', 'FLOW_S26', 'GATE_S26_1', 'GATE_S26_2', 'HWS_S26',\n",
      "       'TWS_S26', 'PUMP_S26', 'MEAN_RAIN'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "train_X_mask, val_X_mask, test_X_mask, \\\n",
    "train_ws_y, val_ws_y, test_ws_y, \\\n",
    "scaler, ws_scaler = baseline_process(n_hours, K, masked_value, split_1, split_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85e89a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77069, 96, 16) (9634, 96, 16) (19268, 96, 16) (77069, 96) (9634, 96) (19268, 96)\n"
     ]
    }
   ],
   "source": [
    "print(train_X_mask.shape, val_X_mask.shape, test_X_mask.shape, train_ws_y.shape, val_ws_y.shape, test_ws_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "839273c5",
   "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": 7,
   "id": "aa031ae1",
   "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": 10,
   "id": "6ed0d841",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===== model parameters ======\n",
    "input_shape = train_X_mask.shape[1:]\n",
    "cnn_unit1 = 64\n",
    "cnn_unit2 = 32\n",
    "cnn_unit3 = 16\n",
    "dropout = 0.1\n",
    "kernel_size = 2\n",
    "pool_size=2,\n",
    "learning_rate = 1e-4\n",
    "decay_steps = 10000\n",
    "decay_rate = 0.95\n",
    "PATIENCE = 500\n",
    "EPOCHS = 3000\n",
    "BATCH = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7d6a860",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-22 22:01:56.126202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10402 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:08:00.0, compute capability: 6.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 72, 16)]          0         \n",
      "                                                                 \n",
      " masking (Masking)           (None, 72, 16)            0         \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 72, 64)            2112      \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1  (None, 36, 64)            0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 36, 64)            0         \n",
      "                                                                 \n",
      " conv1d_1 (Conv1D)           (None, 36, 32)            4128      \n",
      "                                                                 \n",
      " max_pooling1d_1 (MaxPoolin  (None, 18, 32)            0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " conv1d_2 (Conv1D)           (None, 18, 16)            1040      \n",
      "                                                                 \n",
      " max_pooling1d_2 (MaxPoolin  (None, 9, 16)             0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 144)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 96)                13920     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 21200 (82.81 KB)\n",
      "Trainable params: 21200 (82.81 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = cnn(input_shape=input_shape,\n",
    "            cnn_unit1=cnn_unit1,\n",
    "            cnn_unit2=cnn_unit2,\n",
    "            cnn_unit3=cnn_unit3,\n",
    "            dropout=dropout,\n",
    "            kernel_size=kernel_size,\n",
    "            pool_size=pool_size,\n",
    "            masked_value=masked_value\n",
    "            )\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 22:02:13.725298: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8600\n",
      "2023-07-22 22:02:14.718723: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f3b0c01aef0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-07-22 22:02:14.718776: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1\n",
      "2023-07-22 22:02:14.729007: 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 22:02:15.123624: 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 0.17250, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 7s - loss: 0.0797 - mae: 0.2303 - val_loss: 0.0449 - val_mae: 0.1725 - 7s/epoch - 47ms/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 0.17250 to 0.15991, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0177 - mae: 0.1107 - val_loss: 0.0378 - val_mae: 0.1599 - 982ms/epoch - 7ms/step\n",
      "Epoch 3/3000\n",
      "\n",
      "Epoch 3: val_mae improved from 0.15991 to 0.15340, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0156 - mae: 0.1046 - val_loss: 0.0347 - val_mae: 0.1534 - 963ms/epoch - 6ms/step\n",
      "Epoch 4/3000\n",
      "\n",
      "Epoch 4: val_mae improved from 0.15340 to 0.13910, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0141 - mae: 0.0990 - val_loss: 0.0286 - val_mae: 0.1391 - 938ms/epoch - 6ms/step\n",
      "Epoch 5/3000\n",
      "\n",
      "Epoch 5: val_mae improved from 0.13910 to 0.11964, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0116 - mae: 0.0884 - val_loss: 0.0216 - val_mae: 0.1196 - 973ms/epoch - 6ms/step\n",
      "Epoch 6/3000\n",
      "\n",
      "Epoch 6: val_mae improved from 0.11964 to 0.10174, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0085 - mae: 0.0737 - val_loss: 0.0162 - val_mae: 0.1017 - 968ms/epoch - 6ms/step\n",
      "Epoch 7/3000\n",
      "\n",
      "Epoch 7: val_mae improved from 0.10174 to 0.09217, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0059 - mae: 0.0606 - val_loss: 0.0135 - val_mae: 0.0922 - 954ms/epoch - 6ms/step\n",
      "Epoch 8/3000\n",
      "\n",
      "Epoch 8: val_mae improved from 0.09217 to 0.08582, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0045 - mae: 0.0528 - val_loss: 0.0118 - val_mae: 0.0858 - 940ms/epoch - 6ms/step\n",
      "Epoch 9/3000\n",
      "\n",
      "Epoch 9: val_mae improved from 0.08582 to 0.07921, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0035 - mae: 0.0462 - val_loss: 0.0101 - val_mae: 0.0792 - 954ms/epoch - 6ms/step\n",
      "Epoch 10/3000\n",
      "\n",
      "Epoch 10: val_mae improved from 0.07921 to 0.07649, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0029 - mae: 0.0420 - val_loss: 0.0094 - val_mae: 0.0765 - 947ms/epoch - 6ms/step\n",
      "Epoch 11/3000\n",
      "\n",
      "Epoch 11: val_mae improved from 0.07649 to 0.07372, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0026 - mae: 0.0398 - val_loss: 0.0087 - val_mae: 0.0737 - 960ms/epoch - 6ms/step\n",
      "Epoch 12/3000\n",
      "\n",
      "Epoch 12: val_mae improved from 0.07372 to 0.07252, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0024 - mae: 0.0384 - val_loss: 0.0084 - val_mae: 0.0725 - 993ms/epoch - 7ms/step\n",
      "Epoch 13/3000\n",
      "\n",
      "Epoch 13: val_mae improved from 0.07252 to 0.07211, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0023 - mae: 0.0372 - val_loss: 0.0083 - val_mae: 0.0721 - 989ms/epoch - 7ms/step\n",
      "Epoch 14/3000\n",
      "\n",
      "Epoch 14: val_mae improved from 0.07211 to 0.07065, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0022 - mae: 0.0363 - val_loss: 0.0080 - val_mae: 0.0707 - 956ms/epoch - 6ms/step\n",
      "Epoch 15/3000\n",
      "\n",
      "Epoch 15: val_mae improved from 0.07065 to 0.06773, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0021 - mae: 0.0355 - val_loss: 0.0074 - val_mae: 0.0677 - 980ms/epoch - 6ms/step\n",
      "Epoch 16/3000\n",
      "\n",
      "Epoch 16: val_mae did not improve from 0.06773\n",
      "151/151 - 1s - loss: 0.0020 - mae: 0.0347 - val_loss: 0.0075 - val_mae: 0.0685 - 943ms/epoch - 6ms/step\n",
      "Epoch 17/3000\n",
      "\n",
      "Epoch 17: val_mae improved from 0.06773 to 0.06619, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0019 - mae: 0.0341 - val_loss: 0.0070 - val_mae: 0.0662 - 997ms/epoch - 7ms/step\n",
      "Epoch 18/3000\n",
      "\n",
      "Epoch 18: val_mae improved from 0.06619 to 0.06610, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0019 - mae: 0.0334 - val_loss: 0.0070 - val_mae: 0.0661 - 971ms/epoch - 6ms/step\n",
      "Epoch 19/3000\n",
      "\n",
      "Epoch 19: val_mae improved from 0.06610 to 0.06570, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0018 - mae: 0.0329 - val_loss: 0.0069 - val_mae: 0.0657 - 966ms/epoch - 6ms/step\n",
      "Epoch 20/3000\n",
      "\n",
      "Epoch 20: val_mae improved from 0.06570 to 0.06431, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0018 - mae: 0.0324 - val_loss: 0.0066 - val_mae: 0.0643 - 953ms/epoch - 6ms/step\n",
      "Epoch 21/3000\n",
      "\n",
      "Epoch 21: val_mae improved from 0.06431 to 0.06243, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0017 - mae: 0.0319 - val_loss: 0.0063 - val_mae: 0.0624 - 963ms/epoch - 6ms/step\n",
      "Epoch 22/3000\n",
      "\n",
      "Epoch 22: val_mae did not improve from 0.06243\n",
      "151/151 - 1s - loss: 0.0017 - mae: 0.0314 - val_loss: 0.0064 - val_mae: 0.0634 - 909ms/epoch - 6ms/step\n",
      "Epoch 23/3000\n",
      "\n",
      "Epoch 23: val_mae improved from 0.06243 to 0.06202, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0016 - mae: 0.0310 - val_loss: 0.0062 - val_mae: 0.0620 - 987ms/epoch - 7ms/step\n",
      "Epoch 24/3000\n",
      "\n",
      "Epoch 24: val_mae improved from 0.06202 to 0.06171, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0016 - mae: 0.0306 - val_loss: 0.0061 - val_mae: 0.0617 - 943ms/epoch - 6ms/step\n",
      "Epoch 25/3000\n",
      "\n",
      "Epoch 25: val_mae improved from 0.06171 to 0.06145, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0016 - mae: 0.0302 - val_loss: 0.0060 - val_mae: 0.0614 - 992ms/epoch - 7ms/step\n",
      "Epoch 26/3000\n",
      "\n",
      "Epoch 26: val_mae improved from 0.06145 to 0.05867, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0015 - mae: 0.0299 - val_loss: 0.0055 - val_mae: 0.0587 - 955ms/epoch - 6ms/step\n",
      "Epoch 27/3000\n",
      "\n",
      "Epoch 27: val_mae improved from 0.05867 to 0.05816, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0054 - val_mae: 0.0582 - 976ms/epoch - 6ms/step\n",
      "Epoch 28/3000\n",
      "\n",
      "Epoch 28: val_mae improved from 0.05816 to 0.05677, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0015 - mae: 0.0292 - val_loss: 0.0052 - val_mae: 0.0568 - 956ms/epoch - 6ms/step\n",
      "Epoch 29/3000\n",
      "\n",
      "Epoch 29: val_mae did not improve from 0.05677\n",
      "151/151 - 1s - loss: 0.0014 - mae: 0.0289 - val_loss: 0.0052 - val_mae: 0.0569 - 918ms/epoch - 6ms/step\n",
      "Epoch 30/3000\n",
      "\n",
      "Epoch 30: val_mae did not improve from 0.05677\n",
      "151/151 - 1s - loss: 0.0014 - mae: 0.0285 - val_loss: 0.0053 - val_mae: 0.0573 - 924ms/epoch - 6ms/step\n",
      "Epoch 31/3000\n",
      "\n",
      "Epoch 31: val_mae did not improve from 0.05677\n",
      "151/151 - 1s - loss: 0.0014 - mae: 0.0282 - val_loss: 0.0052 - val_mae: 0.0570 - 961ms/epoch - 6ms/step\n",
      "Epoch 32/3000\n",
      "\n",
      "Epoch 32: val_mae improved from 0.05677 to 0.05473, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0013 - mae: 0.0279 - val_loss: 0.0048 - val_mae: 0.0547 - 985ms/epoch - 7ms/step\n",
      "Epoch 33/3000\n",
      "\n",
      "Epoch 33: val_mae did not improve from 0.05473\n",
      "151/151 - 1s - loss: 0.0013 - mae: 0.0277 - val_loss: 0.0050 - val_mae: 0.0557 - 927ms/epoch - 6ms/step\n",
      "Epoch 34/3000\n",
      "\n",
      "Epoch 34: val_mae improved from 0.05473 to 0.05394, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0013 - mae: 0.0274 - val_loss: 0.0047 - val_mae: 0.0539 - 971ms/epoch - 6ms/step\n",
      "Epoch 35/3000\n",
      "\n",
      "Epoch 35: val_mae improved from 0.05394 to 0.05298, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0013 - mae: 0.0272 - val_loss: 0.0045 - val_mae: 0.0530 - 979ms/epoch - 6ms/step\n",
      "Epoch 36/3000\n",
      "\n",
      "Epoch 36: val_mae did not improve from 0.05298\n",
      "151/151 - 1s - loss: 0.0013 - mae: 0.0269 - val_loss: 0.0046 - val_mae: 0.0535 - 1s/epoch - 7ms/step\n",
      "Epoch 37/3000\n",
      "\n",
      "Epoch 37: val_mae improved from 0.05298 to 0.05237, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0012 - mae: 0.0267 - val_loss: 0.0044 - val_mae: 0.0524 - 964ms/epoch - 6ms/step\n",
      "Epoch 38/3000\n",
      "\n",
      "Epoch 38: val_mae improved from 0.05237 to 0.05206, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0012 - mae: 0.0264 - val_loss: 0.0043 - val_mae: 0.0521 - 972ms/epoch - 6ms/step\n",
      "Epoch 39/3000\n",
      "\n",
      "Epoch 39: val_mae improved from 0.05206 to 0.05117, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0012 - mae: 0.0262 - val_loss: 0.0042 - val_mae: 0.0512 - 950ms/epoch - 6ms/step\n",
      "Epoch 40/3000\n",
      "\n",
      "Epoch 40: val_mae improved from 0.05117 to 0.05105, saving model to saved_models/cnn_gate_pump_nofuture.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 0.0012 - mae: 0.0260 - val_loss: 0.0042 - val_mae: 0.0510 - 969ms/epoch - 6ms/step\n",
      "Epoch 41/3000\n",
      "\n",
      "Epoch 41: val_mae improved from 0.05105 to 0.05025, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0012 - mae: 0.0258 - val_loss: 0.0040 - val_mae: 0.0503 - 954ms/epoch - 6ms/step\n",
      "Epoch 42/3000\n",
      "\n",
      "Epoch 42: val_mae improved from 0.05025 to 0.04950, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0012 - mae: 0.0256 - val_loss: 0.0039 - val_mae: 0.0495 - 985ms/epoch - 7ms/step\n",
      "Epoch 43/3000\n",
      "\n",
      "Epoch 43: val_mae improved from 0.04950 to 0.04874, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0254 - val_loss: 0.0038 - val_mae: 0.0487 - 989ms/epoch - 7ms/step\n",
      "Epoch 44/3000\n",
      "\n",
      "Epoch 44: val_mae improved from 0.04874 to 0.04851, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0253 - val_loss: 0.0038 - val_mae: 0.0485 - 953ms/epoch - 6ms/step\n",
      "Epoch 45/3000\n",
      "\n",
      "Epoch 45: val_mae improved from 0.04851 to 0.04807, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0251 - val_loss: 0.0037 - val_mae: 0.0481 - 961ms/epoch - 6ms/step\n",
      "Epoch 46/3000\n",
      "\n",
      "Epoch 46: val_mae did not improve from 0.04807\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0249 - val_loss: 0.0037 - val_mae: 0.0483 - 909ms/epoch - 6ms/step\n",
      "Epoch 47/3000\n",
      "\n",
      "Epoch 47: val_mae improved from 0.04807 to 0.04774, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0248 - val_loss: 0.0036 - val_mae: 0.0477 - 947ms/epoch - 6ms/step\n",
      "Epoch 48/3000\n",
      "\n",
      "Epoch 48: val_mae did not improve from 0.04774\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0246 - val_loss: 0.0037 - val_mae: 0.0482 - 938ms/epoch - 6ms/step\n",
      "Epoch 49/3000\n",
      "\n",
      "Epoch 49: val_mae improved from 0.04774 to 0.04757, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0245 - val_loss: 0.0036 - val_mae: 0.0476 - 960ms/epoch - 6ms/step\n",
      "Epoch 50/3000\n",
      "\n",
      "Epoch 50: val_mae improved from 0.04757 to 0.04639, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0011 - mae: 0.0243 - val_loss: 0.0034 - val_mae: 0.0464 - 960ms/epoch - 6ms/step\n",
      "Epoch 51/3000\n",
      "\n",
      "Epoch 51: val_mae did not improve from 0.04639\n",
      "151/151 - 1s - loss: 0.0010 - mae: 0.0242 - val_loss: 0.0035 - val_mae: 0.0465 - 928ms/epoch - 6ms/step\n",
      "Epoch 52/3000\n",
      "\n",
      "Epoch 52: val_mae did not improve from 0.04639\n",
      "151/151 - 1s - loss: 0.0010 - mae: 0.0240 - val_loss: 0.0037 - val_mae: 0.0479 - 969ms/epoch - 6ms/step\n",
      "Epoch 53/3000\n",
      "\n",
      "Epoch 53: val_mae did not improve from 0.04639\n",
      "151/151 - 1s - loss: 0.0010 - mae: 0.0239 - val_loss: 0.0035 - val_mae: 0.0469 - 924ms/epoch - 6ms/step\n",
      "Epoch 54/3000\n",
      "\n",
      "Epoch 54: val_mae improved from 0.04639 to 0.04587, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 0.0010 - mae: 0.0238 - val_loss: 0.0034 - val_mae: 0.0459 - 970ms/epoch - 6ms/step\n",
      "Epoch 55/3000\n",
      "\n",
      "Epoch 55: val_mae did not improve from 0.04587\n",
      "151/151 - 1s - loss: 0.0010 - mae: 0.0237 - val_loss: 0.0034 - val_mae: 0.0461 - 951ms/epoch - 6ms/step\n",
      "Epoch 56/3000\n",
      "\n",
      "Epoch 56: val_mae did not improve from 0.04587\n",
      "151/151 - 1s - loss: 9.9777e-04 - mae: 0.0236 - val_loss: 0.0034 - val_mae: 0.0463 - 954ms/epoch - 6ms/step\n",
      "Epoch 57/3000\n",
      "\n",
      "Epoch 57: val_mae did not improve from 0.04587\n",
      "151/151 - 1s - loss: 9.8931e-04 - mae: 0.0234 - val_loss: 0.0034 - val_mae: 0.0462 - 943ms/epoch - 6ms/step\n",
      "Epoch 58/3000\n",
      "\n",
      "Epoch 58: val_mae improved from 0.04587 to 0.04587, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.8164e-04 - mae: 0.0234 - val_loss: 0.0034 - val_mae: 0.0459 - 969ms/epoch - 6ms/step\n",
      "Epoch 59/3000\n",
      "\n",
      "Epoch 59: val_mae improved from 0.04587 to 0.04485, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.7228e-04 - mae: 0.0232 - val_loss: 0.0032 - val_mae: 0.0449 - 929ms/epoch - 6ms/step\n",
      "Epoch 60/3000\n",
      "\n",
      "Epoch 60: val_mae improved from 0.04485 to 0.04482, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.6621e-04 - mae: 0.0231 - val_loss: 0.0032 - val_mae: 0.0448 - 950ms/epoch - 6ms/step\n",
      "Epoch 61/3000\n",
      "\n",
      "Epoch 61: val_mae improved from 0.04482 to 0.04449, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.5718e-04 - mae: 0.0230 - val_loss: 0.0032 - val_mae: 0.0445 - 949ms/epoch - 6ms/step\n",
      "Epoch 62/3000\n",
      "\n",
      "Epoch 62: val_mae improved from 0.04449 to 0.04433, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.5119e-04 - mae: 0.0229 - val_loss: 0.0031 - val_mae: 0.0443 - 950ms/epoch - 6ms/step\n",
      "Epoch 63/3000\n",
      "\n",
      "Epoch 63: val_mae improved from 0.04433 to 0.04366, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 9.4587e-04 - mae: 0.0228 - val_loss: 0.0031 - val_mae: 0.0437 - 958ms/epoch - 6ms/step\n",
      "Epoch 64/3000\n",
      "\n",
      "Epoch 64: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.3774e-04 - mae: 0.0227 - val_loss: 0.0031 - val_mae: 0.0443 - 921ms/epoch - 6ms/step\n",
      "Epoch 65/3000\n",
      "\n",
      "Epoch 65: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.3257e-04 - mae: 0.0227 - val_loss: 0.0031 - val_mae: 0.0442 - 993ms/epoch - 7ms/step\n",
      "Epoch 66/3000\n",
      "\n",
      "Epoch 66: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.2420e-04 - mae: 0.0226 - val_loss: 0.0031 - val_mae: 0.0443 - 927ms/epoch - 6ms/step\n",
      "Epoch 67/3000\n",
      "\n",
      "Epoch 67: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.2164e-04 - mae: 0.0225 - val_loss: 0.0031 - val_mae: 0.0437 - 918ms/epoch - 6ms/step\n",
      "Epoch 68/3000\n",
      "\n",
      "Epoch 68: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.1429e-04 - mae: 0.0224 - val_loss: 0.0033 - val_mae: 0.0452 - 917ms/epoch - 6ms/step\n",
      "Epoch 69/3000\n",
      "\n",
      "Epoch 69: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.0879e-04 - mae: 0.0223 - val_loss: 0.0031 - val_mae: 0.0441 - 945ms/epoch - 6ms/step\n",
      "Epoch 70/3000\n",
      "\n",
      "Epoch 70: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 9.0237e-04 - mae: 0.0222 - val_loss: 0.0032 - val_mae: 0.0446 - 919ms/epoch - 6ms/step\n",
      "Epoch 71/3000\n",
      "\n",
      "Epoch 71: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 8.9688e-04 - mae: 0.0222 - val_loss: 0.0032 - val_mae: 0.0446 - 924ms/epoch - 6ms/step\n",
      "Epoch 72/3000\n",
      "\n",
      "Epoch 72: val_mae did not improve from 0.04366\n",
      "151/151 - 1s - loss: 8.9161e-04 - mae: 0.0221 - val_loss: 0.0031 - val_mae: 0.0439 - 917ms/epoch - 6ms/step\n",
      "Epoch 73/3000\n",
      "\n",
      "Epoch 73: val_mae improved from 0.04366 to 0.04355, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 8.8523e-04 - mae: 0.0220 - val_loss: 0.0030 - val_mae: 0.0435 - 952ms/epoch - 6ms/step\n",
      "Epoch 74/3000\n",
      "\n",
      "Epoch 74: val_mae did not improve from 0.04355\n",
      "151/151 - 1s - loss: 8.8123e-04 - mae: 0.0219 - val_loss: 0.0032 - val_mae: 0.0445 - 913ms/epoch - 6ms/step\n",
      "Epoch 75/3000\n",
      "\n",
      "Epoch 75: val_mae did not improve from 0.04355\n",
      "151/151 - 1s - loss: 8.7937e-04 - mae: 0.0219 - val_loss: 0.0032 - val_mae: 0.0448 - 909ms/epoch - 6ms/step\n",
      "Epoch 76/3000\n",
      "\n",
      "Epoch 76: val_mae did not improve from 0.04355\n",
      "151/151 - 1s - loss: 8.7331e-04 - mae: 0.0218 - val_loss: 0.0031 - val_mae: 0.0439 - 908ms/epoch - 6ms/step\n",
      "Epoch 77/3000\n",
      "\n",
      "Epoch 77: val_mae did not improve from 0.04355\n",
      "151/151 - 1s - loss: 8.6749e-04 - mae: 0.0217 - val_loss: 0.0031 - val_mae: 0.0438 - 926ms/epoch - 6ms/step\n",
      "Epoch 78/3000\n",
      "\n",
      "Epoch 78: val_mae did not improve from 0.04355\n",
      "151/151 - 1s - loss: 8.6385e-04 - mae: 0.0217 - val_loss: 0.0031 - val_mae: 0.0439 - 928ms/epoch - 6ms/step\n",
      "Epoch 79/3000\n",
      "\n",
      "Epoch 79: val_mae improved from 0.04355 to 0.04275, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 8.6042e-04 - mae: 0.0216 - val_loss: 0.0029 - val_mae: 0.0427 - 1s/epoch - 7ms/step\n",
      "Epoch 80/3000\n",
      "\n",
      "Epoch 80: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.5707e-04 - mae: 0.0216 - val_loss: 0.0030 - val_mae: 0.0435 - 956ms/epoch - 6ms/step\n",
      "Epoch 81/3000\n",
      "\n",
      "Epoch 81: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.5260e-04 - mae: 0.0215 - val_loss: 0.0030 - val_mae: 0.0436 - 918ms/epoch - 6ms/step\n",
      "Epoch 82/3000\n",
      "\n",
      "Epoch 82: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.4843e-04 - mae: 0.0215 - val_loss: 0.0032 - val_mae: 0.0447 - 915ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83/3000\n",
      "\n",
      "Epoch 83: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.4457e-04 - mae: 0.0214 - val_loss: 0.0030 - val_mae: 0.0429 - 918ms/epoch - 6ms/step\n",
      "Epoch 84/3000\n",
      "\n",
      "Epoch 84: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.4170e-04 - mae: 0.0214 - val_loss: 0.0031 - val_mae: 0.0443 - 989ms/epoch - 7ms/step\n",
      "Epoch 85/3000\n",
      "\n",
      "Epoch 85: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.3783e-04 - mae: 0.0213 - val_loss: 0.0030 - val_mae: 0.0434 - 930ms/epoch - 6ms/step\n",
      "Epoch 86/3000\n",
      "\n",
      "Epoch 86: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.3468e-04 - mae: 0.0213 - val_loss: 0.0030 - val_mae: 0.0437 - 926ms/epoch - 6ms/step\n",
      "Epoch 87/3000\n",
      "\n",
      "Epoch 87: val_mae did not improve from 0.04275\n",
      "151/151 - 1s - loss: 8.3167e-04 - mae: 0.0212 - val_loss: 0.0031 - val_mae: 0.0444 - 907ms/epoch - 6ms/step\n",
      "Epoch 88/3000\n",
      "\n",
      "Epoch 88: val_mae improved from 0.04275 to 0.04268, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 8.2845e-04 - mae: 0.0212 - val_loss: 0.0029 - val_mae: 0.0427 - 960ms/epoch - 6ms/step\n",
      "Epoch 89/3000\n",
      "\n",
      "Epoch 89: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.2640e-04 - mae: 0.0211 - val_loss: 0.0029 - val_mae: 0.0428 - 927ms/epoch - 6ms/step\n",
      "Epoch 90/3000\n",
      "\n",
      "Epoch 90: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.2179e-04 - mae: 0.0211 - val_loss: 0.0030 - val_mae: 0.0437 - 960ms/epoch - 6ms/step\n",
      "Epoch 91/3000\n",
      "\n",
      "Epoch 91: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.1849e-04 - mae: 0.0210 - val_loss: 0.0031 - val_mae: 0.0439 - 944ms/epoch - 6ms/step\n",
      "Epoch 92/3000\n",
      "\n",
      "Epoch 92: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.1612e-04 - mae: 0.0210 - val_loss: 0.0032 - val_mae: 0.0446 - 941ms/epoch - 6ms/step\n",
      "Epoch 93/3000\n",
      "\n",
      "Epoch 93: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.1230e-04 - mae: 0.0209 - val_loss: 0.0030 - val_mae: 0.0432 - 901ms/epoch - 6ms/step\n",
      "Epoch 94/3000\n",
      "\n",
      "Epoch 94: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.0885e-04 - mae: 0.0209 - val_loss: 0.0031 - val_mae: 0.0441 - 925ms/epoch - 6ms/step\n",
      "Epoch 95/3000\n",
      "\n",
      "Epoch 95: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.0619e-04 - mae: 0.0208 - val_loss: 0.0030 - val_mae: 0.0435 - 932ms/epoch - 6ms/step\n",
      "Epoch 96/3000\n",
      "\n",
      "Epoch 96: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.0358e-04 - mae: 0.0208 - val_loss: 0.0030 - val_mae: 0.0430 - 923ms/epoch - 6ms/step\n",
      "Epoch 97/3000\n",
      "\n",
      "Epoch 97: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 8.0042e-04 - mae: 0.0208 - val_loss: 0.0030 - val_mae: 0.0432 - 934ms/epoch - 6ms/step\n",
      "Epoch 98/3000\n",
      "\n",
      "Epoch 98: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.9794e-04 - mae: 0.0207 - val_loss: 0.0029 - val_mae: 0.0427 - 908ms/epoch - 6ms/step\n",
      "Epoch 99/3000\n",
      "\n",
      "Epoch 99: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.9495e-04 - mae: 0.0207 - val_loss: 0.0030 - val_mae: 0.0434 - 927ms/epoch - 6ms/step\n",
      "Epoch 100/3000\n",
      "\n",
      "Epoch 100: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.9164e-04 - mae: 0.0206 - val_loss: 0.0030 - val_mae: 0.0436 - 912ms/epoch - 6ms/step\n",
      "Epoch 101/3000\n",
      "\n",
      "Epoch 101: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.8912e-04 - mae: 0.0206 - val_loss: 0.0030 - val_mae: 0.0430 - 909ms/epoch - 6ms/step\n",
      "Epoch 102/3000\n",
      "\n",
      "Epoch 102: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.8575e-04 - mae: 0.0206 - val_loss: 0.0030 - val_mae: 0.0430 - 928ms/epoch - 6ms/step\n",
      "Epoch 103/3000\n",
      "\n",
      "Epoch 103: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.8299e-04 - mae: 0.0205 - val_loss: 0.0029 - val_mae: 0.0429 - 965ms/epoch - 6ms/step\n",
      "Epoch 104/3000\n",
      "\n",
      "Epoch 104: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.8046e-04 - mae: 0.0205 - val_loss: 0.0030 - val_mae: 0.0436 - 925ms/epoch - 6ms/step\n",
      "Epoch 105/3000\n",
      "\n",
      "Epoch 105: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.7603e-04 - mae: 0.0204 - val_loss: 0.0030 - val_mae: 0.0434 - 904ms/epoch - 6ms/step\n",
      "Epoch 106/3000\n",
      "\n",
      "Epoch 106: val_mae did not improve from 0.04268\n",
      "151/151 - 1s - loss: 7.7564e-04 - mae: 0.0204 - val_loss: 0.0030 - val_mae: 0.0430 - 924ms/epoch - 6ms/step\n",
      "Epoch 107/3000\n",
      "\n",
      "Epoch 107: val_mae improved from 0.04268 to 0.04257, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 7.7074e-04 - mae: 0.0204 - val_loss: 0.0029 - val_mae: 0.0426 - 960ms/epoch - 6ms/step\n",
      "Epoch 108/3000\n",
      "\n",
      "Epoch 108: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.6928e-04 - mae: 0.0204 - val_loss: 0.0030 - val_mae: 0.0433 - 915ms/epoch - 6ms/step\n",
      "Epoch 109/3000\n",
      "\n",
      "Epoch 109: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.6501e-04 - mae: 0.0203 - val_loss: 0.0029 - val_mae: 0.0427 - 905ms/epoch - 6ms/step\n",
      "Epoch 110/3000\n",
      "\n",
      "Epoch 110: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.6302e-04 - mae: 0.0203 - val_loss: 0.0031 - val_mae: 0.0439 - 949ms/epoch - 6ms/step\n",
      "Epoch 111/3000\n",
      "\n",
      "Epoch 111: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.5826e-04 - mae: 0.0202 - val_loss: 0.0031 - val_mae: 0.0437 - 918ms/epoch - 6ms/step\n",
      "Epoch 112/3000\n",
      "\n",
      "Epoch 112: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.5535e-04 - mae: 0.0202 - val_loss: 0.0030 - val_mae: 0.0430 - 932ms/epoch - 6ms/step\n",
      "Epoch 113/3000\n",
      "\n",
      "Epoch 113: val_mae did not improve from 0.04257\n",
      "151/151 - 1s - loss: 7.5339e-04 - mae: 0.0202 - val_loss: 0.0030 - val_mae: 0.0431 - 907ms/epoch - 6ms/step\n",
      "Epoch 114/3000\n",
      "\n",
      "Epoch 114: val_mae improved from 0.04257 to 0.04245, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 7.4931e-04 - mae: 0.0201 - val_loss: 0.0029 - val_mae: 0.0425 - 944ms/epoch - 6ms/step\n",
      "Epoch 115/3000\n",
      "\n",
      "Epoch 115: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.4758e-04 - mae: 0.0201 - val_loss: 0.0030 - val_mae: 0.0434 - 916ms/epoch - 6ms/step\n",
      "Epoch 116/3000\n",
      "\n",
      "Epoch 116: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.4333e-04 - mae: 0.0201 - val_loss: 0.0031 - val_mae: 0.0438 - 928ms/epoch - 6ms/step\n",
      "Epoch 117/3000\n",
      "\n",
      "Epoch 117: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.4033e-04 - mae: 0.0201 - val_loss: 0.0030 - val_mae: 0.0430 - 919ms/epoch - 6ms/step\n",
      "Epoch 118/3000\n",
      "\n",
      "Epoch 118: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.3789e-04 - mae: 0.0200 - val_loss: 0.0031 - val_mae: 0.0438 - 910ms/epoch - 6ms/step\n",
      "Epoch 119/3000\n",
      "\n",
      "Epoch 119: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.3491e-04 - mae: 0.0200 - val_loss: 0.0031 - val_mae: 0.0439 - 924ms/epoch - 6ms/step\n",
      "Epoch 120/3000\n",
      "\n",
      "Epoch 120: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.3163e-04 - mae: 0.0200 - val_loss: 0.0029 - val_mae: 0.0426 - 939ms/epoch - 6ms/step\n",
      "Epoch 121/3000\n",
      "\n",
      "Epoch 121: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.3123e-04 - mae: 0.0200 - val_loss: 0.0030 - val_mae: 0.0434 - 918ms/epoch - 6ms/step\n",
      "Epoch 122/3000\n",
      "\n",
      "Epoch 122: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.2770e-04 - mae: 0.0199 - val_loss: 0.0030 - val_mae: 0.0435 - 899ms/epoch - 6ms/step\n",
      "Epoch 123/3000\n",
      "\n",
      "Epoch 123: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.2479e-04 - mae: 0.0199 - val_loss: 0.0030 - val_mae: 0.0429 - 907ms/epoch - 6ms/step\n",
      "Epoch 124/3000\n",
      "\n",
      "Epoch 124: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.2297e-04 - mae: 0.0199 - val_loss: 0.0031 - val_mae: 0.0437 - 937ms/epoch - 6ms/step\n",
      "Epoch 125/3000\n",
      "\n",
      "Epoch 125: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1975e-04 - mae: 0.0198 - val_loss: 0.0029 - val_mae: 0.0428 - 916ms/epoch - 6ms/step\n",
      "Epoch 126/3000\n",
      "\n",
      "Epoch 126: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1835e-04 - mae: 0.0198 - val_loss: 0.0031 - val_mae: 0.0436 - 926ms/epoch - 6ms/step\n",
      "Epoch 127/3000\n",
      "\n",
      "Epoch 127: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1571e-04 - mae: 0.0198 - val_loss: 0.0030 - val_mae: 0.0435 - 918ms/epoch - 6ms/step\n",
      "Epoch 128/3000\n",
      "\n",
      "Epoch 128: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1436e-04 - mae: 0.0197 - val_loss: 0.0030 - val_mae: 0.0431 - 905ms/epoch - 6ms/step\n",
      "Epoch 129/3000\n",
      "\n",
      "Epoch 129: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1232e-04 - mae: 0.0197 - val_loss: 0.0030 - val_mae: 0.0435 - 941ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 130/3000\n",
      "\n",
      "Epoch 130: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.1132e-04 - mae: 0.0197 - val_loss: 0.0029 - val_mae: 0.0426 - 915ms/epoch - 6ms/step\n",
      "Epoch 131/3000\n",
      "\n",
      "Epoch 131: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.0850e-04 - mae: 0.0197 - val_loss: 0.0029 - val_mae: 0.0428 - 924ms/epoch - 6ms/step\n",
      "Epoch 132/3000\n",
      "\n",
      "Epoch 132: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.0663e-04 - mae: 0.0196 - val_loss: 0.0030 - val_mae: 0.0433 - 900ms/epoch - 6ms/step\n",
      "Epoch 133/3000\n",
      "\n",
      "Epoch 133: val_mae did not improve from 0.04245\n",
      "151/151 - 1s - loss: 7.0523e-04 - mae: 0.0196 - val_loss: 0.0029 - val_mae: 0.0426 - 915ms/epoch - 6ms/step\n",
      "Epoch 134/3000\n",
      "\n",
      "Epoch 134: val_mae improved from 0.04245 to 0.04186, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 7.0349e-04 - mae: 0.0196 - val_loss: 0.0028 - val_mae: 0.0419 - 959ms/epoch - 6ms/step\n",
      "Epoch 135/3000\n",
      "\n",
      "Epoch 135: val_mae did not improve from 0.04186\n",
      "151/151 - 1s - loss: 7.0171e-04 - mae: 0.0196 - val_loss: 0.0030 - val_mae: 0.0435 - 905ms/epoch - 6ms/step\n",
      "Epoch 136/3000\n",
      "\n",
      "Epoch 136: val_mae did not improve from 0.04186\n",
      "151/151 - 1s - loss: 7.0000e-04 - mae: 0.0195 - val_loss: 0.0029 - val_mae: 0.0423 - 922ms/epoch - 6ms/step\n",
      "Epoch 137/3000\n",
      "\n",
      "Epoch 137: val_mae did not improve from 0.04186\n",
      "151/151 - 1s - loss: 6.9880e-04 - mae: 0.0195 - val_loss: 0.0030 - val_mae: 0.0435 - 908ms/epoch - 6ms/step\n",
      "Epoch 138/3000\n",
      "\n",
      "Epoch 138: val_mae did not improve from 0.04186\n",
      "151/151 - 1s - loss: 6.9761e-04 - mae: 0.0195 - val_loss: 0.0030 - val_mae: 0.0429 - 919ms/epoch - 6ms/step\n",
      "Epoch 139/3000\n",
      "\n",
      "Epoch 139: val_mae improved from 0.04186 to 0.04164, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.9477e-04 - mae: 0.0195 - val_loss: 0.0028 - val_mae: 0.0416 - 935ms/epoch - 6ms/step\n",
      "Epoch 140/3000\n",
      "\n",
      "Epoch 140: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.9468e-04 - mae: 0.0195 - val_loss: 0.0029 - val_mae: 0.0423 - 911ms/epoch - 6ms/step\n",
      "Epoch 141/3000\n",
      "\n",
      "Epoch 141: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.9127e-04 - mae: 0.0194 - val_loss: 0.0029 - val_mae: 0.0425 - 921ms/epoch - 6ms/step\n",
      "Epoch 142/3000\n",
      "\n",
      "Epoch 142: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.9067e-04 - mae: 0.0194 - val_loss: 0.0030 - val_mae: 0.0429 - 924ms/epoch - 6ms/step\n",
      "Epoch 143/3000\n",
      "\n",
      "Epoch 143: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8842e-04 - mae: 0.0194 - val_loss: 0.0030 - val_mae: 0.0433 - 908ms/epoch - 6ms/step\n",
      "Epoch 144/3000\n",
      "\n",
      "Epoch 144: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8956e-04 - mae: 0.0194 - val_loss: 0.0029 - val_mae: 0.0425 - 931ms/epoch - 6ms/step\n",
      "Epoch 145/3000\n",
      "\n",
      "Epoch 145: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8706e-04 - mae: 0.0194 - val_loss: 0.0030 - val_mae: 0.0432 - 923ms/epoch - 6ms/step\n",
      "Epoch 146/3000\n",
      "\n",
      "Epoch 146: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8378e-04 - mae: 0.0193 - val_loss: 0.0029 - val_mae: 0.0424 - 938ms/epoch - 6ms/step\n",
      "Epoch 147/3000\n",
      "\n",
      "Epoch 147: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8405e-04 - mae: 0.0193 - val_loss: 0.0029 - val_mae: 0.0425 - 922ms/epoch - 6ms/step\n",
      "Epoch 148/3000\n",
      "\n",
      "Epoch 148: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8318e-04 - mae: 0.0193 - val_loss: 0.0029 - val_mae: 0.0424 - 934ms/epoch - 6ms/step\n",
      "Epoch 149/3000\n",
      "\n",
      "Epoch 149: val_mae did not improve from 0.04164\n",
      "151/151 - 1s - loss: 6.8259e-04 - mae: 0.0193 - val_loss: 0.0029 - val_mae: 0.0423 - 941ms/epoch - 6ms/step\n",
      "Epoch 150/3000\n",
      "\n",
      "Epoch 150: val_mae improved from 0.04164 to 0.04150, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.8070e-04 - mae: 0.0193 - val_loss: 0.0028 - val_mae: 0.0415 - 958ms/epoch - 6ms/step\n",
      "Epoch 151/3000\n",
      "\n",
      "Epoch 151: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.8011e-04 - mae: 0.0193 - val_loss: 0.0029 - val_mae: 0.0426 - 936ms/epoch - 6ms/step\n",
      "Epoch 152/3000\n",
      "\n",
      "Epoch 152: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7865e-04 - mae: 0.0192 - val_loss: 0.0029 - val_mae: 0.0423 - 931ms/epoch - 6ms/step\n",
      "Epoch 153/3000\n",
      "\n",
      "Epoch 153: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7785e-04 - mae: 0.0192 - val_loss: 0.0029 - val_mae: 0.0423 - 951ms/epoch - 6ms/step\n",
      "Epoch 154/3000\n",
      "\n",
      "Epoch 154: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7547e-04 - mae: 0.0192 - val_loss: 0.0030 - val_mae: 0.0427 - 920ms/epoch - 6ms/step\n",
      "Epoch 155/3000\n",
      "\n",
      "Epoch 155: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7388e-04 - mae: 0.0192 - val_loss: 0.0029 - val_mae: 0.0423 - 934ms/epoch - 6ms/step\n",
      "Epoch 156/3000\n",
      "\n",
      "Epoch 156: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7542e-04 - mae: 0.0192 - val_loss: 0.0031 - val_mae: 0.0435 - 929ms/epoch - 6ms/step\n",
      "Epoch 157/3000\n",
      "\n",
      "Epoch 157: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7203e-04 - mae: 0.0191 - val_loss: 0.0030 - val_mae: 0.0434 - 928ms/epoch - 6ms/step\n",
      "Epoch 158/3000\n",
      "\n",
      "Epoch 158: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7233e-04 - mae: 0.0191 - val_loss: 0.0029 - val_mae: 0.0423 - 928ms/epoch - 6ms/step\n",
      "Epoch 159/3000\n",
      "\n",
      "Epoch 159: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7127e-04 - mae: 0.0191 - val_loss: 0.0028 - val_mae: 0.0418 - 929ms/epoch - 6ms/step\n",
      "Epoch 160/3000\n",
      "\n",
      "Epoch 160: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.7031e-04 - mae: 0.0191 - val_loss: 0.0029 - val_mae: 0.0427 - 931ms/epoch - 6ms/step\n",
      "Epoch 161/3000\n",
      "\n",
      "Epoch 161: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6931e-04 - mae: 0.0191 - val_loss: 0.0029 - val_mae: 0.0426 - 925ms/epoch - 6ms/step\n",
      "Epoch 162/3000\n",
      "\n",
      "Epoch 162: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6748e-04 - mae: 0.0191 - val_loss: 0.0030 - val_mae: 0.0428 - 943ms/epoch - 6ms/step\n",
      "Epoch 163/3000\n",
      "\n",
      "Epoch 163: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6767e-04 - mae: 0.0191 - val_loss: 0.0029 - val_mae: 0.0421 - 950ms/epoch - 6ms/step\n",
      "Epoch 164/3000\n",
      "\n",
      "Epoch 164: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6646e-04 - mae: 0.0190 - val_loss: 0.0029 - val_mae: 0.0422 - 955ms/epoch - 6ms/step\n",
      "Epoch 165/3000\n",
      "\n",
      "Epoch 165: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6512e-04 - mae: 0.0190 - val_loss: 0.0029 - val_mae: 0.0426 - 920ms/epoch - 6ms/step\n",
      "Epoch 166/3000\n",
      "\n",
      "Epoch 166: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6320e-04 - mae: 0.0190 - val_loss: 0.0028 - val_mae: 0.0418 - 968ms/epoch - 6ms/step\n",
      "Epoch 167/3000\n",
      "\n",
      "Epoch 167: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6364e-04 - mae: 0.0190 - val_loss: 0.0029 - val_mae: 0.0423 - 927ms/epoch - 6ms/step\n",
      "Epoch 168/3000\n",
      "\n",
      "Epoch 168: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6144e-04 - mae: 0.0190 - val_loss: 0.0029 - val_mae: 0.0420 - 936ms/epoch - 6ms/step\n",
      "Epoch 169/3000\n",
      "\n",
      "Epoch 169: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6136e-04 - mae: 0.0190 - val_loss: 0.0030 - val_mae: 0.0433 - 927ms/epoch - 6ms/step\n",
      "Epoch 170/3000\n",
      "\n",
      "Epoch 170: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.6060e-04 - mae: 0.0189 - val_loss: 0.0029 - val_mae: 0.0421 - 937ms/epoch - 6ms/step\n",
      "Epoch 171/3000\n",
      "\n",
      "Epoch 171: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5870e-04 - mae: 0.0189 - val_loss: 0.0029 - val_mae: 0.0426 - 918ms/epoch - 6ms/step\n",
      "Epoch 172/3000\n",
      "\n",
      "Epoch 172: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5886e-04 - mae: 0.0189 - val_loss: 0.0029 - val_mae: 0.0420 - 946ms/epoch - 6ms/step\n",
      "Epoch 173/3000\n",
      "\n",
      "Epoch 173: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5846e-04 - mae: 0.0189 - val_loss: 0.0028 - val_mae: 0.0417 - 941ms/epoch - 6ms/step\n",
      "Epoch 174/3000\n",
      "\n",
      "Epoch 174: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5745e-04 - mae: 0.0189 - val_loss: 0.0029 - val_mae: 0.0425 - 921ms/epoch - 6ms/step\n",
      "Epoch 175/3000\n",
      "\n",
      "Epoch 175: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5637e-04 - mae: 0.0189 - val_loss: 0.0030 - val_mae: 0.0427 - 929ms/epoch - 6ms/step\n",
      "Epoch 176/3000\n",
      "\n",
      "Epoch 176: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5537e-04 - mae: 0.0189 - val_loss: 0.0030 - val_mae: 0.0429 - 960ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 177/3000\n",
      "\n",
      "Epoch 177: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5251e-04 - mae: 0.0188 - val_loss: 0.0029 - val_mae: 0.0421 - 932ms/epoch - 6ms/step\n",
      "Epoch 178/3000\n",
      "\n",
      "Epoch 178: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5256e-04 - mae: 0.0188 - val_loss: 0.0028 - val_mae: 0.0419 - 922ms/epoch - 6ms/step\n",
      "Epoch 179/3000\n",
      "\n",
      "Epoch 179: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5328e-04 - mae: 0.0188 - val_loss: 0.0030 - val_mae: 0.0429 - 909ms/epoch - 6ms/step\n",
      "Epoch 180/3000\n",
      "\n",
      "Epoch 180: val_mae did not improve from 0.04150\n",
      "151/151 - 1s - loss: 6.5294e-04 - mae: 0.0188 - val_loss: 0.0028 - val_mae: 0.0418 - 932ms/epoch - 6ms/step\n",
      "Epoch 181/3000\n",
      "\n",
      "Epoch 181: val_mae improved from 0.04150 to 0.04104, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.5124e-04 - mae: 0.0188 - val_loss: 0.0027 - val_mae: 0.0410 - 966ms/epoch - 6ms/step\n",
      "Epoch 182/3000\n",
      "\n",
      "Epoch 182: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.5117e-04 - mae: 0.0188 - val_loss: 0.0028 - val_mae: 0.0415 - 931ms/epoch - 6ms/step\n",
      "Epoch 183/3000\n",
      "\n",
      "Epoch 183: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4962e-04 - mae: 0.0188 - val_loss: 0.0030 - val_mae: 0.0429 - 917ms/epoch - 6ms/step\n",
      "Epoch 184/3000\n",
      "\n",
      "Epoch 184: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4908e-04 - mae: 0.0188 - val_loss: 0.0030 - val_mae: 0.0429 - 914ms/epoch - 6ms/step\n",
      "Epoch 185/3000\n",
      "\n",
      "Epoch 185: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4869e-04 - mae: 0.0188 - val_loss: 0.0029 - val_mae: 0.0426 - 965ms/epoch - 6ms/step\n",
      "Epoch 186/3000\n",
      "\n",
      "Epoch 186: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4736e-04 - mae: 0.0187 - val_loss: 0.0028 - val_mae: 0.0415 - 922ms/epoch - 6ms/step\n",
      "Epoch 187/3000\n",
      "\n",
      "Epoch 187: val_mae improved from 0.04104 to 0.04104, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.4667e-04 - mae: 0.0187 - val_loss: 0.0027 - val_mae: 0.0410 - 1s/epoch - 7ms/step\n",
      "Epoch 188/3000\n",
      "\n",
      "Epoch 188: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4746e-04 - mae: 0.0187 - val_loss: 0.0029 - val_mae: 0.0422 - 934ms/epoch - 6ms/step\n",
      "Epoch 189/3000\n",
      "\n",
      "Epoch 189: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4545e-04 - mae: 0.0187 - val_loss: 0.0029 - val_mae: 0.0424 - 970ms/epoch - 6ms/step\n",
      "Epoch 190/3000\n",
      "\n",
      "Epoch 190: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4417e-04 - mae: 0.0187 - val_loss: 0.0029 - val_mae: 0.0425 - 924ms/epoch - 6ms/step\n",
      "Epoch 191/3000\n",
      "\n",
      "Epoch 191: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4543e-04 - mae: 0.0187 - val_loss: 0.0032 - val_mae: 0.0445 - 932ms/epoch - 6ms/step\n",
      "Epoch 192/3000\n",
      "\n",
      "Epoch 192: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4429e-04 - mae: 0.0187 - val_loss: 0.0030 - val_mae: 0.0433 - 920ms/epoch - 6ms/step\n",
      "Epoch 193/3000\n",
      "\n",
      "Epoch 193: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4275e-04 - mae: 0.0187 - val_loss: 0.0030 - val_mae: 0.0432 - 944ms/epoch - 6ms/step\n",
      "Epoch 194/3000\n",
      "\n",
      "Epoch 194: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4295e-04 - mae: 0.0187 - val_loss: 0.0028 - val_mae: 0.0411 - 944ms/epoch - 6ms/step\n",
      "Epoch 195/3000\n",
      "\n",
      "Epoch 195: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4186e-04 - mae: 0.0187 - val_loss: 0.0028 - val_mae: 0.0418 - 922ms/epoch - 6ms/step\n",
      "Epoch 196/3000\n",
      "\n",
      "Epoch 196: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.4117e-04 - mae: 0.0186 - val_loss: 0.0028 - val_mae: 0.0414 - 918ms/epoch - 6ms/step\n",
      "Epoch 197/3000\n",
      "\n",
      "Epoch 197: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.3998e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0420 - 953ms/epoch - 6ms/step\n",
      "Epoch 198/3000\n",
      "\n",
      "Epoch 198: val_mae did not improve from 0.04104\n",
      "151/151 - 1s - loss: 6.3866e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0425 - 933ms/epoch - 6ms/step\n",
      "Epoch 199/3000\n",
      "\n",
      "Epoch 199: val_mae improved from 0.04104 to 0.04091, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.3809e-04 - mae: 0.0186 - val_loss: 0.0027 - val_mae: 0.0409 - 974ms/epoch - 6ms/step\n",
      "Epoch 200/3000\n",
      "\n",
      "Epoch 200: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3955e-04 - mae: 0.0186 - val_loss: 0.0028 - val_mae: 0.0417 - 939ms/epoch - 6ms/step\n",
      "Epoch 201/3000\n",
      "\n",
      "Epoch 201: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3778e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0422 - 926ms/epoch - 6ms/step\n",
      "Epoch 202/3000\n",
      "\n",
      "Epoch 202: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3772e-04 - mae: 0.0186 - val_loss: 0.0028 - val_mae: 0.0415 - 946ms/epoch - 6ms/step\n",
      "Epoch 203/3000\n",
      "\n",
      "Epoch 203: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3739e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0424 - 934ms/epoch - 6ms/step\n",
      "Epoch 204/3000\n",
      "\n",
      "Epoch 204: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3717e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0421 - 915ms/epoch - 6ms/step\n",
      "Epoch 205/3000\n",
      "\n",
      "Epoch 205: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3597e-04 - mae: 0.0186 - val_loss: 0.0028 - val_mae: 0.0413 - 918ms/epoch - 6ms/step\n",
      "Epoch 206/3000\n",
      "\n",
      "Epoch 206: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3498e-04 - mae: 0.0185 - val_loss: 0.0028 - val_mae: 0.0416 - 955ms/epoch - 6ms/step\n",
      "Epoch 207/3000\n",
      "\n",
      "Epoch 207: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3534e-04 - mae: 0.0185 - val_loss: 0.0029 - val_mae: 0.0422 - 937ms/epoch - 6ms/step\n",
      "Epoch 208/3000\n",
      "\n",
      "Epoch 208: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3564e-04 - mae: 0.0186 - val_loss: 0.0029 - val_mae: 0.0421 - 933ms/epoch - 6ms/step\n",
      "Epoch 209/3000\n",
      "\n",
      "Epoch 209: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3306e-04 - mae: 0.0185 - val_loss: 0.0030 - val_mae: 0.0427 - 916ms/epoch - 6ms/step\n",
      "Epoch 210/3000\n",
      "\n",
      "Epoch 210: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3303e-04 - mae: 0.0185 - val_loss: 0.0029 - val_mae: 0.0421 - 929ms/epoch - 6ms/step\n",
      "Epoch 211/3000\n",
      "\n",
      "Epoch 211: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3152e-04 - mae: 0.0185 - val_loss: 0.0030 - val_mae: 0.0432 - 909ms/epoch - 6ms/step\n",
      "Epoch 212/3000\n",
      "\n",
      "Epoch 212: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3221e-04 - mae: 0.0185 - val_loss: 0.0029 - val_mae: 0.0418 - 909ms/epoch - 6ms/step\n",
      "Epoch 213/3000\n",
      "\n",
      "Epoch 213: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3170e-04 - mae: 0.0185 - val_loss: 0.0028 - val_mae: 0.0415 - 903ms/epoch - 6ms/step\n",
      "Epoch 214/3000\n",
      "\n",
      "Epoch 214: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.3113e-04 - mae: 0.0185 - val_loss: 0.0028 - val_mae: 0.0411 - 926ms/epoch - 6ms/step\n",
      "Epoch 215/3000\n",
      "\n",
      "Epoch 215: val_mae improved from 0.04091 to 0.04091, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.3056e-04 - mae: 0.0185 - val_loss: 0.0027 - val_mae: 0.0409 - 977ms/epoch - 6ms/step\n",
      "Epoch 216/3000\n",
      "\n",
      "Epoch 216: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2902e-04 - mae: 0.0184 - val_loss: 0.0030 - val_mae: 0.0429 - 913ms/epoch - 6ms/step\n",
      "Epoch 217/3000\n",
      "\n",
      "Epoch 217: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2921e-04 - mae: 0.0184 - val_loss: 0.0028 - val_mae: 0.0412 - 936ms/epoch - 6ms/step\n",
      "Epoch 218/3000\n",
      "\n",
      "Epoch 218: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2828e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0417 - 947ms/epoch - 6ms/step\n",
      "Epoch 219/3000\n",
      "\n",
      "Epoch 219: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2833e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0423 - 919ms/epoch - 6ms/step\n",
      "Epoch 220/3000\n",
      "\n",
      "Epoch 220: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2742e-04 - mae: 0.0184 - val_loss: 0.0030 - val_mae: 0.0429 - 912ms/epoch - 6ms/step\n",
      "Epoch 221/3000\n",
      "\n",
      "Epoch 221: val_mae did not improve from 0.04091\n",
      "151/151 - 1s - loss: 6.2723e-04 - mae: 0.0184 - val_loss: 0.0030 - val_mae: 0.0426 - 915ms/epoch - 6ms/step\n",
      "Epoch 222/3000\n",
      "\n",
      "Epoch 222: val_mae improved from 0.04091 to 0.04089, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.2634e-04 - mae: 0.0184 - val_loss: 0.0027 - val_mae: 0.0409 - 953ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 223/3000\n",
      "\n",
      "Epoch 223: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2658e-04 - mae: 0.0184 - val_loss: 0.0028 - val_mae: 0.0416 - 940ms/epoch - 6ms/step\n",
      "Epoch 224/3000\n",
      "\n",
      "Epoch 224: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2572e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0424 - 924ms/epoch - 6ms/step\n",
      "Epoch 225/3000\n",
      "\n",
      "Epoch 225: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2417e-04 - mae: 0.0184 - val_loss: 0.0030 - val_mae: 0.0426 - 919ms/epoch - 6ms/step\n",
      "Epoch 226/3000\n",
      "\n",
      "Epoch 226: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2539e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0420 - 917ms/epoch - 6ms/step\n",
      "Epoch 227/3000\n",
      "\n",
      "Epoch 227: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2350e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0418 - 934ms/epoch - 6ms/step\n",
      "Epoch 228/3000\n",
      "\n",
      "Epoch 228: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2391e-04 - mae: 0.0184 - val_loss: 0.0029 - val_mae: 0.0419 - 940ms/epoch - 6ms/step\n",
      "Epoch 229/3000\n",
      "\n",
      "Epoch 229: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2279e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0420 - 912ms/epoch - 6ms/step\n",
      "Epoch 230/3000\n",
      "\n",
      "Epoch 230: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2300e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0420 - 922ms/epoch - 6ms/step\n",
      "Epoch 231/3000\n",
      "\n",
      "Epoch 231: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2330e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0411 - 950ms/epoch - 6ms/step\n",
      "Epoch 232/3000\n",
      "\n",
      "Epoch 232: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2232e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0416 - 976ms/epoch - 6ms/step\n",
      "Epoch 233/3000\n",
      "\n",
      "Epoch 233: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2092e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0414 - 909ms/epoch - 6ms/step\n",
      "Epoch 234/3000\n",
      "\n",
      "Epoch 234: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2094e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0411 - 929ms/epoch - 6ms/step\n",
      "Epoch 235/3000\n",
      "\n",
      "Epoch 235: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2060e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0419 - 916ms/epoch - 6ms/step\n",
      "Epoch 236/3000\n",
      "\n",
      "Epoch 236: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.2085e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0413 - 940ms/epoch - 6ms/step\n",
      "Epoch 237/3000\n",
      "\n",
      "Epoch 237: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1943e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0418 - 918ms/epoch - 6ms/step\n",
      "Epoch 238/3000\n",
      "\n",
      "Epoch 238: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1862e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0422 - 921ms/epoch - 6ms/step\n",
      "Epoch 239/3000\n",
      "\n",
      "Epoch 239: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1985e-04 - mae: 0.0183 - val_loss: 0.0030 - val_mae: 0.0428 - 956ms/epoch - 6ms/step\n",
      "Epoch 240/3000\n",
      "\n",
      "Epoch 240: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1758e-04 - mae: 0.0183 - val_loss: 0.0030 - val_mae: 0.0425 - 940ms/epoch - 6ms/step\n",
      "Epoch 241/3000\n",
      "\n",
      "Epoch 241: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1731e-04 - mae: 0.0183 - val_loss: 0.0029 - val_mae: 0.0419 - 943ms/epoch - 6ms/step\n",
      "Epoch 242/3000\n",
      "\n",
      "Epoch 242: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1680e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0423 - 911ms/epoch - 6ms/step\n",
      "Epoch 243/3000\n",
      "\n",
      "Epoch 243: val_mae did not improve from 0.04089\n",
      "151/151 - 1s - loss: 6.1707e-04 - mae: 0.0183 - val_loss: 0.0028 - val_mae: 0.0415 - 928ms/epoch - 6ms/step\n",
      "Epoch 244/3000\n",
      "\n",
      "Epoch 244: val_mae improved from 0.04089 to 0.04081, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.1562e-04 - mae: 0.0182 - val_loss: 0.0027 - val_mae: 0.0408 - 1s/epoch - 7ms/step\n",
      "Epoch 245/3000\n",
      "\n",
      "Epoch 245: val_mae did not improve from 0.04081\n",
      "151/151 - 1s - loss: 6.1581e-04 - mae: 0.0182 - val_loss: 0.0028 - val_mae: 0.0412 - 957ms/epoch - 6ms/step\n",
      "Epoch 246/3000\n",
      "\n",
      "Epoch 246: val_mae did not improve from 0.04081\n",
      "151/151 - 1s - loss: 6.1559e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0417 - 956ms/epoch - 6ms/step\n",
      "Epoch 247/3000\n",
      "\n",
      "Epoch 247: val_mae did not improve from 0.04081\n",
      "151/151 - 1s - loss: 6.1400e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0422 - 957ms/epoch - 6ms/step\n",
      "Epoch 248/3000\n",
      "\n",
      "Epoch 248: val_mae improved from 0.04081 to 0.04053, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.1546e-04 - mae: 0.0182 - val_loss: 0.0027 - val_mae: 0.0405 - 988ms/epoch - 7ms/step\n",
      "Epoch 249/3000\n",
      "\n",
      "Epoch 249: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1365e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0421 - 947ms/epoch - 6ms/step\n",
      "Epoch 250/3000\n",
      "\n",
      "Epoch 250: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1360e-04 - mae: 0.0182 - val_loss: 0.0028 - val_mae: 0.0415 - 949ms/epoch - 6ms/step\n",
      "Epoch 251/3000\n",
      "\n",
      "Epoch 251: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1361e-04 - mae: 0.0182 - val_loss: 0.0028 - val_mae: 0.0414 - 943ms/epoch - 6ms/step\n",
      "Epoch 252/3000\n",
      "\n",
      "Epoch 252: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1342e-04 - mae: 0.0182 - val_loss: 0.0030 - val_mae: 0.0429 - 983ms/epoch - 7ms/step\n",
      "Epoch 253/3000\n",
      "\n",
      "Epoch 253: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1260e-04 - mae: 0.0182 - val_loss: 0.0030 - val_mae: 0.0428 - 957ms/epoch - 6ms/step\n",
      "Epoch 254/3000\n",
      "\n",
      "Epoch 254: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1152e-04 - mae: 0.0182 - val_loss: 0.0030 - val_mae: 0.0428 - 947ms/epoch - 6ms/step\n",
      "Epoch 255/3000\n",
      "\n",
      "Epoch 255: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1370e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0417 - 974ms/epoch - 6ms/step\n",
      "Epoch 256/3000\n",
      "\n",
      "Epoch 256: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1191e-04 - mae: 0.0182 - val_loss: 0.0029 - val_mae: 0.0417 - 934ms/epoch - 6ms/step\n",
      "Epoch 257/3000\n",
      "\n",
      "Epoch 257: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1011e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0413 - 965ms/epoch - 6ms/step\n",
      "Epoch 258/3000\n",
      "\n",
      "Epoch 258: val_mae did not improve from 0.04053\n",
      "151/151 - 1s - loss: 6.1089e-04 - mae: 0.0181 - val_loss: 0.0029 - val_mae: 0.0418 - 932ms/epoch - 6ms/step\n",
      "Epoch 259/3000\n",
      "\n",
      "Epoch 259: val_mae improved from 0.04053 to 0.04041, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.0959e-04 - mae: 0.0181 - val_loss: 0.0027 - val_mae: 0.0404 - 997ms/epoch - 7ms/step\n",
      "Epoch 260/3000\n",
      "\n",
      "Epoch 260: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.1018e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0412 - 980ms/epoch - 6ms/step\n",
      "Epoch 261/3000\n",
      "\n",
      "Epoch 261: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0985e-04 - mae: 0.0181 - val_loss: 0.0027 - val_mae: 0.0405 - 951ms/epoch - 6ms/step\n",
      "Epoch 262/3000\n",
      "\n",
      "Epoch 262: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0874e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0413 - 969ms/epoch - 6ms/step\n",
      "Epoch 263/3000\n",
      "\n",
      "Epoch 263: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0914e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0410 - 973ms/epoch - 6ms/step\n",
      "Epoch 264/3000\n",
      "\n",
      "Epoch 264: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0844e-04 - mae: 0.0181 - val_loss: 0.0029 - val_mae: 0.0422 - 962ms/epoch - 6ms/step\n",
      "Epoch 265/3000\n",
      "\n",
      "Epoch 265: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0834e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0415 - 920ms/epoch - 6ms/step\n",
      "Epoch 266/3000\n",
      "\n",
      "Epoch 266: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0738e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0411 - 942ms/epoch - 6ms/step\n",
      "Epoch 267/3000\n",
      "\n",
      "Epoch 267: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0726e-04 - mae: 0.0181 - val_loss: 0.0029 - val_mae: 0.0417 - 910ms/epoch - 6ms/step\n",
      "Epoch 268/3000\n",
      "\n",
      "Epoch 268: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0668e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0413 - 939ms/epoch - 6ms/step\n",
      "Epoch 269/3000\n",
      "\n",
      "Epoch 269: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0723e-04 - mae: 0.0181 - val_loss: 0.0029 - val_mae: 0.0416 - 930ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 270/3000\n",
      "\n",
      "Epoch 270: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0570e-04 - mae: 0.0181 - val_loss: 0.0027 - val_mae: 0.0407 - 938ms/epoch - 6ms/step\n",
      "Epoch 271/3000\n",
      "\n",
      "Epoch 271: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0622e-04 - mae: 0.0181 - val_loss: 0.0029 - val_mae: 0.0416 - 917ms/epoch - 6ms/step\n",
      "Epoch 272/3000\n",
      "\n",
      "Epoch 272: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0458e-04 - mae: 0.0180 - val_loss: 0.0027 - val_mae: 0.0405 - 922ms/epoch - 6ms/step\n",
      "Epoch 273/3000\n",
      "\n",
      "Epoch 273: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0578e-04 - mae: 0.0181 - val_loss: 0.0028 - val_mae: 0.0415 - 915ms/epoch - 6ms/step\n",
      "Epoch 274/3000\n",
      "\n",
      "Epoch 274: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0448e-04 - mae: 0.0180 - val_loss: 0.0027 - val_mae: 0.0408 - 941ms/epoch - 6ms/step\n",
      "Epoch 275/3000\n",
      "\n",
      "Epoch 275: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0473e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0411 - 922ms/epoch - 6ms/step\n",
      "Epoch 276/3000\n",
      "\n",
      "Epoch 276: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0408e-04 - mae: 0.0180 - val_loss: 0.0029 - val_mae: 0.0421 - 916ms/epoch - 6ms/step\n",
      "Epoch 277/3000\n",
      "\n",
      "Epoch 277: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0476e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0411 - 962ms/epoch - 6ms/step\n",
      "Epoch 278/3000\n",
      "\n",
      "Epoch 278: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0274e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0416 - 919ms/epoch - 6ms/step\n",
      "Epoch 279/3000\n",
      "\n",
      "Epoch 279: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0341e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0408 - 934ms/epoch - 6ms/step\n",
      "Epoch 280/3000\n",
      "\n",
      "Epoch 280: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0340e-04 - mae: 0.0180 - val_loss: 0.0029 - val_mae: 0.0416 - 895ms/epoch - 6ms/step\n",
      "Epoch 281/3000\n",
      "\n",
      "Epoch 281: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0181e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0415 - 905ms/epoch - 6ms/step\n",
      "Epoch 282/3000\n",
      "\n",
      "Epoch 282: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0159e-04 - mae: 0.0180 - val_loss: 0.0027 - val_mae: 0.0405 - 922ms/epoch - 6ms/step\n",
      "Epoch 283/3000\n",
      "\n",
      "Epoch 283: val_mae did not improve from 0.04041\n",
      "151/151 - 1s - loss: 6.0352e-04 - mae: 0.0180 - val_loss: 0.0029 - val_mae: 0.0417 - 927ms/epoch - 6ms/step\n",
      "Epoch 284/3000\n",
      "\n",
      "Epoch 284: val_mae improved from 0.04041 to 0.04027, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 6.0045e-04 - mae: 0.0180 - val_loss: 0.0027 - val_mae: 0.0403 - 944ms/epoch - 6ms/step\n",
      "Epoch 285/3000\n",
      "\n",
      "Epoch 285: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 6.0077e-04 - mae: 0.0180 - val_loss: 0.0027 - val_mae: 0.0407 - 905ms/epoch - 6ms/step\n",
      "Epoch 286/3000\n",
      "\n",
      "Epoch 286: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9989e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0411 - 929ms/epoch - 6ms/step\n",
      "Epoch 287/3000\n",
      "\n",
      "Epoch 287: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9990e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0409 - 951ms/epoch - 6ms/step\n",
      "Epoch 288/3000\n",
      "\n",
      "Epoch 288: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9917e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0411 - 918ms/epoch - 6ms/step\n",
      "Epoch 289/3000\n",
      "\n",
      "Epoch 289: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9861e-04 - mae: 0.0179 - val_loss: 0.0027 - val_mae: 0.0407 - 938ms/epoch - 6ms/step\n",
      "Epoch 290/3000\n",
      "\n",
      "Epoch 290: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9840e-04 - mae: 0.0179 - val_loss: 0.0029 - val_mae: 0.0419 - 924ms/epoch - 6ms/step\n",
      "Epoch 291/3000\n",
      "\n",
      "Epoch 291: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9810e-04 - mae: 0.0179 - val_loss: 0.0029 - val_mae: 0.0417 - 920ms/epoch - 6ms/step\n",
      "Epoch 292/3000\n",
      "\n",
      "Epoch 292: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9866e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0409 - 940ms/epoch - 6ms/step\n",
      "Epoch 293/3000\n",
      "\n",
      "Epoch 293: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9868e-04 - mae: 0.0180 - val_loss: 0.0028 - val_mae: 0.0412 - 955ms/epoch - 6ms/step\n",
      "Epoch 294/3000\n",
      "\n",
      "Epoch 294: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9832e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0413 - 935ms/epoch - 6ms/step\n",
      "Epoch 295/3000\n",
      "\n",
      "Epoch 295: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9770e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0415 - 941ms/epoch - 6ms/step\n",
      "Epoch 296/3000\n",
      "\n",
      "Epoch 296: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9711e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0415 - 960ms/epoch - 6ms/step\n",
      "Epoch 297/3000\n",
      "\n",
      "Epoch 297: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9612e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0408 - 941ms/epoch - 6ms/step\n",
      "Epoch 298/3000\n",
      "\n",
      "Epoch 298: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9665e-04 - mae: 0.0179 - val_loss: 0.0027 - val_mae: 0.0405 - 989ms/epoch - 7ms/step\n",
      "Epoch 299/3000\n",
      "\n",
      "Epoch 299: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9666e-04 - mae: 0.0179 - val_loss: 0.0029 - val_mae: 0.0416 - 943ms/epoch - 6ms/step\n",
      "Epoch 300/3000\n",
      "\n",
      "Epoch 300: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9718e-04 - mae: 0.0179 - val_loss: 0.0029 - val_mae: 0.0421 - 926ms/epoch - 6ms/step\n",
      "Epoch 301/3000\n",
      "\n",
      "Epoch 301: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9534e-04 - mae: 0.0179 - val_loss: 0.0027 - val_mae: 0.0405 - 945ms/epoch - 6ms/step\n",
      "Epoch 302/3000\n",
      "\n",
      "Epoch 302: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9476e-04 - mae: 0.0179 - val_loss: 0.0029 - val_mae: 0.0419 - 936ms/epoch - 6ms/step\n",
      "Epoch 303/3000\n",
      "\n",
      "Epoch 303: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9473e-04 - mae: 0.0179 - val_loss: 0.0030 - val_mae: 0.0425 - 939ms/epoch - 6ms/step\n",
      "Epoch 304/3000\n",
      "\n",
      "Epoch 304: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9331e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0414 - 970ms/epoch - 6ms/step\n",
      "Epoch 305/3000\n",
      "\n",
      "Epoch 305: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9493e-04 - mae: 0.0179 - val_loss: 0.0027 - val_mae: 0.0403 - 917ms/epoch - 6ms/step\n",
      "Epoch 306/3000\n",
      "\n",
      "Epoch 306: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9350e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0411 - 924ms/epoch - 6ms/step\n",
      "Epoch 307/3000\n",
      "\n",
      "Epoch 307: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9440e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0412 - 929ms/epoch - 6ms/step\n",
      "Epoch 308/3000\n",
      "\n",
      "Epoch 308: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9333e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0410 - 942ms/epoch - 6ms/step\n",
      "Epoch 309/3000\n",
      "\n",
      "Epoch 309: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9410e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0412 - 916ms/epoch - 6ms/step\n",
      "Epoch 310/3000\n",
      "\n",
      "Epoch 310: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9227e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0413 - 914ms/epoch - 6ms/step\n",
      "Epoch 311/3000\n",
      "\n",
      "Epoch 311: val_mae did not improve from 0.04027\n",
      "151/151 - 1s - loss: 5.9378e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0408 - 913ms/epoch - 6ms/step\n",
      "Epoch 312/3000\n",
      "\n",
      "Epoch 312: val_mae improved from 0.04027 to 0.04021, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.9169e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0402 - 948ms/epoch - 6ms/step\n",
      "Epoch 313/3000\n",
      "\n",
      "Epoch 313: val_mae did not improve from 0.04021\n",
      "151/151 - 1s - loss: 5.9226e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0405 - 936ms/epoch - 6ms/step\n",
      "Epoch 314/3000\n",
      "\n",
      "Epoch 314: val_mae did not improve from 0.04021\n",
      "151/151 - 1s - loss: 5.9050e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0405 - 940ms/epoch - 6ms/step\n",
      "Epoch 315/3000\n",
      "\n",
      "Epoch 315: val_mae did not improve from 0.04021\n",
      "151/151 - 1s - loss: 5.9112e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0404 - 954ms/epoch - 6ms/step\n",
      "Epoch 316/3000\n",
      "\n",
      "Epoch 316: val_mae did not improve from 0.04021\n",
      "151/151 - 1s - loss: 5.9279e-04 - mae: 0.0179 - val_loss: 0.0028 - val_mae: 0.0411 - 910ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 317/3000\n",
      "\n",
      "Epoch 317: val_mae improved from 0.04021 to 0.03990, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.9147e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0399 - 967ms/epoch - 6ms/step\n",
      "Epoch 318/3000\n",
      "\n",
      "Epoch 318: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8986e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0414 - 915ms/epoch - 6ms/step\n",
      "Epoch 319/3000\n",
      "\n",
      "Epoch 319: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.9021e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0417 - 911ms/epoch - 6ms/step\n",
      "Epoch 320/3000\n",
      "\n",
      "Epoch 320: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8997e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0405 - 958ms/epoch - 6ms/step\n",
      "Epoch 321/3000\n",
      "\n",
      "Epoch 321: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8976e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0417 - 924ms/epoch - 6ms/step\n",
      "Epoch 322/3000\n",
      "\n",
      "Epoch 322: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8959e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0412 - 936ms/epoch - 6ms/step\n",
      "Epoch 323/3000\n",
      "\n",
      "Epoch 323: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8938e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0412 - 975ms/epoch - 6ms/step\n",
      "Epoch 324/3000\n",
      "\n",
      "Epoch 324: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8863e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0408 - 908ms/epoch - 6ms/step\n",
      "Epoch 325/3000\n",
      "\n",
      "Epoch 325: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8957e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0410 - 950ms/epoch - 6ms/step\n",
      "Epoch 326/3000\n",
      "\n",
      "Epoch 326: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8731e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0414 - 896ms/epoch - 6ms/step\n",
      "Epoch 327/3000\n",
      "\n",
      "Epoch 327: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8769e-04 - mae: 0.0178 - val_loss: 0.0027 - val_mae: 0.0405 - 906ms/epoch - 6ms/step\n",
      "Epoch 328/3000\n",
      "\n",
      "Epoch 328: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8729e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0413 - 913ms/epoch - 6ms/step\n",
      "Epoch 329/3000\n",
      "\n",
      "Epoch 329: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8787e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0419 - 933ms/epoch - 6ms/step\n",
      "Epoch 330/3000\n",
      "\n",
      "Epoch 330: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8755e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0408 - 944ms/epoch - 6ms/step\n",
      "Epoch 331/3000\n",
      "\n",
      "Epoch 331: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8703e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0409 - 960ms/epoch - 6ms/step\n",
      "Epoch 332/3000\n",
      "\n",
      "Epoch 332: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8730e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0415 - 945ms/epoch - 6ms/step\n",
      "Epoch 333/3000\n",
      "\n",
      "Epoch 333: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8648e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0406 - 944ms/epoch - 6ms/step\n",
      "Epoch 334/3000\n",
      "\n",
      "Epoch 334: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8675e-04 - mae: 0.0178 - val_loss: 0.0028 - val_mae: 0.0407 - 989ms/epoch - 7ms/step\n",
      "Epoch 335/3000\n",
      "\n",
      "Epoch 335: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8597e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0404 - 939ms/epoch - 6ms/step\n",
      "Epoch 336/3000\n",
      "\n",
      "Epoch 336: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8538e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0412 - 923ms/epoch - 6ms/step\n",
      "Epoch 337/3000\n",
      "\n",
      "Epoch 337: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8609e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0409 - 943ms/epoch - 6ms/step\n",
      "Epoch 338/3000\n",
      "\n",
      "Epoch 338: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8558e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0413 - 932ms/epoch - 6ms/step\n",
      "Epoch 339/3000\n",
      "\n",
      "Epoch 339: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8604e-04 - mae: 0.0178 - val_loss: 0.0029 - val_mae: 0.0415 - 921ms/epoch - 6ms/step\n",
      "Epoch 340/3000\n",
      "\n",
      "Epoch 340: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8456e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0407 - 931ms/epoch - 6ms/step\n",
      "Epoch 341/3000\n",
      "\n",
      "Epoch 341: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8419e-04 - mae: 0.0177 - val_loss: 0.0029 - val_mae: 0.0419 - 910ms/epoch - 6ms/step\n",
      "Epoch 342/3000\n",
      "\n",
      "Epoch 342: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8323e-04 - mae: 0.0177 - val_loss: 0.0029 - val_mae: 0.0418 - 948ms/epoch - 6ms/step\n",
      "Epoch 343/3000\n",
      "\n",
      "Epoch 343: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8330e-04 - mae: 0.0177 - val_loss: 0.0029 - val_mae: 0.0417 - 964ms/epoch - 6ms/step\n",
      "Epoch 344/3000\n",
      "\n",
      "Epoch 344: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8371e-04 - mae: 0.0177 - val_loss: 0.0029 - val_mae: 0.0417 - 926ms/epoch - 6ms/step\n",
      "Epoch 345/3000\n",
      "\n",
      "Epoch 345: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8434e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0405 - 916ms/epoch - 6ms/step\n",
      "Epoch 346/3000\n",
      "\n",
      "Epoch 346: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8375e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0403 - 934ms/epoch - 6ms/step\n",
      "Epoch 347/3000\n",
      "\n",
      "Epoch 347: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8306e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0410 - 913ms/epoch - 6ms/step\n",
      "Epoch 348/3000\n",
      "\n",
      "Epoch 348: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8225e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0402 - 901ms/epoch - 6ms/step\n",
      "Epoch 349/3000\n",
      "\n",
      "Epoch 349: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8307e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0408 - 909ms/epoch - 6ms/step\n",
      "Epoch 350/3000\n",
      "\n",
      "Epoch 350: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8148e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0404 - 963ms/epoch - 6ms/step\n",
      "Epoch 351/3000\n",
      "\n",
      "Epoch 351: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8189e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0410 - 925ms/epoch - 6ms/step\n",
      "Epoch 352/3000\n",
      "\n",
      "Epoch 352: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8261e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0407 - 901ms/epoch - 6ms/step\n",
      "Epoch 353/3000\n",
      "\n",
      "Epoch 353: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8208e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0404 - 909ms/epoch - 6ms/step\n",
      "Epoch 354/3000\n",
      "\n",
      "Epoch 354: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8122e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0409 - 915ms/epoch - 6ms/step\n",
      "Epoch 355/3000\n",
      "\n",
      "Epoch 355: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8055e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0410 - 950ms/epoch - 6ms/step\n",
      "Epoch 356/3000\n",
      "\n",
      "Epoch 356: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8084e-04 - mae: 0.0177 - val_loss: 0.0027 - val_mae: 0.0404 - 937ms/epoch - 6ms/step\n",
      "Epoch 357/3000\n",
      "\n",
      "Epoch 357: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8064e-04 - mae: 0.0177 - val_loss: 0.0028 - val_mae: 0.0411 - 909ms/epoch - 6ms/step\n",
      "Epoch 358/3000\n",
      "\n",
      "Epoch 358: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7961e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0416 - 904ms/epoch - 6ms/step\n",
      "Epoch 359/3000\n",
      "\n",
      "Epoch 359: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7995e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0417 - 937ms/epoch - 6ms/step\n",
      "Epoch 360/3000\n",
      "\n",
      "Epoch 360: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8205e-04 - mae: 0.0177 - val_loss: 0.0029 - val_mae: 0.0417 - 956ms/epoch - 6ms/step\n",
      "Epoch 361/3000\n",
      "\n",
      "Epoch 361: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.8025e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0414 - 972ms/epoch - 6ms/step\n",
      "Epoch 362/3000\n",
      "\n",
      "Epoch 362: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7932e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0410 - 933ms/epoch - 6ms/step\n",
      "Epoch 363/3000\n",
      "\n",
      "Epoch 363: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7890e-04 - mae: 0.0176 - val_loss: 0.0027 - val_mae: 0.0400 - 943ms/epoch - 6ms/step\n",
      "Epoch 364/3000\n",
      "\n",
      "Epoch 364: val_mae did not improve from 0.03990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.7961e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0415 - 912ms/epoch - 6ms/step\n",
      "Epoch 365/3000\n",
      "\n",
      "Epoch 365: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7873e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0408 - 937ms/epoch - 6ms/step\n",
      "Epoch 366/3000\n",
      "\n",
      "Epoch 366: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7831e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0407 - 924ms/epoch - 6ms/step\n",
      "Epoch 367/3000\n",
      "\n",
      "Epoch 367: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7754e-04 - mae: 0.0176 - val_loss: 0.0027 - val_mae: 0.0405 - 929ms/epoch - 6ms/step\n",
      "Epoch 368/3000\n",
      "\n",
      "Epoch 368: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7847e-04 - mae: 0.0176 - val_loss: 0.0030 - val_mae: 0.0421 - 944ms/epoch - 6ms/step\n",
      "Epoch 369/3000\n",
      "\n",
      "Epoch 369: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7889e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0412 - 950ms/epoch - 6ms/step\n",
      "Epoch 370/3000\n",
      "\n",
      "Epoch 370: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7848e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0406 - 968ms/epoch - 6ms/step\n",
      "Epoch 371/3000\n",
      "\n",
      "Epoch 371: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7722e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0411 - 927ms/epoch - 6ms/step\n",
      "Epoch 372/3000\n",
      "\n",
      "Epoch 372: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7801e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0411 - 991ms/epoch - 7ms/step\n",
      "Epoch 373/3000\n",
      "\n",
      "Epoch 373: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7759e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0406 - 967ms/epoch - 6ms/step\n",
      "Epoch 374/3000\n",
      "\n",
      "Epoch 374: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7769e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0407 - 910ms/epoch - 6ms/step\n",
      "Epoch 375/3000\n",
      "\n",
      "Epoch 375: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7662e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0416 - 911ms/epoch - 6ms/step\n",
      "Epoch 376/3000\n",
      "\n",
      "Epoch 376: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7670e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0406 - 933ms/epoch - 6ms/step\n",
      "Epoch 377/3000\n",
      "\n",
      "Epoch 377: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7631e-04 - mae: 0.0176 - val_loss: 0.0029 - val_mae: 0.0417 - 953ms/epoch - 6ms/step\n",
      "Epoch 378/3000\n",
      "\n",
      "Epoch 378: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7581e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0411 - 918ms/epoch - 6ms/step\n",
      "Epoch 379/3000\n",
      "\n",
      "Epoch 379: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7527e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0412 - 931ms/epoch - 6ms/step\n",
      "Epoch 380/3000\n",
      "\n",
      "Epoch 380: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7577e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0408 - 934ms/epoch - 6ms/step\n",
      "Epoch 381/3000\n",
      "\n",
      "Epoch 381: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7636e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0405 - 937ms/epoch - 6ms/step\n",
      "Epoch 382/3000\n",
      "\n",
      "Epoch 382: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7561e-04 - mae: 0.0176 - val_loss: 0.0027 - val_mae: 0.0405 - 950ms/epoch - 6ms/step\n",
      "Epoch 383/3000\n",
      "\n",
      "Epoch 383: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7606e-04 - mae: 0.0176 - val_loss: 0.0027 - val_mae: 0.0404 - 963ms/epoch - 6ms/step\n",
      "Epoch 384/3000\n",
      "\n",
      "Epoch 384: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7422e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0411 - 953ms/epoch - 6ms/step\n",
      "Epoch 385/3000\n",
      "\n",
      "Epoch 385: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7417e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0408 - 934ms/epoch - 6ms/step\n",
      "Epoch 386/3000\n",
      "\n",
      "Epoch 386: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7428e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0412 - 909ms/epoch - 6ms/step\n",
      "Epoch 387/3000\n",
      "\n",
      "Epoch 387: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7356e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0402 - 908ms/epoch - 6ms/step\n",
      "Epoch 388/3000\n",
      "\n",
      "Epoch 388: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7427e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0410 - 909ms/epoch - 6ms/step\n",
      "Epoch 389/3000\n",
      "\n",
      "Epoch 389: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7449e-04 - mae: 0.0176 - val_loss: 0.0028 - val_mae: 0.0413 - 946ms/epoch - 6ms/step\n",
      "Epoch 390/3000\n",
      "\n",
      "Epoch 390: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7302e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0403 - 950ms/epoch - 6ms/step\n",
      "Epoch 391/3000\n",
      "\n",
      "Epoch 391: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7295e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0404 - 916ms/epoch - 6ms/step\n",
      "Epoch 392/3000\n",
      "\n",
      "Epoch 392: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7328e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0414 - 947ms/epoch - 6ms/step\n",
      "Epoch 393/3000\n",
      "\n",
      "Epoch 393: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7351e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0407 - 917ms/epoch - 6ms/step\n",
      "Epoch 394/3000\n",
      "\n",
      "Epoch 394: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7261e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0406 - 947ms/epoch - 6ms/step\n",
      "Epoch 395/3000\n",
      "\n",
      "Epoch 395: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7210e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0418 - 956ms/epoch - 6ms/step\n",
      "Epoch 396/3000\n",
      "\n",
      "Epoch 396: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7121e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0413 - 930ms/epoch - 6ms/step\n",
      "Epoch 397/3000\n",
      "\n",
      "Epoch 397: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7276e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0420 - 934ms/epoch - 6ms/step\n",
      "Epoch 398/3000\n",
      "\n",
      "Epoch 398: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7155e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0411 - 943ms/epoch - 6ms/step\n",
      "Epoch 399/3000\n",
      "\n",
      "Epoch 399: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7107e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0418 - 915ms/epoch - 6ms/step\n",
      "Epoch 400/3000\n",
      "\n",
      "Epoch 400: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7279e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0410 - 926ms/epoch - 6ms/step\n",
      "Epoch 401/3000\n",
      "\n",
      "Epoch 401: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7065e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0405 - 952ms/epoch - 6ms/step\n",
      "Epoch 402/3000\n",
      "\n",
      "Epoch 402: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7165e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0418 - 924ms/epoch - 6ms/step\n",
      "Epoch 403/3000\n",
      "\n",
      "Epoch 403: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7095e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0403 - 936ms/epoch - 6ms/step\n",
      "Epoch 404/3000\n",
      "\n",
      "Epoch 404: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7112e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0412 - 941ms/epoch - 6ms/step\n",
      "Epoch 405/3000\n",
      "\n",
      "Epoch 405: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6991e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0410 - 934ms/epoch - 6ms/step\n",
      "Epoch 406/3000\n",
      "\n",
      "Epoch 406: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7068e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0414 - 958ms/epoch - 6ms/step\n",
      "Epoch 407/3000\n",
      "\n",
      "Epoch 407: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6982e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0409 - 957ms/epoch - 6ms/step\n",
      "Epoch 408/3000\n",
      "\n",
      "Epoch 408: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7143e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0405 - 928ms/epoch - 6ms/step\n",
      "Epoch 409/3000\n",
      "\n",
      "Epoch 409: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6991e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0411 - 943ms/epoch - 6ms/step\n",
      "Epoch 410/3000\n",
      "\n",
      "Epoch 410: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7016e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0401 - 917ms/epoch - 6ms/step\n",
      "Epoch 411/3000\n",
      "\n",
      "Epoch 411: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6871e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0415 - 939ms/epoch - 6ms/step\n",
      "Epoch 412/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 412: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.7019e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0417 - 947ms/epoch - 6ms/step\n",
      "Epoch 413/3000\n",
      "\n",
      "Epoch 413: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6863e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0406 - 930ms/epoch - 6ms/step\n",
      "Epoch 414/3000\n",
      "\n",
      "Epoch 414: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6897e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0409 - 931ms/epoch - 6ms/step\n",
      "Epoch 415/3000\n",
      "\n",
      "Epoch 415: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6948e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0405 - 958ms/epoch - 6ms/step\n",
      "Epoch 416/3000\n",
      "\n",
      "Epoch 416: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6815e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0414 - 924ms/epoch - 6ms/step\n",
      "Epoch 417/3000\n",
      "\n",
      "Epoch 417: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6834e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0418 - 915ms/epoch - 6ms/step\n",
      "Epoch 418/3000\n",
      "\n",
      "Epoch 418: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6868e-04 - mae: 0.0175 - val_loss: 0.0028 - val_mae: 0.0406 - 949ms/epoch - 6ms/step\n",
      "Epoch 419/3000\n",
      "\n",
      "Epoch 419: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6841e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0409 - 945ms/epoch - 6ms/step\n",
      "Epoch 420/3000\n",
      "\n",
      "Epoch 420: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6819e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0408 - 933ms/epoch - 6ms/step\n",
      "Epoch 421/3000\n",
      "\n",
      "Epoch 421: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6861e-04 - mae: 0.0175 - val_loss: 0.0029 - val_mae: 0.0412 - 910ms/epoch - 6ms/step\n",
      "Epoch 422/3000\n",
      "\n",
      "Epoch 422: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6818e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0411 - 919ms/epoch - 6ms/step\n",
      "Epoch 423/3000\n",
      "\n",
      "Epoch 423: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6835e-04 - mae: 0.0175 - val_loss: 0.0027 - val_mae: 0.0400 - 946ms/epoch - 6ms/step\n",
      "Epoch 424/3000\n",
      "\n",
      "Epoch 424: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6792e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0411 - 937ms/epoch - 6ms/step\n",
      "Epoch 425/3000\n",
      "\n",
      "Epoch 425: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6672e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0407 - 924ms/epoch - 6ms/step\n",
      "Epoch 426/3000\n",
      "\n",
      "Epoch 426: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6791e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0410 - 938ms/epoch - 6ms/step\n",
      "Epoch 427/3000\n",
      "\n",
      "Epoch 427: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6667e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 948ms/epoch - 6ms/step\n",
      "Epoch 428/3000\n",
      "\n",
      "Epoch 428: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6599e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 931ms/epoch - 6ms/step\n",
      "Epoch 429/3000\n",
      "\n",
      "Epoch 429: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6647e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 930ms/epoch - 6ms/step\n",
      "Epoch 430/3000\n",
      "\n",
      "Epoch 430: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6684e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0409 - 936ms/epoch - 6ms/step\n",
      "Epoch 431/3000\n",
      "\n",
      "Epoch 431: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6511e-04 - mae: 0.0174 - val_loss: 0.0027 - val_mae: 0.0401 - 907ms/epoch - 6ms/step\n",
      "Epoch 432/3000\n",
      "\n",
      "Epoch 432: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6637e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0405 - 971ms/epoch - 6ms/step\n",
      "Epoch 433/3000\n",
      "\n",
      "Epoch 433: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6518e-04 - mae: 0.0174 - val_loss: 0.0027 - val_mae: 0.0402 - 954ms/epoch - 6ms/step\n",
      "Epoch 434/3000\n",
      "\n",
      "Epoch 434: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6476e-04 - mae: 0.0174 - val_loss: 0.0027 - val_mae: 0.0399 - 918ms/epoch - 6ms/step\n",
      "Epoch 435/3000\n",
      "\n",
      "Epoch 435: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6542e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0412 - 922ms/epoch - 6ms/step\n",
      "Epoch 436/3000\n",
      "\n",
      "Epoch 436: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6576e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 989ms/epoch - 7ms/step\n",
      "Epoch 437/3000\n",
      "\n",
      "Epoch 437: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6493e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0413 - 937ms/epoch - 6ms/step\n",
      "Epoch 438/3000\n",
      "\n",
      "Epoch 438: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6462e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0405 - 936ms/epoch - 6ms/step\n",
      "Epoch 439/3000\n",
      "\n",
      "Epoch 439: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6518e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0410 - 920ms/epoch - 6ms/step\n",
      "Epoch 440/3000\n",
      "\n",
      "Epoch 440: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6611e-04 - mae: 0.0174 - val_loss: 0.0027 - val_mae: 0.0400 - 986ms/epoch - 7ms/step\n",
      "Epoch 441/3000\n",
      "\n",
      "Epoch 441: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6413e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0407 - 947ms/epoch - 6ms/step\n",
      "Epoch 442/3000\n",
      "\n",
      "Epoch 442: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6464e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0417 - 1s/epoch - 7ms/step\n",
      "Epoch 443/3000\n",
      "\n",
      "Epoch 443: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6308e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0411 - 947ms/epoch - 6ms/step\n",
      "Epoch 444/3000\n",
      "\n",
      "Epoch 444: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6496e-04 - mae: 0.0174 - val_loss: 0.0027 - val_mae: 0.0402 - 953ms/epoch - 6ms/step\n",
      "Epoch 445/3000\n",
      "\n",
      "Epoch 445: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6567e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0407 - 979ms/epoch - 6ms/step\n",
      "Epoch 446/3000\n",
      "\n",
      "Epoch 446: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6342e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0408 - 922ms/epoch - 6ms/step\n",
      "Epoch 447/3000\n",
      "\n",
      "Epoch 447: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6363e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0413 - 939ms/epoch - 6ms/step\n",
      "Epoch 448/3000\n",
      "\n",
      "Epoch 448: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6306e-04 - mae: 0.0174 - val_loss: 0.0029 - val_mae: 0.0413 - 954ms/epoch - 6ms/step\n",
      "Epoch 449/3000\n",
      "\n",
      "Epoch 449: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6416e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 909ms/epoch - 6ms/step\n",
      "Epoch 450/3000\n",
      "\n",
      "Epoch 450: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6419e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0406 - 913ms/epoch - 6ms/step\n",
      "Epoch 451/3000\n",
      "\n",
      "Epoch 451: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6275e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0409 - 941ms/epoch - 6ms/step\n",
      "Epoch 452/3000\n",
      "\n",
      "Epoch 452: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6326e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0411 - 953ms/epoch - 6ms/step\n",
      "Epoch 453/3000\n",
      "\n",
      "Epoch 453: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6276e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0411 - 954ms/epoch - 6ms/step\n",
      "Epoch 454/3000\n",
      "\n",
      "Epoch 454: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6132e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0407 - 920ms/epoch - 6ms/step\n",
      "Epoch 455/3000\n",
      "\n",
      "Epoch 455: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6215e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0404 - 925ms/epoch - 6ms/step\n",
      "Epoch 456/3000\n",
      "\n",
      "Epoch 456: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6207e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0407 - 955ms/epoch - 6ms/step\n",
      "Epoch 457/3000\n",
      "\n",
      "Epoch 457: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6139e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0411 - 972ms/epoch - 6ms/step\n",
      "Epoch 458/3000\n",
      "\n",
      "Epoch 458: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6325e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0407 - 957ms/epoch - 6ms/step\n",
      "Epoch 459/3000\n",
      "\n",
      "Epoch 459: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6139e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0408 - 940ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 460/3000\n",
      "\n",
      "Epoch 460: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6245e-04 - mae: 0.0174 - val_loss: 0.0028 - val_mae: 0.0409 - 958ms/epoch - 6ms/step\n",
      "Epoch 461/3000\n",
      "\n",
      "Epoch 461: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6161e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0407 - 953ms/epoch - 6ms/step\n",
      "Epoch 462/3000\n",
      "\n",
      "Epoch 462: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6120e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0412 - 930ms/epoch - 6ms/step\n",
      "Epoch 463/3000\n",
      "\n",
      "Epoch 463: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6123e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0404 - 913ms/epoch - 6ms/step\n",
      "Epoch 464/3000\n",
      "\n",
      "Epoch 464: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6097e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0410 - 912ms/epoch - 6ms/step\n",
      "Epoch 465/3000\n",
      "\n",
      "Epoch 465: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5999e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0409 - 932ms/epoch - 6ms/step\n",
      "Epoch 466/3000\n",
      "\n",
      "Epoch 466: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6108e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0412 - 973ms/epoch - 6ms/step\n",
      "Epoch 467/3000\n",
      "\n",
      "Epoch 467: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6204e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0411 - 941ms/epoch - 6ms/step\n",
      "Epoch 468/3000\n",
      "\n",
      "Epoch 468: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6171e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0404 - 932ms/epoch - 6ms/step\n",
      "Epoch 469/3000\n",
      "\n",
      "Epoch 469: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5920e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0403 - 930ms/epoch - 6ms/step\n",
      "Epoch 470/3000\n",
      "\n",
      "Epoch 470: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6029e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0405 - 966ms/epoch - 6ms/step\n",
      "Epoch 471/3000\n",
      "\n",
      "Epoch 471: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6093e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0406 - 916ms/epoch - 6ms/step\n",
      "Epoch 472/3000\n",
      "\n",
      "Epoch 472: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5928e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0409 - 922ms/epoch - 6ms/step\n",
      "Epoch 473/3000\n",
      "\n",
      "Epoch 473: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5882e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0408 - 913ms/epoch - 6ms/step\n",
      "Epoch 474/3000\n",
      "\n",
      "Epoch 474: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5957e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0408 - 957ms/epoch - 6ms/step\n",
      "Epoch 475/3000\n",
      "\n",
      "Epoch 475: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.6017e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0412 - 948ms/epoch - 6ms/step\n",
      "Epoch 476/3000\n",
      "\n",
      "Epoch 476: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5879e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0400 - 954ms/epoch - 6ms/step\n",
      "Epoch 477/3000\n",
      "\n",
      "Epoch 477: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5951e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0412 - 952ms/epoch - 6ms/step\n",
      "Epoch 478/3000\n",
      "\n",
      "Epoch 478: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5948e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0405 - 938ms/epoch - 6ms/step\n",
      "Epoch 479/3000\n",
      "\n",
      "Epoch 479: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5872e-04 - mae: 0.0173 - val_loss: 0.0030 - val_mae: 0.0420 - 930ms/epoch - 6ms/step\n",
      "Epoch 480/3000\n",
      "\n",
      "Epoch 480: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5783e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0404 - 931ms/epoch - 6ms/step\n",
      "Epoch 481/3000\n",
      "\n",
      "Epoch 481: val_mae did not improve from 0.03990\n",
      "151/151 - 1s - loss: 5.5893e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0403 - 936ms/epoch - 6ms/step\n",
      "Epoch 482/3000\n",
      "\n",
      "Epoch 482: val_mae improved from 0.03990 to 0.03950, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.5854e-04 - mae: 0.0173 - val_loss: 0.0026 - val_mae: 0.0395 - 997ms/epoch - 7ms/step\n",
      "Epoch 483/3000\n",
      "\n",
      "Epoch 483: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5849e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0403 - 952ms/epoch - 6ms/step\n",
      "Epoch 484/3000\n",
      "\n",
      "Epoch 484: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5786e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0401 - 920ms/epoch - 6ms/step\n",
      "Epoch 485/3000\n",
      "\n",
      "Epoch 485: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5792e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0406 - 943ms/epoch - 6ms/step\n",
      "Epoch 486/3000\n",
      "\n",
      "Epoch 486: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5803e-04 - mae: 0.0173 - val_loss: 0.0029 - val_mae: 0.0415 - 932ms/epoch - 6ms/step\n",
      "Epoch 487/3000\n",
      "\n",
      "Epoch 487: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5834e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0408 - 926ms/epoch - 6ms/step\n",
      "Epoch 488/3000\n",
      "\n",
      "Epoch 488: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5737e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0406 - 926ms/epoch - 6ms/step\n",
      "Epoch 489/3000\n",
      "\n",
      "Epoch 489: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5642e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0407 - 928ms/epoch - 6ms/step\n",
      "Epoch 490/3000\n",
      "\n",
      "Epoch 490: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5865e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0408 - 936ms/epoch - 6ms/step\n",
      "Epoch 491/3000\n",
      "\n",
      "Epoch 491: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5709e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0403 - 928ms/epoch - 6ms/step\n",
      "Epoch 492/3000\n",
      "\n",
      "Epoch 492: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5648e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0407 - 942ms/epoch - 6ms/step\n",
      "Epoch 493/3000\n",
      "\n",
      "Epoch 493: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5625e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0408 - 911ms/epoch - 6ms/step\n",
      "Epoch 494/3000\n",
      "\n",
      "Epoch 494: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5692e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0409 - 916ms/epoch - 6ms/step\n",
      "Epoch 495/3000\n",
      "\n",
      "Epoch 495: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5735e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0401 - 954ms/epoch - 6ms/step\n",
      "Epoch 496/3000\n",
      "\n",
      "Epoch 496: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5743e-04 - mae: 0.0173 - val_loss: 0.0027 - val_mae: 0.0402 - 959ms/epoch - 6ms/step\n",
      "Epoch 497/3000\n",
      "\n",
      "Epoch 497: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5591e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 937ms/epoch - 6ms/step\n",
      "Epoch 498/3000\n",
      "\n",
      "Epoch 498: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5687e-04 - mae: 0.0173 - val_loss: 0.0028 - val_mae: 0.0406 - 927ms/epoch - 6ms/step\n",
      "Epoch 499/3000\n",
      "\n",
      "Epoch 499: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5614e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0405 - 914ms/epoch - 6ms/step\n",
      "Epoch 500/3000\n",
      "\n",
      "Epoch 500: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5625e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0409 - 935ms/epoch - 6ms/step\n",
      "Epoch 501/3000\n",
      "\n",
      "Epoch 501: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5624e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0399 - 933ms/epoch - 6ms/step\n",
      "Epoch 502/3000\n",
      "\n",
      "Epoch 502: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5576e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0404 - 908ms/epoch - 6ms/step\n",
      "Epoch 503/3000\n",
      "\n",
      "Epoch 503: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5556e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0405 - 951ms/epoch - 6ms/step\n",
      "Epoch 504/3000\n",
      "\n",
      "Epoch 504: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5613e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 983ms/epoch - 7ms/step\n",
      "Epoch 505/3000\n",
      "\n",
      "Epoch 505: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5658e-04 - mae: 0.0172 - val_loss: 0.0029 - val_mae: 0.0411 - 963ms/epoch - 6ms/step\n",
      "Epoch 506/3000\n",
      "\n",
      "Epoch 506: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5498e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 983ms/epoch - 7ms/step\n",
      "Epoch 507/3000\n",
      "\n",
      "Epoch 507: val_mae did not improve from 0.03950\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.5544e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0403 - 937ms/epoch - 6ms/step\n",
      "Epoch 508/3000\n",
      "\n",
      "Epoch 508: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5573e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0404 - 981ms/epoch - 6ms/step\n",
      "Epoch 509/3000\n",
      "\n",
      "Epoch 509: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5484e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 934ms/epoch - 6ms/step\n",
      "Epoch 510/3000\n",
      "\n",
      "Epoch 510: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5362e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 927ms/epoch - 6ms/step\n",
      "Epoch 511/3000\n",
      "\n",
      "Epoch 511: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5472e-04 - mae: 0.0172 - val_loss: 0.0029 - val_mae: 0.0411 - 950ms/epoch - 6ms/step\n",
      "Epoch 512/3000\n",
      "\n",
      "Epoch 512: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5442e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 927ms/epoch - 6ms/step\n",
      "Epoch 513/3000\n",
      "\n",
      "Epoch 513: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5443e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0401 - 903ms/epoch - 6ms/step\n",
      "Epoch 514/3000\n",
      "\n",
      "Epoch 514: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5500e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0408 - 926ms/epoch - 6ms/step\n",
      "Epoch 515/3000\n",
      "\n",
      "Epoch 515: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5405e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0410 - 967ms/epoch - 6ms/step\n",
      "Epoch 516/3000\n",
      "\n",
      "Epoch 516: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5482e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0409 - 949ms/epoch - 6ms/step\n",
      "Epoch 517/3000\n",
      "\n",
      "Epoch 517: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5447e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 932ms/epoch - 6ms/step\n",
      "Epoch 518/3000\n",
      "\n",
      "Epoch 518: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5395e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0397 - 919ms/epoch - 6ms/step\n",
      "Epoch 519/3000\n",
      "\n",
      "Epoch 519: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5441e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 929ms/epoch - 6ms/step\n",
      "Epoch 520/3000\n",
      "\n",
      "Epoch 520: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5285e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0399 - 934ms/epoch - 6ms/step\n",
      "Epoch 521/3000\n",
      "\n",
      "Epoch 521: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5421e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0396 - 966ms/epoch - 6ms/step\n",
      "Epoch 522/3000\n",
      "\n",
      "Epoch 522: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5386e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 940ms/epoch - 6ms/step\n",
      "Epoch 523/3000\n",
      "\n",
      "Epoch 523: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5284e-04 - mae: 0.0172 - val_loss: 0.0029 - val_mae: 0.0416 - 952ms/epoch - 6ms/step\n",
      "Epoch 524/3000\n",
      "\n",
      "Epoch 524: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5373e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0410 - 933ms/epoch - 6ms/step\n",
      "Epoch 525/3000\n",
      "\n",
      "Epoch 525: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5333e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0405 - 967ms/epoch - 6ms/step\n",
      "Epoch 526/3000\n",
      "\n",
      "Epoch 526: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5286e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0407 - 928ms/epoch - 6ms/step\n",
      "Epoch 527/3000\n",
      "\n",
      "Epoch 527: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5360e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0403 - 944ms/epoch - 6ms/step\n",
      "Epoch 528/3000\n",
      "\n",
      "Epoch 528: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5260e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0401 - 951ms/epoch - 6ms/step\n",
      "Epoch 529/3000\n",
      "\n",
      "Epoch 529: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5298e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0401 - 942ms/epoch - 6ms/step\n",
      "Epoch 530/3000\n",
      "\n",
      "Epoch 530: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5297e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 948ms/epoch - 6ms/step\n",
      "Epoch 531/3000\n",
      "\n",
      "Epoch 531: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5237e-04 - mae: 0.0172 - val_loss: 0.0029 - val_mae: 0.0412 - 968ms/epoch - 6ms/step\n",
      "Epoch 532/3000\n",
      "\n",
      "Epoch 532: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5360e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0396 - 932ms/epoch - 6ms/step\n",
      "Epoch 533/3000\n",
      "\n",
      "Epoch 533: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5280e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0398 - 930ms/epoch - 6ms/step\n",
      "Epoch 534/3000\n",
      "\n",
      "Epoch 534: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5313e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0403 - 949ms/epoch - 6ms/step\n",
      "Epoch 535/3000\n",
      "\n",
      "Epoch 535: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5179e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 929ms/epoch - 6ms/step\n",
      "Epoch 536/3000\n",
      "\n",
      "Epoch 536: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5144e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0395 - 935ms/epoch - 6ms/step\n",
      "Epoch 537/3000\n",
      "\n",
      "Epoch 537: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5255e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0401 - 923ms/epoch - 6ms/step\n",
      "Epoch 538/3000\n",
      "\n",
      "Epoch 538: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5181e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 936ms/epoch - 6ms/step\n",
      "Epoch 539/3000\n",
      "\n",
      "Epoch 539: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5108e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 947ms/epoch - 6ms/step\n",
      "Epoch 540/3000\n",
      "\n",
      "Epoch 540: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5203e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0399 - 933ms/epoch - 6ms/step\n",
      "Epoch 541/3000\n",
      "\n",
      "Epoch 541: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5201e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0405 - 933ms/epoch - 6ms/step\n",
      "Epoch 542/3000\n",
      "\n",
      "Epoch 542: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5172e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 965ms/epoch - 6ms/step\n",
      "Epoch 543/3000\n",
      "\n",
      "Epoch 543: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5206e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 911ms/epoch - 6ms/step\n",
      "Epoch 544/3000\n",
      "\n",
      "Epoch 544: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5107e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0406 - 921ms/epoch - 6ms/step\n",
      "Epoch 545/3000\n",
      "\n",
      "Epoch 545: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5053e-04 - mae: 0.0172 - val_loss: 0.0029 - val_mae: 0.0413 - 937ms/epoch - 6ms/step\n",
      "Epoch 546/3000\n",
      "\n",
      "Epoch 546: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5124e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0402 - 956ms/epoch - 6ms/step\n",
      "Epoch 547/3000\n",
      "\n",
      "Epoch 547: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5108e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0397 - 939ms/epoch - 6ms/step\n",
      "Epoch 548/3000\n",
      "\n",
      "Epoch 548: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5102e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 934ms/epoch - 6ms/step\n",
      "Epoch 549/3000\n",
      "\n",
      "Epoch 549: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5064e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0397 - 920ms/epoch - 6ms/step\n",
      "Epoch 550/3000\n",
      "\n",
      "Epoch 550: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5024e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0398 - 930ms/epoch - 6ms/step\n",
      "Epoch 551/3000\n",
      "\n",
      "Epoch 551: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5064e-04 - mae: 0.0172 - val_loss: 0.0028 - val_mae: 0.0405 - 932ms/epoch - 6ms/step\n",
      "Epoch 552/3000\n",
      "\n",
      "Epoch 552: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4976e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0407 - 940ms/epoch - 6ms/step\n",
      "Epoch 553/3000\n",
      "\n",
      "Epoch 553: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5035e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 925ms/epoch - 6ms/step\n",
      "Epoch 554/3000\n",
      "\n",
      "Epoch 554: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5016e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0397 - 964ms/epoch - 6ms/step\n",
      "Epoch 555/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 555: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5075e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0396 - 932ms/epoch - 6ms/step\n",
      "Epoch 556/3000\n",
      "\n",
      "Epoch 556: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5090e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0400 - 921ms/epoch - 6ms/step\n",
      "Epoch 557/3000\n",
      "\n",
      "Epoch 557: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4943e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0407 - 928ms/epoch - 6ms/step\n",
      "Epoch 558/3000\n",
      "\n",
      "Epoch 558: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4946e-04 - mae: 0.0171 - val_loss: 0.0029 - val_mae: 0.0416 - 935ms/epoch - 6ms/step\n",
      "Epoch 559/3000\n",
      "\n",
      "Epoch 559: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.5057e-04 - mae: 0.0172 - val_loss: 0.0027 - val_mae: 0.0399 - 957ms/epoch - 6ms/step\n",
      "Epoch 560/3000\n",
      "\n",
      "Epoch 560: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4960e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0403 - 914ms/epoch - 6ms/step\n",
      "Epoch 561/3000\n",
      "\n",
      "Epoch 561: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4831e-04 - mae: 0.0171 - val_loss: 0.0029 - val_mae: 0.0412 - 922ms/epoch - 6ms/step\n",
      "Epoch 562/3000\n",
      "\n",
      "Epoch 562: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4815e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0395 - 905ms/epoch - 6ms/step\n",
      "Epoch 563/3000\n",
      "\n",
      "Epoch 563: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4983e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 914ms/epoch - 6ms/step\n",
      "Epoch 564/3000\n",
      "\n",
      "Epoch 564: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4892e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0403 - 933ms/epoch - 6ms/step\n",
      "Epoch 565/3000\n",
      "\n",
      "Epoch 565: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4884e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0397 - 918ms/epoch - 6ms/step\n",
      "Epoch 566/3000\n",
      "\n",
      "Epoch 566: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4924e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0405 - 937ms/epoch - 6ms/step\n",
      "Epoch 567/3000\n",
      "\n",
      "Epoch 567: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4889e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0402 - 964ms/epoch - 6ms/step\n",
      "Epoch 568/3000\n",
      "\n",
      "Epoch 568: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4871e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0406 - 935ms/epoch - 6ms/step\n",
      "Epoch 569/3000\n",
      "\n",
      "Epoch 569: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4859e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0407 - 906ms/epoch - 6ms/step\n",
      "Epoch 570/3000\n",
      "\n",
      "Epoch 570: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4797e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0398 - 926ms/epoch - 6ms/step\n",
      "Epoch 571/3000\n",
      "\n",
      "Epoch 571: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4841e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0399 - 916ms/epoch - 6ms/step\n",
      "Epoch 572/3000\n",
      "\n",
      "Epoch 572: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4794e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0399 - 923ms/epoch - 6ms/step\n",
      "Epoch 573/3000\n",
      "\n",
      "Epoch 573: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4835e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0395 - 921ms/epoch - 6ms/step\n",
      "Epoch 574/3000\n",
      "\n",
      "Epoch 574: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4841e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0397 - 913ms/epoch - 6ms/step\n",
      "Epoch 575/3000\n",
      "\n",
      "Epoch 575: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4784e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0399 - 924ms/epoch - 6ms/step\n",
      "Epoch 576/3000\n",
      "\n",
      "Epoch 576: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4721e-04 - mae: 0.0171 - val_loss: 0.0029 - val_mae: 0.0410 - 946ms/epoch - 6ms/step\n",
      "Epoch 577/3000\n",
      "\n",
      "Epoch 577: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4809e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0404 - 944ms/epoch - 6ms/step\n",
      "Epoch 578/3000\n",
      "\n",
      "Epoch 578: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4776e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 928ms/epoch - 6ms/step\n",
      "Epoch 579/3000\n",
      "\n",
      "Epoch 579: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4687e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0401 - 955ms/epoch - 6ms/step\n",
      "Epoch 580/3000\n",
      "\n",
      "Epoch 580: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4745e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0397 - 915ms/epoch - 6ms/step\n",
      "Epoch 581/3000\n",
      "\n",
      "Epoch 581: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4735e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0403 - 944ms/epoch - 6ms/step\n",
      "Epoch 582/3000\n",
      "\n",
      "Epoch 582: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4681e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0407 - 941ms/epoch - 6ms/step\n",
      "Epoch 583/3000\n",
      "\n",
      "Epoch 583: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4732e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 932ms/epoch - 6ms/step\n",
      "Epoch 584/3000\n",
      "\n",
      "Epoch 584: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4614e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0404 - 926ms/epoch - 6ms/step\n",
      "Epoch 585/3000\n",
      "\n",
      "Epoch 585: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4718e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 913ms/epoch - 6ms/step\n",
      "Epoch 586/3000\n",
      "\n",
      "Epoch 586: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4812e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0404 - 917ms/epoch - 6ms/step\n",
      "Epoch 587/3000\n",
      "\n",
      "Epoch 587: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4646e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0405 - 905ms/epoch - 6ms/step\n",
      "Epoch 588/3000\n",
      "\n",
      "Epoch 588: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4685e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0398 - 951ms/epoch - 6ms/step\n",
      "Epoch 589/3000\n",
      "\n",
      "Epoch 589: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4565e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0402 - 928ms/epoch - 6ms/step\n",
      "Epoch 590/3000\n",
      "\n",
      "Epoch 590: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4657e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0401 - 926ms/epoch - 6ms/step\n",
      "Epoch 591/3000\n",
      "\n",
      "Epoch 591: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4669e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 934ms/epoch - 6ms/step\n",
      "Epoch 592/3000\n",
      "\n",
      "Epoch 592: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4573e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0408 - 920ms/epoch - 6ms/step\n",
      "Epoch 593/3000\n",
      "\n",
      "Epoch 593: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4655e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0399 - 946ms/epoch - 6ms/step\n",
      "Epoch 594/3000\n",
      "\n",
      "Epoch 594: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4549e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0402 - 917ms/epoch - 6ms/step\n",
      "Epoch 595/3000\n",
      "\n",
      "Epoch 595: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4670e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0402 - 923ms/epoch - 6ms/step\n",
      "Epoch 596/3000\n",
      "\n",
      "Epoch 596: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4602e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0398 - 918ms/epoch - 6ms/step\n",
      "Epoch 597/3000\n",
      "\n",
      "Epoch 597: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4504e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 905ms/epoch - 6ms/step\n",
      "Epoch 598/3000\n",
      "\n",
      "Epoch 598: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4669e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0397 - 943ms/epoch - 6ms/step\n",
      "Epoch 599/3000\n",
      "\n",
      "Epoch 599: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4582e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0403 - 972ms/epoch - 6ms/step\n",
      "Epoch 600/3000\n",
      "\n",
      "Epoch 600: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4523e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 949ms/epoch - 6ms/step\n",
      "Epoch 601/3000\n",
      "\n",
      "Epoch 601: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4385e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0405 - 955ms/epoch - 6ms/step\n",
      "Epoch 602/3000\n",
      "\n",
      "Epoch 602: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4551e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 935ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 603/3000\n",
      "\n",
      "Epoch 603: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4541e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0403 - 917ms/epoch - 6ms/step\n",
      "Epoch 604/3000\n",
      "\n",
      "Epoch 604: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4561e-04 - mae: 0.0171 - val_loss: 0.0028 - val_mae: 0.0408 - 939ms/epoch - 6ms/step\n",
      "Epoch 605/3000\n",
      "\n",
      "Epoch 605: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4571e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0396 - 918ms/epoch - 6ms/step\n",
      "Epoch 606/3000\n",
      "\n",
      "Epoch 606: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4499e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0396 - 919ms/epoch - 6ms/step\n",
      "Epoch 607/3000\n",
      "\n",
      "Epoch 607: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4441e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0404 - 937ms/epoch - 6ms/step\n",
      "Epoch 608/3000\n",
      "\n",
      "Epoch 608: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4410e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 953ms/epoch - 6ms/step\n",
      "Epoch 609/3000\n",
      "\n",
      "Epoch 609: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4448e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 920ms/epoch - 6ms/step\n",
      "Epoch 610/3000\n",
      "\n",
      "Epoch 610: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4442e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 933ms/epoch - 6ms/step\n",
      "Epoch 611/3000\n",
      "\n",
      "Epoch 611: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4490e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0400 - 959ms/epoch - 6ms/step\n",
      "Epoch 612/3000\n",
      "\n",
      "Epoch 612: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4486e-04 - mae: 0.0171 - val_loss: 0.0027 - val_mae: 0.0401 - 914ms/epoch - 6ms/step\n",
      "Epoch 613/3000\n",
      "\n",
      "Epoch 613: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4377e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0405 - 924ms/epoch - 6ms/step\n",
      "Epoch 614/3000\n",
      "\n",
      "Epoch 614: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4334e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 928ms/epoch - 6ms/step\n",
      "Epoch 615/3000\n",
      "\n",
      "Epoch 615: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4303e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 922ms/epoch - 6ms/step\n",
      "Epoch 616/3000\n",
      "\n",
      "Epoch 616: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4358e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0402 - 933ms/epoch - 6ms/step\n",
      "Epoch 617/3000\n",
      "\n",
      "Epoch 617: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4351e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0402 - 955ms/epoch - 6ms/step\n",
      "Epoch 618/3000\n",
      "\n",
      "Epoch 618: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4391e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0395 - 935ms/epoch - 6ms/step\n",
      "Epoch 619/3000\n",
      "\n",
      "Epoch 619: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4306e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 954ms/epoch - 6ms/step\n",
      "Epoch 620/3000\n",
      "\n",
      "Epoch 620: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4402e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0407 - 974ms/epoch - 6ms/step\n",
      "Epoch 621/3000\n",
      "\n",
      "Epoch 621: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4480e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0406 - 929ms/epoch - 6ms/step\n",
      "Epoch 622/3000\n",
      "\n",
      "Epoch 622: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4234e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 918ms/epoch - 6ms/step\n",
      "Epoch 623/3000\n",
      "\n",
      "Epoch 623: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4294e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 976ms/epoch - 6ms/step\n",
      "Epoch 624/3000\n",
      "\n",
      "Epoch 624: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4299e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0395 - 942ms/epoch - 6ms/step\n",
      "Epoch 625/3000\n",
      "\n",
      "Epoch 625: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4385e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 935ms/epoch - 6ms/step\n",
      "Epoch 626/3000\n",
      "\n",
      "Epoch 626: val_mae did not improve from 0.03950\n",
      "151/151 - 1s - loss: 5.4229e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0395 - 927ms/epoch - 6ms/step\n",
      "Epoch 627/3000\n",
      "\n",
      "Epoch 627: val_mae improved from 0.03950 to 0.03908, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.4346e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0391 - 989ms/epoch - 7ms/step\n",
      "Epoch 628/3000\n",
      "\n",
      "Epoch 628: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4357e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0394 - 943ms/epoch - 6ms/step\n",
      "Epoch 629/3000\n",
      "\n",
      "Epoch 629: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4268e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0392 - 917ms/epoch - 6ms/step\n",
      "Epoch 630/3000\n",
      "\n",
      "Epoch 630: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4161e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 929ms/epoch - 6ms/step\n",
      "Epoch 631/3000\n",
      "\n",
      "Epoch 631: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4298e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 934ms/epoch - 6ms/step\n",
      "Epoch 632/3000\n",
      "\n",
      "Epoch 632: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4182e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 932ms/epoch - 6ms/step\n",
      "Epoch 633/3000\n",
      "\n",
      "Epoch 633: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4204e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 925ms/epoch - 6ms/step\n",
      "Epoch 634/3000\n",
      "\n",
      "Epoch 634: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4250e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 931ms/epoch - 6ms/step\n",
      "Epoch 635/3000\n",
      "\n",
      "Epoch 635: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4266e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0398 - 943ms/epoch - 6ms/step\n",
      "Epoch 636/3000\n",
      "\n",
      "Epoch 636: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4286e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0401 - 946ms/epoch - 6ms/step\n",
      "Epoch 637/3000\n",
      "\n",
      "Epoch 637: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4205e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0405 - 918ms/epoch - 6ms/step\n",
      "Epoch 638/3000\n",
      "\n",
      "Epoch 638: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4173e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 957ms/epoch - 6ms/step\n",
      "Epoch 639/3000\n",
      "\n",
      "Epoch 639: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4261e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 926ms/epoch - 6ms/step\n",
      "Epoch 640/3000\n",
      "\n",
      "Epoch 640: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4278e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0392 - 929ms/epoch - 6ms/step\n",
      "Epoch 641/3000\n",
      "\n",
      "Epoch 641: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4176e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 924ms/epoch - 6ms/step\n",
      "Epoch 642/3000\n",
      "\n",
      "Epoch 642: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4091e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0402 - 937ms/epoch - 6ms/step\n",
      "Epoch 643/3000\n",
      "\n",
      "Epoch 643: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4263e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0395 - 932ms/epoch - 6ms/step\n",
      "Epoch 644/3000\n",
      "\n",
      "Epoch 644: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4180e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 938ms/epoch - 6ms/step\n",
      "Epoch 645/3000\n",
      "\n",
      "Epoch 645: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4107e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 930ms/epoch - 6ms/step\n",
      "Epoch 646/3000\n",
      "\n",
      "Epoch 646: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4098e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 953ms/epoch - 6ms/step\n",
      "Epoch 647/3000\n",
      "\n",
      "Epoch 647: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4180e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 951ms/epoch - 6ms/step\n",
      "Epoch 648/3000\n",
      "\n",
      "Epoch 648: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4189e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0405 - 977ms/epoch - 6ms/step\n",
      "Epoch 649/3000\n",
      "\n",
      "Epoch 649: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4093e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 950ms/epoch - 6ms/step\n",
      "Epoch 650/3000\n",
      "\n",
      "Epoch 650: val_mae did not improve from 0.03908\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.4092e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0401 - 937ms/epoch - 6ms/step\n",
      "Epoch 651/3000\n",
      "\n",
      "Epoch 651: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4025e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 944ms/epoch - 6ms/step\n",
      "Epoch 652/3000\n",
      "\n",
      "Epoch 652: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4146e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 970ms/epoch - 6ms/step\n",
      "Epoch 653/3000\n",
      "\n",
      "Epoch 653: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4051e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 945ms/epoch - 6ms/step\n",
      "Epoch 654/3000\n",
      "\n",
      "Epoch 654: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4052e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 939ms/epoch - 6ms/step\n",
      "Epoch 655/3000\n",
      "\n",
      "Epoch 655: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4003e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0401 - 940ms/epoch - 6ms/step\n",
      "Epoch 656/3000\n",
      "\n",
      "Epoch 656: val_mae did not improve from 0.03908\n",
      "151/151 - 1s - loss: 5.4002e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0398 - 935ms/epoch - 6ms/step\n",
      "Epoch 657/3000\n",
      "\n",
      "Epoch 657: val_mae improved from 0.03908 to 0.03905, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.4071e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0391 - 989ms/epoch - 7ms/step\n",
      "Epoch 658/3000\n",
      "\n",
      "Epoch 658: val_mae did not improve from 0.03905\n",
      "151/151 - 1s - loss: 5.4049e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 927ms/epoch - 6ms/step\n",
      "Epoch 659/3000\n",
      "\n",
      "Epoch 659: val_mae did not improve from 0.03905\n",
      "151/151 - 1s - loss: 5.4082e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0394 - 919ms/epoch - 6ms/step\n",
      "Epoch 660/3000\n",
      "\n",
      "Epoch 660: val_mae did not improve from 0.03905\n",
      "151/151 - 1s - loss: 5.3971e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0395 - 957ms/epoch - 6ms/step\n",
      "Epoch 661/3000\n",
      "\n",
      "Epoch 661: val_mae did not improve from 0.03905\n",
      "151/151 - 1s - loss: 5.4028e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0393 - 981ms/epoch - 6ms/step\n",
      "Epoch 662/3000\n",
      "\n",
      "Epoch 662: val_mae did not improve from 0.03905\n",
      "151/151 - 1s - loss: 5.4034e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0404 - 930ms/epoch - 6ms/step\n",
      "Epoch 663/3000\n",
      "\n",
      "Epoch 663: val_mae improved from 0.03905 to 0.03898, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.4029e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0390 - 946ms/epoch - 6ms/step\n",
      "Epoch 664/3000\n",
      "\n",
      "Epoch 664: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.4065e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0394 - 904ms/epoch - 6ms/step\n",
      "Epoch 665/3000\n",
      "\n",
      "Epoch 665: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.4041e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0397 - 931ms/epoch - 6ms/step\n",
      "Epoch 666/3000\n",
      "\n",
      "Epoch 666: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3859e-04 - mae: 0.0170 - val_loss: 0.0028 - val_mae: 0.0402 - 947ms/epoch - 6ms/step\n",
      "Epoch 667/3000\n",
      "\n",
      "Epoch 667: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3951e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0400 - 947ms/epoch - 6ms/step\n",
      "Epoch 668/3000\n",
      "\n",
      "Epoch 668: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3985e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0398 - 935ms/epoch - 6ms/step\n",
      "Epoch 669/3000\n",
      "\n",
      "Epoch 669: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3880e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 920ms/epoch - 6ms/step\n",
      "Epoch 670/3000\n",
      "\n",
      "Epoch 670: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3948e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0399 - 979ms/epoch - 6ms/step\n",
      "Epoch 671/3000\n",
      "\n",
      "Epoch 671: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3860e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0396 - 921ms/epoch - 6ms/step\n",
      "Epoch 672/3000\n",
      "\n",
      "Epoch 672: val_mae did not improve from 0.03898\n",
      "151/151 - 1s - loss: 5.3853e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 938ms/epoch - 6ms/step\n",
      "Epoch 673/3000\n",
      "\n",
      "Epoch 673: val_mae improved from 0.03898 to 0.03883, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.3834e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0388 - 964ms/epoch - 6ms/step\n",
      "Epoch 674/3000\n",
      "\n",
      "Epoch 674: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3887e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0400 - 964ms/epoch - 6ms/step\n",
      "Epoch 675/3000\n",
      "\n",
      "Epoch 675: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3832e-04 - mae: 0.0169 - val_loss: 0.0028 - val_mae: 0.0404 - 912ms/epoch - 6ms/step\n",
      "Epoch 676/3000\n",
      "\n",
      "Epoch 676: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3917e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0389 - 947ms/epoch - 6ms/step\n",
      "Epoch 677/3000\n",
      "\n",
      "Epoch 677: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3844e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0398 - 932ms/epoch - 6ms/step\n",
      "Epoch 678/3000\n",
      "\n",
      "Epoch 678: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3840e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 932ms/epoch - 6ms/step\n",
      "Epoch 679/3000\n",
      "\n",
      "Epoch 679: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3866e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 943ms/epoch - 6ms/step\n",
      "Epoch 680/3000\n",
      "\n",
      "Epoch 680: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3871e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0398 - 912ms/epoch - 6ms/step\n",
      "Epoch 681/3000\n",
      "\n",
      "Epoch 681: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3889e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 933ms/epoch - 6ms/step\n",
      "Epoch 682/3000\n",
      "\n",
      "Epoch 682: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3843e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 943ms/epoch - 6ms/step\n",
      "Epoch 683/3000\n",
      "\n",
      "Epoch 683: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3888e-04 - mae: 0.0170 - val_loss: 0.0026 - val_mae: 0.0394 - 947ms/epoch - 6ms/step\n",
      "Epoch 684/3000\n",
      "\n",
      "Epoch 684: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3848e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 931ms/epoch - 6ms/step\n",
      "Epoch 685/3000\n",
      "\n",
      "Epoch 685: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3750e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0396 - 924ms/epoch - 6ms/step\n",
      "Epoch 686/3000\n",
      "\n",
      "Epoch 686: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3776e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0390 - 939ms/epoch - 6ms/step\n",
      "Epoch 687/3000\n",
      "\n",
      "Epoch 687: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3923e-04 - mae: 0.0170 - val_loss: 0.0027 - val_mae: 0.0398 - 931ms/epoch - 6ms/step\n",
      "Epoch 688/3000\n",
      "\n",
      "Epoch 688: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3822e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0396 - 942ms/epoch - 6ms/step\n",
      "Epoch 689/3000\n",
      "\n",
      "Epoch 689: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3815e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0400 - 954ms/epoch - 6ms/step\n",
      "Epoch 690/3000\n",
      "\n",
      "Epoch 690: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3737e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 913ms/epoch - 6ms/step\n",
      "Epoch 691/3000\n",
      "\n",
      "Epoch 691: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3815e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 952ms/epoch - 6ms/step\n",
      "Epoch 692/3000\n",
      "\n",
      "Epoch 692: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3871e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0399 - 926ms/epoch - 6ms/step\n",
      "Epoch 693/3000\n",
      "\n",
      "Epoch 693: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3754e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 939ms/epoch - 6ms/step\n",
      "Epoch 694/3000\n",
      "\n",
      "Epoch 694: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3760e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 918ms/epoch - 6ms/step\n",
      "Epoch 695/3000\n",
      "\n",
      "Epoch 695: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3722e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0398 - 971ms/epoch - 6ms/step\n",
      "Epoch 696/3000\n",
      "\n",
      "Epoch 696: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3817e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 939ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 697/3000\n",
      "\n",
      "Epoch 697: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3736e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0396 - 935ms/epoch - 6ms/step\n",
      "Epoch 698/3000\n",
      "\n",
      "Epoch 698: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3778e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 911ms/epoch - 6ms/step\n",
      "Epoch 699/3000\n",
      "\n",
      "Epoch 699: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3716e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 927ms/epoch - 6ms/step\n",
      "Epoch 700/3000\n",
      "\n",
      "Epoch 700: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3726e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0394 - 935ms/epoch - 6ms/step\n",
      "Epoch 701/3000\n",
      "\n",
      "Epoch 701: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3703e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 952ms/epoch - 6ms/step\n",
      "Epoch 702/3000\n",
      "\n",
      "Epoch 702: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3787e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 932ms/epoch - 6ms/step\n",
      "Epoch 703/3000\n",
      "\n",
      "Epoch 703: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3761e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 935ms/epoch - 6ms/step\n",
      "Epoch 704/3000\n",
      "\n",
      "Epoch 704: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3624e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 941ms/epoch - 6ms/step\n",
      "Epoch 705/3000\n",
      "\n",
      "Epoch 705: val_mae did not improve from 0.03883\n",
      "151/151 - 1s - loss: 5.3738e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 922ms/epoch - 6ms/step\n",
      "Epoch 706/3000\n",
      "\n",
      "Epoch 706: val_mae improved from 0.03883 to 0.03871, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.3699e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0387 - 963ms/epoch - 6ms/step\n",
      "Epoch 707/3000\n",
      "\n",
      "Epoch 707: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3673e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 915ms/epoch - 6ms/step\n",
      "Epoch 708/3000\n",
      "\n",
      "Epoch 708: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3662e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 941ms/epoch - 6ms/step\n",
      "Epoch 709/3000\n",
      "\n",
      "Epoch 709: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3670e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 937ms/epoch - 6ms/step\n",
      "Epoch 710/3000\n",
      "\n",
      "Epoch 710: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3703e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0388 - 922ms/epoch - 6ms/step\n",
      "Epoch 711/3000\n",
      "\n",
      "Epoch 711: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3666e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 919ms/epoch - 6ms/step\n",
      "Epoch 712/3000\n",
      "\n",
      "Epoch 712: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3721e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 941ms/epoch - 6ms/step\n",
      "Epoch 713/3000\n",
      "\n",
      "Epoch 713: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3641e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 937ms/epoch - 6ms/step\n",
      "Epoch 714/3000\n",
      "\n",
      "Epoch 714: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3560e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 966ms/epoch - 6ms/step\n",
      "Epoch 715/3000\n",
      "\n",
      "Epoch 715: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3583e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 948ms/epoch - 6ms/step\n",
      "Epoch 716/3000\n",
      "\n",
      "Epoch 716: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3603e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 965ms/epoch - 6ms/step\n",
      "Epoch 717/3000\n",
      "\n",
      "Epoch 717: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3552e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0396 - 934ms/epoch - 6ms/step\n",
      "Epoch 718/3000\n",
      "\n",
      "Epoch 718: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3557e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 941ms/epoch - 6ms/step\n",
      "Epoch 719/3000\n",
      "\n",
      "Epoch 719: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3552e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 944ms/epoch - 6ms/step\n",
      "Epoch 720/3000\n",
      "\n",
      "Epoch 720: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3600e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 959ms/epoch - 6ms/step\n",
      "Epoch 721/3000\n",
      "\n",
      "Epoch 721: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3662e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 968ms/epoch - 6ms/step\n",
      "Epoch 722/3000\n",
      "\n",
      "Epoch 722: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3534e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0390 - 929ms/epoch - 6ms/step\n",
      "Epoch 723/3000\n",
      "\n",
      "Epoch 723: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3565e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 916ms/epoch - 6ms/step\n",
      "Epoch 724/3000\n",
      "\n",
      "Epoch 724: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3519e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 924ms/epoch - 6ms/step\n",
      "Epoch 725/3000\n",
      "\n",
      "Epoch 725: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3555e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0389 - 949ms/epoch - 6ms/step\n",
      "Epoch 726/3000\n",
      "\n",
      "Epoch 726: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3552e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 940ms/epoch - 6ms/step\n",
      "Epoch 727/3000\n",
      "\n",
      "Epoch 727: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3529e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0396 - 927ms/epoch - 6ms/step\n",
      "Epoch 728/3000\n",
      "\n",
      "Epoch 728: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3602e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0394 - 922ms/epoch - 6ms/step\n",
      "Epoch 729/3000\n",
      "\n",
      "Epoch 729: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3605e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0388 - 956ms/epoch - 6ms/step\n",
      "Epoch 730/3000\n",
      "\n",
      "Epoch 730: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3562e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0389 - 929ms/epoch - 6ms/step\n",
      "Epoch 731/3000\n",
      "\n",
      "Epoch 731: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3550e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0393 - 919ms/epoch - 6ms/step\n",
      "Epoch 732/3000\n",
      "\n",
      "Epoch 732: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3486e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 930ms/epoch - 6ms/step\n",
      "Epoch 733/3000\n",
      "\n",
      "Epoch 733: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3554e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 920ms/epoch - 6ms/step\n",
      "Epoch 734/3000\n",
      "\n",
      "Epoch 734: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3415e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 949ms/epoch - 6ms/step\n",
      "Epoch 735/3000\n",
      "\n",
      "Epoch 735: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3496e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 938ms/epoch - 6ms/step\n",
      "Epoch 736/3000\n",
      "\n",
      "Epoch 736: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3527e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0397 - 948ms/epoch - 6ms/step\n",
      "Epoch 737/3000\n",
      "\n",
      "Epoch 737: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3486e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 939ms/epoch - 6ms/step\n",
      "Epoch 738/3000\n",
      "\n",
      "Epoch 738: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3339e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 937ms/epoch - 6ms/step\n",
      "Epoch 739/3000\n",
      "\n",
      "Epoch 739: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3512e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 986ms/epoch - 7ms/step\n",
      "Epoch 740/3000\n",
      "\n",
      "Epoch 740: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3516e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0398 - 910ms/epoch - 6ms/step\n",
      "Epoch 741/3000\n",
      "\n",
      "Epoch 741: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3485e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 923ms/epoch - 6ms/step\n",
      "Epoch 742/3000\n",
      "\n",
      "Epoch 742: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3423e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0395 - 946ms/epoch - 6ms/step\n",
      "Epoch 743/3000\n",
      "\n",
      "Epoch 743: val_mae did not improve from 0.03871\n",
      "151/151 - 1s - loss: 5.3326e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 923ms/epoch - 6ms/step\n",
      "Epoch 744/3000\n",
      "\n",
      "Epoch 744: val_mae improved from 0.03871 to 0.03866, saving model to saved_models/cnn_gate_pump_nofuture.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.3442e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0387 - 970ms/epoch - 6ms/step\n",
      "Epoch 745/3000\n",
      "\n",
      "Epoch 745: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3504e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0387 - 916ms/epoch - 6ms/step\n",
      "Epoch 746/3000\n",
      "\n",
      "Epoch 746: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3351e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0390 - 925ms/epoch - 6ms/step\n",
      "Epoch 747/3000\n",
      "\n",
      "Epoch 747: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3373e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 919ms/epoch - 6ms/step\n",
      "Epoch 748/3000\n",
      "\n",
      "Epoch 748: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3355e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 924ms/epoch - 6ms/step\n",
      "Epoch 749/3000\n",
      "\n",
      "Epoch 749: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3381e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 912ms/epoch - 6ms/step\n",
      "Epoch 750/3000\n",
      "\n",
      "Epoch 750: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3340e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 937ms/epoch - 6ms/step\n",
      "Epoch 751/3000\n",
      "\n",
      "Epoch 751: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3336e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 924ms/epoch - 6ms/step\n",
      "Epoch 752/3000\n",
      "\n",
      "Epoch 752: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3371e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 904ms/epoch - 6ms/step\n",
      "Epoch 753/3000\n",
      "\n",
      "Epoch 753: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3361e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 927ms/epoch - 6ms/step\n",
      "Epoch 754/3000\n",
      "\n",
      "Epoch 754: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3296e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 918ms/epoch - 6ms/step\n",
      "Epoch 755/3000\n",
      "\n",
      "Epoch 755: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3317e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0389 - 941ms/epoch - 6ms/step\n",
      "Epoch 756/3000\n",
      "\n",
      "Epoch 756: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3354e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0388 - 925ms/epoch - 6ms/step\n",
      "Epoch 757/3000\n",
      "\n",
      "Epoch 757: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3335e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 899ms/epoch - 6ms/step\n",
      "Epoch 758/3000\n",
      "\n",
      "Epoch 758: val_mae did not improve from 0.03866\n",
      "151/151 - 1s - loss: 5.3359e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 911ms/epoch - 6ms/step\n",
      "Epoch 759/3000\n",
      "\n",
      "Epoch 759: val_mae improved from 0.03866 to 0.03862, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.3303e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0386 - 977ms/epoch - 6ms/step\n",
      "Epoch 760/3000\n",
      "\n",
      "Epoch 760: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3346e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0389 - 915ms/epoch - 6ms/step\n",
      "Epoch 761/3000\n",
      "\n",
      "Epoch 761: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3298e-04 - mae: 0.0169 - val_loss: 0.0027 - val_mae: 0.0394 - 919ms/epoch - 6ms/step\n",
      "Epoch 762/3000\n",
      "\n",
      "Epoch 762: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3368e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 924ms/epoch - 6ms/step\n",
      "Epoch 763/3000\n",
      "\n",
      "Epoch 763: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3300e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0392 - 963ms/epoch - 6ms/step\n",
      "Epoch 764/3000\n",
      "\n",
      "Epoch 764: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3257e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 938ms/epoch - 6ms/step\n",
      "Epoch 765/3000\n",
      "\n",
      "Epoch 765: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3244e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 942ms/epoch - 6ms/step\n",
      "Epoch 766/3000\n",
      "\n",
      "Epoch 766: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3305e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 924ms/epoch - 6ms/step\n",
      "Epoch 767/3000\n",
      "\n",
      "Epoch 767: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3291e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0391 - 925ms/epoch - 6ms/step\n",
      "Epoch 768/3000\n",
      "\n",
      "Epoch 768: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3211e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0396 - 931ms/epoch - 6ms/step\n",
      "Epoch 769/3000\n",
      "\n",
      "Epoch 769: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3261e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 931ms/epoch - 6ms/step\n",
      "Epoch 770/3000\n",
      "\n",
      "Epoch 770: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3311e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0390 - 928ms/epoch - 6ms/step\n",
      "Epoch 771/3000\n",
      "\n",
      "Epoch 771: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3259e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 945ms/epoch - 6ms/step\n",
      "Epoch 772/3000\n",
      "\n",
      "Epoch 772: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3149e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 944ms/epoch - 6ms/step\n",
      "Epoch 773/3000\n",
      "\n",
      "Epoch 773: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3228e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 938ms/epoch - 6ms/step\n",
      "Epoch 774/3000\n",
      "\n",
      "Epoch 774: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3278e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 952ms/epoch - 6ms/step\n",
      "Epoch 775/3000\n",
      "\n",
      "Epoch 775: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3235e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 951ms/epoch - 6ms/step\n",
      "Epoch 776/3000\n",
      "\n",
      "Epoch 776: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3235e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 931ms/epoch - 6ms/step\n",
      "Epoch 777/3000\n",
      "\n",
      "Epoch 777: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3254e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0395 - 928ms/epoch - 6ms/step\n",
      "Epoch 778/3000\n",
      "\n",
      "Epoch 778: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3269e-04 - mae: 0.0169 - val_loss: 0.0026 - val_mae: 0.0393 - 957ms/epoch - 6ms/step\n",
      "Epoch 779/3000\n",
      "\n",
      "Epoch 779: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3124e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 933ms/epoch - 6ms/step\n",
      "Epoch 780/3000\n",
      "\n",
      "Epoch 780: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3157e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0397 - 948ms/epoch - 6ms/step\n",
      "Epoch 781/3000\n",
      "\n",
      "Epoch 781: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3232e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 950ms/epoch - 6ms/step\n",
      "Epoch 782/3000\n",
      "\n",
      "Epoch 782: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3144e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0395 - 955ms/epoch - 6ms/step\n",
      "Epoch 783/3000\n",
      "\n",
      "Epoch 783: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3105e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 941ms/epoch - 6ms/step\n",
      "Epoch 784/3000\n",
      "\n",
      "Epoch 784: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3202e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 945ms/epoch - 6ms/step\n",
      "Epoch 785/3000\n",
      "\n",
      "Epoch 785: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3166e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 933ms/epoch - 6ms/step\n",
      "Epoch 786/3000\n",
      "\n",
      "Epoch 786: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3129e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 976ms/epoch - 6ms/step\n",
      "Epoch 787/3000\n",
      "\n",
      "Epoch 787: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3099e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 926ms/epoch - 6ms/step\n",
      "Epoch 788/3000\n",
      "\n",
      "Epoch 788: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3165e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 926ms/epoch - 6ms/step\n",
      "Epoch 789/3000\n",
      "\n",
      "Epoch 789: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3023e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 954ms/epoch - 6ms/step\n",
      "Epoch 790/3000\n",
      "\n",
      "Epoch 790: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3189e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 920ms/epoch - 6ms/step\n",
      "Epoch 791/3000\n",
      "\n",
      "Epoch 791: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3103e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 933ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 792/3000\n",
      "\n",
      "Epoch 792: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3089e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0389 - 950ms/epoch - 6ms/step\n",
      "Epoch 793/3000\n",
      "\n",
      "Epoch 793: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3076e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 939ms/epoch - 6ms/step\n",
      "Epoch 794/3000\n",
      "\n",
      "Epoch 794: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3100e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 949ms/epoch - 6ms/step\n",
      "Epoch 795/3000\n",
      "\n",
      "Epoch 795: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3061e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 957ms/epoch - 6ms/step\n",
      "Epoch 796/3000\n",
      "\n",
      "Epoch 796: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3000e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 936ms/epoch - 6ms/step\n",
      "Epoch 797/3000\n",
      "\n",
      "Epoch 797: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3107e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 975ms/epoch - 6ms/step\n",
      "Epoch 798/3000\n",
      "\n",
      "Epoch 798: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3092e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 925ms/epoch - 6ms/step\n",
      "Epoch 799/3000\n",
      "\n",
      "Epoch 799: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2971e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0399 - 913ms/epoch - 6ms/step\n",
      "Epoch 800/3000\n",
      "\n",
      "Epoch 800: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3117e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 903ms/epoch - 6ms/step\n",
      "Epoch 801/3000\n",
      "\n",
      "Epoch 801: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2980e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 924ms/epoch - 6ms/step\n",
      "Epoch 802/3000\n",
      "\n",
      "Epoch 802: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3039e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 942ms/epoch - 6ms/step\n",
      "Epoch 803/3000\n",
      "\n",
      "Epoch 803: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2968e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 940ms/epoch - 6ms/step\n",
      "Epoch 804/3000\n",
      "\n",
      "Epoch 804: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2988e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0389 - 934ms/epoch - 6ms/step\n",
      "Epoch 805/3000\n",
      "\n",
      "Epoch 805: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3052e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 950ms/epoch - 6ms/step\n",
      "Epoch 806/3000\n",
      "\n",
      "Epoch 806: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3090e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 947ms/epoch - 6ms/step\n",
      "Epoch 807/3000\n",
      "\n",
      "Epoch 807: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2946e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0395 - 962ms/epoch - 6ms/step\n",
      "Epoch 808/3000\n",
      "\n",
      "Epoch 808: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2984e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 960ms/epoch - 6ms/step\n",
      "Epoch 809/3000\n",
      "\n",
      "Epoch 809: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3070e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 929ms/epoch - 6ms/step\n",
      "Epoch 810/3000\n",
      "\n",
      "Epoch 810: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2923e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 1s/epoch - 7ms/step\n",
      "Epoch 811/3000\n",
      "\n",
      "Epoch 811: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3040e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0386 - 938ms/epoch - 6ms/step\n",
      "Epoch 812/3000\n",
      "\n",
      "Epoch 812: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3031e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 938ms/epoch - 6ms/step\n",
      "Epoch 813/3000\n",
      "\n",
      "Epoch 813: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2922e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 918ms/epoch - 6ms/step\n",
      "Epoch 814/3000\n",
      "\n",
      "Epoch 814: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2936e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 955ms/epoch - 6ms/step\n",
      "Epoch 815/3000\n",
      "\n",
      "Epoch 815: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2941e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 949ms/epoch - 6ms/step\n",
      "Epoch 816/3000\n",
      "\n",
      "Epoch 816: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.3036e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 946ms/epoch - 6ms/step\n",
      "Epoch 817/3000\n",
      "\n",
      "Epoch 817: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2936e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0393 - 946ms/epoch - 6ms/step\n",
      "Epoch 818/3000\n",
      "\n",
      "Epoch 818: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2968e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 974ms/epoch - 6ms/step\n",
      "Epoch 819/3000\n",
      "\n",
      "Epoch 819: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2956e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 957ms/epoch - 6ms/step\n",
      "Epoch 820/3000\n",
      "\n",
      "Epoch 820: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2869e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 913ms/epoch - 6ms/step\n",
      "Epoch 821/3000\n",
      "\n",
      "Epoch 821: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2936e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 933ms/epoch - 6ms/step\n",
      "Epoch 822/3000\n",
      "\n",
      "Epoch 822: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2983e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 976ms/epoch - 6ms/step\n",
      "Epoch 823/3000\n",
      "\n",
      "Epoch 823: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2996e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 971ms/epoch - 6ms/step\n",
      "Epoch 824/3000\n",
      "\n",
      "Epoch 824: val_mae did not improve from 0.03862\n",
      "151/151 - 1s - loss: 5.2973e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 928ms/epoch - 6ms/step\n",
      "Epoch 825/3000\n",
      "\n",
      "Epoch 825: val_mae improved from 0.03862 to 0.03853, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.2889e-04 - mae: 0.0168 - val_loss: 0.0025 - val_mae: 0.0385 - 964ms/epoch - 6ms/step\n",
      "Epoch 826/3000\n",
      "\n",
      "Epoch 826: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2878e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0393 - 943ms/epoch - 6ms/step\n",
      "Epoch 827/3000\n",
      "\n",
      "Epoch 827: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2918e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 935ms/epoch - 6ms/step\n",
      "Epoch 828/3000\n",
      "\n",
      "Epoch 828: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2843e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 954ms/epoch - 6ms/step\n",
      "Epoch 829/3000\n",
      "\n",
      "Epoch 829: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2842e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 928ms/epoch - 6ms/step\n",
      "Epoch 830/3000\n",
      "\n",
      "Epoch 830: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2871e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 928ms/epoch - 6ms/step\n",
      "Epoch 831/3000\n",
      "\n",
      "Epoch 831: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2936e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 964ms/epoch - 6ms/step\n",
      "Epoch 832/3000\n",
      "\n",
      "Epoch 832: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2880e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 926ms/epoch - 6ms/step\n",
      "Epoch 833/3000\n",
      "\n",
      "Epoch 833: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2895e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 936ms/epoch - 6ms/step\n",
      "Epoch 834/3000\n",
      "\n",
      "Epoch 834: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2808e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 917ms/epoch - 6ms/step\n",
      "Epoch 835/3000\n",
      "\n",
      "Epoch 835: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2821e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 903ms/epoch - 6ms/step\n",
      "Epoch 836/3000\n",
      "\n",
      "Epoch 836: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2916e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 939ms/epoch - 6ms/step\n",
      "Epoch 837/3000\n",
      "\n",
      "Epoch 837: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2904e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 922ms/epoch - 6ms/step\n",
      "Epoch 838/3000\n",
      "\n",
      "Epoch 838: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2848e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 949ms/epoch - 6ms/step\n",
      "Epoch 839/3000\n",
      "\n",
      "Epoch 839: val_mae did not improve from 0.03853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.2814e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 949ms/epoch - 6ms/step\n",
      "Epoch 840/3000\n",
      "\n",
      "Epoch 840: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2806e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0389 - 955ms/epoch - 6ms/step\n",
      "Epoch 841/3000\n",
      "\n",
      "Epoch 841: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2851e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 928ms/epoch - 6ms/step\n",
      "Epoch 842/3000\n",
      "\n",
      "Epoch 842: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2831e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 917ms/epoch - 6ms/step\n",
      "Epoch 843/3000\n",
      "\n",
      "Epoch 843: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2775e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0393 - 934ms/epoch - 6ms/step\n",
      "Epoch 844/3000\n",
      "\n",
      "Epoch 844: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2845e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0395 - 969ms/epoch - 6ms/step\n",
      "Epoch 845/3000\n",
      "\n",
      "Epoch 845: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2715e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 960ms/epoch - 6ms/step\n",
      "Epoch 846/3000\n",
      "\n",
      "Epoch 846: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2730e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0392 - 918ms/epoch - 6ms/step\n",
      "Epoch 847/3000\n",
      "\n",
      "Epoch 847: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2844e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 940ms/epoch - 6ms/step\n",
      "Epoch 848/3000\n",
      "\n",
      "Epoch 848: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2708e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0390 - 925ms/epoch - 6ms/step\n",
      "Epoch 849/3000\n",
      "\n",
      "Epoch 849: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2820e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 952ms/epoch - 6ms/step\n",
      "Epoch 850/3000\n",
      "\n",
      "Epoch 850: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2778e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 927ms/epoch - 6ms/step\n",
      "Epoch 851/3000\n",
      "\n",
      "Epoch 851: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2784e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0389 - 939ms/epoch - 6ms/step\n",
      "Epoch 852/3000\n",
      "\n",
      "Epoch 852: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2748e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0388 - 969ms/epoch - 6ms/step\n",
      "Epoch 853/3000\n",
      "\n",
      "Epoch 853: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2857e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 926ms/epoch - 6ms/step\n",
      "Epoch 854/3000\n",
      "\n",
      "Epoch 854: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2733e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0393 - 952ms/epoch - 6ms/step\n",
      "Epoch 855/3000\n",
      "\n",
      "Epoch 855: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2665e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 916ms/epoch - 6ms/step\n",
      "Epoch 856/3000\n",
      "\n",
      "Epoch 856: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2755e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 925ms/epoch - 6ms/step\n",
      "Epoch 857/3000\n",
      "\n",
      "Epoch 857: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2676e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 942ms/epoch - 6ms/step\n",
      "Epoch 858/3000\n",
      "\n",
      "Epoch 858: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2593e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0387 - 934ms/epoch - 6ms/step\n",
      "Epoch 859/3000\n",
      "\n",
      "Epoch 859: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2627e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 935ms/epoch - 6ms/step\n",
      "Epoch 860/3000\n",
      "\n",
      "Epoch 860: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2636e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 920ms/epoch - 6ms/step\n",
      "Epoch 861/3000\n",
      "\n",
      "Epoch 861: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2707e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0393 - 932ms/epoch - 6ms/step\n",
      "Epoch 862/3000\n",
      "\n",
      "Epoch 862: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2744e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 945ms/epoch - 6ms/step\n",
      "Epoch 863/3000\n",
      "\n",
      "Epoch 863: val_mae improved from 0.03853 to 0.03853, saving model to saved_models/cnn_gate_pump_nofuture.h5\n",
      "151/151 - 1s - loss: 5.2675e-04 - mae: 0.0167 - val_loss: 0.0025 - val_mae: 0.0385 - 975ms/epoch - 6ms/step\n",
      "Epoch 864/3000\n",
      "\n",
      "Epoch 864: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2668e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 939ms/epoch - 6ms/step\n",
      "Epoch 865/3000\n",
      "\n",
      "Epoch 865: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2700e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 932ms/epoch - 6ms/step\n",
      "Epoch 866/3000\n",
      "\n",
      "Epoch 866: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2723e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0394 - 971ms/epoch - 6ms/step\n",
      "Epoch 867/3000\n",
      "\n",
      "Epoch 867: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2783e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0387 - 939ms/epoch - 6ms/step\n",
      "Epoch 868/3000\n",
      "\n",
      "Epoch 868: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2726e-04 - mae: 0.0168 - val_loss: 0.0027 - val_mae: 0.0395 - 918ms/epoch - 6ms/step\n",
      "Epoch 869/3000\n",
      "\n",
      "Epoch 869: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2647e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 946ms/epoch - 6ms/step\n",
      "Epoch 870/3000\n",
      "\n",
      "Epoch 870: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2694e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 933ms/epoch - 6ms/step\n",
      "Epoch 871/3000\n",
      "\n",
      "Epoch 871: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2599e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 930ms/epoch - 6ms/step\n",
      "Epoch 872/3000\n",
      "\n",
      "Epoch 872: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2663e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 919ms/epoch - 6ms/step\n",
      "Epoch 873/3000\n",
      "\n",
      "Epoch 873: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2630e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 918ms/epoch - 6ms/step\n",
      "Epoch 874/3000\n",
      "\n",
      "Epoch 874: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2647e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 944ms/epoch - 6ms/step\n",
      "Epoch 875/3000\n",
      "\n",
      "Epoch 875: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2725e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0389 - 919ms/epoch - 6ms/step\n",
      "Epoch 876/3000\n",
      "\n",
      "Epoch 876: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2661e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 927ms/epoch - 6ms/step\n",
      "Epoch 877/3000\n",
      "\n",
      "Epoch 877: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2635e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 971ms/epoch - 6ms/step\n",
      "Epoch 878/3000\n",
      "\n",
      "Epoch 878: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2573e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 972ms/epoch - 6ms/step\n",
      "Epoch 879/3000\n",
      "\n",
      "Epoch 879: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2666e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0385 - 949ms/epoch - 6ms/step\n",
      "Epoch 880/3000\n",
      "\n",
      "Epoch 880: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2636e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 945ms/epoch - 6ms/step\n",
      "Epoch 881/3000\n",
      "\n",
      "Epoch 881: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2575e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 938ms/epoch - 6ms/step\n",
      "Epoch 882/3000\n",
      "\n",
      "Epoch 882: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2665e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 947ms/epoch - 6ms/step\n",
      "Epoch 883/3000\n",
      "\n",
      "Epoch 883: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2703e-04 - mae: 0.0168 - val_loss: 0.0026 - val_mae: 0.0391 - 923ms/epoch - 6ms/step\n",
      "Epoch 884/3000\n",
      "\n",
      "Epoch 884: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2524e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 907ms/epoch - 6ms/step\n",
      "Epoch 885/3000\n",
      "\n",
      "Epoch 885: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2667e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 918ms/epoch - 6ms/step\n",
      "Epoch 886/3000\n",
      "\n",
      "Epoch 886: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2539e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 959ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 887/3000\n",
      "\n",
      "Epoch 887: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2629e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 935ms/epoch - 6ms/step\n",
      "Epoch 888/3000\n",
      "\n",
      "Epoch 888: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2661e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 931ms/epoch - 6ms/step\n",
      "Epoch 889/3000\n",
      "\n",
      "Epoch 889: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2491e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 961ms/epoch - 6ms/step\n",
      "Epoch 890/3000\n",
      "\n",
      "Epoch 890: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2561e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0387 - 967ms/epoch - 6ms/step\n",
      "Epoch 891/3000\n",
      "\n",
      "Epoch 891: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2553e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 975ms/epoch - 6ms/step\n",
      "Epoch 892/3000\n",
      "\n",
      "Epoch 892: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2516e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 927ms/epoch - 6ms/step\n",
      "Epoch 893/3000\n",
      "\n",
      "Epoch 893: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2618e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 949ms/epoch - 6ms/step\n",
      "Epoch 894/3000\n",
      "\n",
      "Epoch 894: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2571e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 953ms/epoch - 6ms/step\n",
      "Epoch 895/3000\n",
      "\n",
      "Epoch 895: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2504e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 939ms/epoch - 6ms/step\n",
      "Epoch 896/3000\n",
      "\n",
      "Epoch 896: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2532e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 918ms/epoch - 6ms/step\n",
      "Epoch 897/3000\n",
      "\n",
      "Epoch 897: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2517e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 973ms/epoch - 6ms/step\n",
      "Epoch 898/3000\n",
      "\n",
      "Epoch 898: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2507e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 915ms/epoch - 6ms/step\n",
      "Epoch 899/3000\n",
      "\n",
      "Epoch 899: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2536e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 931ms/epoch - 6ms/step\n",
      "Epoch 900/3000\n",
      "\n",
      "Epoch 900: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2534e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 946ms/epoch - 6ms/step\n",
      "Epoch 901/3000\n",
      "\n",
      "Epoch 901: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2538e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 967ms/epoch - 6ms/step\n",
      "Epoch 902/3000\n",
      "\n",
      "Epoch 902: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2521e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 946ms/epoch - 6ms/step\n",
      "Epoch 903/3000\n",
      "\n",
      "Epoch 903: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2490e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 940ms/epoch - 6ms/step\n",
      "Epoch 904/3000\n",
      "\n",
      "Epoch 904: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2620e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 943ms/epoch - 6ms/step\n",
      "Epoch 905/3000\n",
      "\n",
      "Epoch 905: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2529e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 921ms/epoch - 6ms/step\n",
      "Epoch 906/3000\n",
      "\n",
      "Epoch 906: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2490e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 920ms/epoch - 6ms/step\n",
      "Epoch 907/3000\n",
      "\n",
      "Epoch 907: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2449e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0387 - 904ms/epoch - 6ms/step\n",
      "Epoch 908/3000\n",
      "\n",
      "Epoch 908: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2507e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 961ms/epoch - 6ms/step\n",
      "Epoch 909/3000\n",
      "\n",
      "Epoch 909: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2534e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 907ms/epoch - 6ms/step\n",
      "Epoch 910/3000\n",
      "\n",
      "Epoch 910: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2558e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 916ms/epoch - 6ms/step\n",
      "Epoch 911/3000\n",
      "\n",
      "Epoch 911: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2342e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 922ms/epoch - 6ms/step\n",
      "Epoch 912/3000\n",
      "\n",
      "Epoch 912: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2521e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 923ms/epoch - 6ms/step\n",
      "Epoch 913/3000\n",
      "\n",
      "Epoch 913: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2438e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 907ms/epoch - 6ms/step\n",
      "Epoch 914/3000\n",
      "\n",
      "Epoch 914: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2478e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 923ms/epoch - 6ms/step\n",
      "Epoch 915/3000\n",
      "\n",
      "Epoch 915: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2378e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 915ms/epoch - 6ms/step\n",
      "Epoch 916/3000\n",
      "\n",
      "Epoch 916: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2413e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 933ms/epoch - 6ms/step\n",
      "Epoch 917/3000\n",
      "\n",
      "Epoch 917: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2491e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 948ms/epoch - 6ms/step\n",
      "Epoch 918/3000\n",
      "\n",
      "Epoch 918: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2396e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 938ms/epoch - 6ms/step\n",
      "Epoch 919/3000\n",
      "\n",
      "Epoch 919: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2564e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 940ms/epoch - 6ms/step\n",
      "Epoch 920/3000\n",
      "\n",
      "Epoch 920: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2453e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 942ms/epoch - 6ms/step\n",
      "Epoch 921/3000\n",
      "\n",
      "Epoch 921: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2383e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 923ms/epoch - 6ms/step\n",
      "Epoch 922/3000\n",
      "\n",
      "Epoch 922: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2378e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 944ms/epoch - 6ms/step\n",
      "Epoch 923/3000\n",
      "\n",
      "Epoch 923: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2493e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 950ms/epoch - 6ms/step\n",
      "Epoch 924/3000\n",
      "\n",
      "Epoch 924: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2405e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 917ms/epoch - 6ms/step\n",
      "Epoch 925/3000\n",
      "\n",
      "Epoch 925: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2423e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 946ms/epoch - 6ms/step\n",
      "Epoch 926/3000\n",
      "\n",
      "Epoch 926: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2368e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 933ms/epoch - 6ms/step\n",
      "Epoch 927/3000\n",
      "\n",
      "Epoch 927: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2352e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 963ms/epoch - 6ms/step\n",
      "Epoch 928/3000\n",
      "\n",
      "Epoch 928: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2345e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 946ms/epoch - 6ms/step\n",
      "Epoch 929/3000\n",
      "\n",
      "Epoch 929: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2322e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 932ms/epoch - 6ms/step\n",
      "Epoch 930/3000\n",
      "\n",
      "Epoch 930: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2365e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 933ms/epoch - 6ms/step\n",
      "Epoch 931/3000\n",
      "\n",
      "Epoch 931: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2386e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 943ms/epoch - 6ms/step\n",
      "Epoch 932/3000\n",
      "\n",
      "Epoch 932: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2386e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 934ms/epoch - 6ms/step\n",
      "Epoch 933/3000\n",
      "\n",
      "Epoch 933: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2361e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 953ms/epoch - 6ms/step\n",
      "Epoch 934/3000\n",
      "\n",
      "Epoch 934: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2426e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 962ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 935/3000\n",
      "\n",
      "Epoch 935: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2321e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 918ms/epoch - 6ms/step\n",
      "Epoch 936/3000\n",
      "\n",
      "Epoch 936: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2367e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 921ms/epoch - 6ms/step\n",
      "Epoch 937/3000\n",
      "\n",
      "Epoch 937: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2389e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 938ms/epoch - 6ms/step\n",
      "Epoch 938/3000\n",
      "\n",
      "Epoch 938: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2419e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 926ms/epoch - 6ms/step\n",
      "Epoch 939/3000\n",
      "\n",
      "Epoch 939: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2352e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 924ms/epoch - 6ms/step\n",
      "Epoch 940/3000\n",
      "\n",
      "Epoch 940: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2381e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 921ms/epoch - 6ms/step\n",
      "Epoch 941/3000\n",
      "\n",
      "Epoch 941: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2399e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 924ms/epoch - 6ms/step\n",
      "Epoch 942/3000\n",
      "\n",
      "Epoch 942: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2369e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 947ms/epoch - 6ms/step\n",
      "Epoch 943/3000\n",
      "\n",
      "Epoch 943: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2271e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 925ms/epoch - 6ms/step\n",
      "Epoch 944/3000\n",
      "\n",
      "Epoch 944: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2257e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 933ms/epoch - 6ms/step\n",
      "Epoch 945/3000\n",
      "\n",
      "Epoch 945: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2316e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 932ms/epoch - 6ms/step\n",
      "Epoch 946/3000\n",
      "\n",
      "Epoch 946: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2214e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 939ms/epoch - 6ms/step\n",
      "Epoch 947/3000\n",
      "\n",
      "Epoch 947: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2313e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 940ms/epoch - 6ms/step\n",
      "Epoch 948/3000\n",
      "\n",
      "Epoch 948: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2224e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 951ms/epoch - 6ms/step\n",
      "Epoch 949/3000\n",
      "\n",
      "Epoch 949: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2276e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 976ms/epoch - 6ms/step\n",
      "Epoch 950/3000\n",
      "\n",
      "Epoch 950: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2301e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 954ms/epoch - 6ms/step\n",
      "Epoch 951/3000\n",
      "\n",
      "Epoch 951: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2270e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 973ms/epoch - 6ms/step\n",
      "Epoch 952/3000\n",
      "\n",
      "Epoch 952: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2314e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 968ms/epoch - 6ms/step\n",
      "Epoch 953/3000\n",
      "\n",
      "Epoch 953: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2261e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0393 - 928ms/epoch - 6ms/step\n",
      "Epoch 954/3000\n",
      "\n",
      "Epoch 954: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2251e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 923ms/epoch - 6ms/step\n",
      "Epoch 955/3000\n",
      "\n",
      "Epoch 955: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2296e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0397 - 936ms/epoch - 6ms/step\n",
      "Epoch 956/3000\n",
      "\n",
      "Epoch 956: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2229e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0396 - 903ms/epoch - 6ms/step\n",
      "Epoch 957/3000\n",
      "\n",
      "Epoch 957: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2172e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 921ms/epoch - 6ms/step\n",
      "Epoch 958/3000\n",
      "\n",
      "Epoch 958: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2282e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 945ms/epoch - 6ms/step\n",
      "Epoch 959/3000\n",
      "\n",
      "Epoch 959: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2271e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0387 - 957ms/epoch - 6ms/step\n",
      "Epoch 960/3000\n",
      "\n",
      "Epoch 960: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2201e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 951ms/epoch - 6ms/step\n",
      "Epoch 961/3000\n",
      "\n",
      "Epoch 961: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2256e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 948ms/epoch - 6ms/step\n",
      "Epoch 962/3000\n",
      "\n",
      "Epoch 962: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2249e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 924ms/epoch - 6ms/step\n",
      "Epoch 963/3000\n",
      "\n",
      "Epoch 963: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2286e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 951ms/epoch - 6ms/step\n",
      "Epoch 964/3000\n",
      "\n",
      "Epoch 964: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2188e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 952ms/epoch - 6ms/step\n",
      "Epoch 965/3000\n",
      "\n",
      "Epoch 965: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2260e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 964ms/epoch - 6ms/step\n",
      "Epoch 966/3000\n",
      "\n",
      "Epoch 966: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2243e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0396 - 989ms/epoch - 7ms/step\n",
      "Epoch 967/3000\n",
      "\n",
      "Epoch 967: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2205e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0395 - 958ms/epoch - 6ms/step\n",
      "Epoch 968/3000\n",
      "\n",
      "Epoch 968: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2195e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 944ms/epoch - 6ms/step\n",
      "Epoch 969/3000\n",
      "\n",
      "Epoch 969: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2139e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0391 - 940ms/epoch - 6ms/step\n",
      "Epoch 970/3000\n",
      "\n",
      "Epoch 970: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2263e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 939ms/epoch - 6ms/step\n",
      "Epoch 971/3000\n",
      "\n",
      "Epoch 971: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2161e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 945ms/epoch - 6ms/step\n",
      "Epoch 972/3000\n",
      "\n",
      "Epoch 972: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2238e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0389 - 915ms/epoch - 6ms/step\n",
      "Epoch 973/3000\n",
      "\n",
      "Epoch 973: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2142e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 909ms/epoch - 6ms/step\n",
      "Epoch 974/3000\n",
      "\n",
      "Epoch 974: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2151e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0392 - 942ms/epoch - 6ms/step\n",
      "Epoch 975/3000\n",
      "\n",
      "Epoch 975: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2093e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0389 - 921ms/epoch - 6ms/step\n",
      "Epoch 976/3000\n",
      "\n",
      "Epoch 976: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2121e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 945ms/epoch - 6ms/step\n",
      "Epoch 977/3000\n",
      "\n",
      "Epoch 977: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2227e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 947ms/epoch - 6ms/step\n",
      "Epoch 978/3000\n",
      "\n",
      "Epoch 978: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2144e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0388 - 944ms/epoch - 6ms/step\n",
      "Epoch 979/3000\n",
      "\n",
      "Epoch 979: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2161e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0392 - 952ms/epoch - 6ms/step\n",
      "Epoch 980/3000\n",
      "\n",
      "Epoch 980: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2199e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 930ms/epoch - 6ms/step\n",
      "Epoch 981/3000\n",
      "\n",
      "Epoch 981: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2098e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 936ms/epoch - 6ms/step\n",
      "Epoch 982/3000\n",
      "\n",
      "Epoch 982: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2161e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 939ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 983/3000\n",
      "\n",
      "Epoch 983: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2216e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 953ms/epoch - 6ms/step\n",
      "Epoch 984/3000\n",
      "\n",
      "Epoch 984: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2022e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 977ms/epoch - 6ms/step\n",
      "Epoch 985/3000\n",
      "\n",
      "Epoch 985: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2091e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 924ms/epoch - 6ms/step\n",
      "Epoch 986/3000\n",
      "\n",
      "Epoch 986: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2121e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 924ms/epoch - 6ms/step\n",
      "Epoch 987/3000\n",
      "\n",
      "Epoch 987: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2102e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 967ms/epoch - 6ms/step\n",
      "Epoch 988/3000\n",
      "\n",
      "Epoch 988: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2168e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0396 - 964ms/epoch - 6ms/step\n",
      "Epoch 989/3000\n",
      "\n",
      "Epoch 989: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2208e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0393 - 956ms/epoch - 6ms/step\n",
      "Epoch 990/3000\n",
      "\n",
      "Epoch 990: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2083e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 970ms/epoch - 6ms/step\n",
      "Epoch 991/3000\n",
      "\n",
      "Epoch 991: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2139e-04 - mae: 0.0167 - val_loss: 0.0026 - val_mae: 0.0390 - 945ms/epoch - 6ms/step\n",
      "Epoch 992/3000\n",
      "\n",
      "Epoch 992: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2064e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 944ms/epoch - 6ms/step\n",
      "Epoch 993/3000\n",
      "\n",
      "Epoch 993: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2066e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 942ms/epoch - 6ms/step\n",
      "Epoch 994/3000\n",
      "\n",
      "Epoch 994: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2021e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 916ms/epoch - 6ms/step\n",
      "Epoch 995/3000\n",
      "\n",
      "Epoch 995: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2012e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 946ms/epoch - 6ms/step\n",
      "Epoch 996/3000\n",
      "\n",
      "Epoch 996: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2145e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 931ms/epoch - 6ms/step\n",
      "Epoch 997/3000\n",
      "\n",
      "Epoch 997: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1995e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0389 - 967ms/epoch - 6ms/step\n",
      "Epoch 998/3000\n",
      "\n",
      "Epoch 998: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2057e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 909ms/epoch - 6ms/step\n",
      "Epoch 999/3000\n",
      "\n",
      "Epoch 999: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2052e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 941ms/epoch - 6ms/step\n",
      "Epoch 1000/3000\n",
      "\n",
      "Epoch 1000: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2033e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 922ms/epoch - 6ms/step\n",
      "Epoch 1001/3000\n",
      "\n",
      "Epoch 1001: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2066e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0386 - 940ms/epoch - 6ms/step\n",
      "Epoch 1002/3000\n",
      "\n",
      "Epoch 1002: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2142e-04 - mae: 0.0167 - val_loss: 0.0027 - val_mae: 0.0394 - 935ms/epoch - 6ms/step\n",
      "Epoch 1003/3000\n",
      "\n",
      "Epoch 1003: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2010e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 927ms/epoch - 6ms/step\n",
      "Epoch 1004/3000\n",
      "\n",
      "Epoch 1004: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1991e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 916ms/epoch - 6ms/step\n",
      "Epoch 1005/3000\n",
      "\n",
      "Epoch 1005: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2026e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0389 - 927ms/epoch - 6ms/step\n",
      "Epoch 1006/3000\n",
      "\n",
      "Epoch 1006: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1991e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 938ms/epoch - 6ms/step\n",
      "Epoch 1007/3000\n",
      "\n",
      "Epoch 1007: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2059e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 911ms/epoch - 6ms/step\n",
      "Epoch 1008/3000\n",
      "\n",
      "Epoch 1008: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2094e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0388 - 922ms/epoch - 6ms/step\n",
      "Epoch 1009/3000\n",
      "\n",
      "Epoch 1009: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1945e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0388 - 916ms/epoch - 6ms/step\n",
      "Epoch 1010/3000\n",
      "\n",
      "Epoch 1010: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2087e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 918ms/epoch - 6ms/step\n",
      "Epoch 1011/3000\n",
      "\n",
      "Epoch 1011: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2022e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 920ms/epoch - 6ms/step\n",
      "Epoch 1012/3000\n",
      "\n",
      "Epoch 1012: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2061e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 913ms/epoch - 6ms/step\n",
      "Epoch 1013/3000\n",
      "\n",
      "Epoch 1013: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1929e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 917ms/epoch - 6ms/step\n",
      "Epoch 1014/3000\n",
      "\n",
      "Epoch 1014: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1987e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 939ms/epoch - 6ms/step\n",
      "Epoch 1015/3000\n",
      "\n",
      "Epoch 1015: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2030e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 930ms/epoch - 6ms/step\n",
      "Epoch 1016/3000\n",
      "\n",
      "Epoch 1016: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1948e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 919ms/epoch - 6ms/step\n",
      "Epoch 1017/3000\n",
      "\n",
      "Epoch 1017: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2031e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 925ms/epoch - 6ms/step\n",
      "Epoch 1018/3000\n",
      "\n",
      "Epoch 1018: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1867e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 945ms/epoch - 6ms/step\n",
      "Epoch 1019/3000\n",
      "\n",
      "Epoch 1019: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1974e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 926ms/epoch - 6ms/step\n",
      "Epoch 1020/3000\n",
      "\n",
      "Epoch 1020: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1946e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 943ms/epoch - 6ms/step\n",
      "Epoch 1021/3000\n",
      "\n",
      "Epoch 1021: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1926e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 944ms/epoch - 6ms/step\n",
      "Epoch 1022/3000\n",
      "\n",
      "Epoch 1022: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1928e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 935ms/epoch - 6ms/step\n",
      "Epoch 1023/3000\n",
      "\n",
      "Epoch 1023: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1956e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 945ms/epoch - 6ms/step\n",
      "Epoch 1024/3000\n",
      "\n",
      "Epoch 1024: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2011e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0399 - 918ms/epoch - 6ms/step\n",
      "Epoch 1025/3000\n",
      "\n",
      "Epoch 1025: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.2000e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 945ms/epoch - 6ms/step\n",
      "Epoch 1026/3000\n",
      "\n",
      "Epoch 1026: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1894e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 915ms/epoch - 6ms/step\n",
      "Epoch 1027/3000\n",
      "\n",
      "Epoch 1027: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1927e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 943ms/epoch - 6ms/step\n",
      "Epoch 1028/3000\n",
      "\n",
      "Epoch 1028: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1964e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 942ms/epoch - 6ms/step\n",
      "Epoch 1029/3000\n",
      "\n",
      "Epoch 1029: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1835e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 925ms/epoch - 6ms/step\n",
      "Epoch 1030/3000\n",
      "\n",
      "Epoch 1030: val_mae did not improve from 0.03853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.1878e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 920ms/epoch - 6ms/step\n",
      "Epoch 1031/3000\n",
      "\n",
      "Epoch 1031: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1944e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 944ms/epoch - 6ms/step\n",
      "Epoch 1032/3000\n",
      "\n",
      "Epoch 1032: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1976e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 921ms/epoch - 6ms/step\n",
      "Epoch 1033/3000\n",
      "\n",
      "Epoch 1033: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1944e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 923ms/epoch - 6ms/step\n",
      "Epoch 1034/3000\n",
      "\n",
      "Epoch 1034: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1947e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 921ms/epoch - 6ms/step\n",
      "Epoch 1035/3000\n",
      "\n",
      "Epoch 1035: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1900e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 941ms/epoch - 6ms/step\n",
      "Epoch 1036/3000\n",
      "\n",
      "Epoch 1036: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1978e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0399 - 930ms/epoch - 6ms/step\n",
      "Epoch 1037/3000\n",
      "\n",
      "Epoch 1037: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1867e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 924ms/epoch - 6ms/step\n",
      "Epoch 1038/3000\n",
      "\n",
      "Epoch 1038: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1926e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 943ms/epoch - 6ms/step\n",
      "Epoch 1039/3000\n",
      "\n",
      "Epoch 1039: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1916e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 929ms/epoch - 6ms/step\n",
      "Epoch 1040/3000\n",
      "\n",
      "Epoch 1040: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1886e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 944ms/epoch - 6ms/step\n",
      "Epoch 1041/3000\n",
      "\n",
      "Epoch 1041: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1954e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 933ms/epoch - 6ms/step\n",
      "Epoch 1042/3000\n",
      "\n",
      "Epoch 1042: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1890e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 906ms/epoch - 6ms/step\n",
      "Epoch 1043/3000\n",
      "\n",
      "Epoch 1043: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1960e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 938ms/epoch - 6ms/step\n",
      "Epoch 1044/3000\n",
      "\n",
      "Epoch 1044: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1857e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 942ms/epoch - 6ms/step\n",
      "Epoch 1045/3000\n",
      "\n",
      "Epoch 1045: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1934e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 964ms/epoch - 6ms/step\n",
      "Epoch 1046/3000\n",
      "\n",
      "Epoch 1046: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1860e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 929ms/epoch - 6ms/step\n",
      "Epoch 1047/3000\n",
      "\n",
      "Epoch 1047: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1972e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 934ms/epoch - 6ms/step\n",
      "Epoch 1048/3000\n",
      "\n",
      "Epoch 1048: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1829e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 963ms/epoch - 6ms/step\n",
      "Epoch 1049/3000\n",
      "\n",
      "Epoch 1049: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1796e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 935ms/epoch - 6ms/step\n",
      "Epoch 1050/3000\n",
      "\n",
      "Epoch 1050: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1768e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 939ms/epoch - 6ms/step\n",
      "Epoch 1051/3000\n",
      "\n",
      "Epoch 1051: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1861e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 951ms/epoch - 6ms/step\n",
      "Epoch 1052/3000\n",
      "\n",
      "Epoch 1052: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1924e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 940ms/epoch - 6ms/step\n",
      "Epoch 1053/3000\n",
      "\n",
      "Epoch 1053: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1852e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 944ms/epoch - 6ms/step\n",
      "Epoch 1054/3000\n",
      "\n",
      "Epoch 1054: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1812e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 928ms/epoch - 6ms/step\n",
      "Epoch 1055/3000\n",
      "\n",
      "Epoch 1055: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1841e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 931ms/epoch - 6ms/step\n",
      "Epoch 1056/3000\n",
      "\n",
      "Epoch 1056: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1830e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 938ms/epoch - 6ms/step\n",
      "Epoch 1057/3000\n",
      "\n",
      "Epoch 1057: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1861e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 942ms/epoch - 6ms/step\n",
      "Epoch 1058/3000\n",
      "\n",
      "Epoch 1058: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1872e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 947ms/epoch - 6ms/step\n",
      "Epoch 1059/3000\n",
      "\n",
      "Epoch 1059: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1786e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 928ms/epoch - 6ms/step\n",
      "Epoch 1060/3000\n",
      "\n",
      "Epoch 1060: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1845e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 924ms/epoch - 6ms/step\n",
      "Epoch 1061/3000\n",
      "\n",
      "Epoch 1061: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1838e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 957ms/epoch - 6ms/step\n",
      "Epoch 1062/3000\n",
      "\n",
      "Epoch 1062: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1832e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 947ms/epoch - 6ms/step\n",
      "Epoch 1063/3000\n",
      "\n",
      "Epoch 1063: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1901e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 978ms/epoch - 6ms/step\n",
      "Epoch 1064/3000\n",
      "\n",
      "Epoch 1064: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1739e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 939ms/epoch - 6ms/step\n",
      "Epoch 1065/3000\n",
      "\n",
      "Epoch 1065: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1809e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 928ms/epoch - 6ms/step\n",
      "Epoch 1066/3000\n",
      "\n",
      "Epoch 1066: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1808e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 933ms/epoch - 6ms/step\n",
      "Epoch 1067/3000\n",
      "\n",
      "Epoch 1067: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1705e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 912ms/epoch - 6ms/step\n",
      "Epoch 1068/3000\n",
      "\n",
      "Epoch 1068: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1773e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 915ms/epoch - 6ms/step\n",
      "Epoch 1069/3000\n",
      "\n",
      "Epoch 1069: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1801e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 919ms/epoch - 6ms/step\n",
      "Epoch 1070/3000\n",
      "\n",
      "Epoch 1070: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1897e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 926ms/epoch - 6ms/step\n",
      "Epoch 1071/3000\n",
      "\n",
      "Epoch 1071: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1821e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 928ms/epoch - 6ms/step\n",
      "Epoch 1072/3000\n",
      "\n",
      "Epoch 1072: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1706e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 908ms/epoch - 6ms/step\n",
      "Epoch 1073/3000\n",
      "\n",
      "Epoch 1073: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1756e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 939ms/epoch - 6ms/step\n",
      "Epoch 1074/3000\n",
      "\n",
      "Epoch 1074: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1764e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 922ms/epoch - 6ms/step\n",
      "Epoch 1075/3000\n",
      "\n",
      "Epoch 1075: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1746e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 925ms/epoch - 6ms/step\n",
      "Epoch 1076/3000\n",
      "\n",
      "Epoch 1076: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1784e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 925ms/epoch - 6ms/step\n",
      "Epoch 1077/3000\n",
      "\n",
      "Epoch 1077: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1776e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 954ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1078/3000\n",
      "\n",
      "Epoch 1078: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1682e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 927ms/epoch - 6ms/step\n",
      "Epoch 1079/3000\n",
      "\n",
      "Epoch 1079: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1779e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 923ms/epoch - 6ms/step\n",
      "Epoch 1080/3000\n",
      "\n",
      "Epoch 1080: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1786e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 926ms/epoch - 6ms/step\n",
      "Epoch 1081/3000\n",
      "\n",
      "Epoch 1081: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1821e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 918ms/epoch - 6ms/step\n",
      "Epoch 1082/3000\n",
      "\n",
      "Epoch 1082: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1784e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0399 - 939ms/epoch - 6ms/step\n",
      "Epoch 1083/3000\n",
      "\n",
      "Epoch 1083: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1723e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 947ms/epoch - 6ms/step\n",
      "Epoch 1084/3000\n",
      "\n",
      "Epoch 1084: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1691e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 929ms/epoch - 6ms/step\n",
      "Epoch 1085/3000\n",
      "\n",
      "Epoch 1085: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1786e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 962ms/epoch - 6ms/step\n",
      "Epoch 1086/3000\n",
      "\n",
      "Epoch 1086: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1706e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 981ms/epoch - 6ms/step\n",
      "Epoch 1087/3000\n",
      "\n",
      "Epoch 1087: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1663e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0398 - 959ms/epoch - 6ms/step\n",
      "Epoch 1088/3000\n",
      "\n",
      "Epoch 1088: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1756e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 964ms/epoch - 6ms/step\n",
      "Epoch 1089/3000\n",
      "\n",
      "Epoch 1089: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1746e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0398 - 958ms/epoch - 6ms/step\n",
      "Epoch 1090/3000\n",
      "\n",
      "Epoch 1090: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1725e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 941ms/epoch - 6ms/step\n",
      "Epoch 1091/3000\n",
      "\n",
      "Epoch 1091: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1728e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 971ms/epoch - 6ms/step\n",
      "Epoch 1092/3000\n",
      "\n",
      "Epoch 1092: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1741e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 965ms/epoch - 6ms/step\n",
      "Epoch 1093/3000\n",
      "\n",
      "Epoch 1093: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1581e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 917ms/epoch - 6ms/step\n",
      "Epoch 1094/3000\n",
      "\n",
      "Epoch 1094: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1705e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 947ms/epoch - 6ms/step\n",
      "Epoch 1095/3000\n",
      "\n",
      "Epoch 1095: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1689e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 970ms/epoch - 6ms/step\n",
      "Epoch 1096/3000\n",
      "\n",
      "Epoch 1096: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1702e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 972ms/epoch - 6ms/step\n",
      "Epoch 1097/3000\n",
      "\n",
      "Epoch 1097: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1756e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 944ms/epoch - 6ms/step\n",
      "Epoch 1098/3000\n",
      "\n",
      "Epoch 1098: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1666e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 923ms/epoch - 6ms/step\n",
      "Epoch 1099/3000\n",
      "\n",
      "Epoch 1099: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1760e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 935ms/epoch - 6ms/step\n",
      "Epoch 1100/3000\n",
      "\n",
      "Epoch 1100: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1635e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 923ms/epoch - 6ms/step\n",
      "Epoch 1101/3000\n",
      "\n",
      "Epoch 1101: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1598e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 920ms/epoch - 6ms/step\n",
      "Epoch 1102/3000\n",
      "\n",
      "Epoch 1102: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1638e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 942ms/epoch - 6ms/step\n",
      "Epoch 1103/3000\n",
      "\n",
      "Epoch 1103: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1597e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 910ms/epoch - 6ms/step\n",
      "Epoch 1104/3000\n",
      "\n",
      "Epoch 1104: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1689e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 919ms/epoch - 6ms/step\n",
      "Epoch 1105/3000\n",
      "\n",
      "Epoch 1105: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1620e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 911ms/epoch - 6ms/step\n",
      "Epoch 1106/3000\n",
      "\n",
      "Epoch 1106: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1707e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 911ms/epoch - 6ms/step\n",
      "Epoch 1107/3000\n",
      "\n",
      "Epoch 1107: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1642e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 926ms/epoch - 6ms/step\n",
      "Epoch 1108/3000\n",
      "\n",
      "Epoch 1108: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1614e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0398 - 927ms/epoch - 6ms/step\n",
      "Epoch 1109/3000\n",
      "\n",
      "Epoch 1109: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1665e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 944ms/epoch - 6ms/step\n",
      "Epoch 1110/3000\n",
      "\n",
      "Epoch 1110: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1645e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 943ms/epoch - 6ms/step\n",
      "Epoch 1111/3000\n",
      "\n",
      "Epoch 1111: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1647e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 909ms/epoch - 6ms/step\n",
      "Epoch 1112/3000\n",
      "\n",
      "Epoch 1112: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1631e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 919ms/epoch - 6ms/step\n",
      "Epoch 1113/3000\n",
      "\n",
      "Epoch 1113: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1660e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 935ms/epoch - 6ms/step\n",
      "Epoch 1114/3000\n",
      "\n",
      "Epoch 1114: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1645e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 927ms/epoch - 6ms/step\n",
      "Epoch 1115/3000\n",
      "\n",
      "Epoch 1115: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1696e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 951ms/epoch - 6ms/step\n",
      "Epoch 1116/3000\n",
      "\n",
      "Epoch 1116: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1715e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 964ms/epoch - 6ms/step\n",
      "Epoch 1117/3000\n",
      "\n",
      "Epoch 1117: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1520e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0399 - 958ms/epoch - 6ms/step\n",
      "Epoch 1118/3000\n",
      "\n",
      "Epoch 1118: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1706e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 947ms/epoch - 6ms/step\n",
      "Epoch 1119/3000\n",
      "\n",
      "Epoch 1119: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1610e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 923ms/epoch - 6ms/step\n",
      "Epoch 1120/3000\n",
      "\n",
      "Epoch 1120: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1579e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 912ms/epoch - 6ms/step\n",
      "Epoch 1121/3000\n",
      "\n",
      "Epoch 1121: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1545e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0393 - 957ms/epoch - 6ms/step\n",
      "Epoch 1122/3000\n",
      "\n",
      "Epoch 1122: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1660e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 928ms/epoch - 6ms/step\n",
      "Epoch 1123/3000\n",
      "\n",
      "Epoch 1123: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1621e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 945ms/epoch - 6ms/step\n",
      "Epoch 1124/3000\n",
      "\n",
      "Epoch 1124: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1589e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 971ms/epoch - 6ms/step\n",
      "Epoch 1125/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1125: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1604e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 948ms/epoch - 6ms/step\n",
      "Epoch 1126/3000\n",
      "\n",
      "Epoch 1126: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1641e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0390 - 965ms/epoch - 6ms/step\n",
      "Epoch 1127/3000\n",
      "\n",
      "Epoch 1127: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1642e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0395 - 931ms/epoch - 6ms/step\n",
      "Epoch 1128/3000\n",
      "\n",
      "Epoch 1128: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1611e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 933ms/epoch - 6ms/step\n",
      "Epoch 1129/3000\n",
      "\n",
      "Epoch 1129: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1582e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 940ms/epoch - 6ms/step\n",
      "Epoch 1130/3000\n",
      "\n",
      "Epoch 1130: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1674e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 949ms/epoch - 6ms/step\n",
      "Epoch 1131/3000\n",
      "\n",
      "Epoch 1131: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1572e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 936ms/epoch - 6ms/step\n",
      "Epoch 1132/3000\n",
      "\n",
      "Epoch 1132: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1566e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 972ms/epoch - 6ms/step\n",
      "Epoch 1133/3000\n",
      "\n",
      "Epoch 1133: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1514e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 932ms/epoch - 6ms/step\n",
      "Epoch 1134/3000\n",
      "\n",
      "Epoch 1134: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1507e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 969ms/epoch - 6ms/step\n",
      "Epoch 1135/3000\n",
      "\n",
      "Epoch 1135: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1613e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 943ms/epoch - 6ms/step\n",
      "Epoch 1136/3000\n",
      "\n",
      "Epoch 1136: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1554e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0392 - 957ms/epoch - 6ms/step\n",
      "Epoch 1137/3000\n",
      "\n",
      "Epoch 1137: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1410e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 967ms/epoch - 6ms/step\n",
      "Epoch 1138/3000\n",
      "\n",
      "Epoch 1138: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1595e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 948ms/epoch - 6ms/step\n",
      "Epoch 1139/3000\n",
      "\n",
      "Epoch 1139: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1564e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0393 - 953ms/epoch - 6ms/step\n",
      "Epoch 1140/3000\n",
      "\n",
      "Epoch 1140: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1546e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0393 - 945ms/epoch - 6ms/step\n",
      "Epoch 1141/3000\n",
      "\n",
      "Epoch 1141: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1564e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 969ms/epoch - 6ms/step\n",
      "Epoch 1142/3000\n",
      "\n",
      "Epoch 1142: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1618e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 946ms/epoch - 6ms/step\n",
      "Epoch 1143/3000\n",
      "\n",
      "Epoch 1143: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1594e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 963ms/epoch - 6ms/step\n",
      "Epoch 1144/3000\n",
      "\n",
      "Epoch 1144: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1503e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 951ms/epoch - 6ms/step\n",
      "Epoch 1145/3000\n",
      "\n",
      "Epoch 1145: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1577e-04 - mae: 0.0166 - val_loss: 0.0026 - val_mae: 0.0391 - 1s/epoch - 7ms/step\n",
      "Epoch 1146/3000\n",
      "\n",
      "Epoch 1146: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1507e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 964ms/epoch - 6ms/step\n",
      "Epoch 1147/3000\n",
      "\n",
      "Epoch 1147: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1489e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 920ms/epoch - 6ms/step\n",
      "Epoch 1148/3000\n",
      "\n",
      "Epoch 1148: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1603e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0394 - 917ms/epoch - 6ms/step\n",
      "Epoch 1149/3000\n",
      "\n",
      "Epoch 1149: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1524e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0392 - 916ms/epoch - 6ms/step\n",
      "Epoch 1150/3000\n",
      "\n",
      "Epoch 1150: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1487e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0393 - 960ms/epoch - 6ms/step\n",
      "Epoch 1151/3000\n",
      "\n",
      "Epoch 1151: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1535e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 991ms/epoch - 7ms/step\n",
      "Epoch 1152/3000\n",
      "\n",
      "Epoch 1152: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1516e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 941ms/epoch - 6ms/step\n",
      "Epoch 1153/3000\n",
      "\n",
      "Epoch 1153: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1503e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 968ms/epoch - 6ms/step\n",
      "Epoch 1154/3000\n",
      "\n",
      "Epoch 1154: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1536e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 965ms/epoch - 6ms/step\n",
      "Epoch 1155/3000\n",
      "\n",
      "Epoch 1155: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1475e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0393 - 932ms/epoch - 6ms/step\n",
      "Epoch 1156/3000\n",
      "\n",
      "Epoch 1156: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1433e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 935ms/epoch - 6ms/step\n",
      "Epoch 1157/3000\n",
      "\n",
      "Epoch 1157: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1534e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0397 - 929ms/epoch - 6ms/step\n",
      "Epoch 1158/3000\n",
      "\n",
      "Epoch 1158: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1410e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0392 - 929ms/epoch - 6ms/step\n",
      "Epoch 1159/3000\n",
      "\n",
      "Epoch 1159: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1516e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 954ms/epoch - 6ms/step\n",
      "Epoch 1160/3000\n",
      "\n",
      "Epoch 1160: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1447e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 904ms/epoch - 6ms/step\n",
      "Epoch 1161/3000\n",
      "\n",
      "Epoch 1161: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1460e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0392 - 918ms/epoch - 6ms/step\n",
      "Epoch 1162/3000\n",
      "\n",
      "Epoch 1162: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1383e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0393 - 952ms/epoch - 6ms/step\n",
      "Epoch 1163/3000\n",
      "\n",
      "Epoch 1163: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1532e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 929ms/epoch - 6ms/step\n",
      "Epoch 1164/3000\n",
      "\n",
      "Epoch 1164: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1470e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 911ms/epoch - 6ms/step\n",
      "Epoch 1165/3000\n",
      "\n",
      "Epoch 1165: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1396e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 934ms/epoch - 6ms/step\n",
      "Epoch 1166/3000\n",
      "\n",
      "Epoch 1166: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1380e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 932ms/epoch - 6ms/step\n",
      "Epoch 1167/3000\n",
      "\n",
      "Epoch 1167: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1425e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 937ms/epoch - 6ms/step\n",
      "Epoch 1168/3000\n",
      "\n",
      "Epoch 1168: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1476e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 952ms/epoch - 6ms/step\n",
      "Epoch 1169/3000\n",
      "\n",
      "Epoch 1169: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1353e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 945ms/epoch - 6ms/step\n",
      "Epoch 1170/3000\n",
      "\n",
      "Epoch 1170: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1389e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 926ms/epoch - 6ms/step\n",
      "Epoch 1171/3000\n",
      "\n",
      "Epoch 1171: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1431e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0394 - 940ms/epoch - 6ms/step\n",
      "Epoch 1172/3000\n",
      "\n",
      "Epoch 1172: val_mae did not improve from 0.03853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.1551e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 945ms/epoch - 6ms/step\n",
      "Epoch 1173/3000\n",
      "\n",
      "Epoch 1173: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1441e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 921ms/epoch - 6ms/step\n",
      "Epoch 1174/3000\n",
      "\n",
      "Epoch 1174: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1456e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 932ms/epoch - 6ms/step\n",
      "Epoch 1175/3000\n",
      "\n",
      "Epoch 1175: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1349e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 947ms/epoch - 6ms/step\n",
      "Epoch 1176/3000\n",
      "\n",
      "Epoch 1176: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1536e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 920ms/epoch - 6ms/step\n",
      "Epoch 1177/3000\n",
      "\n",
      "Epoch 1177: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1443e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 941ms/epoch - 6ms/step\n",
      "Epoch 1178/3000\n",
      "\n",
      "Epoch 1178: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1425e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 946ms/epoch - 6ms/step\n",
      "Epoch 1179/3000\n",
      "\n",
      "Epoch 1179: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1354e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 945ms/epoch - 6ms/step\n",
      "Epoch 1180/3000\n",
      "\n",
      "Epoch 1180: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1420e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 944ms/epoch - 6ms/step\n",
      "Epoch 1181/3000\n",
      "\n",
      "Epoch 1181: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1323e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 935ms/epoch - 6ms/step\n",
      "Epoch 1182/3000\n",
      "\n",
      "Epoch 1182: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1390e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 930ms/epoch - 6ms/step\n",
      "Epoch 1183/3000\n",
      "\n",
      "Epoch 1183: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1526e-04 - mae: 0.0166 - val_loss: 0.0027 - val_mae: 0.0396 - 941ms/epoch - 6ms/step\n",
      "Epoch 1184/3000\n",
      "\n",
      "Epoch 1184: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1396e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 958ms/epoch - 6ms/step\n",
      "Epoch 1185/3000\n",
      "\n",
      "Epoch 1185: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1384e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 958ms/epoch - 6ms/step\n",
      "Epoch 1186/3000\n",
      "\n",
      "Epoch 1186: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1353e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 931ms/epoch - 6ms/step\n",
      "Epoch 1187/3000\n",
      "\n",
      "Epoch 1187: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1328e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0391 - 929ms/epoch - 6ms/step\n",
      "Epoch 1188/3000\n",
      "\n",
      "Epoch 1188: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1353e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 955ms/epoch - 6ms/step\n",
      "Epoch 1189/3000\n",
      "\n",
      "Epoch 1189: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1342e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 927ms/epoch - 6ms/step\n",
      "Epoch 1190/3000\n",
      "\n",
      "Epoch 1190: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1414e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 911ms/epoch - 6ms/step\n",
      "Epoch 1191/3000\n",
      "\n",
      "Epoch 1191: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1475e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 908ms/epoch - 6ms/step\n",
      "Epoch 1192/3000\n",
      "\n",
      "Epoch 1192: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1333e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 951ms/epoch - 6ms/step\n",
      "Epoch 1193/3000\n",
      "\n",
      "Epoch 1193: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1296e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 964ms/epoch - 6ms/step\n",
      "Epoch 1194/3000\n",
      "\n",
      "Epoch 1194: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1386e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 922ms/epoch - 6ms/step\n",
      "Epoch 1195/3000\n",
      "\n",
      "Epoch 1195: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1372e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 952ms/epoch - 6ms/step\n",
      "Epoch 1196/3000\n",
      "\n",
      "Epoch 1196: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1330e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 962ms/epoch - 6ms/step\n",
      "Epoch 1197/3000\n",
      "\n",
      "Epoch 1197: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1356e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 940ms/epoch - 6ms/step\n",
      "Epoch 1198/3000\n",
      "\n",
      "Epoch 1198: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1374e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 950ms/epoch - 6ms/step\n",
      "Epoch 1199/3000\n",
      "\n",
      "Epoch 1199: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1305e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 921ms/epoch - 6ms/step\n",
      "Epoch 1200/3000\n",
      "\n",
      "Epoch 1200: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1366e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0392 - 917ms/epoch - 6ms/step\n",
      "Epoch 1201/3000\n",
      "\n",
      "Epoch 1201: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1364e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 951ms/epoch - 6ms/step\n",
      "Epoch 1202/3000\n",
      "\n",
      "Epoch 1202: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1317e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 949ms/epoch - 6ms/step\n",
      "Epoch 1203/3000\n",
      "\n",
      "Epoch 1203: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1363e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0392 - 925ms/epoch - 6ms/step\n",
      "Epoch 1204/3000\n",
      "\n",
      "Epoch 1204: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1244e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 937ms/epoch - 6ms/step\n",
      "Epoch 1205/3000\n",
      "\n",
      "Epoch 1205: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1287e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 966ms/epoch - 6ms/step\n",
      "Epoch 1206/3000\n",
      "\n",
      "Epoch 1206: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1355e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 921ms/epoch - 6ms/step\n",
      "Epoch 1207/3000\n",
      "\n",
      "Epoch 1207: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1346e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0394 - 928ms/epoch - 6ms/step\n",
      "Epoch 1208/3000\n",
      "\n",
      "Epoch 1208: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1307e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 924ms/epoch - 6ms/step\n",
      "Epoch 1209/3000\n",
      "\n",
      "Epoch 1209: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1240e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 941ms/epoch - 6ms/step\n",
      "Epoch 1210/3000\n",
      "\n",
      "Epoch 1210: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1357e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 966ms/epoch - 6ms/step\n",
      "Epoch 1211/3000\n",
      "\n",
      "Epoch 1211: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1298e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 922ms/epoch - 6ms/step\n",
      "Epoch 1212/3000\n",
      "\n",
      "Epoch 1212: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1354e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 925ms/epoch - 6ms/step\n",
      "Epoch 1213/3000\n",
      "\n",
      "Epoch 1213: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1363e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 925ms/epoch - 6ms/step\n",
      "Epoch 1214/3000\n",
      "\n",
      "Epoch 1214: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1276e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 965ms/epoch - 6ms/step\n",
      "Epoch 1215/3000\n",
      "\n",
      "Epoch 1215: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1277e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 924ms/epoch - 6ms/step\n",
      "Epoch 1216/3000\n",
      "\n",
      "Epoch 1216: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1310e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 928ms/epoch - 6ms/step\n",
      "Epoch 1217/3000\n",
      "\n",
      "Epoch 1217: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1401e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 926ms/epoch - 6ms/step\n",
      "Epoch 1218/3000\n",
      "\n",
      "Epoch 1218: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1245e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 932ms/epoch - 6ms/step\n",
      "Epoch 1219/3000\n",
      "\n",
      "Epoch 1219: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1238e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 938ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1220/3000\n",
      "\n",
      "Epoch 1220: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1261e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 921ms/epoch - 6ms/step\n",
      "Epoch 1221/3000\n",
      "\n",
      "Epoch 1221: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1355e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 920ms/epoch - 6ms/step\n",
      "Epoch 1222/3000\n",
      "\n",
      "Epoch 1222: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1278e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 931ms/epoch - 6ms/step\n",
      "Epoch 1223/3000\n",
      "\n",
      "Epoch 1223: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1355e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 939ms/epoch - 6ms/step\n",
      "Epoch 1224/3000\n",
      "\n",
      "Epoch 1224: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1259e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 902ms/epoch - 6ms/step\n",
      "Epoch 1225/3000\n",
      "\n",
      "Epoch 1225: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1292e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 951ms/epoch - 6ms/step\n",
      "Epoch 1226/3000\n",
      "\n",
      "Epoch 1226: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1255e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 926ms/epoch - 6ms/step\n",
      "Epoch 1227/3000\n",
      "\n",
      "Epoch 1227: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1346e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 946ms/epoch - 6ms/step\n",
      "Epoch 1228/3000\n",
      "\n",
      "Epoch 1228: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1314e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 953ms/epoch - 6ms/step\n",
      "Epoch 1229/3000\n",
      "\n",
      "Epoch 1229: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1219e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 941ms/epoch - 6ms/step\n",
      "Epoch 1230/3000\n",
      "\n",
      "Epoch 1230: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1233e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0393 - 948ms/epoch - 6ms/step\n",
      "Epoch 1231/3000\n",
      "\n",
      "Epoch 1231: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1301e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 937ms/epoch - 6ms/step\n",
      "Epoch 1232/3000\n",
      "\n",
      "Epoch 1232: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1268e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 906ms/epoch - 6ms/step\n",
      "Epoch 1233/3000\n",
      "\n",
      "Epoch 1233: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1246e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 944ms/epoch - 6ms/step\n",
      "Epoch 1234/3000\n",
      "\n",
      "Epoch 1234: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1237e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 922ms/epoch - 6ms/step\n",
      "Epoch 1235/3000\n",
      "\n",
      "Epoch 1235: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1244e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 939ms/epoch - 6ms/step\n",
      "Epoch 1236/3000\n",
      "\n",
      "Epoch 1236: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1318e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 915ms/epoch - 6ms/step\n",
      "Epoch 1237/3000\n",
      "\n",
      "Epoch 1237: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1187e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 922ms/epoch - 6ms/step\n",
      "Epoch 1238/3000\n",
      "\n",
      "Epoch 1238: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1270e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 908ms/epoch - 6ms/step\n",
      "Epoch 1239/3000\n",
      "\n",
      "Epoch 1239: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1248e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 934ms/epoch - 6ms/step\n",
      "Epoch 1240/3000\n",
      "\n",
      "Epoch 1240: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1222e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 924ms/epoch - 6ms/step\n",
      "Epoch 1241/3000\n",
      "\n",
      "Epoch 1241: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1160e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 930ms/epoch - 6ms/step\n",
      "Epoch 1242/3000\n",
      "\n",
      "Epoch 1242: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1291e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 927ms/epoch - 6ms/step\n",
      "Epoch 1243/3000\n",
      "\n",
      "Epoch 1243: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1219e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0393 - 938ms/epoch - 6ms/step\n",
      "Epoch 1244/3000\n",
      "\n",
      "Epoch 1244: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1177e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 927ms/epoch - 6ms/step\n",
      "Epoch 1245/3000\n",
      "\n",
      "Epoch 1245: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1178e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 912ms/epoch - 6ms/step\n",
      "Epoch 1246/3000\n",
      "\n",
      "Epoch 1246: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1186e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 899ms/epoch - 6ms/step\n",
      "Epoch 1247/3000\n",
      "\n",
      "Epoch 1247: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1210e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 911ms/epoch - 6ms/step\n",
      "Epoch 1248/3000\n",
      "\n",
      "Epoch 1248: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1239e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 953ms/epoch - 6ms/step\n",
      "Epoch 1249/3000\n",
      "\n",
      "Epoch 1249: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1110e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 914ms/epoch - 6ms/step\n",
      "Epoch 1250/3000\n",
      "\n",
      "Epoch 1250: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1185e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 963ms/epoch - 6ms/step\n",
      "Epoch 1251/3000\n",
      "\n",
      "Epoch 1251: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1159e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 934ms/epoch - 6ms/step\n",
      "Epoch 1252/3000\n",
      "\n",
      "Epoch 1252: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1158e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 934ms/epoch - 6ms/step\n",
      "Epoch 1253/3000\n",
      "\n",
      "Epoch 1253: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1187e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 941ms/epoch - 6ms/step\n",
      "Epoch 1254/3000\n",
      "\n",
      "Epoch 1254: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1190e-04 - mae: 0.0165 - val_loss: 0.0026 - val_mae: 0.0394 - 920ms/epoch - 6ms/step\n",
      "Epoch 1255/3000\n",
      "\n",
      "Epoch 1255: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1208e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 904ms/epoch - 6ms/step\n",
      "Epoch 1256/3000\n",
      "\n",
      "Epoch 1256: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1165e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 930ms/epoch - 6ms/step\n",
      "Epoch 1257/3000\n",
      "\n",
      "Epoch 1257: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1164e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 967ms/epoch - 6ms/step\n",
      "Epoch 1258/3000\n",
      "\n",
      "Epoch 1258: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1170e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 932ms/epoch - 6ms/step\n",
      "Epoch 1259/3000\n",
      "\n",
      "Epoch 1259: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1154e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 922ms/epoch - 6ms/step\n",
      "Epoch 1260/3000\n",
      "\n",
      "Epoch 1260: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1119e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 926ms/epoch - 6ms/step\n",
      "Epoch 1261/3000\n",
      "\n",
      "Epoch 1261: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1157e-04 - mae: 0.0165 - val_loss: 0.0028 - val_mae: 0.0403 - 952ms/epoch - 6ms/step\n",
      "Epoch 1262/3000\n",
      "\n",
      "Epoch 1262: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1202e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 940ms/epoch - 6ms/step\n",
      "Epoch 1263/3000\n",
      "\n",
      "Epoch 1263: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1161e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 909ms/epoch - 6ms/step\n",
      "Epoch 1264/3000\n",
      "\n",
      "Epoch 1264: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1163e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 953ms/epoch - 6ms/step\n",
      "Epoch 1265/3000\n",
      "\n",
      "Epoch 1265: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1168e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 930ms/epoch - 6ms/step\n",
      "Epoch 1266/3000\n",
      "\n",
      "Epoch 1266: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1092e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 947ms/epoch - 6ms/step\n",
      "Epoch 1267/3000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1267: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1185e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 974ms/epoch - 6ms/step\n",
      "Epoch 1268/3000\n",
      "\n",
      "Epoch 1268: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1188e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 969ms/epoch - 6ms/step\n",
      "Epoch 1269/3000\n",
      "\n",
      "Epoch 1269: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1161e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 963ms/epoch - 6ms/step\n",
      "Epoch 1270/3000\n",
      "\n",
      "Epoch 1270: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1113e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 962ms/epoch - 6ms/step\n",
      "Epoch 1271/3000\n",
      "\n",
      "Epoch 1271: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1045e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 950ms/epoch - 6ms/step\n",
      "Epoch 1272/3000\n",
      "\n",
      "Epoch 1272: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1162e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 908ms/epoch - 6ms/step\n",
      "Epoch 1273/3000\n",
      "\n",
      "Epoch 1273: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1116e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 928ms/epoch - 6ms/step\n",
      "Epoch 1274/3000\n",
      "\n",
      "Epoch 1274: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1227e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 973ms/epoch - 6ms/step\n",
      "Epoch 1275/3000\n",
      "\n",
      "Epoch 1275: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1136e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 920ms/epoch - 6ms/step\n",
      "Epoch 1276/3000\n",
      "\n",
      "Epoch 1276: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1147e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0394 - 925ms/epoch - 6ms/step\n",
      "Epoch 1277/3000\n",
      "\n",
      "Epoch 1277: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1054e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 967ms/epoch - 6ms/step\n",
      "Epoch 1278/3000\n",
      "\n",
      "Epoch 1278: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1172e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 935ms/epoch - 6ms/step\n",
      "Epoch 1279/3000\n",
      "\n",
      "Epoch 1279: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1052e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 919ms/epoch - 6ms/step\n",
      "Epoch 1280/3000\n",
      "\n",
      "Epoch 1280: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1085e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 913ms/epoch - 6ms/step\n",
      "Epoch 1281/3000\n",
      "\n",
      "Epoch 1281: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1020e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 971ms/epoch - 6ms/step\n",
      "Epoch 1282/3000\n",
      "\n",
      "Epoch 1282: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1110e-04 - mae: 0.0165 - val_loss: 0.0028 - val_mae: 0.0401 - 968ms/epoch - 6ms/step\n",
      "Epoch 1283/3000\n",
      "\n",
      "Epoch 1283: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1174e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 931ms/epoch - 6ms/step\n",
      "Epoch 1284/3000\n",
      "\n",
      "Epoch 1284: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1078e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 914ms/epoch - 6ms/step\n",
      "Epoch 1285/3000\n",
      "\n",
      "Epoch 1285: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1133e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 936ms/epoch - 6ms/step\n",
      "Epoch 1286/3000\n",
      "\n",
      "Epoch 1286: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1127e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 936ms/epoch - 6ms/step\n",
      "Epoch 1287/3000\n",
      "\n",
      "Epoch 1287: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1122e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 929ms/epoch - 6ms/step\n",
      "Epoch 1288/3000\n",
      "\n",
      "Epoch 1288: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1079e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 942ms/epoch - 6ms/step\n",
      "Epoch 1289/3000\n",
      "\n",
      "Epoch 1289: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1089e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 929ms/epoch - 6ms/step\n",
      "Epoch 1290/3000\n",
      "\n",
      "Epoch 1290: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1072e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 923ms/epoch - 6ms/step\n",
      "Epoch 1291/3000\n",
      "\n",
      "Epoch 1291: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1017e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 968ms/epoch - 6ms/step\n",
      "Epoch 1292/3000\n",
      "\n",
      "Epoch 1292: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1063e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 957ms/epoch - 6ms/step\n",
      "Epoch 1293/3000\n",
      "\n",
      "Epoch 1293: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1045e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 918ms/epoch - 6ms/step\n",
      "Epoch 1294/3000\n",
      "\n",
      "Epoch 1294: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1000e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 947ms/epoch - 6ms/step\n",
      "Epoch 1295/3000\n",
      "\n",
      "Epoch 1295: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1011e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 967ms/epoch - 6ms/step\n",
      "Epoch 1296/3000\n",
      "\n",
      "Epoch 1296: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0994e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 914ms/epoch - 6ms/step\n",
      "Epoch 1297/3000\n",
      "\n",
      "Epoch 1297: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1030e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 934ms/epoch - 6ms/step\n",
      "Epoch 1298/3000\n",
      "\n",
      "Epoch 1298: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1048e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 942ms/epoch - 6ms/step\n",
      "Epoch 1299/3000\n",
      "\n",
      "Epoch 1299: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1024e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 943ms/epoch - 6ms/step\n",
      "Epoch 1300/3000\n",
      "\n",
      "Epoch 1300: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0999e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 932ms/epoch - 6ms/step\n",
      "Epoch 1301/3000\n",
      "\n",
      "Epoch 1301: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0996e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 944ms/epoch - 6ms/step\n",
      "Epoch 1302/3000\n",
      "\n",
      "Epoch 1302: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1055e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 926ms/epoch - 6ms/step\n",
      "Epoch 1303/3000\n",
      "\n",
      "Epoch 1303: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1072e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 996ms/epoch - 7ms/step\n",
      "Epoch 1304/3000\n",
      "\n",
      "Epoch 1304: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1088e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 941ms/epoch - 6ms/step\n",
      "Epoch 1305/3000\n",
      "\n",
      "Epoch 1305: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1024e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 936ms/epoch - 6ms/step\n",
      "Epoch 1306/3000\n",
      "\n",
      "Epoch 1306: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1025e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 920ms/epoch - 6ms/step\n",
      "Epoch 1307/3000\n",
      "\n",
      "Epoch 1307: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0996e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 939ms/epoch - 6ms/step\n",
      "Epoch 1308/3000\n",
      "\n",
      "Epoch 1308: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1145e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 961ms/epoch - 6ms/step\n",
      "Epoch 1309/3000\n",
      "\n",
      "Epoch 1309: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0929e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 941ms/epoch - 6ms/step\n",
      "Epoch 1310/3000\n",
      "\n",
      "Epoch 1310: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0973e-04 - mae: 0.0165 - val_loss: 0.0028 - val_mae: 0.0402 - 931ms/epoch - 6ms/step\n",
      "Epoch 1311/3000\n",
      "\n",
      "Epoch 1311: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1034e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 930ms/epoch - 6ms/step\n",
      "Epoch 1312/3000\n",
      "\n",
      "Epoch 1312: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0906e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0396 - 961ms/epoch - 6ms/step\n",
      "Epoch 1313/3000\n",
      "\n",
      "Epoch 1313: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1085e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 923ms/epoch - 6ms/step\n",
      "Epoch 1314/3000\n",
      "\n",
      "Epoch 1314: val_mae did not improve from 0.03853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151/151 - 1s - loss: 5.0991e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 952ms/epoch - 6ms/step\n",
      "Epoch 1315/3000\n",
      "\n",
      "Epoch 1315: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1017e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 916ms/epoch - 6ms/step\n",
      "Epoch 1316/3000\n",
      "\n",
      "Epoch 1316: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0971e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 947ms/epoch - 6ms/step\n",
      "Epoch 1317/3000\n",
      "\n",
      "Epoch 1317: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1089e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 946ms/epoch - 6ms/step\n",
      "Epoch 1318/3000\n",
      "\n",
      "Epoch 1318: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1024e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 920ms/epoch - 6ms/step\n",
      "Epoch 1319/3000\n",
      "\n",
      "Epoch 1319: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1094e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 908ms/epoch - 6ms/step\n",
      "Epoch 1320/3000\n",
      "\n",
      "Epoch 1320: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1015e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 935ms/epoch - 6ms/step\n",
      "Epoch 1321/3000\n",
      "\n",
      "Epoch 1321: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0877e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0401 - 925ms/epoch - 6ms/step\n",
      "Epoch 1322/3000\n",
      "\n",
      "Epoch 1322: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1023e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 929ms/epoch - 6ms/step\n",
      "Epoch 1323/3000\n",
      "\n",
      "Epoch 1323: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1039e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 916ms/epoch - 6ms/step\n",
      "Epoch 1324/3000\n",
      "\n",
      "Epoch 1324: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0992e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0395 - 946ms/epoch - 6ms/step\n",
      "Epoch 1325/3000\n",
      "\n",
      "Epoch 1325: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0926e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 940ms/epoch - 6ms/step\n",
      "Epoch 1326/3000\n",
      "\n",
      "Epoch 1326: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1026e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 930ms/epoch - 6ms/step\n",
      "Epoch 1327/3000\n",
      "\n",
      "Epoch 1327: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1070e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 931ms/epoch - 6ms/step\n",
      "Epoch 1328/3000\n",
      "\n",
      "Epoch 1328: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0908e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0396 - 918ms/epoch - 6ms/step\n",
      "Epoch 1329/3000\n",
      "\n",
      "Epoch 1329: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1110e-04 - mae: 0.0165 - val_loss: 0.0028 - val_mae: 0.0401 - 911ms/epoch - 6ms/step\n",
      "Epoch 1330/3000\n",
      "\n",
      "Epoch 1330: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0945e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 925ms/epoch - 6ms/step\n",
      "Epoch 1331/3000\n",
      "\n",
      "Epoch 1331: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0987e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 910ms/epoch - 6ms/step\n",
      "Epoch 1332/3000\n",
      "\n",
      "Epoch 1332: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0922e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 917ms/epoch - 6ms/step\n",
      "Epoch 1333/3000\n",
      "\n",
      "Epoch 1333: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0986e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0401 - 913ms/epoch - 6ms/step\n",
      "Epoch 1334/3000\n",
      "\n",
      "Epoch 1334: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0890e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 891ms/epoch - 6ms/step\n",
      "Epoch 1335/3000\n",
      "\n",
      "Epoch 1335: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0978e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 919ms/epoch - 6ms/step\n",
      "Epoch 1336/3000\n",
      "\n",
      "Epoch 1336: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1023e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 912ms/epoch - 6ms/step\n",
      "Epoch 1337/3000\n",
      "\n",
      "Epoch 1337: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0859e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0398 - 917ms/epoch - 6ms/step\n",
      "Epoch 1338/3000\n",
      "\n",
      "Epoch 1338: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0969e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 924ms/epoch - 6ms/step\n",
      "Epoch 1339/3000\n",
      "\n",
      "Epoch 1339: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0963e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 915ms/epoch - 6ms/step\n",
      "Epoch 1340/3000\n",
      "\n",
      "Epoch 1340: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0908e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0400 - 949ms/epoch - 6ms/step\n",
      "Epoch 1341/3000\n",
      "\n",
      "Epoch 1341: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0937e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0396 - 953ms/epoch - 6ms/step\n",
      "Epoch 1342/3000\n",
      "\n",
      "Epoch 1342: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0889e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 964ms/epoch - 6ms/step\n",
      "Epoch 1343/3000\n",
      "\n",
      "Epoch 1343: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0839e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0397 - 951ms/epoch - 6ms/step\n",
      "Epoch 1344/3000\n",
      "\n",
      "Epoch 1344: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0948e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 956ms/epoch - 6ms/step\n",
      "Epoch 1345/3000\n",
      "\n",
      "Epoch 1345: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0951e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0399 - 933ms/epoch - 6ms/step\n",
      "Epoch 1346/3000\n",
      "\n",
      "Epoch 1346: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0924e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0400 - 957ms/epoch - 6ms/step\n",
      "Epoch 1347/3000\n",
      "\n",
      "Epoch 1347: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0888e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0396 - 932ms/epoch - 6ms/step\n",
      "Epoch 1348/3000\n",
      "\n",
      "Epoch 1348: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0907e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0398 - 916ms/epoch - 6ms/step\n",
      "Epoch 1349/3000\n",
      "\n",
      "Epoch 1349: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0934e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0397 - 911ms/epoch - 6ms/step\n",
      "Epoch 1350/3000\n",
      "\n",
      "Epoch 1350: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0932e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0398 - 926ms/epoch - 6ms/step\n",
      "Epoch 1351/3000\n",
      "\n",
      "Epoch 1351: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0935e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0397 - 952ms/epoch - 6ms/step\n",
      "Epoch 1352/3000\n",
      "\n",
      "Epoch 1352: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0852e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 938ms/epoch - 6ms/step\n",
      "Epoch 1353/3000\n",
      "\n",
      "Epoch 1353: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.1012e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0397 - 913ms/epoch - 6ms/step\n",
      "Epoch 1354/3000\n",
      "\n",
      "Epoch 1354: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0887e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 912ms/epoch - 6ms/step\n",
      "Epoch 1355/3000\n",
      "\n",
      "Epoch 1355: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0989e-04 - mae: 0.0165 - val_loss: 0.0027 - val_mae: 0.0400 - 921ms/epoch - 6ms/step\n",
      "Epoch 1356/3000\n",
      "\n",
      "Epoch 1356: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0881e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0400 - 939ms/epoch - 6ms/step\n",
      "Epoch 1357/3000\n",
      "\n",
      "Epoch 1357: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0838e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0397 - 917ms/epoch - 6ms/step\n",
      "Epoch 1358/3000\n",
      "\n",
      "Epoch 1358: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0797e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0395 - 940ms/epoch - 6ms/step\n",
      "Epoch 1359/3000\n",
      "\n",
      "Epoch 1359: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0926e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 943ms/epoch - 6ms/step\n",
      "Epoch 1360/3000\n",
      "\n",
      "Epoch 1360: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0819e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0395 - 924ms/epoch - 6ms/step\n",
      "Epoch 1361/3000\n",
      "\n",
      "Epoch 1361: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0866e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0396 - 912ms/epoch - 6ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1362/3000\n",
      "\n",
      "Epoch 1362: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0824e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0399 - 924ms/epoch - 6ms/step\n",
      "Epoch 1363/3000\n",
      "\n",
      "Epoch 1363: val_mae did not improve from 0.03853\n",
      "151/151 - 1s - loss: 5.0776e-04 - mae: 0.0164 - val_loss: 0.0027 - val_mae: 0.0400 - 925ms/epoch - 6ms/step\n",
      "Epoch 1363: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f3dce66ad40>"
      ]
     },
     "execution_count": 12,
     "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/cnn_gate_pump_nofuture.h5', \n",
    "                     monitor='val_mae', \n",
    "                     mode='min', \n",
    "                     verbose=2, \n",
    "                     save_best_only=True\n",
    "                    )\n",
    "\n",
    "\n",
    "model.fit([train_X_mask], train_ws_y,\n",
    "          validation_data=([test_X_mask], test_ws_y),\n",
    "          epochs=EPOCHS,\n",
    "          batch_size=BATCH,\n",
    "          verbose=2,\n",
    "          shuffle=True,\n",
    "          callbacks=[es, mc]\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d04c0b",
   "metadata": {},
   "source": [
    "### Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c60c3dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603/603 [==============================] - 1s 2ms/step\n",
      "inv_y.shape, inv_yhat.shape (19268, 96) (19268, 96)\n",
      "MAE = 0.203\n",
      "RMSE = 0.2656\n"
     ]
    }
   ],
   "source": [
    "saved_model = load_model('saved_models/cnn_gate_pump_nofuture.h5')\n",
    "\n",
    "yhat = saved_model.predict(test_X_mask)\n",
    "\n",
    "inv_yhat = ws_scaler.inverse_transform(yhat)\n",
    "inv_y = ws_scaler.inverse_transform(test_ws_y)\n",
    "\n",
    "print(\"inv_y.shape, inv_yhat.shape\", inv_y.shape, inv_yhat.shape)\n",
    "print('MAE = {}'.format(float(\"{:.4f}\".format(mae(inv_y, inv_yhat)))))\n",
    "print('RMSE = {}'.format(float(\"{:.4f}\".format(sqrt(mse(inv_y, inv_yhat))))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10108ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dn5/PXeYTTzyBtm3b4p133sGGDRuQnZ2N+vXrY/z48Rg/fjwzCQtBEARBEARBEARBEARBlBYEURTFYFeC8CUjIwOJiYlIT09HQkJCsKtDEEQ5JmncfABA8uRhQa4JQRAEQRAEQRBE+SUUdEUUI5EgCIIgCIIgCIIgCIIgCENIkUgQBEEQBEEQBEEQBEEQhCGkSCQIgiAIgiAIQhNRFJGeWxjsahAEQRAEEQKQIpEgCIIgCIIgCE0+XX4I7V9ZhIIiV7CrQoQYZzPzUVRM7YIgCKI8QYpEgiAIgouDZ7KCXQWCIAgiCKw+eA4AUEgKI0JFl/8txqQFe4NdDYIgCCKAkCKRIAiC4OKK91cEuwoEQRBEEBAgAADEINeDCE3WHjof7CoQBEEQAYQUiQRRyhj87nKsOnAu2NUgyiHFLppCEgRB2EUURfy27USpsu7bl5oJAPhnz+kg14QIRQQh2DUgCIIgAgkpEgmiFCGKIg6eycI7/+wLdlUIgiAIgrDA1pQ0PDZnG6avOhLsqnBzNjMfAPDfqcwg14QIRUiRSBAEUb6IsHNyTk4OVq5cieXLl2Pt2rU4efIkzp49i7y8PFStWhXVq1dHy5Yt0a9fP/Tr1w/Nmzd3qt4EUS4RySCMIAiCIEo1eQXFAFAqsyAXFZMgQngRSwRTyfWdIAiCKB9YUiRu2bIFn332GWbPno3s7GwA3oFE4sSJEzhx4gS2bduGOXPmAAA6duyI++67D6NHj0ZcXJzNqhNE+cNV8p2RQpEoC2TmFSI+JjLY1SAIgiA4cZEAQsig5kAQBFE+MaVI3LZtG55++mksXbrUoziMiYlBp06d0LFjR1SrVg1VqlRBhQoVcOHCBVy4cAFHjhzB+vXrcezYMWzZsgX3338/nn32WUyYMAGPPfYYoqKi/HJjBFEWIXmNKCv8tu0EHpuzDesnDELNhJhgV4cgCILggBSJhBypNZBrM0EQRPmCW5F4xx134Pvvv4fL5UK1atUwatQojB49Gl26dEFEhHExqamp+P333/Hdd99h1apVGDduHD799FN888036NOnj62bIIjyAsnvRFlhw5ELANxxt0iRSBAEUTqgpFuEHLVHGkEQBFE+4E628u2336JFixaYPXs2Tp06hQ8//BA9evTgUiICQM2aNXHvvfdi+fLlOHz4MMaOHYuTJ09i6dKllitPEOUNsgQgygrUkgmCKO+UJiMuyeKM5BBCjsciMai1IAiCIAINtyJx9uzZ2LlzJ0aNGoXw8HBbF23QoAE+/fRTHDx4EIMGDbJVFkEQBEEQBEGUFkqjO2hYSWUp2Qohh/TKBEEQ5RNu1+ZRo0Y5fvG6deuibt26jpdLEGUVsgQgygqlaP5MEAThKNJYHlaKNIlhAlAMoJjkEEKGKKnFS1FbJgiCIOzDbZFIEETwkeR3EuMJgiAIonQihRksTaoXoaS2LoqRSMgQS2FbJgiCIOxjS5E4cOBAjBgxgvv4m2++mVyZCcIGkhUDCWxEWeKHjcewZG9qsKtBEAQREDxjeSmy4vLGSAxuPYjQQiSDRIIgiHIJt2szi2XLlqFWrVrcx69btw7Hjh2zc0mCKNecTs8DQBaJROlH3oaf+3knACB58rDgVIYgCCKASJluw8NKj/ZFUhSRazMhRySJlCAIolwSUNdml8tVqlZfCSLUuOGTNcGuAkEQBEGUeybM24mPlx60dG5pdAcl12aCRWlsywRBEIR9AqZILC4uxpkzZxAXFxeoSxJEmSMzvyjYVSAIR6BJh/9JzynE/d9uRhb1GwThOLPWH8OUv/dZOre4RBn3zj/7naySX/G6NpMikfBCrYEgCKJ8Ysq1OSMjA2lpaYptxcXFSElJ8bhpqBFFEWlpafj666+Rn5+Pdu3aWa4sQRAEUTagyYf/+XnLcSzcfRpX/lcL13a4JNjVIQiihNJo1HdJpQo4cCYL1eOjg10VIoQQS2G8T4Igyg6iKOL79ccwonNdREeEB7s65QpTisT33nsPr776qmLbuXPnkJSUxHW+IAi47bbbzFySIAgGJK4RBGGENK8jAyKCCDVK30c5sGUNHDiThV6NqwW7KkQIUfpaMkEQZYl1hy/g+V93IaegCGP7Ng52dcoVplybRVFU/CcIgs82rf/q1KmDV199FQ8//LC/7oUgyg1GglvSuPn4dm1yIKpCEESIIi04UDB8gggtAm2RuD0lDdl2QxyU1Lk0WlMS/kNaqCpFeYMIgihDSGNbYTENToHGlEXiE088gTFjxgBwKxUbNWqE6tWrY8OGDZrnhIWFISEhAYmJifZqShCEKaYtP4zbeiQFuxoEQQSJsJKZHVkkEkRoEeg4g9d+vBpXtqmFT2+91HZZtDBByPG4NpOvDEEQQaC4pA8Ko/AKAYdbkfjkk08iMTERL730kmdbv379ULVqVTRo0MAvlSMIwjqh3J9eOXUl7uzZAKO61A92VYggEcLNs8wgPWOyICKI0CIY3+Ths9mOlEMLE4Qcag8EQQQTT+Z4mlgEHG7X5vfffx+fffaZYtvy5cuxZs0axytFEIR9QrlD/e9UBp77eSfyCouDXRUiSNDcIwAIkkUiPW2CCCVK8zdZemseuiSNm49Z648FuxqW8LSHEJY5CYIo+1AXFHi4FYmCIMDlcvmzLgRBcMLTWYaKm4koiliyN5U5cfp164kg1KjsU1jswuajF4NdDSLIeGMkEgQRSgTatdlJSrMSNJT5Zk1ysKtgCWoPRHpuIYrJ9YEIGtT2ggW3IjExMREXLlxAXl6eP+sTUH766Sf0798flStXRlxcHNq3b4+33noLhYWFpsqZMWMGBEHQ/W/hwoV+uguCYBMqFonL95/FXTM24fftJ4NdlXLDh/8ewPBP1yA1o+z014R5QqUPIAhCSSDX5Z1S9EilkN7IP5TW/lpqDqW0+oQDtH9lEV77c0+wq0GUc0prH1qa4Y6R2KFDByxfvhx33HEH7rjjDlSsWBEAUFBQgJUrV5oSVPr27Wu+pg7z+OOPY+rUqYiIiMDAgQNRsWJFLFmyBM899xz++OMPLFq0CBUqVDBVZuPGjdG7d2/mvksuucSJahOEId7A14EnLacA+05nolujqp5tBUXuGVPKhZwg1Kh8cqzkWecWhL7rOA38AYAm/gQn/acsxTUdLsGTlzULdlXKNIH8JJ1W/FGyFUIOKZYJAFi67wxeRutgV4MgiADCrUh8/PHHsWzZMsydOxdz5871bL948SL69+/PfUFBEFBUVGSqkk7z66+/YurUqahYsSKWL1+OTp06AQDOnTuHgQMHYtWqVXjhhRfw9ttvmyq3d+/emDFjhh9qTBBK9OQ2yb0gGNmrHpm9FSsPnEPy5GGebTGR4QCA/CJfEwySP/2DUPLu+7+9DF/c3hmXtaoZ5BoRwSBUwhsQpYfk8zn44N8DpEj0M4F0bZau5ZRIQIojQo6kWKZFwfIN9QtEsKC2Fzy4XZuvueYa/PTTT+jatSsqVKgAURQhCAJEUTT1XyjEWXzjjTcAAOPGjfMoEQGgWrVq+OSTTwAAH330EdLT04NSP4KwgyuIfibHL+YCULpShXkSPgS+PgQwbfmhYFeBCDJkQUQQoUUg48pJMgHPJb9dm4z5O07pHhPqY/mekxk4dDYr2NUoN3gyptLCFUEQRLmCW5EIAMOHD8fatWuRlZUFl8sFURRRq1YtuFwuU/8FkxMnTmDjxo0AgNGjR/vs7927N+rVq4f8/Hz89ddfga4eQXChJ665gujaLCGfaEir1KU5uHxpQ/7u1RPWJ37Yhms+WhXYCulAzcJ/SN8ePWOCUNL6xYX4c0fw4vby5iUoKnbBZTOJgZmFhBd+242HZm1hl1PSkYR6dzL0g5UY9M7yYFeDm9KerMSjSCQ9IkEQQaB096ClG1OKxLLA1q1bAQBVqlRBw4YNmcd07txZcSwvBw8exPPPP4+xY8fiySefxFdffYVz587Zq3A5Iz2nEEnj5mPXCbIGtYrXjSl4Up1caaiXObaUy8+hi+zVq+eg87aewI7j9H2VB2heRxBssguK8fHS4Flr82Y4bTJxAW75cr2tazmt6KFFQWdxed5P6eyxyeKdAKgdEMGHrKIDD3eMRBZHjhxBeHi4U3UJCEeOHAEA1K9fX/OYevXqKY7lZfXq1Vi9erViW0xMDF5++WU899xzJmtaPlm67wwA4M2Fe/Ht3d2CXJvSiTRBCWZ3WiyKns5FEo5p8hE4StNgWkrnTgEjadx83N6jAV69to3lMujLI4jQwky/t/bweVvXcnzs1SmusNiFBbtO4+p2tUutYizQBDNBnhOQRSJBEET5xJZFYoMGDVC3bl2n6hIQMjMzAQBxcXGax0gZqTMyMrjKrFWrFiZOnIj169fj7NmzyMjIwMaNG3H77bcjPz8f48aN88Rl1CI/Px8ZGRmK/8oj1eOjAQAd6lUKbkVKMa4gCnUe60PSXAQV+bunV1H6mb3hmKXzaGJHEL54XHTLyUAVyKzNX606gkdnb8XWlDRnL1qGkZ7mnlOlU+4vH18RoYW3Pw1yRYhyD8m8gYdbkZiTk+OXCvir3EAyZMgQvP766+jatSuqVauG+Ph4dO7cGd98840n8/Orr76K1NRUzTImTZqExMREz3+SVWR5gwYi+7g8FomB71Gl18eygGDVh1whCMIYu/0i9asE4SUUvge7cQ9NXcuhG/5ipdtLR6+4izmFAIC8wmJHrlkeCIX2aAdJkbT64PmAtmsiNCjt7Zco/VAbDB7cisSGDRvi3XffRV5eniMX3rRpE6666iq88847jpTHS3x8PAAgOztb85isLHe2t4SEBNvXe+yxx1CtWjXk5+dj0aJFmseNHz8e6enpnv9SUlJsX7s0Egrx/UoDen2m9xkal5OakYdek5fgTKYz37W3Dt6/SVkYeBSvPsRH2BCvXqmmNLm4E0SgCIUuJ5D6FqevpVecNN5T38OPUzLSxewC5BQUOVKWGeRjeDEN6OUOCltEEOUXbkViQUEBnnnmGSQlJWHcuHHYuXOn6YtlZWVh5syZGDx4MLp164a//voLiYmJpsuxQ1JSEgDoKuqkfdKxdggPD0fTpk0BAMePH9c8Ljo6GgkJCYr/yiPScEQiqD56LlnFJpSxC3edxom0XKw+6GxSIFYgedINBw561oQcUuYThBdXCLjinc/KD9zFHL5PHsWBnTHopd924d6Zm6wXUMr4efMJR8rp+No/uPrDVY6UZQZ5cyCdki+7TqRj4a5Twa6G35DEfXr3RLAgGTd4cCdbOXjwIF5++WV89tlnmDJlCqZMmYJWrVqhT58+6Nq1K9q3b4/q1aujSpUqiI6OxsWLF3HhwgUcPnwYGzZswPr167F8+XLk5uZCFEW0atUKb731FoYOHerP+/OhY8eOAIDz58/jyJEjzMzNmza5BZhOnTo5cs3z592BsiVrSEIbSUEWRpoQXfQGbE/ga45yikokgPAwZxK4e2MkKkwSNSHBw/+E+iOmT90Yy++Qni1hguz8wFszBYNQGHc+WHLQ8Bj1gtzhs1m4/L0VWDdhEKpVjOa+ltMWQ7rFOXCpb9YetV9IKWKdzWQ6cg6d1fa28hfySTxN6N28v3g/rmpXG01qxOOqEuXu1hcuQ+W4qCDXzHnonROhAnkzBh5u7UHVqlXx4YcfYvfu3bj33nsRFxeH3bt347PPPsPdd9+Nzp07o0GDBoiPj0dUVBRq1qyJli1bYtiwYXjllVewYMEC5OTkoEuXLvjmm2+wY8eOgCsRAaBu3bro0qULAGDWrFk++1etWoWUlBRER0c7Ur8tW7Zg//79AICuXbvaLq+s47FIpL5Al7TcAqTlFDD3/bLFmdVtO8jnPzMdnhR8uuwQlu4942iZBFEWoeRHhBlum74+2FUICB6LxBCfAE9bfkjxe8Gu0yhyidh2LM1UOY4rEjmOIRGOn9BuhcaQRaIv7y8+gLEzNyu23fJl2exf6Z0TwYbaYPAwbYbUtGlTTJs2DSdPnsTnn3+O66+/HtWqVYMoisz/wsPD0aVLFzz77LPYunUr1q1bh9tuuw1hDllAWWHChAkAgMmTJ2PLli2e7efPn8eDDz4IAHj44YcVbtfz5s1DixYtMGjQIEVZOTk5+Pjjjz3ZoOWsWLECw4cPBwD07t2bFIk8UGfARcqFXHR49R/mvhlr3AHR95zKQMsXFuqW4y9hXz5xWbj7tOa1rLzuNxfuxZgZG61VjCBskHIhBwt2Bt5FyW522X2nfccnglCzxaSCqrQT6pOP4xdzFb9FT9gSc+U4fpsaDy63oBj5RS6LRYr4YeMxvyVp+Wz5ISSNm++Xsu1S2rOHl+7a+48ilUXxwbNZQaqJfynlzZcgCBtwuzarqVixIu655x7cc889AIAjR47g5MmTOHv2LPLy8lC1alVUr14dTZs2RVxcnGMVdoLrrrsOjz76KD744AN0794dgwYNQlxcHP7991+kpaWhV69eeO211xTnpKenY9++fT7JZgoKCvDwww/jqaeeQseOHVG/fn0UFRVh//792LVrFwCgbdu2+PHHHwN2f6UZyUIgLAjL2UfPZ6NB1dBqq3bJ5RTKnRZkuTP3kQTid0L1EYdqvfQY9dlanEzPQ/LkYQG9rtVHJbl5fLvuKF67ro1zFSKIUozXIjHUUdbQap/phEWiXEbQKq3li96FS7MuZrtPZuC5n3fihEp56hQfLzV2JddjzcFzKCh2oX/zGg7VyEtpHAvlyNtGaUm8MXLaWvRpWg2PDGrqt2uoQxOU1YzWpeWdE2UXaoHBw7IiUU3Dhg2Z8QZDlalTp6JXr174+OOPsWbNGhQWFqJx48YYN24cnnjiCURF8cWxiI2NxQsvvIBNmzZh79692L17N3Jzc1G5cmUMHjwYI0aMwJ133sldXnmnqDg4WZv/3n0a9327Gb8/3Avt6lYK6LWdRmtMP52ehwvZBWhVx5vIx+nHfPicOz7PyfQ81EiIUewjd/XgEOrue6WJCxrhBILBQ7O2oG7lChh/ZctgV4UgShXSfN6phCdbjl1Ep/qVHSlLjnosl36ajSHtxDxfXoY/FCKSJWOmn+J0ZuTZK3d0iVuqPxaRWMnpShPy2pcWndKG5AvYkHzBr4pENWU1o7U3eVXZvD+i9EDTzMDjmCKxNDJy5EiMHDmS69g777wTd955p8/2qKgovPrqqw7XrPzy7j/7g3LdwyUBqk+l56Fd3aBUwe/0m7IU+UUuv1lTyYXh6z5e7XerrbScAlSKLT0K+q9WHcEfO05i3oO9gl2VkIAUy/aYv8PtYq2nSKRHTBC+SBPeizmFSD6XjaRq9jwRjp3P0VUkfr/+KNYeOo+PRptL4Ce39Fmx/6xXSWPyw7ZjMZRXWIxil4iYyHDPNq4YiSbrKB1/JiOA2axDhFUHzwW7CrZQKJlJmaRJWX00oupfggg0pMQOHsELVEgQDPaWxPIKlpKhLEy8z2SyBXFW7CIn77ew2HxsJDtdfyanhUGorPa/+ucebC0DMciOng98Vkg9TqfnIWncfGw9djHYVfEbJCMRhHPIh4QTaf5xpZUzcd4u/LnDfGxV+Xf/46YUj3W52XHbTv9xzUer0PqlvxUKopN+fGYZeYV+K9uIHzYeQ9K4+ZZkGTsE+npOw+P2Xp4ob0oNsaT5lrPbJggCpEgkQhTWgHTobBZyCvzj9uK5rs3z1xw8FzKKKzM4IQAUGAjDgsNqWp46rzl4Do0n/IVj53McvXZpwmnhLuWC9UnktpQ0zWzjVtmX6l58CGQm74Nn9IOmn83Mx/BP1yA9N3iTYoCsPgmCiaxPDOR4raVg0Nyu+ju3wFoikvcXuz09jELGfLvuqM+2/alZJXX0bvti5RHDa5rtegLVVem5Zf+y5QSAwC8+2rlaTkGR5XbhFKXRtdmflMIpgC2kBY7TGXkGRxKEf5AWukjmDTykSCSCyrJ9Z5A0bj4uZiuVCyxhb9A7y/HQ91t8tpslO7/IR7HkROfz36kMjP5yPWauTQbgXllv+cJC7D2dYb9wE7SWxUA0wslYlAUWszVaRR77z+USmZYMG5IvAAD6TlmK53/dGbC6BRunlbZOcd3Hq3H7VxuQX1TsWJwteZbPpfvOoNfkJY6Uq8fgd5fr7v916wlsPnoRqw4E12WtLAtVoihi0DvLcCrd/xZlRNlCbl1nNt4gC94iZm9I0agP+3i1UuazFYdLrmeuzj9uOs513Au/7tLc54TLam5BMTI1LA6le5J7GpzV8K6wgzqTrhxJgRhoZZgdC7ZWL/6Nrm8sdrA25pFXv7xZ47Eob8+gvClOidDDVTL9lMKUEYGDFIlEUPlnTyoAd2xCHrYfT7d9zTFfb0TfKUuZ++yM/5L1UWpJjJ8DqZnILSzGz5vZQvyWYxfx73+p1i8ItsAy4lLzQR6dSMhh5J7DmvvYed5y4WXqvwfQ7uVFusd/t+6Y9YuVMqy+z0Aog3ccT0fz5xdizkb2pNos93272fP3e//sx4m03HInyJdHFu1JxaGz2egxyf+KY6JsIe8dwhxQtvPGdl5ziL2woKWk0+rHgrE+4IQicfC7y9HWYJzelpLm+dsfFt0Ldmm7mEt3+ObCvY5fVw+7j5Y3zIu/kMsbZXHoXb7/rKmQLuVNsUZxMYlgI7VBllU94V9IkUiEFI8PdmdQW3fkPHO/HQF63+lMHDuf47FSc6pcCakjkyYm0gq7llBxwydrcPc3m2xdk+VmGm5iZuSkxVJhkb4w4fTkRy68LNt/FoB74vX0T9vxbYlVKMHP0r1n0Oz5BTh+Ud8N3Oks0E65kZF7lS+hapnqBHJLVIIwg3zscMIq/6jN0Bla/ZWo8SMQlsYbjijlJLP9dC7j+9SLR3maYVk8+N3l2H1SuXj87j/70fblv7HrRDq+KLHQNEKukOWxcpyxJpmrXKeQt8f8otLXryksEoNXDb9xx1cbcNl7K7iPL2+KtXJ2u0QIQk0weJAikQgq6o8/ISYSALD6IFuRCAC7T6bjTKb5WBxXvL9C2xLRdGnahSSfz8bZzHyPyxSvUFFU7MLVH67CrhP8VpdjZmzw2fbCb7u5z5cQReCDfw+gw6v61gJ6GMVIdOIZyycELNdYlwjM3XwcL/y2Gy6XaEvAeWvhXhw6qx8Lzwn2nMzAY3O2Bt2KTlKwG8WTdLqa36wxt4KYkVeIy99bjpQLvvWUlEv+epJm791ppStBEPaRf8d6626bki8gO9//1l5mLRIDwdpDShnMZdJY/e4Z5hZJJy9gWwH+pHLL/uDfA8jMK8KIaWvxv7/+4ypb/hj1XJs3Hw1Owi55lUqjUqY8ZG0OdOie0kSwZVeCoDYYPEiRSIQE0gq7UVcgCMCwD1Zh6NSVtq/lNJIw+NfO0+g3ZalngsLq31guOxdyCrDzRDoe/2Eb9zWzbE5y5I/i3X/2Iy2nED9uTLEkNLFcm+VWQywB02znrxRYfXfIr9Fowl+aGax5+GTZIQx6Z7nfB6jnf92J37ad9Fv5PNXfcuwiVh90u93ZNRBMzyk0lXnz5y188bskNh+9iP2pWZirChkgit7EAMGezPijj7FigVeWYyQShFXkfXqYjibxxmlr8ezcHYptZzLzfKz1uK+rWR/j4+fv9LrkOhHX0Qj1IkixQZ9apBr/jRYW1URFmJuOSJ4XC3edxpMGMpN8PFDXU+JclvPxGMsLZd212Sz+TPxQUOQKubZKr5wINM/8tB3LSzzRgPIXTiCUsKVInDlzJmbOnInUVHtx3ghCgldpcy7L2cyvTo/3OQXFHrdCllKDtfouHW+UEVZOUbG93nNRSYxKeRWf/XkHZqwxzsoocdWHK/Hy77uZisQWLyz0/M3q6DeZtACQFyG5Wr3+5x5P7Ez1oz6jyiK3cNdpU9cDgOMXA5PMwUkBXO7SylPsDZ+swY6SZ2g0YWRx9Hw2rv1oFXIKitD+1UVo/4p1y1YjpDvLL3IpJoWK5DshNJs5ci5bESd15YGzeHbudtPl/Lr1hJPVKvU0rREPAOjfvHqQa0KUNuRjkXrsP5eVr8gsn6yKjTb80zUY+dlaaxcuua5aztHqr3iTsAQCI9dmo3HDKLmWtAjEi6T/vf+7zfjFoG+UX7lQQ2YyivHsT5Kqxnr+DqWxixdKtqLE5fnOnS/78R+2ovPrwU2uo0ZahCYIp1l98BySxs3HeZXy/KfNx3G/LD56aew3ywq2FIl33nkn7rnnHsTHxztVH4LwQRTFAK7AWe+M1Cv4go5FYj7DushK0Hc77hZ5hcVYWZJVVl3FrHx+66ddJzIwY02yj4DOM1n6c4d24HMW8jKkv79cdYS5HwAiw5Vd3P3fbUaoIcXocnIY5HGpVbuuSdzxla+7vBHfrz+G7cfTseuEO0O5P8d06XlNW34Id3ztrWsg3MO0LAw6vLrIkzhKjigCN366Bk/95FUc3vftZu4sqnICYYVUmogIdz+PmvExQa4J4W9+3XrCURlA3j+qYyR2fn0xOrz6j6ZChBWXGHDHYOZFrVMz69ocjEmTkYLIqEpbjjnrNmwmtqX8eWkpRD9broy3mJVfhIW7TiOnwNfrI2ncfEyXyR12qZng7cOcihkcSMp6jESz+FOZunzfWeODAsyTP5pfGCUIHiS52sigoxR2m2UGW4rEKlWqICEhAbGxscYHEwQD9XjLEpB/2nRctgJnfzLtr+m4tnsSZ4xECz2hnQmFFYWRnAvZBcgt8CocfRWHyuMjw7S7m9f/3IMXf9tleE2jWEfqxxFp0l3K6JoAkJlXqGm9kJ5bqJml2/g6gR0Jx860luiHVctAqrjk15LHUpV/C4GeZ6flFOKjpQeZ++yGH5DQc8FU1sVZa+1QYe7m41h/2Pu+pXdc2uJQpuUU4JsAJ3Mo7Tz+wzY8NmcrcgqKcIqRlMMsPEOt2T7kivetJ2PQtDzkPN8u6bmFuolQAH2Lwx82HvOx3FTj9ETPzLqKPO5vu7qJzGPUCVbavvw37v9uM17SiDn9i8mQHHpc06GO5+/SOCEuba7N/lbWSsX7Y+2PVw4giLLE5yv1E2uRJXTwsDXLbtGiBdLT05GV5f+EBETZYum+M0jP8Y2jxuoLnF7JZqFnPWgVKZNjagafJcX4X3aavkaxKOLzFYew1cIzWi+L86TuhC9m+yoj2r38N9746z/8sd0dz6/Ta//g+k9We/YvUllkqSc7cdERmnX5ctURzFxrnHTDyLJAPdmJcEDoUisq2r68CDd9vk6h1JD43/w9eOqn7cy2bXwd+0iuvgrXZocHWLny2HMNR69gjWAFfNd7vjPWJCNfZTVstWo8LfnPHSfR4dV/PIlo9FzOUi7kOGpV42+e/mk7Rn2+zvO7tCkQJV76fTde+n13yMW5CnUycotw65fr0WPSEttlKb+FwFn9bT+eBoAxdoluZV7SuPno89YSxXYWT/64Hb3fXGK6b9fqQ4ZOXYlek5XPVV20nvLluZ934tYv1+teO4MRF5oHJ8avA7JwMbFR2nKI8rruf0+rwqNI9XFSSSR3+w7EhLigyIXkc/qKXzOUtmQr437eYXyQDfz5DskzgSiPzDfwXjMKnUH4D9uuzcXFxfjyyy+dqg9RThjz9UY8/sNWn+1MayfZuOnE5Mtf/Y262CV7zwBwZ35UwwpEvrFEsRcXFc59TZcIvPHXXlz/yRr+inLw7TpfpV5GXhE+X3EYj8z2vre9Mneuz1coV4zUChReAfNMZh5WHzyHmWuTkSlL2rFg5ylFzEWWsHbFe/xWIXrIy2ZVe/PRiwqlhkRaiQJx9yn+zNtOCZ0ZeYVoMnEBt6WE1asGe+VPS46WT3QDOZnR60/sZgFVtEOO47cdSwPgnfzqWYo88P1mvPbnHlv1CyYei0Q/vOqMvEL8sycVkxfsxZnMPOMTTCAp4kvBfDvk2FLSvu3Gs1Mq2DWOsXUFNpJ7FssT46dNKQCUrtNa/diF7AIcv5iLvlOWmrq+WikmYWSNCBhnbTaKWy0P7xBoHvx+i+dvs2ODFP7Fe777X8FBO3y5d4U/rOXkFpkA0Oz5Bej/9jLHxnJjtXxo4c/kdoB/+/ZQN0jUSmZEWCOvsNizMExoYzNdAGEDW4rEe+65B8OHD8dzzz2HTz75BEVFzrhwEeWD0xn5PkqBYE+u7Cz2qYUyKTwfSzBkZW2WBNzsgmKcychDKkPor19FGUaAdxVGftySvamKbFeAf4S/z5cfUvxetNs40UlOQRG6/u9f3PLlerz4225MkiWlUdeZ5dqsnhA5ISibKUFqP6O/0LfOYPGoTEHLy8Jdp9H1f263f8kKcpnJGDqs2H561EjQjkkXiMVyrRV5+XfG886Kir3ZD0VRxLTlh5jfpRG8E9NdJ/iVy96yzR2vfjTyyb+6qMKi0i15+XOsGPfzDtw7cxOmLT+E/83/z38XIgyR+nC5UvyLlYcxc22yjTJlf2scI33X/mhnast5lyiigmwB0eUSUVjsMuxbJKXjjuNp2FFi7ajHhewC7sVYdV9iJQmXHCt9q13yi3yt583eR9ekKorf/likUi6COVv28v1n0XfKUmw+6rug7dStKBdeS8G44mc5xZ8LmWZigwaDJhMXBLsKZYrH5mxFn7fMLRiVJfT6k9zCYs9Cb2leFC/t2FIk3nXXXahYsSKio6PxyCOPoFatWrjyyitxxx134K677mL+d/fddztVd6KU4pkYiCJXjMRA8tWqZKbAZYXwMClrs+++/075BmaXH9f1jX/R7Y1/fY6pHh+tOofveX212uvCeNeMTabjI1pZJT+vco/efjydGbhcTlaecv+qA+ew9dhF3PLlOszZmKLY9+Mm5W8WPLV+++99aPa8Uvix6qpjp/kuUGWU/nPHScN4d+/9sx9nMpUTQzNVKCp24V6DWIkXfNzcfa8QyMkDjxgtciyKv/rnHk/s1cPnsjF5wV5mNnVFuYzbvH2677fEEvav+nAV1jHc4fWvZ2+CprAUMZnQ4ci5bFz/yWrkMRJDhQKSYskfLe8MZzgKO5RW1+xAw2qeby3chxd/240jGu6ZOQVFuHfmJp9MjxKKpF0aY5s/uzTp+jd0vKTkNxAd4VUkPvj9FjSduAA5jDASLK75aDWu+Wg1V/+Smce/4C/vG4KVBETrqoVqjwdG/Zo/v9BHmWi2H61TSblwJj0HJy3Dijldm0VR1EySpsWRs263bska9tm5XstQJ+TtHcfTkFfofRdlXY94IbvAMCSIvz6VrPwiR2IgZ+YVYn8qf3IowkuvyUtML77bwa5XS1lnz8kMn23jft6BjLzAL1yVV2wpEmfMmIGZM2ciKysLoijiwoUL+Pvvv/Htt99ixowZiv+++eYbz99E+YblkmZlkW3lgbOOr3JvSL6A4Z+utXSuWnaoXtGt9KscF+lzLMsqgEeo4w3SrkZrwuXBoJwR08y7TlvKKK1qB8cu5OD6T9YokmpI/LLlhGFxPC4sHy09iIIiF37cmILhn7rvU6mAMSzCg1MCZFGxCw/P2oqnf/JvLB+t+sqtYcfM2GhYzterkwEAB1Ltx8sVRdGSkGvWtXmVzGVNmrxZcZlcWzJ55+nCUjPyPAoknr7LqsuYt4/1tdJcuvcMXC7RUw91G9iUfAGtXlyIT5YexNZjaTh4JrRiIEtWx4GasIa2/UfZR+81ay1MrTl4Hv/sSfVZfJJwMb4Ln+s61L7OZvqO9dJCh5R5XBRFjwcDACzksN5n8cUK/YD0avQmW6IIzNpwzPNb3acaLQo6hVbs5GyVklUrWZ06xIoVz8vVB+VjRckfDlqGyeu+MVlbcfDXztO4+Yt1WHmA3+tAWtSS6v3jJm/oE7vySmGxC9d8tBov/+5NSCNdJyOvkMsLpbTx7NzteO3PPfrfjp8Wifq+tdQRGfPubzbhcofCAJUnTqTl4kRaruHiu7Po9zNnM/Px3NwdtsN9hBKn0nO93kIWzp+zMQWz1x8zPpBwBL6owxrcfvvtIW9mTYQerEk+a+Lrhd3Gbpu+AS1qxWPh43199m1LSUP7uonc7fNkmgNxsFRV79G4Gj5YchDD2tbxObRCZDhyC9Ur5RyXsChEGFkTHDHIuCjFpjIDKw5kKPPcLzs0nq8pFY6tOgz7YCV+f7i3pxSj1WdvkiDRI0gUu1wAvNYtem2m+yRfq1cAeOKHbZh1b3cAwHFVfBZWedJE6K+d7IDIPSf9iycua6ZdERm/bDmBp37ajgWP9UHL2gm+B2h80vK4nlxvgVGOv5VTxS6vFfbQqSuxetxA3eP/3GEulpO6v1MrxNcdPo8xMzbizeFtPdvn7zyFa9p7+6hv1h5FTkGxR5ALtpW4miV7z+C27g38GiPRn3dc1kSmB77bjJ0n0rHqOf22bBU9C63bpm/Alhcu89keXqKg0xr3eBaKdp9M9znWCvJJp/TuJffa8DC39tAlev+WY9YKkKdtSc/znz2puHfmJix7ur/msScuekOFqJOL3fn1Rvx4Xw9T9fMnRS4Xohi2EU54vcxmKFSd/Izl73n+zpMY1q428zipT/b1EtBGahPHLuQgadx8xT67Ci/pWRw4k+mzbcIvO/HnjlPY+9oQxETyx/0OBFrfSX5RMY6ez0GzmvEAgEdmb/UkF5SQkifqJT2RmhjrkIW7TiOnoAg3dKprut5m3rseO4+bD7NCADn5gQ/fZmT5/OGSA/hhUwpGd6uP9vUqBaRO/kZKqJY8eViQa0LwYEuRSNaFhBUkmUnhXiSyrWOMkCf7kFhz8BxGf7ken912Ka5oXYurnBlrkpnbC4td6Pz6Ynx6Syf0bFJN8/wNRy74WG55Xe98b6p+lVjsU1ldsY7bn5rpEWoA/7mQfrrskPFBJmFV1ej9OhnAnBdBcNc1TBA8EzyjZCta8CmDRYXCR37K7pMZGP/LDrx+XVtP3XhwicCvW90Wmn/tPI1Rnev5XHPZvrNoXitesV1LMM3SEZj03qFkYaPmZHoedwyTYyWKy9MZeUxFIk8b6fTaP5h0Q1vc3LW+4bGfLjuENxfquzQ7hXzCyJPg4IkfZAkKrHz6ovxP0ZM5VR524NHZWxWKROnpSm1U631n5xchPEwI+CRRcmH0p2twqYjzZZP03EJ8seIwnrysGcJs+GmqQzLYYf6OU5i14Si+v6e7zz7WK9Hqv8JL2q6WlRpPyAAp0+++0xlYd/g8ujeqqlt3Lc5ney0SwzzflPuaESXPXYToqbMc8+7E/O9xyzG35dtJnX5Irix54bfdin0bSpLD6cVpPp+VDxFAtYrRmseYRRqv1WgqjdVeHBZMuuTjtfTu/OXa3LpOIgB34jhBgGKhXKoGr8u7HJaLpFPdnEsxzri5WLIIGiyXeD20ZIiXf9+N2RtSPAoMtRJx3+lMr1xf8vBSLuTgjx0n8WD/Jp7jxn67ueQY32vc/517nxVFopzCYhciw42dCouKXfhq9RGM6dXQc7y/w2os3HUan604hHkP9vLrdQJNBMfzdhreLN2lYYEyr7AYny0/jIcGNHb8WfZsXBVrZGEfSsPzKCsE/qsgyj3KQUwalKH41w5nS1ZtT3FM1LWQhITMvCL3hGul0mWo2CUqYu+8xVJE6NwTa1WcJW+pU96HoEymCauqLlH0EeylJCGBotglwuUS8fv2k3j5993e1WPZMfIaFrlEzFh9BMcv+mZO+2Gj0nxe/l61BOhn5+q7Ky/YeVomrOoeqrjuB0sOAgCa1ayo2CdCxKI9qRgzYyN6Tl7CXZ4WrIl33coVDOtbyJlWTZpcF2sczysgqL8dn3JK/v1aFj/ULi6XiGX7zmjvFwMTGY+VoEKudHlr4T7s13BDl56vNFHWagutX/obV324yoHamsMbY9d337J9Z3wylFq6hu0SQuUi2ny05AA+WnoQ2zmSdASKZ+du9wljIT0mnr5wU/IF7DqR7u1DNFIN8yRbefG3XQDcY+5Nn68zvjiMFdDSoqD0TcnjKMvnVdI3uP6IuXjNXBaJJf96LOs0ThI5ynvpt134a5d2P3vp64s9cWiTqsZqHmeEFJNs0e7Tmu3gj+3seqiHYSsylPwxSE1K67mlZuR5MnCreWjWFh+rQMAtK1SMdtt11CxJZrYvNZO5UA4A43/ZCUC54KfV9qRaspROoug+78N/D5jOTn82M5/5LqR6hBko882SkVfomCWdVruWYpdryW7yrOfSEY/O2Yq3Fu5TPP/tKWlOVFPBpmRlX3DLl3wJ/f7YcRJv/LXXs9AM+H8e8cJvu7DVghdTqBMRhJTZRpcsTWue3607ivcW78dSkwkheWhQNc5n25ZjFykJSwAgRSIRcNjCR8m/MrGexyqkUqxv/MEwA0saHhqO/0vxWy40FhS5cM83G9H8+YWebaxLeSdAvnu1lGxGx+mpIdTXiYm093kbBf22cr7L5Ztg573F+03XzQ6tXlyIqz5chUdnb1VYomqt/M1YnYyX/9iD3m8u9dn33M87Fb/lt6aloPpp83HmdonM/CIMfHu57jFq5G2nl8pyVoCA0+nmJglFMiWeOmkO681f2qCy5j4JnmyZS/amBmwlUfqm9VZ8j1/Mwd8m4jx9v+EY7vx6I7ZpTCTs9Ek8faP6TuSHDZ260lQoEm9sLe1KBzN+4saSiZX8udz59UYMmeps7KeyFr6lqNiFo+ezPbHieJpkTkERko3i7DqAvC67TqTjQGqmqYnSjdPW4qoPV3msHT5eqrS0X3ngLH7fflLRF2kpynkXPuT8vt03FIHc+qlPU3ffLF1Smpi6XKKiH/Ln5FC9aKs3UTVq+9+sPYqHZ23lum67upW4jmMhuYdLll4sJi1gZ1dXv1/5u//vVAb+N994oil/RlJ5R89nQxRFvLton2KRptsb/+KZuTuQy7Aa1FrcKnKJiCyx5n/6p+2KfcUub9gS+dsY8v4KpMjCjhjFjGa9Z5co4mJOId75Zz8GmZA59p7OQJf/Lca///kumrlE935pbLBiAcrinhmbcPVHzixcabVqSbFfpLEAoYitWnKIdH+s23Ry6LhxmjJ2+wbORQapH5PHCvW3xb0UF5bldfHtuqOaIXCs8uofe3Dl1JWOlskiGKJAZIR7LNMO01GyIFQKojkXeb4V/vbHf6jvgfd+s8kwMRJhH8cUiampqfj4448xZswYDBs2DMOGDcOYMWPwySefIDU1cBmOiNBH6kRE1ja5pQBHB8I6Ru0+ZAfWgNvs+QU+Kyqs43aeSNesI2sCzlNdDfkGgFv5KRdeX7q6ta2Bb9XBcziRlouDZ/QzTGvBVpb6btMS2vxFfpELe075ZvqSPyv5uzCzUi8/T56wxCySALZJJ2ObKIoei4WL2V6rTpbAYVZw1HNHMmut6DmPo9Gs2H/O8wyPlsR02pR8AUXFLiSNm4/nf93piLi05tA5j4Wp3jcyYtpa3Kcxeb38veU+Aa4lATpLIxB7sUu0bIkmf7RzDZTR0qHyfuYip+Wv9DikiWeohTqV7u31+WzFgRW3P59rODDPyi0oZloxB5tPlh1CvynLPMkyeO71we+3oP/by/xbMRVXfbgKl723wlJ2bq0QC7dN34BHZ2/FkPe9k075/e897TsuaMHKnpvG+Y1JEyopluO+05k4eNa+Uv5MRh6Sxs3HYY6yDp91K4b13NqtGOBUqxjF3J5UzddixEm0slGr27cU+zLlQg5GTFuLL1YaTzRZrs3nsgpw6Gw2PlhyEJMZSkwzcoNLFDXdVO/5ZiOaTlyAKX/vVYzLe09nKha5XpIlPDmVLlPg6Axwi/ac9txPpon4b1LszO/XsxLhiBjy/kqcKlm8dEIO/3FjCjYkm7POtYLk/l2ksYggX9jwLGAJ+vFYefh+/VEkjZvvFwVfOGNB0GpV/zd/D75UeWfpybm9GB4wL/y6Cw9+v8VaBTT4avUR/MeQ6Z1GeoQtVCGC/ElUSb+gzj4voRePM9Bk5xfphuyR6jp383GuMcr3fG1jG/UuAYJnYYDwL7YVicXFxZgwYQIaNGiARx99FDNnzsSCBQuwYMECzJw5E4888ggaNGiAiRMnorjYvoBPlH7kH/zsDW4XkMy8opJEBLIVv5J/9TpIlpDiTUBht6ayOnAeJ2fyAnbcNVOWHaqbMLoleSbFMMHrumKF26ZvQK/JSzD4XV8LHy7hkHEIy7VTLtR0+d9iU3XMdjD4scIiBL7tkIfl+70KZmmyuOXYRfyokT2Uh1f/2INdJ3xdenaf9ApO8qQpWlkuzXDgTBZum852nRFFtzu63BqCFZrgbGa+QpHCY5EoT9Czo8Tl8sZpaz2Wn9+tO+aIhdjoL9Yjr9B9rVMqa8303EL8+5978et8lnZw8/2pWZ44UGo03QVtdEryM1mKcMA3wdGXNlZjnVyQsUJqRh4zM6xPdfxQPSeKvHfmJqYVczAtB/IKi/HuP24LcK2MxhK/bPEqqzeVZJJ1yrrIDLzN75BsYmLGBU2eIVmuYDTiVo3+UQ2rKuN+doe3iCxJsHLPzE14a+E+7mtrsb3E9ZPHdWxxSR+na5Fooa2+PaK96XN+1HAFtoK6jar7rwolMV37vLVUNxawhCAonwLrEyh2uT0Q5MnR/t59mnshoahYW5EovcuPlx7CqoO+ymsWPSYtwdGSBHp6b/CJH7YrXF55kYY3dcJAwPd7daLHePZn/XAwLLYcu+hJTqPGSIbQcsf+c8cpj9ylDomjpUjcc9JYwTWvxJpUzwpaHbKGFymHkyKOJaNTzcwrNFR+f7HyCF6f/x/e/tvbV33lYGiYUEcdkiIQhHkUwQG7pGXu+GoDU3ksIcnL/+xJxQiVhS0PC0viMTMVioznI21ycnwhfLGtSLz99tvx5ptvoqCgAFFRUejZsydGjRqFUaNGoWfPnoiKikJBQQEmT56MO++804EqE6Ud1sT01unrMeGXnZjFyI5nFqmPX77/LApKzPnPMwQKPvdc979Gugu9ouS7Pl12yMeyI48hjElIce+819Gvs1xAklxSd3MIMmbheTcsN2xWjEQ7ypXHf9hm+Vw1LKEYMBbAtPh6dTIA4IZP1ugKwkZFfrX6CDMWnZn4dEbu1CxWHjjHjOckisCQqSvQ5y2vkkRy6Vt18JxnW7c3FisUKXwWxt6D5J/czzKlhj9XXkWImDBvJ+7+ZhMKi12eNiwpctW3ECHLtMqVUVEQLAc6lz8/qW2p0druuTzHdaQ+RHrOwVAerTpwDt3e+Be3csSBkrts+QOrzU3+LaTnFiJp3HzFQkMw5gVsdzh2TZ780etiKSlczMbsM4udydKgd7yumfJFoZNpuRqWU25mrT+muc8J4qK9OQ2l/m3lAXfbsJPkxjrqh6xRB1G01NdGaQXR13m5nyw9qLnPLJKCVEKt4DHror8tJQ07ZAt5UvlJVWM9ZZ/Lyscrf+xRWAW+8dderv7LXUcXoiKMp2PqR6tlhQkA/aYswxHZvWq9S60wHHpIqlWW8ky9KdAKkOz8IhQWu3DDJ2tws0ZsU6NmXaRhhp+e67U4lmRg6flpycS5hdrvKK+wGNtS0jxKZPVCoJzoCGtJzaR35fX6EpnK8CveW4Gu//vXdweDj2Tfq9V6lUaCoczzyGEhpEl8+ffduO7j1T7bJQ8qrRixn6/wWrOqQybxkFby/ckfRU6+e96mJ1cbxaUn7GFLkfjrr79i9uzZEEURTz75JE6dOoWVK1di9uzZmD17NlauXInTp0/j6aefhiiKmDVrFn7//Xen6k6UUrTmpQt3n8Y5mQUQV78pO2b1Qcld0d3zrjp4DqM+d696fLfOd7LAZ1THZ5OoV5TXWktkZod94DvtuD9aZfGwr8Tt9bmfdxhan5jFqts5a5sdl5BDforRpnSx9/6QXFdLG04qk12i6GPFxz7OfNmiaKxg8ffUWwoPcCG7wNMOtJS28kyrxy7kIFPDpTlU4Hkl0v1Lro9OuFya4WJ2gcfaa8uxNGSonulv25QWNPN3ngp4wiazSO5ffzJi6AWS27/a4LNNdxFMtdMn3lwAlMzy8VML9Xcnr+fjP2zDxHm7AqIQZylrEiv4xnGW8Ffwft0QE2oXMJ0qWKlesGOKLtuvtMa85iPlhPfXbSd9FEUbky+gdmIMs7wj57IVISI+W+6Ou1nkEj0K6rWH3ZaCv21Tft88Fo9SWZEa7vhy1PF81x/xWiiyrvX2on2GymB5UgyuxTDAMwizlLLqCX1g0ou5ycgrROuX/vbI1AfOZGFsSXxNBQbPRKtf65pUxfO3CKWBgpW7fH3+Hlz38WqPAnHq4v2aWdTtWsEJcC8KStna1ZyUyXSz1h/D/B2nuOJDlyfnUX8p80TRndBR8rA6ci7bpx1oLXh5ZqgBfBEz1iT7LEDIx+AJ83aCB57EePL+Q/oE5G+hSpw7lIb61TgR4obgw5Yicfr06RAEARMnTsTbb7+NSpUq+RyTmJiIt956CxMnToQoivjiiy/sXJIoxWTkua0ypEC76jiBauF33eHzhoKY/IxbvlyPYR+sUnSokpB07IJvh8U1JMgsEtNzCvHePxqJQXQGmGX7z2Bj8gVNxYqZDFZGA5lcAJKEzmBZJC7Y5SuEGLk2hyLyZ3rn1xuDWJPQwKwwZcaKU36oXhIUHnYy3MEB+MQ1lCNAQEKM24IoK7/IuI9QVVHqb/RqblUWdWJCxvPu9qe6FyCk+Jsv/rZb73DHWarKev2tylV/OyNzZ1qu+dVtXRyeMIR7sghrlzt383GFBVGg0LtTtaud+pN0OuMzq43ztHv1c5NPgCUFYo6O5b8/kS+6qJVsWrEc/YkZ5a+eUlBrV7BjdakVxiz3VnW7HTFtLfcnL4XoKHaJHgu1Ag2r6MJiUdfjREIdI1GrPLUyyajOhUUuvPfPAQDaburymGa8oWKkkjIYFpHqkNeBNKSS3CoXy5LALNrjG6PfqIkWanwjjWt43YtFEcjjSmCifTVpsU4al79YeQQ9Jy9hLkhaXXSQ+s/D57LRaMJfWCNzj39Ow1JrwrydeGjWFs340BIul4ip/x6wVK8le0tf7gR/zVX2p2bh5T/2eOaXA95ehp4lbVlSKE7SCJXldbEPbscrjyvKm6is0CA+vlqRzQq3ExVR4vqtOjfQSTzLM7YUiRs3bkRYWBiefvppw2OffvpphIWFYeNGmoiXV6R4Y4s4s6De/tUGzYFOIiu/CGdkwX7TcwuZ1ily10iJk2m5hitu8s7p3X/2aQ6aet3m4bPZGDFtreXVLLkgbFSGtP/W7vX9KtBbtUTZdSKDERTXOjxCulnUE5E1sqD6iiDmIYw/3SDMlmymKiJkWb21PO44y0rPLcTrf+7BH9tP4nxWvmeS9D+NJB2AWygJD/NmyTNUgqp2W3ETCyRcVllBVgSoH/lXQci6p6iC7HlczC7AN7JM77xIk8CVB89pxvB9+qftuPHTNabL9idqJZ5auW+1m9mWkoZNJpMn6F1LPYlKzfCOmZLLKCuLrtNIdSwqduGebzZhf2qmQsmp7k/Cg6B1O5WeiydlIUHumO5rpQoAEARdGULrfWguAAXgXpPGzeeSTYZ/6hufi3fMlOa+xS7RxwJRTXpuIVq8sNBnu9qSTx0jceeJNGZ5amWS0a0WuUSP/LiQQ+52iSLmbDiGP3fo35eee7j6+QfCJfO9f/bj1T/26Lp65xQUabosA8pv88hZ9v3J780nRA/jeAECDqT6JisEoBs/8zSHx4cRRcUuzN5wDDuPuw0JpAXCrTIZ5QebseOsuKdKLN3Lb0ARKkjv3GnjDOkbySks9ln4kCvrk8bNR25BsUrODA1LDJ7v/Oau9RS/jUYE9XPWi9vNSkAo9yCTwssY9W2EeWwpEi9evIjExEQkJiYaHisdd/Ei26yaKPtInbBWd8Pafjwt17Cz6fqGMq4Hb2DmQe8sN15xK6mzAO0AzADfhMqqQNX59cUedz6jEopdIsLDBLSoleDXSYrVlbl7Z27yeQ52qqkV19AORS5R831q3fauE+n4UKVkjom0HYLWMv608jQb09JMu3e7NkvfHLthaGVUZPHlqiN4ZPZWXPr6Ygz9wJ1IYbNOJuzZG1I89S0qNraFsmIlaPXVyB/joBY1FPv+2nlK4QKs9cgjtOKXhTDnswv4Xe4cQuv5vfj7brz0+27T9ZEE4LOZ+ZrWRgCQzUgu42+ke01hWe0bNFarffd1H6/GjZzB1iXrBr1+RF0PdliNwE24TqTlYvF/qRj9BTtGm4QTQfvN3tddMzbhF1mCDc1svaLI7IPH9ErSLT9MULqAyssD3IrdN/76D0nj5mPXiXQUu0Qkc7i48cKT1IsF7zglKZPMLqY+NMubqXa4asGg2CUqYktqKml9LBL166Bnfc/CJQLjftmJh2dt1T3u5T/2aO67+iNlGJBAeJxM/feAYdKPVi/+jcfmbAPAtrSV11MrkZI6GaTiN6vPgagZn/oVnWfoxCP7afNxjP9lp+e5SN+y+s55ksH4g0C6vDuFv2rsUZC5RNz5tcbCTgkT5u1UxCfkjePvb3j6XfUik1EYDPVeKSS5lS5eMjD6h2GhTNjD1qyicuXKSE9PR0aGcUeUnp6O9PR0VK5c2c4liVKM4bfPOMCfscj1AhtLuGSdtO7qPMcQY2cesz0lnauMYpfoUSb6M7PY7pNst1Ee1Pdg14XVaYpdoub7LNZQYt38+Tq8889+JFWN9WzTczU4lZ5rmCHPDvL679NYEbdctsl27PRE4rMVhyydd/R8DopdxgkENpZYSg39YKXiXllWAk7qJk6l52LgO8twkUNJJRfACotdePD7LXhalhzjp81sS4PYKOPA6Ha+xg6vLsLPFhL7GPHQ91sUv1Mz/Pft6JFTonQx+4zkbU70/OvbeIIR5mHkZ26FXgrDQsZn0cfPdWF9TyOmrWHWRbccxrPVm+gcOZfNTCylew2O+pxTZX1XJ7txYuxjKbTkVVPHGDUDq3pa2YW95wjo2rAKLqlUQbFdqqYoiphRYtV7x1cbsFqWlEgP3vdjNRam+l1pIbUjs9ZY83ec8vyttpwrKpHZJLTuQL04LIWf0MK8ItH5DiiQCnwj5u88BZGRROiVP3bjsCoW8H6G3KRMvKfqrzRuU0sO/2dPqqZ1uhOPTMtNfYcq5Iu0wEoY4y/rWqlLLXaJSLng9XpiKb0kC3ep3w8VRaKVR8P6NJarYtzO2eCVZVkZrHmvK4U60jMIIqxhS5HYpUsXuFwuvPfee4bHvvfee3C5XOjcubOdSzrKTz/9hP79+6Ny5cqIi4tD+/bt8dZbb6Gw0JrgtXnzZowYMQI1a9ZETEwMGjZsiEceeQRnzpwxPrkc4A2arrE/cFVRoLeyLBdK9RRDPJ0Zb/wZZj0ka06DC0nWVmGCfzNCjv6CLxshC/VgrOeOokX/5tVROVY7iL0dCl0uzfepZb0hHc4baL7HpCWeDHn+aPf+lN19szLqX8ycAsCL1qNkWU7xsurgOcN3dPwi2339BCMIutad6bsEss/6c/spHD6brXCl17rW4XPeSY/0fE/K3O5/2aJMSCLBihemxk6yhLScQryrFUeWE9bk93y2st7d3uDLLuk0Hgt1k49I/kylv1jDjj8m3cfO5+APjiQvLEtfnwmuU5Vi4HKJzAzc+1OzDK+t9z4k61G9cV5SpprByrNQx/fkHaIr6Yx1RpYg7/9jLYYZwK6fUay2MIF9nvTtuETvN1BY7LJsQaiFCYN1S/jjGy0WRUW8TK1LmF0c5o1V5k9CSI8IAFi467RCkj9yLhtfr07GZe+tUBx3ueo3oOxD1HINa/Ei5UKuz+IBC183aUZfbFiKPlIfaTZhoBPhg3adSPe9R4Mb0nNDDxYGIf1MU+xJfsP+rlnKbKn/VctJwfrONiVfQGZeIZeFNo/ngDoPgVzxJ8lR8m/v+V93cdXz9+1umTgQSdfKG7YUiWPGjIEoinjttdfwwgsvICvLN7tjZmYmnn/+ebz22msQBAF33323nUs6xuOPP46RI0di9erV6Nq1K4YMGYJjx47hueeew8CBA5Gbay4W2ty5c9G9e3fMnTsXDRo0wLXXXouwsDB89NFHaNeuHQ4ePOinOylNGLso+ZzBeSgrPgIvj83RduOQB7JlxTT5eCn/e/1sxWHTdZOQC+F6PF4S+0gQhKDEX+JBfQvzd55iHqdHuCDgYk6hXwTl9/85YEpoE0XRkxRI/sSD+fj9aQUg/347vroI2Qaxx0zFSJQdq/X4ejWpxl+giqJiF6zmNxAE+DRes895myxDphqPS3dJ/XwEaVHEx0sP4lxWvidIe8lmAL5C7soDvjGIJNcuPcw+nn0GVjFm+G7dUYz7xTfjX/I551wfeZBP5OQLSC7RdxsPrKNZAq0/ZNybPl+LR2Zrj3Hea/te/LzKSkt9SFqOd/8KlSVBXmExDpnI+L3BKGaizrPxeR+yYyWrLb1P1ezk2qg8rSzvAPCBLAQG72Lfwsf6au5jKV/l7desVZocVjs3ViS63Tfk7WnF/rMehaFLZhEmev7nHP6eKLImzJ0b6HtaPSGLSckss1jkUhKaXRy28+7NUEcj4zVgz4qr56R/8d26o5bPZ3EyPU+xsHMhm//bl9+L+q6kBQ8rqMsKpvJVPZ7fOM1e3N4txy7iqg9XmZLzF+46jSYTFyiyYocC8vfvcol495/9ePLHbZbLm77qMG74ZA2OnnfLc6LqGiwjC8kqLxixSAH4eFLdOG0tHp61Fb8w8hAYwaoxK9GQBCtrs5FVtoQ0lpFFovPYUiTecMMNGDlyJFwuF9544w3UrFkT/fv3xy233IJbbrkF/fr1Q61atTBp0iSIooiRI0fi+uuvd6rulvn1118xdepUVKxYEevXr8fff/+Nn3/+GQcOHEDbtm2xatUqvPDCC9zlnTx5EnfccQeKiorw2WefYcOGDfjhhx+wf/9+3HrrrUhNTcXo0aNDysQ/GEi3r7VywXo+vMqYp3/abnyQBn/uYA9waw6ewwrZZJyVXXnK3/sA8A38VizvJLxWLHxtKFzwr2uzHZwY8P7d67byNcrqbYWvVh8x9a2u4nTNCiQ8Y6XVZyd/NBdzCnHMIL6VuYUDb3xKrdhC2fnWV8jTcwu5BQ81rK9J+87Y3x4r6VPbl/7G5ysOyRYtgNUHz6HJxAWK485k5mPK3/vQ+fXFyjpIikTVc37WIFGVmsJiF9Pty4gXflOuCNsZ57RWl3nCUDiJ1i1Y7btExt+7T6YjRxUT0WoSKz00Y+CpYF2575SlzORlEl+tSvb8fftXythO437egUHvLOe6NmD8bA9rJHg4ej7bJ8nRWcYE1B/PVo3U9vXGernFLq9rs95Qvv6wrwLWCVHz+MVcZl8gJaPSIkwQECYo63D7Vxvw2fLDnrrJ47U5HSvN3++ZVX59WUgTFvO2qizES55rRl4h8ouKkZZboGgLWn2oWYnO7EIr67LTVx3BtbKYbGax8zpOpudhsipbrVbyEl58Eh4ZtGc58nf/pqpeVqyaNyZfLKmTcrv0Ozu/CDd/vi6goTyueF9pibnrhL0YitIijdrTQ69ZrDronmudDlIIEwlRFDFr/TGPQl7+nopFER/8ewC/bDmBg2eysC0lzXQYCcmNWS6LyxdCWGFupAU36Sip/7Ta58/ZcAyNJ/zFfbzkSSVnf2qmx3XYDHtPZaDpxL8UC5JyJb9a+ayXbMUI6ZxAyAHlDduR17/99ls8+eSTCA8PR25uLlasWIE5c+Zgzpw5WLlyJXJzcxEeHo6nnnoKM2fOdKLOtnnjjTcAAOPGjUOnTp0826tVq4ZPPvkEAPDRRx8hPZ3vw3j//feRk5ODwYMHY+zYsZ7t4eHh+PTTT5GYmIiNGzdi0aJFDt5F6UP6fLVWBFiWTQKCZ9k1+sv1nomtUcY7nq7JCVN93v7ThGwUcMTQ81jwwcxYI1dsFcrMwnIKih0L7NuwWpyp4/2atVlVttG1eF0PPOUb7Gcp43h5Z9F+5FjM3spy+bXymNVtKzO/yMcdeMyMjT7nnUxjC9Vea2VlwWYSEe09nYGmExdg+qojpq3t1JRlMW3lAfeigZ3vS/p+7v9ui+YC2MEzmY4tPPIsKG1MvqD54rJ0EsBE6pj3qmNxGWE1XmC/KcswYZ7SilV6T3ICZbFhBu57lh12b5+Gil2spF5yGcuqou6XrScsvRNBcCsKeZ63nTAKWvj7PbNKt3LJ5s8vQLuXF6H58wux7vAFLNlrHALJ7GXM9iGszOav/bkH21WKejPYfR/qe1C7IJvlbFa+YoQzo6T7ZJk3PvPv20/ili+VIX7cC6H89ysl3tJyk95w5ALWHj6P2RuOmX6X6m9L71vuP2WpYXm+rrR89ZGuupHh4j1/xyks2HkKp9KVSkaprov3nMHBM87G+DbDqoPnMGHeTvSfsgyrDpxT9KXyjMIPfr8Z1328Gg9+t4VVDDe5hcWKebDePM6TvFSE4l+zfLT0oG3lmnxxyAwPfL8FhcUidp5Ix/7UTLz02y4cPOO17JX/DcgsEi3MIaVbDEU5oLRjW90QGRmJt99+G8nJyZg6dSpuu+02XH755bj88stx2223YerUqUhOTsaUKVMQGemfmGZmOHHiBDZudE/SRo8e7bO/d+/eqFevHvLz8/HXX3xa+nnz5mmWV7FiRVxzzTUAgF9++cVqtcsE3g6P/0MWBAGxURE2rxs4SwQ9nFgI4b2XMEEIevBdOZNuaOv52wkLBL8bW5qoovw5FxYpT7QTz09OlMlsu4GNkah/vI81hg7+/lIrRkeYVspKsC0S2TW+mGMuEL8oyuNsso/RUqBqPbM0HUsyNV+tOgIAWCQLAG+VYMhpTl9T4WLPeB52JvTy74flDnfkXDYGv7sCczayE+aYhUcZdMsX6/kShqmO0S3b5EPyd9Itf1iv24V3LJQ/G7WCgCUfSZ4SdmG9Ep6ETWGC/uuXh3Fw+tvVmigOaF7d8Nx2dRMNj2ElmzE7ES8ocjFd0j3XiGCP90s5lI12eGaudc8eLay+X8nTwSh0ilk+W35Y0a7/O2Xd4u6YSr4b9sEqdHrtH9PlaFkkSt/JFp2QKHfN2IiFu4zdhvW6V56s6W+r+hS3zMK/WPCvqu3uOZmBh2ZtwQPfb0GPSUsU+6T+7r3F+zH4XXuKYztIit4Tabm4dfp6xdjNajdmQnnIkd73P7vVhgfaL82TvMrzO3gKMt5xTEvZKIrAfd9uxjdrjcIYWLdIVCteCeewpaFZscL9gbdr1w516tTBI4884kil/MnWrVsBAFWqVEHDhg2Zx3Tu3BkpKSnYunUrbr75Zt3yMjMzPfEPtRLJdO7cGd9++63n2uUVqbMxq1AzisljRKhYMmt1fvWqVGBuZ2HmVuxaFlll72tD0OKFhYptXRtW8fztxPuIDA/TFcTtYmag2nvKu2KqDhxf5GB05ju/3oAqcVF4d2QHw2P9ab6vFhqcDJbvb6X/Ld3r44uV1mKVMgVxjepKSY/MIN36/SZXtT0JQGx876xEMmqkrKlDWtfCJ7d00jxO/e1k5RfhZFoumtWMt1w/I5xsNflFxYbfj1lBVn64PDsyq71n5LoVwE7FnuRpFQXF2gmmhsmyei7fdxY9G3tjlPozoZcd2l6S6ONqdc1H1t0z/QVvHGOFItGzzT2WDv/UXgwzPVi1i2ZYQMqRYiDq9eXyMA5OU6Dhzlsr0VjO4hk3WQpvpxPG7DnJVm6dSjfn6mk2jIdcEXLwTBYzk7tZrI7poz437yrMizxD9/uLrScjUrPHolJS/YTUVmYr9p9Fp/qVmOcu2XsG6w6fx5A2tS1dm5ddJ5X9qQg+pYzWN64OR6E4R3VSflExoiOMFzCcRn1/8nFf3lfY/fwlmVodwkX321Erny1e26n1Ox7PB71r8YgSrBiJvJAlov+wZZHYv39/DBo0qFTF/jtyxG15Ub9+fc1j6tWrpzhWj+TkZM/fWmWaKa8sI8UNMtNeNh+9aLuj87uriyhyXUPLpbtmvDtQdXyMtl7/m7VHMXXxAe57Sc3IQ0Wd8vxJTKTvgC9XBjvxPsxa6JnFTA0PyoRvddvmydjHy7J9ZzWz8arxZ5tXN2Mnr+XvkUQQBGZ22mCTX+TCp8usJeSS3Dzs9JOrD7ozRbtDSegXtHD3aYW7q7qNq5/uvd9sYmbAVGMnDpRRG/x792lu5W7z5xdin0EcLjNNXm2loEiUo3Pe6fQ8ZuDxL1cethUTWAstHYrcsvWzFYcVSo6WtRM0y5OK230yHUUl8Tf14i06KUfWiI92rCx/YqSUk2B9kROHtXK2MgCiVJZwrL7AqH8oconYcixNoaxRI03C/eHaXKixwBgmALf3aKB77m4NBZ4cdVxTALZcf1mYDQfiFPI+YPC7yzHma98QGyz0EhhYXdNMzzW2qHe5RExf5cy8qkO9So6UYwWeBEGsI6TFPdb4p/6ynP7WXKp5j1aYCyuXVS+K/uiQdb5dlJ4Fvs9cFN1Jxv43fw/yi/gtabWGPv3vSmlhFwhFmW/mbe/vBgZxYo2IMBGPy8q9hqDYX2awNRtPTExEYmIiKlfWz1gWSmRmuicIcXHa7m0VK1YEAGRkGAsVUnl6ZfKUl5+fj4yMDMV/ZY0R09wrjKd1VlUrx0aiS5KyPR06ww6yzou/O9hl+85yZYL6Y/tJ5nYR7oC3lWOjdM9/b/F+pGbwZTGLCg8Lkj0imwiZ4s+J96Hl+uMUZia1cvlJfZo/skrzoL5q4+rW3HlZPKrKAJtq0krCCHlWU7OM7dvI8Bir74Rl8Wf37cqtzjIsJmNyMlnBuax8psvOusPnFb/VTyL5nG8WaQm1JYMWCyxkb5dQ11kdUuC+bzdzWW49w1DQsfrRC4wg6FoMemc5zpnIPimNEwt3n8aQ91f67H99/n+Yq5GIiIU0kTMKBM/b5w39YKUn3m+TGhUNyxv2wSq8t3g/pi0/jPavLjI1wbKK0xZi/qICY9FNTa2EGFtu32YeRZPqyvepthKpFBtpKFdk5hUZxvuT5CV1UhYn0MpUHBcd4UgCOlaMXXUiidKKVU+GMzpZz/0hf0uKtw3JF/Dan3scKVPPQs7fqJ/R1R+5s76f5xxnWK/N3z2gW5Ho/a3VR939zSbTZatlGr14z6yEJE6hviVFshVFHFo3pzPy8OGSA/hi5RHMlyXxNBr/tT67XTpxhkXPv4Fz2fW10HT/m55bqIgfagUe7wY7cQ49rs2mzySMsDUbb9KkCTIzM5GfH1op2ksjkyZN8ihmExMTPVaMZRG9LJIu0ddE2m5sI393sG/9vU9hZWKWMAEY98tOn3grdvDHSr8d5BaJrCyTZvG3R50ZeVqeVEA9gfVX0zPKlKxWCuQV+s8N/IHv7QWYlmP3W+VpFloTTSNYChC79bUTn0nCSTf2Qxr92E2fr9M9T+6So257vEqQl/+wPiF8eJZSub0x2Vofo5UpXI06s6UR13+iocRkvDr5ohSPy7kR0ut41eD5mmlFF0zGAD10JhtrDrkToGgp8p3sK/0R2cEf4SJqc7jb1kiIhiCX1CX3LoPOR23l1KymttLXc46qTLUcERFmHHuZx7pKdgUTx/KhleE9PEwIWrgXXh4d2CTYVdCElYiFB59EIqKINQd9kyEZnScnr2Qsfn2+M0rEYKN1p9+t88aM26oTJ5FnEchuy2fFcZRflyfUkNW5nN7tTVnkTDxYURQVffyx8zlYuk+5ICLv2pSuzd6/t6ekl2xz/z6QmonOry/WTbyo1dbzdBSorO/KClKfqHf+Y3O2YtzPO3yuKf3mnV/otUEeJzNPHS3cqt6zJOxhS5F40003obCwED/++KNT9fE78fHuOE3Z2dqKn6wstytSQoK22466PL0yecobP3480tPTPf+lpISGKbc/0BPIXaKouWocbdEKzd/p3u0qA0JduJXzYP/Gls6Tv9OXf99tux5OWBboYcbKS65EVie48FfYhzEzNujuVzd5JxQSgeB3DatdPRqZsLbMLSjicpliceO0tT7twq41oFwws+pa5e/+jYdHZ2/Fiv1nAfi64wRjTeOZuTv8fo2j5+1ZygNsedhfFvRZBhavZi4rlaV+tedllhfy4uRtIBChcHJ1Mk1bRT6pd4JLG1TmGsdu7d5A8ZwleUFLYSahXrDlcR1Tx9RTf7thgrEyrvUlxnKzP9FbROURG7rJ4jkHmv4tagTt2ka8t3i/pfPmbFDOZX7ZcgKjv1zvY+Wuhqeb2HWibHhuaSnfq8TpeylJcMUqdDiJmksUTS+kDn5nuaVrTVqw1/N3Rl4h1h46r3msEZuPXlScL/HED9vQeII3werQD1biu3XHFMfIx+ZKGh5kkjz2+/aT2Hk83RPXVB2zV4GFIdFTFY+Vnv7xg99drpudW+85/rbtJOZsTPG5xq8mEinqIYIvXrCL815ZWIlbTvBhS5H42GOPoUePHnj44Ye5MxwHm6SkJADQVdRJ+6Rj9WjQoIHn72PHjjGP4SkvOjoaCQkJiv/KKizXEAlR9LVgSS6ZsI3oXNfS9UI9yCpvnCQz+OOeG1WPs6zAk7/TAgeSpKhjOTlOIJsMx7tSK6yMVgBDQbkUKMxYA1tVIkqYzVhthDxUgVXXKklxZzawvh3Ut733dCYem+O2Cix2iUgaN9+zYKDuz0VRRHpuIZLGzfdki955nM/9mRd5+z+Tac/1/qfNxzFZNpGR6DdlmaabrpHFsIRZpZqVrK3S4zfSI5mpS36Rizn5vfT1xTr10B87zGQZN2JjsvOTBl43Q17CBL4FseY14z3fUFLVWESVxNLYYjAx6vjqIsVvrRhm+nVUnsNT3xolMZ/5cDIwg5sKOvKU0Vx1VOd6QVn4kKiVYObZBZaDZ9wGEWd13JhZfKtSwEtu0Gk5Bfhly3GfdirhpAh7qYVMyoFES16/ql0d7vOz8ovwyxavRb2vV4qzFlku0a18M8NpnTjI8rrr8ficbbj5C20PiX/2pOKATozj4Z+uYZ7/6zblIjbLelJudSlfwJY/6bUlCvLl+8/i6o9WIaKk32V5wkjfhtb7T6wQqXEXvq66RuP3wTNZSD6fg81HL2DZPl85gmfOqO6tX5//n+E5W49dxOajxh4ifK7NouJfM3iyXIe4PqA0YisbwxtvvIG+ffti586duPrqq9G6dWv06tULNWrUQHi4duyXF1980c5lbdGxY0cAwPnz53HkyBFm5uZNm9wxHTp10s5MKZGQkIAmTZrg4MGD2LRpE9q2bWurvLJMu7qJ2GEwYRRF0Ud4leLPPDG4mc8KEQ+hrlNpWTsBKw8Yu3qYwR99ZaUKkYYdeO8m1Zjb5atNeq7tvESaCMxrBafajNPvFQAmLfjP2L2svA6WBs/lp038seVYqBW0dp/ymwt9FVRmCYbSWGTosaVaSAL4jDXJePma1j6vZPqqIx4BdO7m4xjTK8kTE8of3P/tZt39TSf+hWevaIF7deJrTlt+CFe1882KmVvAzia54sBZrrqx3pzep2vVZRswtnw304qunLoSPRtXxc1dtZPWmeX+7/Tfk5reTaphFYeLpFNY9YjQQhAErgD1guBV6FWPj8at3Rtg2/F0dG1YBYv/01Ysq7sFJzJshwls1+bIcEHhsh4VHmZoMQn4R05Zd/hCSdZo331GYRZa1UnA0Qv2LY2twmuBFgyW7D2DI+eyMeDtZY6V+c6i/biosYDgpAzj9CKA02jdKe8n6xKBcT/vwJ87TqFrwyqoWznWZ9F0nc2QQmolkksUsclBK68nf9yOGzq5jUX0lJ5S/FVRFH0Wp0Z+ttaT/C158jCfc62650vc8ZXXE0iRAEanqfJ4nGmJcLFR2ioatQKRVwwc/qk7V4H0fKSQWntPZ6LNJYm65/pYpXJcVArrwnofEgL4LBIv5hTgZFouxTkMMWwpEl9++WUIguBpyLt27cLu3cZui8FUJNatWxddunTBxo0bMWvWLEycOFGxf9WqVUhJSUF0dDSGDh3KVeb111+PKVOmYNasWRgzZoxiX1ZWFv744w8AwA033ODMTZRhXKL24Gk14HggVyCGtK6FhbtPcx/foGos08zeLiJ8s0Df07shvmRkuHvj+raYMG+nYZk8cRe13DMFh/V+oeTaHAjkGTA/W34Y9aoYx9YqjxhlZNYLCm+FUFjdtBrz0Q4sy06tR6HuN9bI3ZIAzNpgfnGIhZTBUk2agRVqYbGID5cc0FUkAsBVH/oqO7WUuHZahV7fYzUKEgDMN0hmo05QY8SaQ+dxE0ORmJlXiPiYSJ/2ILUCnnvg+a78pUSsFBvJtI78bdsJRBkEcjLzfopdou5EUY70CYkiUCMhBjPv6oqFu8wlJ+KZqKkZ/4tSLtAqQv26XrqmFSbOM8487K/eU7P5GDyCkZ3rYeEufvnNaewk1QkEp9IDEyLl4JlM3QSGITDsOooTi4F/liT3kLxVPlxy0HaZEnsY2cxZi4lOMYcjS7NLVCY6BOBRImoxaQHbgo7HYk6NvI48b0/vy9aaf3VqUMlj4agmNSMPNWUWzHbnLHtOZhgqEtXKfbNJzbTmkCL45nQv/rYbL/62GyufHWDquoR/sTW979u3L/r27Yt+/fp5/pO26f0XbCZMmAAAmDx5MrZs8SYKOH/+PB588EEAwMMPP4zERO9HNW/ePLRo0QKDBg3yKe/xxx9HbGwsFi9ejC+++MKzvbi4GA8++CDS0tLQpUsXXH755f66pTKDi7HKZL9MR4vTxayCK0wQuDI+m0UURdzRM0mx7bqOl/gcFxEmoGIM52QG1gS44Z3qWprE6MFbZ6uoX0mfpmxLS7vwCuZmssQC5dci8du1zsYxMyIUHvMuhpDvbx6a5ZtgR8ttXLLkGv3FOqRcyFEI1KIo4kBqlj+q6IFncp6RV4Q2L/1tumwnuu4fNh7jVuL5s71NYrhvG8FS+D31o2/ma0CpDDMuV3//mF5JxoVYRMvFen9qFv73l7ErFy+nObPdC/BaAcofS93KxtaMcpxQUmm9F/XmhtX44tbqKYxHXFoX9auYu0f9awEVDRS3FaLCDUMASFzaoLIDtVLi7yRydnEynvf9323RjN2sl1gEKD1Z2XnRXoSzVJqdqjBJzcxjxkh0mi3H+C0crShftSxTUy74T0H+2Qp3NmMRwJK9qUgaN587vJPevOmaj1Z7ygX0x0wey0Geb0pdjNY7aFdXXyHJwsz4pLWI2N7CdQn72JqNL1u2zKFqBJbrrrsOjz76KD744AN0794dgwYNQlxcHP7991+kpaWhV69eeO211xTnpKenY9++fcjL8xX+6tSpgxkzZuDmm2/G2LFjMX36dCQlJWHjxo04fPgwatasiVmzZoVcJt1Qo2ZCNC5mF2qKKlYHrkAqVcwqEgUARX6wKBJFIFJlPcFabQoTBG7hVRCsZV0bd2ULx1fa6yRWwFakOVqmHPUER4oP5DT+Uny5Am+kFhIUFLvw+nVt8PyvxtYwZYVsB0IFmOXwWf7vQYrNtubQeY9QzdrvNHmFxbrxeNVY6dukfmLtofOeuEvJk4dxa/yOns/Bcz/v5HJxBULPUpqFFIpEXtfUjDzNbM0sjI586erW+Hp1soXa+ZecgmK0fZmtkI6OCOPKbMpCGj/l45KR9YhPGQ54BYiiyJTP1DKW1gS4zSUJigQZIrSViVNGtMfwT9d4XO/sIkLEvX0bQRCAtxdpJw7hlVU61KvkeAB/f3tahAI8j9dIZn/n73145do2DtUo+GgpVK0obv0y3RF9+2T1O2pRKx7vjeqAK6eutHyZGz5Zo+sCK0dSYjkxtZB/d0nj5mPDRF+DIass2+cNczK7JPFQem4hqsdHG57LY2TiSWSsc+j7/x4wLIdHMat+56z6dW9UxdI4Z+Y9qi3ln5u7A5HhAqrHxwBwNt42YYwt0WLmzJmYOXMmUlO105qHKlOnTsUPP/yAHj16YM2aNfjrr79Qt25dTJ48GUuWLEGFCuZcB0eMGIH169fjhhtuwOHDhzFv3jwUFxfjoYcewvbt29GkSRM/3UnZIUwQSiwS2futrkIGUpEYYVYQFPgGC7PwTjjDwvgFZwECZlpQfIUJzmdu9btrs+rxneK0HjHLJ8sOYbuFRBM5+foKErtt/nqG9Wpp4dbuDQJ2rVAwjOCx2qmd6GwQfzOLYupj1adGGLiLWuXemZvQ6bV/FIsAu/QyJ1pAGpNWHeSLiagFt8IzBNqbHN5+ZsuxNG/WSi6LRP/faBKn8tYMR85lI1MjO7Z6zNJSHqiRx0i0Iyo4MWZqXV79urSuVTVOOXlOyylUlKmOEei07BYTGY67evvGRZfD27f5QwIJdWODV/4wDl3lBEbt/JsAex4QvqhfUZggcCnHnLu+hb5B4xT1YqbZcFO845V0nSLOlX6jOe/JtFzPQkuhy6WpDPyAQ5GYkee1xNe6H7U7O+t6DarEWZKL7XR9P2xKQWGxyGUUcybD2fBGhE1F4p133om7774b8fHxTtUnoIwcORLLly9Heno6cnJysHPnTjz33HOIivINeHznnXdCFEUkJydrlnfppZfi559/xpkzZ5Cfn4/k5GR89NFHqFmzph/vovQwpE0tw2NK1piY++I44wmp4XUhcgIrrs1mY5xV5QjILfXvU2/qoHtcuCDwWwta7OjDBMFxxV+TGhVNHb/1hcvw3d3duI+3O3/xt4m9UfBwu/UP9VhNoUIoWIjxTLYvqeRsTE3e1rF4TyqOnPMmL3BXVXm22nLaKViJjlhxDu3gVIxEXqv04Lc2Jaymt+dUBvIKizX7IL7skP6nasXATXoBazEKJczEl1QjneOEW6oo8k34tBK7sN69FNsNAOpUUi54sD6vzc8PNq6ADlKcy5oJ7PfP+5TOZvlnQtqrSVVcUqmCYTzOYLD3tHY2XCcJRgIxQhtWtuX/TvknpIpcoSVH3Sakn7PW88dY1pLXwlXm2mYV+jyt9dNlhzzl8rbvYgMr/p6Tl2BbShoAYMzXGzHsA7Y1KCsMQ9K4+Tgn68PeWrgPgDvbdMPxfzET3vCM3eez87EtJc30N+zEnIOnjH06Gb0Ja9gaqapUqYLExETExjq/skuUPR7o11h3vyi6OyotnVNcdAT2vjYEV7evY+q69zGydsZE+kdIa17LnFJdAFAx2pyCVOor9eIQNSrZZ1SfsDB+12arhJlRVnIytK1vBlU9KsdFmVL6qI99oL9+21Xz8S3BzdBu15LDSoyT8hifJBQsEnkEtggN9+HfH+5l6Zq8t33PzE2654miBSvuEEJ6/3aVNFqZS32vFwINToZW0/tu3VHueHrMY3QO+uaurhwl2OPrO7tYOk+v3nayJnuGTxvv34khmHdc0VKaspT7f2w/6fnbJ6sq43qJFSJtKdkkC2itxFzc9+hwv3VdB7dc27h6RUSEC1xZr0sjWk8tPbfQE2fXX6FkygNFLhFfMZIq2mH8Lzt9Ou7bpm9Q/BYEZ8byTq/+w9yelVekGP+ctFZW19vsbfhrWDbrraal6K9UIZK5XQpDIuf7dW5rX1aoF57aLP7PnVX7wBnfuujJLzsNvEX4FrCMjyGcx9Zjb9GiBdLT05GVRZ0+YYzRKo9LFA1XvGMiw9GytjllHcsttU0d/yg9WtRK4D52WLvaEARfdx5jfOMlqbm8tdv602hgDw/jV/IJAGKjwvmqKMPtPm36NB+6JFVG32bV3WVaKI9nTO5UvxIAeFb5JCrHsgdiLYJt0WdHyKoRH21J0d6kRvAt0xM1BCZ/sfd04BOdqDF611e3r4N1h9lZCdvVrWTpmlYVWmsPnffp30tzXDCtOE3+mlgE2mXICK22p9cmedqO3qKP3vj/71P9DMuW0Hss8X5I5mW1mQuCV3YK9rciwtmEG2oOnc1W/G7FkNPCwwRr3hGqJqWlqOO1pIl0eNb6zsgOANyyw9HzzsSFLE20f2UR2r+yCAAwY01ycCsTAjw2Zyse/2Gb6fN+23YSr/65x/H6GC3ECwJQKdY7l0mw2IdqKc8Kil34ecsJb32seDZrnKPuV83K72blbV6LR6csc7VDUvjuke5F2iVPDGPmPlkLNVpn7zqRrpnkTILnnQR73lVese3aXFxcjC+//NKp+hDlGClAa4SBgHZfX3PWYSz81d+0rpPALeyP7dMIAgTLgwXPWUbKHTNux4IAvGoQ4JpVVJggOBL7RxS9lpZW3CF5bvNYSfa2s5lKtyWzA1SwBzS78gdPsOQuSUp3iagI9z3rWTNGRfh3yfDj0W5L0H4lCmd/U2AigYS/0IrJpgVvRlU9rN71kXPZyqzNEIP+rdghr4gd29CO5aDeqf5sbY8MtBDH2UKFePomvWegp8hyqiVJ41XXhlVMnqe9z64S8PlhLfHhaG1L90oGi13OZG3me+FWF7I6lizkSdx4qW+sXsFEgjg9tELKsOo+oHl17Hz5csW2ChYWVfWQ2kcp7g412S5bmGXdX6hZWjvBVe3Mec2o+W3bSeODGLBcUgNJ+3qVALiTJTmJKIp4+qftit9OESiLRNGjpOM74dt1zsQC1Yx5yNxaYqhSsvd/871KaTP9uvrYzLxCzfMvGoRqAvjeSbAX2sortmZ199xzD4YPH47nnnsOn3zyCYqKAp89kig7SG4NRp2V1c5CbmXlr1X1ynFROPTGUO7jBcG60K11WiMTigK3ko/vWAEChnfST8LRQTURkK7hBCKAZ4c0x7sj26MeR4IJNTzvXKqqOhuj6WzcDtxyC5Nu8nICIZjHqVzyJeVuMALG//JgT3xwc0f0bloNAPDprYFxLS8OgfTYj83Zprtf3Rb+d539bJdONq9AhwJz8tsY8r47JpG6xdu5gt65liwSOY+zYs2rNXaJot7khcMiUc9FuOSGWLGCnRprpP7eQu40TV68urWlukjj1j19GunGOh3eqS5zu8f93oFHw9v+rC5kVY7l885w4j1rLVizvoOYyHDExyi3S94Z/Zs7u2hVmhdWtLj249Wev1lyGMvF0ghpvhCqlOaQHSwKDBaXpfcq3XU3k4swRqi7FCfDaPomhDP37ngTZ5UGfbl061Jd5TEFzdS/yCXinUX7PL/bvrwIF7PZ3yxP2A+ed8LTd5qNr08YY8t/46677kLFihURHR2NRx55BC+++CK6dOmCGjVqIDycvVonCAKmT59u57JEGadfs+pYsOu04+XKBRh/y2q3dW9guJoUHua21DNrkdinaTXM23oCLWrFezJ2yWlzCb/bdriZrM2CcWc+sIVvYiGzz7pybCQzZtgb17dFbFQEbtCYMBnBUw/pEHX7k1ZZnbyWETGR1i0erGY4N4PaEk56Z9tVbuGBoFP9yuhU32sh6U/3OzmlMYxVtAPxYe0kmVm0J9VbThAEa5cIaISMDAn0gseHQnIfOVq1mbEmWfsL5LFI1DlIGoMeGdgEL/+xR7XPuGwepHmNk/0IT3Z1O4zoXBdfrT7i803N3uBuT058ayLAqZlmX2xgixpYsveM5lm8uhe7ns1TbmyHDvUq4bL3Vvgc9+bwdvh7NztOmxxJbnJa8VfWrWr+99d/PtvMJhwEgE+WHnSiOn5DncCjtLP9OF8MO+lfp71P1ItWLlHELoO4eryoxxt/KfOlW/Cn3COKIrcilFUP6UzpecvHQDNGL7PWH8PczccV27QS6fywMcWwPCNFNsA3/lP8VeexpUicMWMGBEHwrDxfuHABf//9N/NY6ThSJBJGVDYdM9A8/lYkju3bCN+uO4pqFaNwLottti2UhPrZalLxMu7KFhjbtxEaVI1Fqxd9vzfWSqiU0OXPR3rjTGYe7prhToJgJmuz1Wdm9ryr2tXBzhPpPnEKzSaysYLWs6iVEMPcbrYcc2VYP9esoVzL2gmeLHy8ooJaAc6beTYQBMqoY9WBs4G5kA3ScgpxR48G+GatcmHj7t4NLZfplDXA+ewCU8qahtXiFFmgrSCKItYdvoC8wmL0b17DVlmAO/thryZVVdewXSwTnnJf/3MPftiUgp0vX2GqbCtZaLUmFqy4xN5zjMvlsUiMivBdaHFqAiiV42Q/YjVrs5nTBCj7b6ez37pE0ZZqtVpFI9lOWbpWO7g0qQpW7Hf3vVXionCBwzVO7sUwonM9zeMqcVhFymNoOq33y7fgmioIpcPaSQsnE2eECloWiTUTopGa4Z+M30Dw3MQF1b9GIarMou7KRBG46sNVpsrQejT3fqNMCueUgtLn+lDGH/QHi/ak4oqSGPmAe2F/Y/JFjaN9KyKNN79sOYGHBijDnZgZTtRKRICdbAtwzrq4LFpzlwZsKRJvv/32oLixEWUbVosa3NL+hE9qqv2aVcfy/f5VAIR54t1ofx+VY6Ow55RvsoY5Y7vjps/XaZ4XESagZW3tpC56K9pua0WvxaIgCNyZrqxaZ7DOi40KR446Q2MJIkS/KIKMiowKD9O8rtkByonqbzmWZvlcs4K5usnwnK5uZ7FR3uFk9r3dER0Zhhs+WWOqHmYIE7QFm0AJFL9ajGMUSFYdPKeI9eaIEOuQIHwhu4BpVa2FM5ln4elfHx/c1H6BAFYfPK/4Haxp8faUNHxpMWPnZ8sPmz7HUsB7HtdmnX3SeOJP60y1dY0TWJ1X26mDXKnAelqdG1TGpqNak0xWeey+9aWrW+EVmXVojXj2wpsdK3s5027t5FlE3fLCZbhy6krPQhiLOWO723K1VN+yoPPLLkbZS1moFchmUb+/wON9hryKMH/NPZ++vBneXrTfdjnhDLP3py5rhgf6N0aTiQtsl6+Fw2sHpvFXYih1u7CrMP1oyQGM7dsYURFhyFbNRfzlNi+9G3+OXVkqTyF5WAE1wz9d67PtTEl8+Cl/7/NRJIZ6LNMybswdsti2SCQIp2EJqk4oBu7p3RAfLDmI6Xd09utADnhXI/WsEOpoxDtixX6SYyRAsbp6rVMiwv1vkRjJEKiMigr0eLB+wiBEhYdprnCavfdgr4yZHe8fGdgUczYeQ05+Me7t2wgnLhord9RtW74C36NxVfXhJRUzVy89BresqXCTlUPrW/5FK+OpFcwEFG9cvSIOn7VpkShrhO8vPmCrrECjJ8inZuQpJg2/bTuBazvox7S1C29sKDk8fZOepa9QopBzMWbM8snrrd3r47t12m7iekixnNQKYjv4w2W1W8MqWH/Em5FdUJmmqZ9Q7cQYhbVow2pxJhWJvot8D/RvjDG9GioUUVoxjC9tUBkz1/J/71pNRb5oJdVLjw71KunKTZViIxWhMXhoXMMdizqhgrMZvs22kyta18Q/GuMgL8GO5ye/50LOBGbTlh/yS10iHAray3qmYWGCY+Vrwaukql8l1tQiniEl31e9yhV8Yow7gfoTt6Iw3Xvau9jw9qL9qBEfg5FdfK2Tz3NYONvBn/o4s7JvkxoVFe6+W3UMGGasTrZWqQBR1sNChCplK4gDUSZgrdxLSRTsMKhlTSRPHubYQF5BZ3XdyDVKL16SkaJQ3lf2ZWSnXX/Ed/KjpdgKD/NVJD7QvzGqVYzWrYMZpPu5uWs9n20sRNF3/129rLthei+qvatmQgwqx0Vpvi+zw1OgFInXdqij+P3R6I4A2BaJLIWuRFK1WMwY0xU/3t8Dl7XyjXPJA0/AZAAY0yvJUvlyaiXEIELnfoKtyDUi2s/Zq+Xc0PESZvsN7SfEJiEmEvMf7W2rjEAsqv+0yTjmjxX0qp6rsqp4bM42prLNSfILzSuUeayl7/9ui+Y+qd2yYnDJtyXEmE8eI3Ehx9mJ5ItXteJybWZlVNfzBPjunm665akf9eWqvn2wyb6e9eoqRvMr0vRkppIrGF7PCkaPftuLl+OrO7v4bI8vubeTab6u+kPb1sZXd3ZGR5Pxk41gjV0zxvjWTULuxiinp9ZiHuuaITQBb/a8fxf5jWB9p1e2YT9j3XKC9Ex5u/wYB2Ils/jf9W0x695ujt8/K0aiWZLPKxWnLlFEOiMe+x/b/eNpIlU5zY+JgsyKvjXi3XO923s0MCzPqreDXXjjC4e63F9WIUUiEXIIgoCkqsqOo38z+67NRp1M7yb8ysqJQ1viT50JrTSGal1THmNHjXTK4JY1MOmGtoz93jJn3tUVm58fjMEtvROCSIYmtmVtdnzBiDBBI2sqK3YGfyfNyi4pH/eNSlILCROHteS+thY8codmGzE7PgVoPGtXtxIaVfdOPlvUcru8s4QsvfdnZQCuW1n5jvWKkDJcAsBLFrOXymlcI053gh1C8yIm8x7sZbuMZ65oznVc/aqxnnfz5e2dPYsEgYg56jTuuLL2Xq6/FYnjf9mBvaczjQ/k5IN/D3gsrvTqzvr+RPjXzdrKZI4Vty8rv4hxJBupr2Il3YqUDWZGE9lAOmkNaVNLU1kj32w2pmGkavBWX0FtneSTndTU1dzPTN3OqpiIaS2XU5jlO/hSVj47wKOYtdpnTBrulr/U8ZoFQUB0RDgGtqjpuBKO1W676rhlhwkCc2yfdtul/NcM8gQ8lGIkst7nJ7d0Ml2OnpXnm8N95Xqn4H2UkuXnowObGBzJh3S3cdER6NnYvuGHGvV9OdFiNiRfQPtXFzlQEi/uWl/38WqfhT+nMNvXZZeMvU6FnfAHvLIuzzxm/JUt7FaHUGFKkbhixQqsW6cdu82Id999F6+++qrl84nygQDg27u74Z0R7b3bHJBzjMowc42r29dB4+raaeSl+YBW5rIv7+isXY+SfytGR+DmrvV996vqWbVitKI8dcbe+Y/2xue3s68XERamWMGsEheF4Z0uYa5qmnkFvz9sXVESJghIrOC1JkmePMzW6ua0W90CNU/cE602ILduUSvQWARLkSVdlyVM6tXJSnXfuKEtZt/bHTUT3IqpYh2XJFF0T5AedkhoNRKWeZXeDar6N5OqhNpqx4k45P2b+1ojsxAg4Kp2dRAfE4FODSojqVocVj47ADdeai37ebCxOxb4O6P57A3OWiO++89+jyu5bjZjxlfsEkXHlDOssWzGmmTT5bDcyW/WiQmsRnr/aiUaoLT0tWqdoLbyX/xkP+5zVx1kB5MPDxM0lTXysY2lSOS9DVH0nVyrXV7VZZltGqIo+rSz6zvyu88bKd3UCiWzSc7k1KsSiyEllmRWx+NuDd1Wfe1VVofy5+i0FQyrOL1rSEn71MjbWzzDanTXK95kTMG25PGHK6xVWG1FLk8Ma1ubq5x9qb7ZYaUFoUsbmHOjN8PZTO1EV3KkLLiPD27myHX93YTUMdW32YghLrE/1bkFPx7k3dsfO/xj9Wj2PUjZuFneC1uPXcSaQ86F9/A3rG9XbdQyyIF8C4QSU9OZ/v37Y/jw4cx9Xbt2RePGjXXPnzJlCl555RUzlyTKIWGCgHpVYtFH5s7sxKqvkTKqaQ1+Cx2jzlpyI1VbVkrUTmQro8LDBBSVdOisiRJgLPRJAoJE6zqJmm5eEeECMmQKti0vXIYmNeKZMYfMDFBSHeXuNYoidcp6+vLmeHdkB/6LGSDVm8c95eEBvoqu8DDBZ7Xu14d6YfGTfTXLsWKJagX1e5LaONMiUeeh+wST53jZMZHh6NG4Kmbf2x1D29ZCVZ2MnCJEHHpjKB4d5ExyCzl/PKzv6vruyPbY/tLlim3zH+2NnS9fjstKLGT8HSPqlu7KBQGzE7cPbu7os4135VkQgKRqcdj58hUe66F6VWJLZaI0AQ4oEg2svpxI7OU0PO+a9VzcikRnNIkdHHLh/HXbCZ9tZhJM6D0L+XdsVTnyucqSq0kN7QVDNVrZqgXBV/6QXMrk36Gdd1UnsYJPv//wrK2KstVJUMxeziX6tjMnLVnU1alfNRYPDdCfVwDa9/HUZc2wZtxAzXA22168DNtfvJy5D/DKcQk647bTQwerfRvJrkaLI6zkfHEyL4FguzY/O3dHUK8vx6jfGMWIqcdiBSOho/Sa/Km4XfzfGa7jpL7Cn+/eqWRmAHDb9PWK3w/N0g5/IWfhrtN48PvNSBo332dfoC1x5V+pv9t8UbELvSYv4T6eJRZd78dkif4gnjHPVYdAKo1yb6hj2i5CS9BJSUlBcnKy3foQ5QRJiGbhjS8onxSYK//Jy3xX2YwG7+a1+CcMRtWpFBuFWfd2w8RhrQyOU3Z8Cx7r4xE2tFwPjZ6F3I3UCEEQ0K5uJZ/trMn2xKH87sXxMRGoVjFaoTiSW9Po3UJibKQpdymJ5jXZz0u6Fs8A0pERcN3HXUx0T6orxZqvo7/96aQ2zppYvHVjO83z1M/GzPfWqHpFfHLLpQGNCXRN+zqoleieFEv/smhVOwE3dKqrsHAF3Ek74mMiPZNiKbYkCyeUjOOvVH47ZkqMDBcQyagDrzxU1sQmu67NRnEDg22do4dZxc+f20855jb4sgMhCQDlPWyQJQrhRe/1hIcJnnEgTAAWPt4Hzw5RukUZxY6LiQxHlyT3OFBdR04xQ5ggKCbsS57qh4WP9/XUU4LVb/O2xsTYSFSM0l+ourdPQ9UWc21DhOjXiRirqT4+uBkzxIucRwaxLd0jwsM0k9oBbjktMVY7liafAt/Z57GHkX1ar09yiSJa1vJVFMrlQKNQJ37O/wEAOHo+Gxl5/osN5xQ8ixpm4oLKkd5CKIwxU2/qaMqa2AjWHRU6kJRNsqY1mwBFsja8/7vN+GvnaeYxVr9debz3UEO6p+z8YlPJ0PyZSdouvDVjxRhWd33B//LKHhQjkQgKegOpJFjLFRPqlaOFj/dBUx1LAVZQbyN9gN0JqpqejasZJlVQP4f6VWLRvFY8vr+nm2aCEaN6mpn8aD2T/13vFdxb10nA4if7oqmGoo5FRHgYNj0/GN0bsS0SBUEwTLqREBPBLbAlTx6GO3qyy2MJC/880dfj8izHjM7IijBoZahuVtNAwa14ru5/Uy74ChB6MUB97sXCvbHOkJTPeRaSMuhxU9f6eOaK5pgxpotmex93ZQvmOwZ871edXKhyyeRy0/ODNcuwg7pNDmqhbQUnCIJpdzc53Q0UJ38/3te4jZUhikqhIlES8r9fz85CnF9UjM9W+GYxPZ2R59j0oFUdX2WFXeRZNHnRez2CIHgUh4Lgjhn7YP8maCWzyPpotDvemZ71n5QVuLbOIoUZwgSla3Oj6hU9i2Xy7awFPJaVhRbzHuqlO66qLfOky/GGSXCJ/p2IdWa4fEaGh3lCvLBiLwPAVe3qOKb0VVBys1J//XVJQhb5M3C6v7jAUJjIrxCnWigWRWDWvb5JdwRBwNYXLsPIznVxaZK+K20dDQ8ZO9zXr5Hid78pyzBy2lrHr+M0WXn68VrtLJhK5wYrEYu8b+jasAreG9XBr9eT5D6Wpw8v16gSCvJy+XsrDI+x+hqsrs1ZtTjXs4hWI/j8wUcIhSn1gfXc2lziK48Igm9SSfWYQRaJzkOKRCIo6MZrK/nQ5ceoP/4WtRLwxyPmsncamfBLk7Wr2xsPXLyZn436LLUQKv3s1aSaZn21ypSysLlM6GzCNJQU8mdQv0osmphw+9ZCPoEXBPhYic0Y0wVTb+rg+b3x+cHY9Pxg29dlPa6mNeM98ZPkmJkUaB05655ujq7tdWJYSUqox1d9Bb32Pn8NrVrKXSeIiQxH/+baCrj7+zVGfY3QApIgP6jEtblBVeVKpqRY9Ndzkb+KFrXi9ZUjYAs/PE01efIwdEnSDtQPuC2f9eK9hhLPXdnCtmuzkYVeuE428GAhikCmjjXPj5uO47t1vkpG0cEYiQAwqrOzlhhWnrRRHy3dr/yb+euxPp6/pYmGTkhXTxuTSnjhKn3PAiPCBUEzLqp8nGcpEs3EU21SoyKX/CIhPSutMCpqqsZFaX5/E4e2xNOX24u3piejzXuwJ+Y91FNz/9pxA9GvGZ9ClBvV6xhQsuAj38yqsjrOphY9GvFlVpY/lx4lC0NSJlNRdFtWvs9QClWOi8JbN7ZHlMH7bWzCfZ+XBlV8rYOcTELlL7Ta94P93S72ai+iJzhiDEqLkZJ3Q7B0GU6Fp2DBklHyi9xxDVmyNi/+1G9ZVeiy3NZ54M2l9c8TyrBJWrH2WXjGLpO35mQYFLuwYrqqCRMEvHqt0ktCEJRzogf6N8antyoTJYWedFf6IUUiERTkg448FiLgnejJj2F1ijGR4YgMFxSZAK26IcrdQHiEO1bHrhYwAGOlnvq+IjhmDVoTqegI9z0UmdAkXtehjqlByg6SUAG4O3P1mNW/eQ1c28HrahEdEe5I/CUzEzEeRaLoaZ/s/W3rJvpYDajPNQNvDJtaCTH69ddV3usfGhcVjvZ1E7nqISdYK+8s/nd9G8/fUrW6N6qK5MnDUD0+Gm/d2M7zLUgCd7SD8b/kwqHcCikzrwhG4g1rr5NPNkTkR0OqVYy2HZPMKEZisDOYaqFXba1+RRTtxd377aFeuKxVTY9l3ps3tsMt3XwTgFklNSPf9DlGb8cT+0vjPUpKs2KdcdJr1eH+S8+amwchTKddyV7PAMbCiNn2WKSnIVUfW/IMlu/jmxj/dH8PzX339m2Ehwc6HwNXomP9yj4xHuVEhIf5LdatXqms18O7FnFbjwbm61JywXpV3JY2Unu/ruMlmvE8g9G/ixAdjwnNA691rRZan9vTlzfHD2O7o3WdREV7UMdgYyG9o2BbJLJcP52CdUd1K7uV3ZUthCqSSMsx59JsBqvWxCc1YuEakZWvb+0qYec9WfWsc4mioWwUKNTW5SwFuCjCZ4EkTBAUxgvPDWlhLQQVYQpSJBJBQT6QDlVlQZOShfAMtgf+N1SRsViuiFKjJ2CPZmRH1uKtG9sxXW5/us9XyI6O1P/E1HfII19ojX2Se6KJeQRu65GkmYhFwkgIfXdke/0DSshXubcGasjSG1jVsRh55Aqp3lom8m5XVPY+K/csVwSoB9h2dRNRsySz5crnBhhY+ursUz0jloA1e2x3rJ8wyLC+ysyphocHBdb7Gdm5nichz0MDmmDVcwNQMTrCsXYqDw0gn3ycSMvF4v9SWafo1tdJF41Qjo+jxq4roaEiMQQbrShCt/PQeiYuRiZfM7SvVwlf3N4Za8d7v3u71nlyPlp60PQ5Ru9fer1ah0mKRD15QPq2pCLsxpALEwTNdiXPCMyacJtt73Uq8btjZ5a4cRZwxjKrWznW8kT1Bo54bO0Z8ZrN4HQvVjEmAu3rJuIJVcxteZxV+fuRvCx4++YhrfUttViLd1Izkt6D/J41FxRkR22cOBjTVFY68tAeVuJTq8sAnHeDly8E6lG3sj03bfW7k7K2h4UJ6MYwMjCKuQt4xxyprQQrfAYrHjoAPDKwCboaeC4Ywbqle/s0wu8P99IMScBDuBlLAJM4EcPRDLzZydXto7IJZZjamp4XUeS3mNRC7lHmJGqPIcAdy5TV5ozkE6diRhNeSJFIBAW5UK0VH0y+usz77bt0rMX0LPUmDuNPJDJSw72LNQmomRCDH8Z25y6bRwDVWnX/+Ba3cKhnaSFRK4F/smE0sb6hU12ucuSTFUFtg+4Amo9Op/q/P9wL393tjS/EpUg0mKjqPS4rtyxXfMitbwGgW6Oq+OSWTvh6TBdEhofpth9dt2fVSNCytq8re2xUBGomxODN4W3ZQmdJ+Z/f3lm2KXSUMjzPXrIIDg8TPKvp/oDH8lhCq9pGj/b16/gmXqUNu4o+I0Xiff0aYXS3+rYnVk6SW1iMORvZ8RH1eG/xfo+ySMLuJxkTGa6buMnfyOs/695uWPnsANUR0qSdfb7UfkQRir5fjkdZU/LvmUzzlpPycTpclWxFzme3XYq3R7T31Om9Ue3RraG37ZlVOtStHIuhbY1dCZOqxgY0pIF0j3r0bmrP8tNpwsME/PZwbx+LGLkruHyMk6wEr+WM68ZqE3IPnTlje3jCu/Rq4lZieZMRlhwk6854xIvq8dEY0sa9eH97jwYYXWJhXLVEfn1ruLVvW+2OeW2HOo6O/zElHjdq5WqvJlWx65UrPL/tKkPU30RlhqdRo+pexQbP5aQxR+p7ghmH9+sxXXB374aKbU9d3hw/6lgbWyU8jJ3MUWLdeOOF6T+2n3SwRkq2HEvzW9l2UDcPtXuuHlbFo+/XH7OtZDMySnEaHgMINSFidFmmIEUiERR0v/eSfVbcVPTcuOS7buqiVAbyCjyvXauduVKrvqxVTE+duK6qRKuuVePcK8I8i2xmJuNOWei8IUvgEkgxSu9adSvHKiYvTgh40uDGSrRjxfJLPvCxVr8rx0V5XOJ0LRJ1rqFuU+rs1fL9o7rUZwqd0v2yvoPBLbVjGQYKnic/cVhLPHlZM4VVA49r6FMlFiu834qWHvELmRLWWwH2KqpRWzWTfEDrFo0y3AYDPfdGHliZceVUiY3CG9e39SzMhAIf/HsAkxbsdaQs3nFV7ziWC26gkPdFPRtXQ70qSoW/1EWqv4//Xh2Cva8NAQA8c0VzfHprJ1TUcL2U+nCphHoWFhXkVmyCoO2iHBcd4XHHHNyqBq7vWBcz7+7q2W/FICdOJ3vzxKEtMbpbfSx7ZoDl7LNWUCvNeCwUzbL64DnHy1Tz2nVtMEe2OCy/LcnVrn8z5fchjSc8fbJ8Yb1CVLjH0k/KzKzuvsxM/tVZel+9to1HLpNK6Vi/End5ctTvNyEm0tFYgFL9LmFYHMrbsd31aXW3x4qH/s1d3u9TFH3jfatpUbIwK1kfB9PofUDzGo5alUtYsVSOMfDYKq+o5fHqpmQe643LrmtzZHgYFsjiEVuFtxbqxHl8hiCkSXQa019xQUEBVq5ciRUrVij+KyhwxzFg7VMfQxByIb96fDSuk63get2JvMfwCiR6br1ygWv8UH4LRDlXqtyw5VhRuMn7NK24erxI1+/dxHjyP/PurobCxKMD3ZnWbuikLfBfZyKjWr0qsZ6VXEFQDhb+tHAxsyrOFSOxpOZaAYGlItSrvmb5sySZkLzdGilBdBOq6Nwaq+nK3wnPE7y7d0NMGNrCJ8bo7leuwOe3MRRknCRPHmb5XAUcAkSVuCg8OqipaUuKm0pCI/BmeY0OV37rd5TEyqrBmGiKYCfMCMRcJFgyl1asL8A9ubbTJozuSZoQV4+Pxl297H3DTqG2KrTDqC710KKWcfKsRSoLIzkh6P3tQUuxUiHKG3P3oQFN0EjHGk/6/KUx1Ur25l5NqqFBSbInPddmwO0Wmjx5GHo2di9qyZWOVha3WCFVpMdyb99GHuWRVHQni8ojO2jJYKvHDfS4k+rxxe2dMf9RZcI9pzI3vzOiPeY9yE7sclv3BoowFfL3I4XpUT//riUWpj+M7Y5Z97itYJM0EoFpZemW2k+xx+vG17XZiBsvNfYesWpFyGreTlreSQoA9ed9qWrR066iQL1Iwvpu5XHXwgR3iBk9WtRKwMH/XemxbA3F8Bm2sXBLPMrH5jXtJ3os7Zj5jKRjrXwFdi0S21ySgJa1fbMps+CRQQDg0gbu77vtJcpvrEtSZczbelyxjac9kUWi85hejrx48SL69++vuV9vnyiKIeXqRgQPaRxtVzcRgiDgxatb49dtShN2eVtRx1/RQi9eSYta3g7O6kBuNSuuFnKhZ9tLl1uqk5wtL1xmuDoKuN03jNyanrisGe7t20hTsAWA92/qaKp+h89ml/wlKATClrX4Bh8WRgpYM2+Fp1l8dWcXd7maMRKV/8oxM05LCYDkbbrIwNxUPzOzubY7snM9/LQpBRuT+eK6xESGY2zfxj7b4wJo9eIPeF6Z9Ph4+4DE2Ej8+1Q/DHpnOQBZ3DZGWAJWwoznhhhnLzbTH93XrxEW7fGN0xis2Il2F1X00b8n+VMb1aUevlp9xI914ePQ2SzHynrt2ja4cupKw+P0Ar4HayJ8WauahsdIn4qZxFlaZUjxZ63Ire3repMyhAnWF7SsyBVmQicAQKs6CdxuflZE+Ju7+oaDidRIUsEbT43VFjrVr4zjF3PNVY7BcA6Fm4T8U7i5a32M6dVQ8X0cfmMoJv66E4C7DfRsUg3/PNHXI9Oqo7xoZVCWFjhcnnh77u2Kyb9G18Yjd0jfAuvT/vvxvrji/RW657Paqd1e4r5+jfDZ8sOKbep7eUyVNVltpWSGmgnRaF+vEpInD8NNn6/FusMXDC24eb+HCA13+LKC1vdsl95Nq2Ffauhn+7ZLrYQYnM5gJ3GxkgDOik4wLafQ/EklCAK4EpuECW5lXu8m1TSzuMvvtk0d99zw1Wtb4/pP1ni2925SHf/uVcqs0qe6bvwgzVBmFCPReUxbJIolKcKt/EcQEpLQ0axktUke4Fk+yL47sj0WP6ltGaFGzzQ7ijMJhH5SCiWH3hgqK5Ovs39CJvjIO7VIuxHd4X6OTk3yBEHQVSLyTOq0y1YqKazIVUuf7o//Xd8GS5/p7y5D51pO0rqO/uqzXjsw0wtKk8Fi0atINxSSNS6dWEHbzSgyXGBmHA9VnlQFvufFKWsVFtI7Zyne1CuvksAtnzBLFiys1+sSRfy9+7RimyAY9zcDTGSvvLQBOx5gsIZtf64amyk7VOZ721LSdPebeVyCILBd6BnHacGbSd5pYjkUzC4dhYgaredWISocH4/uhEk3tGWW1Z6RRVKN/PmFhwmmxnf5o7fi2swrA/B83/2bV8dvD/XC12NKFtBM1mXJU/0w6QZfjwOrSVv0mGDR08QO6vesfvZhYQLGDWmJZ4c091ghNq0Z74mpvWHCYEWcT1aGUsCrTChSJe6Qv0OjDMJ6e6ViWN99cw7rIaYi0cEO9Or2dXBXr4a4rqPSC0b9vI0WW/Wonegdk5vWiGeWr0ZvQb5vM/YY7I2VaLaG7JA5oYCl+QvH/YfIEOx3xl3ZQnNfmCBg+h18Xj0ewwMLMlSft5ZyH6u2Gued/3plZR0YRam/w0Eta/h4sklVqJUYoxnjnFRRzmPKVGTpUv5GRhBG/PlIb6YLm7y74E3kIcG7GmlViFV3lvLOTS/exwtXtcJrf+4BAIzt28izvTT2aTGRYcgrdOFTmzHEejWuho+XHgJgzeqiYbU4XasZCTPvOr/IuhCaEBOBjLwi/auZeOHh4V4LhJGd6+KTZYcMV9O0BNNaCTGaSpED/xvK3hFEoiPCNN/FQwOaWCrzCoMMmXaQFspSLvhawyx8XLkQwmqPD/RvgnBBQCeVmxbgbjKpGeaSPUSFhzFjO5klWEKXGUvIO3o0wDdrj/KXbVC0fHdZncTUqxKL5MnDMH3VEfy+/SS2Gygq1QQrWQCfZZX7Xx4lhrq82fd6Y98Na+cNY6Iu6/HBTTHm642G5XuyP5t8XvLjrTxr1jigv0CqvfPpy5ujjcytjMfjAXAv3JzNzNd2IfdDE6plwQXdLvL3I3/G3RtVwfojFwC4LdAf7M8et6wucEmXkjfhL27vjH5Tlvkca26hwVJ1FAv1dstiERMZjhevboUle30t5+UU2liFkp/5/FXuWKJGCrIr29bGrA3sRFjT7+jMzAwsfZ/hYQJcevGYGOx7/UokjZtv6pxAYGQFPf/R3hj3807sPJFuqtxQWcyT06haHA6fyzY+0AQVosJxb5+G+GKlrweEIAB1dCy146LCkV1QDAD4c8cp3RBcTqEXfkYPd38p6s5lWK9cPQ5Ghoehea141E6Mwal0tyUnzzhbI8F/BgXlFVOKxH79jOOWEAQPgiAohFO7XNO+Dn6XZfeykqhFip+kVghWio30mnzrWjJq75QCWNerUgEVZFYVeq7Yocpjg5rhrb/32lJUCAB6NqmGy1vVxKI9qZasLtRoPUl52X8+0hspF3LsX4yB9P71LRL537dkgeASRY8wZRQMWa8N2rIACbAwZ8YqmL9Ma2fyKC8KNSYDTPd2T1ZZ786K0RF48vLmmtdXx8YUNMqWcOJ7AoLnBtKkekXsOpGhe8xbw9vhuV924OVrWmNIm9q4+Yt1XGUb3VOFSG//XPp6Z32m3tRB8fvu3g2RllNgWpEYyni/L76j5fTgTC4UafED+/WhXqbd1CwpEnktEhktfFTnevhhU4rmOT2b8GVWNqpBKCoIrCB/1PIxVq6UNsuD/Rvj6Hl9OcUTI1HWnzWoGodfH+qlnURH55lLMq68vY24tC6OGChMbuh0CbYeS2Na7tl9xdLzlCuvNTwWPVixSJx5V1fc/tUGxbOMjgjnjvemRWR4GFMRKT3j8DBBU3bQIyJMsOXC7Q/qMpLgyGldJxGvX9cG1368Gi1qxeO9UR0QFxXucXXVIhTdwCcOa4m7v9nkaJkRYQImDG2JZ4f4WiaGCYJnfmrE/J2n8DECE5ZGvojL63UaFgagWF+uZr1zdf8iWUnPfaAnek1e4j5Po7wuSZWxMfkiEmIiuMOkEfyEpo00QZhECiJ9W48GeGJwM0/yAy1iIsMwqnM9vDW8HT68uSMA4Kp2dfDadW1wTftLUFXmaj20rdwywbesNpcYCxsHU90xrtQWS6XRzPqB/o1xZJK9BBjqOIL+tHCRT9zaXJKou1pnJAzxIF2O5RZu5n1Lc9ViRYxE/vhu6jrZecSBFuW6JLFdbQF796HlNmYXLSsdVpIZKQOsdB+dG/haIarprZq8G7k2m42RBrjjYKmz2QcLnvc0sks9HJk0DIIgcCuAAGguJFSIDMd/rw5RxPTU+14fHdSU+5r+ILfEAgEAftJR/Mi5toNv4iwrY5A8huXTl1sLNeAvJEWDPyegRp+XlFRMTYd6ldDWIDGDz7Us3IbZ8VR+uFpxpFeUlusmYKyE99eYH+j4ncp4lt7tgiBYboPPDmlhmDXek1RB9aA71KvkYy1kSu6QVXnKiPaY+4A76czOl9kxvAc0r4GlT/fXraMdvh7TxZN4DjBuV1YUc1I7rswR440F6z3ryZIeRaLFByTFbvUXVj4hPddcCcn1vmP9ymhZOwER4WE4bDCXCD01on9Ce4SFufsLtuJZP2ZxJMMaOBBzS/klePtdHoMG1hFa5deWfQtat/zeqA4AgD464xVhHVIkEiGHlbG1b7Pq+OeJvujXrDoeG9zUZ/VGbWUoCALevLEdRnaph6vbu2OuhIcJuK17A4SHCYqBwiiD4s8P9MTuV67QrV+L2u7VE3USgcx857JxlibUg4kTkwqtEsxMLARB8JkYm62aJFSyMr6ysvJqIbdIlLCatTkyPIy5T22hpMaMm6CTtKubiMYak3E7dZkztju2vHCZybOMJTLJyriWSsBXxxGdM7Y75tzntlSR3gdPfEq1y7MAQbddWplMN68Vj0k3tFXE61Jb71XgXBV3An/Fghr77WbmdkGAwloc0LdeDHbizZlrkz1/7zhuzl1MjhWrU/k32KqOPaudzUeNkzmpM8HrId0PTzdhdbJlpAB49Zo2AJzJOGqlv2PVj3WvzPsX1D+1r/+VTtyubg3Zi0Gf33Ypnr68md8UBFteuAzbXjTbx1tHEc8ygOOkx7WZoxFL1kl8k3j2MXrxstVIGVadiIM5oHkN1KvijXdm1F/JXYm3v3g5emosMn17d1fF72m3Xor3SxQOdvj5gR6Gx3hjJFp7PrwZb82y5Cm316EI4Lu7u5k6l8dirlG1iqhbuQJu79HASvVCBn985zyJPLsksRedWQvHgbBRkX+LvBaTPHLTNe29cVClK2g9H/nmrLzyOZ8ONqRIJIKCXl9iVfhoqiO0my1TfrR8Us4qJToi3DAzrZRYwaw1QqD49u6ueOYKtmslL2aUZOoxwZ+TcquKQLvIYwZJru3f3NVV42gvvzzYE7891MvT7uQWiUauzWpFiEREOPsLYFkosagZ4LgiAgT8+lAvrBs/CABwa/f66NqwiidYvVViIsMViZ2cRt50JKFcTvdGVVEj3q1sDA8TMPWmDphyY3vDcrs2rIL46Ag80N+bFVuvT7OaQVEQBMWkTd3aFjPuyR8Ew1Cb9cT0FYnB1STaiecqx+6ztmL9KmfGmmTuY7Xq+swVzdG+ZGw1mnjwlGeEkaJe2v3uqPYKayoz1LER74/tOaEte8gPNzMW64U3eWdke6wZN9Bn++Wta+HhgU395tqcWCGSK3uoUyjiWfpRkPFYIJb8lvompy0w7b6X2okx+KOkzfvjHRspTuXeRImxkZoJmtSLE0Pa1PIkwDGL/Da1EpfJkV6Z1fbywc0dbctCLKR4pqII9GrCv3jDS4WocKx6biCXy/jAFjUAhJ5r81OXNQv4IqLRI1DLe+sOn/drgtvlJQkub+ue5N2oc7nXr2vj+dubJIp9QlLVWDw80BtPVjpMq5+Tt4+5m4/r1JrwF+VSkZiZmYkJEyagefPmqFChAqpVq4Zhw4ZhyZIllsrr37+/x42B9V+tWv4L8l9a0Y2B5odOOrew2PggDqxOHqXz/JGp0An6NK1uOYkF4HZ7WVYyuPAgeP61tzJr7mp8/LBR6SbIOx7r3cIPY3tg9ytX6Cq7JTrVr4z29Sp5BkiXyJ9ESIvI8DBL35V0zvQ7uti6vhXiYyI9wfNfv64tfryvB5Y9M8DgLOexKo9pJhmQcW2HS7gmL1ERYdj5yhVoVSKAG71Lp+LAqO89kAK0PNlFsNB798G2SCxwSJFoNw6mUaZYIzJyCw2PMWrvDw1ogt8edisvpAkKz/upp5HZ0bg++oXXL5nkx0ZFWI4FbeetsGrHckFnXUPtsm91aI6OCNdNEBCqspBZghVHtk/T6qgeH43LWnHML0qqyPNN2BXF5I/DH3Jdt4ZVPQvzLF6TKS0A7WzCTtZNnkARcC98SiGTWEj9h5VY7gAQFx2hsAQf2ta5OWZS1Vi8eFWroCvwXCb68UASHcn27vEn3nioGvtVv2/6nC9etFWkGKzybO56/eB1Hb0GCzd3q4/oiDDFgrWcZc8MYMdI5HjmweqLyzvlTpF45swZdO7cGZMmTUJmZiauvvpqtG7dGgsWLMDgwYPx4YcfWi77iiuuwB133OHz36hRoxy8g9KNXmZjCauDayCwOn4E23rF38THRCI2ylTuJgX+fD5mi76YXcDcPnFoSwyzmA0tKiLM0GpVjbQC5xJFzyBq1frgrl4NIQgCBresYeo8aVwOdIDiUP5cRlyqnUneiWrrhUlQZBTWuNjlrWpyWb7yoBbLAtmP8VhqyvlhrPXkBgBbOcSyAJYmserjm9W0lsXQKgUWkgqwsCt727VIXL7/rL0KqIiOkJKmGbta1UqMQfJk8/F+jT6DuhYVlHJ438vfj/fFMo0YdXJY1oOixw3ce0M9G/vGY/UH6nLtekQEC39a/siRLLSalsQ/rBEfjY0TB3NlqvYoZTjkB94+Xiuhi5NJHlhVqRwXhdUMS1cJtQu2liLRbruuER/tkQV6qWIYv35dW3RUhSNhYceCVd7sWtQytvDTCjWgZtkzA3BX74ZWq+UYd/RIAgDbCW+cxiisjD/R+rJYsos/eyWz15M383Z1E7Hv9SuZ88W592uHBeARM0iPGBysz/xLKWPHjsX+/fsxaNAg/P7774iNdQt8f/31F6655ho8/vjj6NevH9q1a2e67HHjxqF///4O17hssfCxvuj/9jLdjrhqCGRVkvdHTgiKgkEnGMK6U79wMj0PgCzZisNLGt0bVcG6wxcsnSvV6ZJKFXAiLdfTFu5VrTrz8MPY7pYDY8dGhqPNJQl4fHAzNK8Vj7joCIzsXA9d/rfYVDmbnx/s+aaiLMadC2XFXiBJqhqLKSPa4yc/ulDwKJwFQdueZ0ibWo4FY1f3fYFqBqJoXmnerVFV1IiPxpnMfMfqwYpJKn0L6vrxxghyCqMwB7zYHd8CkdzCyB1KztXt6yAjrxBXtatjeGxZoDkjXpqeJaCclrUT0LlBZdzVq6Gmi7k67qtTqMcUOx4RwURK7uMPV1A5HetXRvLkYfhs+SHT50pdBc+XyqtIfHhgE0xesFex7dGBTXB569DywBrUsgZ+337SZ7tdi7sNEwfbOh+wnmyFl7aXJGLniXQAfGF1QoXG1eMwoEUNJE8ehl0l9Q8lgmUYomVxx5o/yZOxOQ3r7vWGZnlsbc9YzlA9VtcJj8UjZ2g9n5oJMWhfNxEP9S+dY0yoU64sEvfs2YPffvsN4eHhmD59ukeJCABDhw7FnXfeCZfLhUmTJgWxlmUbnv5XnXEuGMhX7+Rdk9UBxOgsXqHmtWtbW7p+qOPEwCwPsi3Pomu2ZMlyQ8q8aWei3a1RVSTpZFvTIyxMwJ+P9MGlDSqjYnQEHhrQBNXjo01bByqV4paq4jceGcge2ENJb2nmkfnbHShQ1i8AEB8T4dNegunuNOte4+Dvfz5qLR4dwG5zrCzpUl+llmudHrdm3aN/v0658dgtJhCKRDPNLjxMwO09kkzXy8wiSyj1TyxGcWZfj4kMx9wHenpcsdUkTx7mt3iD8qWQKTeaX7gPFaTPR23JGUqYSZrG27ZZnkNPXt5c4cpf6JDVtB2u7XAJbtTwIrAaS9gpagQw9rSVha7yZuDAgyDAr3G2Ja5s46uQH9mZ3a+z5k9zNh5zvE4SrASB+uHKBI/FuV6b0gt3waN017LwjQwPw28P97adGI5gU64UifPmzQMA9OrVCw0a+GaNGj16NADgjz/+QGGhcdwewjxGsQIDmRVUj3dGet3q5BM2q/NoaZIysgtboOEpdv2EQbitxNyf8EWefEBurGNW+eFVFPCfN+7KFo4o6Rr4IYC2E/hLf1Q7kc9ypjSx5YXLsNV0ZmglrEQtgNva8M6eSbipSz2/ajN2v3IFNkwYrMic3K9Z9YBNLFifUreGxhY/UiIbp6ha0T1huFvm6iU9g0KVkrGywwqXnk30FRNSfzN25iZb12EZNrLcfWfMmOGJ+7xq1SrP9nCZteDxT+7E0Tevwpm5r7CvlZeFo29fj6NvXoXCcynMY87Nfw9H37xK8d9393TH0TevwrQ7jTOiWuGfJ/oqspXLWbNmDXr37o1j7wxHyke34sLiz5CTnWVYZkpKCl555RV07doVlStXRrVq1dC/f38sXuxrUX7q1CmMGzcOAwYMQHx8PARBQCuB/XxcLhemTZuGDh06oGLFiqhZsyauvPJKrFmzxnNMsOOb8SCvopmMwKGGmSzhwaJvM3dfUq+K8Xjr5H1oJX/jRcst2Sxvj2jPDKu07JkB+PWhXo5cwyzTbr0UX96unfXcDFqyp103c9ZizNdjuuA3h5/Z2vEDNfvfUPuuWtZOQDOOWOd2uZvhXn5z1/rM5+RvC8kO9Sp5/r6idU12DEMD4VBaBJfkDVab1bsNnjAAN3flW0AjnKVcKRK3bt0KAOjcmd15S9uzs7Nx4MAB0+XPmzcPjz/+OO677z68+OKLWLhwIVyu4K/IhRJSRxFqg4MaKc4S4Ezw6OiIcCRPHobrO7IViWN6JRmW4ZS7YjC5SpVAQZ2J0A7ywU0URdzZM8lSOZLsKq2489RNgDOr778+yC+g8VrchJoVohwtQVfPxSHQmHl+7eomokpclOXsjxJaiVqiI8Lx8jWtERcdgUoV2Ndo50Bm+LjoCFSICkf3ksyWM8Z0wTd3dQ24S89DA+RZqgNP4+oVsWbcQFzd3usmK/UzP21SKnoCbb0hKTAW7Um1VY7ZyWZMTAxmzZrl+R0WBjw8oAnyU3aiOPMcEK6tFMreuwqCICA8rjKy9izTvkh4JKpe9ZTnvyEP/w9Vr3oKA8e+ZKquvDStGc8cX7dt24ZBgwYhJycHlQfejYrtrkDmtoV49sExhmX+9ttvePPNN9GkSRO8/vrreOGFF5CZmYnLLrsMX3/9teLYffv24c0338SJEyfQtm1bAMCt3Rpg3+tDfMp95pln8MADD6Bt27Z499138dRTT2H//v3o168fNmzYYPEJBB7551KaLZ88cSYD1ENJMRHNWEX1b+52E9VbaJEyqzqphK5UQV9BvNHAPfiBfo1199vlkkoVFEoSO3x4c0fMNOE+PKRNLb/LOXZlP9b5A5rXQHuHnplE7cQKmsk3QiUp055Xr8DKZwf4xMP0F1qvLpFhDeg06kznfZtV98Ql1Xofs+/Vj1EtxUKevcFtKcnbNCX5hMci8boOlxgeQzhPuYqReOTIEQBA/fr1mfsTEhKQkJCAjIwMHDlyBK1atTJV/gcffOCzrVmzZvjuu+/QpUvgs56GIqVhpVyNwiLRT9eYOMxcWyut8GSytcot3erjTGYePlt+WCEAmX1nHotESZGoM+J9cksnPPj9FgDAK9e01owzxUt8DE+XzD+wuo/23oBVwdJfwpxWmLdbuvlajAeL9vX0FXMd61cCACx+sp9uNkmniYoIQ7OaFbE/1WsdFR8TgSY1nFstl5qYlFAj0IrEAc1r4OOlhxR1CTR1KlVQxF2UqqEey4zGNsfjNzoWI9Hc8UOHDsVPP/2E2DFDIISFIzxMwNNXNMfOOQcxp1YTFOdkaJ6bvWcZKjTqjPCE6sjZswyV+97GPE4IC0fF1l7Li2nP9Ee/KcvQNMCZvCdMmIDKlStj2bJlaPfGSgBARGINrFn4IWrU6IcKDTv5nNO9kTssyoABA3Ds2DFUq+adeN5///3o0KEDXnzxRYwZ41VGXnrppTh//jyqVKmCuXPnYsSIEQgLExQLmgBQVFSETz/9FDfeeCO+/fZbz/YRI0agUaNG+P7779G1q7VYaC9d3Qrzd5yydK4V5H1JaZQLJSTL5EC5yV7Tvg7qVq6ASxvwJc/g5dbuDXBrd/e4O+XGdsyQLLUTY3CqJL51g6pxnm1W0VvE2PPqFboWjeOubIEGMuXTtFsvRTRHMkd/IV9s4sXf7b6sJ3k0wslQMLFREYit4iufC4J1ubpB1VgcPZ9j6pyEmEjUrVwBxy/m6h5nR2ZXh02RL/RoxbOXhzRgUVDiMZaVX+TeoBN/Wo43LINu8SXHlO/2HizKlUViZmYmACAuTjtmWcWKbkVHRoa2MKymT58++OKLL7Bv3z5kZ2fj+PHjmDdvHlq3bo39+/dj8ODB+O+//3TLyM/PR0ZGhuK/skhpXHkuKLLv2ky48ef7j4kM96xgi5BnpDRXjjoGGiseiIR8kBvRuR7mP9rHsPxqFaNwSzffxYz4mAhmZk2ta3LHALMhS0mn+q3dM4SJu3o1tJXN0GnqVo71cfWUWxB/NNqtTGhSo6JtVy6zOKRL0kT9FoySRjkFawLAKyQ+fXkzS9cM51UEeJSrakWi/mlOW5849e7NTrZuvvlmnD9/HnlH3B4e4YKAgoIC/PHbPCS06a95XlHGGeSn7EZsy76Ia9kXRempyDuuLxdJSN9V1QDEppLIyMjAP//8g1tvvRUJCd7YShXbDERsXEXk7F3FPE9K8tW6dWuFEhEAoqOjMXToUBw/ftwjjwJAfHw8qlQxVgwVFhYiNzcXNWvWVGyvUaMGwsLCUKGC9YWMMb0aYu4DPS2fbxb59xI6vb15ikq8jlgxA/2BIAiOKxHVjOhcD12SfK+x4DGvfHNZq5pY/GRfdGYc5wRG2eDv79cYV7b1LiwMaVMLA5q7M1tXio3EKI14cmUFnm57CCPOnhn8FR/ViEEtvf1bhA0FfSC8caTarR430B1yxgQzxnTFgObVmfs8oUsYSR79nclaHbblslY1ZfMpvvdxaYPKit+STO9xbWacw1N2l6TK2P7i5Vx1IAIDt0Vio0bmM5ayEAQBhw6Zzzr27LPP4vfffzd93pdffoneva0HYOfhtddeU/yOjY3FJZdcgiuvvBJ9+vTBxo0bMX78ePz666+aZUyaNAmvvMKOK1SW8MZILD1cUsm94tq4ehyteNhEvUIqrZo5tXJYMToCtRJiMLZvIyzYeUpxDbMIEDDt1k5cg7aZa2x63h0/776+jdF3ylLPdrNxV7hdmxV/W3vOftMjMra9eHXoW+eO6dkQX69OBuA/5fjc+3vgxmlrdY9xKuGGFtElMWudzqpuhBQby0p3azVOol48LlY11N+fGeuPRtXjcPhsNvfxLJzqM1mZqfVISkpCjx49sP2/5ajQuDPCwgQsWLAA6enpaNCqHy5uYMtp2XtWQIiKQYXGXRAWGY2ISrWRvWcZYuq2ZNcrJ93zd1h+FiYPbYABrYxdly5evIjiYuOMlbGxsYqEe2p27tyJoqIin1A4Qngkmrdqg93H3HLshzd3xCOztxpeT+L06dOG19aiQoUK6NatG2bMmIEePXqgT58+SEtLw2uvvYbKlStj7NixpssMFnJZKtD9i5MUliw0R5pI1lNaUSuWDK3fDfpEPWWhnSRO2xiKhlBxkXUKVsxHNZHhAtpckoBdJ6wZpvzyQE98suwg5mxkx2v1F+OvbOH5u6mNJGb+lo+Akn5MFHFJpQro26y6qWfVsFoc2terhKX7zvrs84ZMYFxT9dvp+3x+WEu88scez+9qFaM9FoXqa8++tzu2HLuo2Lbv9SGICAtD4wl/ebZJYQ7qlnjtMGMkMuoiJZeUFJE1E2IC4t5N8MOtSExOTnbkglYVMSdPnsS+fftMn5eVJXP7incPetnZ2gK8dLx8Bdoq0dHRmDhxIq677josXLgQhYWFiIxkfwDjx4/Hk08+6fmdkZGBevXK3opaIIfyGy+ti90nM/DfKXvWnQkVIpnB5wNF+3qVsD0lLWjXdxK1bJhUzT2ZspJRjkVEeBjWTRgEAPhrpzU3LUm2dYkihrTRd6WzE8y6ftVYVImLwoXsAndZJoUBboNEB2QMfynQ1XXr2tC/lhZ2uaMk7qb8cfC6mJuFx9LD5906LDff3bshoiPC0J0j0YmT3GQnaLbF1xFpcuKqVhx2Vq3AqxFFoH/z6li276wjLmebj17EL1uOcx+/6Xl2PDK1ZSNP1UaPHo21Tz4DV2E+BADff/89+vXrh+MVtdtJ9p5liG3SDWGR7olBbMs+yNq2EFUGj4UQpuz/xcI8HP/wFs/v6h+6/73iiiuwcOFC3bp17NgRR48eNbyHl156CS+//LLm/lOn3ONH7druMUCu2K9esxaK97jlUTPZng8ePIhffvkFI0aMQHi4tTHvu+++w6hRo3Drrbd6tjVq1AirV692bME/0JRmJU+j6m4Ppxa1/J+AIRTo26w6Vuz3VXxYoUpclKZraCCywYcCPRtXxZpD502f9/I1rfHrtpMAtOVQUXQretJyrCUPrV81Fle0rsVUjr0/qgMe/2GbpXKNkMubgiDgug51PPdqhrwi/+coiI0KR2ae2103UK7k6uuwwpzwVCUyXPCxPgTc1ukd6lXC9Z+4E3gJ0PaE6tG4Kno0Vo776pAcgDve9NJ9Zz0yNNvrxLeODw1oAsDt0v3W8Ha4orU9K1vCebgVierg0IHmu+++w3fffWerjKSkJGzZsgXHjrHTostdipOSkmxdS6JlS/dqe35+Ps6dO+cRStVER0cjOjp0Egz4C88AodHLOdkPvz2iPY6dz0HfKUtxReuaxieEKPMe6BmQlbVg8PjgZhjUsqZn1clJzMTWkBNZokks4vAdtHoNiaSqsR5FYru6lUydyytoS5lnQxG1MPHFbc5kMfQH8sUEeSymYLphq/sFu0le1MREhuOePl7lRJRDWTSNkARRKwsMVt8Gr2uzdJT6+zMKQJ+ZX4gaiFaUYYcDZ7Lw5I/buY/X6mOtDC0jR478f3t3HhdV1f8B/HMZmGEHEVEREETcEUVxRcWd3DfUzK00y+XR1FyyLNPMFh971Cyt3Cr9mZlmpvXkWrk+brlV5oaKWxoKiAgC5/cHzjjDzDD7Bp/368VLueuZuYdzz/3es2Dc+AnIuXAY2dkt8P3332PRokV456Lug+X9fQmPbqcisM0w1TKf2q2ReWA9ci4dg3e05hjSkrscFfq+rvp9zcimRZ8h2PBA92vWrEFOTsnjRwGGe9koj6Gsl6kH9uUKBQrz8wyeQ92DBw+QkpICLy8vvPPOOybtq87Pzw9169ZF8+bN0b59e9y8eRPvvPMOevXqhV9//dWo78jpuHDMqHFkEI7N7GjS5CeubNXwBKu8r1IG4DvWrqg1YZSfouwM32/uWLfGdDsuFEUzolsyK7q+IGWd0CeNbQy9RLPUgzzDLcx1Gf3lUZO23za+Fbos+tWkfTaOboEjl4ta5JkT/PaRF+X14nUr5bOy7i7Amr8XGpGHJrSPwcKdRZPInni9E3LzCzB+3XHVUBza59A8SXiQF8a2jcazLaMMnqskyq9IV11V1wsl9Rd1/U3sOk72YXRpPWzYMMMbObn4+Hhs3LgRR44c0bleudzHxwc1apg3zlJx//zz5E2TskVkWaYsRHQVt13rV0a/eN2zGpsrorw3dk5ug2o6Bo42li1jeB8/E4/fDLQ2dHOT4ObKNW01d+5rPnx5yNwQH2GbSkhyvUr44uBlkyfAaBhRDhfvZBtVKVB2ey4+Hoix1G/Wr3bV3cWvOGV2NObtZ/2wAI0um+ZPtmIbWslxkWyuHuBSOLBLW/FA4rpRJc+cZylPD5lVuuUaq26o5TNQG6ukVlG6BhIvHkA29PeYo/YwVKuyP879fb+Ere3HmIeQ4ipUqADPqg2Q/fse/Pf7KBQUFKBfv35YtuQIUnVsn31mNyQPT3gEVMKju0UtSyR3OWQBFZF9Zo9WIBGSG7wiG6h+7dCh5Nld1bVs2dLkz6OLcrzB3FztCXLycnPh5m584KigoAADBw7E77//jh9++AGhoaZPzAAUTbbSoUMHJCUlYfHixarlHTp0QN26dfH+++/j3XffNevYphjVuho++eWi1Y7nIsW+XmUliAhY78XZzkltrHIcY1nSe8SWokN8ceiS7mCOpWz5mZVVj/6Nw/BevzirHFNfC7nigWZj7T1/x6Tt1YOj6no20F9ex1T0Q8zjYYnMec86rEUkfBTuqkm6lJTPH8a0Nnyka5tiv7erFaIKJBZ1DfYosc4jFftFkiRM6VxL3+Z6Rel59u4RF4oJ637TPKer3wjKqNI/qIeaXr16AQD27duns1Xi2rVrAQDdu3fX2wXZVOvWrQNQ1DJROZFLWVZSF8klg+LRtlaI1c8ZXcHXoq6ZtrwZPxVbGa90MS6AVBpYOquxKVpWD0bqO11NbqU1slXRGzdjWklWD/FF6jtd0dDMYKh6rixpjDZ1ylZ8oUYESIvnelNnFlSfFdEWehRLj6tUJNSDRsZeN1soLNZzx5g8YakKVmw9HFnecP4ydYINc7sXlfR8LHOTEFpsdlLl2LlKhs5au7K/6uFrWnJNM1JoGwOahGt89tAA4/KQT502yLl4FGtXL8dTTz2FwMBAbBzdAt7FJhwSQiD7j18gHj3E9eWjcf2TUaqfgoxbyDl/EIV5Ra3/epXwwGas27dv4+bNmwZ/1Ie90UXZe0TZxRkAfnypFX6a2Bq3b92CzNf4YRief/55fP/991i1ahXatWtn3gcD8Msvv+D06dPo0aOHxvKYmBjUrl0b+/btM/vY6v43oz2O6ukKDwAzutS26nAvHHu6dCrpqobbuG5RXAMDLcYd5fVudbBlXKJZsz7bQ/GX/8VZc1iC4693wqlZ1ptIw9Kuxr6PW8Z+0L+BUdubU47J3d0wqGmE1r6VHtc3YnW8xCy+rTF528/T3ewJocy9xvumt8PmcZov9p5M4Kh9TN4FXFOZCiTWrVsXPXv2REFBAUaMGKHR/eWHH37AqlWr4ObmhldeeUVr36FDh6JWrVr48MMPNZbv3r0be/bs0eqil5eXh3feeUf11njy5Mk2+ESuR1mOudLwJ6W0VzHpERHkDTcJSGls3daxulhS0Vn5bILhjYrpHheK1He64pMhjTDViGDG3N6x2PBic5t13y3vq8DzrSzrKuEI6uMi2mtcHFf369S2WkGmpjYYe9HcsbUMXcfit4HiXXwMZYOPBzdS/V8uc8PipxuakjybiY8oh4vzngSFvjFy5l7vGs0hSW44fuR/GDRoEICi4EDx7ui5V0+hIOsOAhKfQXDP6Ro/QZ3HQTzKxYNzBwEAHepYPgRJQkICKleubPBn/vz5JR6nXr16cHd31+jBUquSPyLLKXD291PwCDFuPMIpU6Zg5cqV+OCDD/D0009b9Nlu3SpqmaNrMplHjx4hPz9f9bslwxCE+HuivA2GG9HHleqDZDxjqs72un0uH5aAhQMb2OdkJvD0kCE2LEAVRAnwMr0RixDAy520e9FFlje/J5ZSkJ4u1LZoYOGrcLeoG7Y1JFZ/MjTEDxNaYe3Ipjrrv1vGJeKXKW01ljWvZr36TJVALxx5rQN6NdSeYKx4ap5vVQ2HX9V88VP86uiqF017qpbebunq+5v7N1ol0Av+j6+nqcfYMi4RO+zcapnMU3YGonjsk08+we+//44dO3YgOjoarVq1wt9//42ff/4ZQggsXLgQ9evX19rvypUrOHv2LO7c0WwqfeLECUycOBEVK1ZEgwYNUL58edy+fRsnT55UVfpefvlljBgxwi6fz9kp30K40sM344hli4/CXePB2qYs+DMwpsWkvrzbqW4lGPPe10suM2rSD0t0qlsJn/56CYDrvJHUmGzFgU/Bq59LwPbf/8a7P/5pt3MWL7qVE4gYEh7kjZiKfhoTRxkz86Sptwpzr4fCwHiMPo9bJ+i7h0mQEBcWgBNpGTr391Ub90uSJHSNrWzSbL/2UinAuFmv+zatji/OjsGQel7o3r273u2yz+yB5OGJgKZ9IenoDpz5v43IPrMHvnXb6tjbdNYaIzEgIAAdOnTAl19+iZkzZ6qGpvniiy/wIPs+QmolqrYtfPQQBZm34eblD5n3kxYk77//PubPn48ZM2ZgwoQJZn6iJ5RD7qxbtw7Jycmq5ceOHcPZs2c1Zm0+/WZn1HjtB4vPaQ+uPNkKWUbXi3pb1Ll9FO52bwVpCuV9y9RJ95TGtYvB/J/+0ljWJbbkyQKN4edZcpjAhR7ljFKjop+qS3R4kLfePBMbpt1S0FqTRirpq+MXb83nJgEV/DS3Ld4l2k2StK5Vg/BAbBjdApHTt2qdQ33YHI8SZle3prsPHiHE/3FLTB3fLzknqwQST5w4gSVLlmDv3r1IS0srcVZkSZI03praW0hICI4cOYJ58+bhm2++webNm+Hj44POnTvj5ZdfRvv27U06Xps2bTB69GgcPXoUJ0+eRHp6Otzc3BAaGorOnTvjhRdeQIsWxr3hLwuetEh0nbsPWySSrSi7GoxMNL5VnrmDczsr9ZLAVbq4uWm0SHRcOqqH+KF6iJ9dA4nqutWvjAntY4wKJAIoCrSpBRJHtqqG1Qcua2zz6dDiE+6Y9gW3rG7eZBOGruOqZxNw8GI6lv58AUDR4OMaJGDzuESdlfLiJKlorLGu9Stj60nzZpd3tMjyPvCNbY9XZrSHl5fu4KPIf4QHZ/fBM7KBziAiAHhXb4rMo9+hIPvek/0KC3D/zG7M6VkXHjI3jYn2evfuDR8f/S1trDVGIgDMnTsXLVq0QJs2bTBq1CikpaXh3//+N1q0aYdr1Z60MM278Rdu/d8MBLR8GoGJRbNNb9q0CVOnTlV1Oy4+WWDHjh1RseKTFphvvfUWAODMmTMAigKWe/fuBQC89tprAIBGjRqhY8eOWL16NTIzM9GpUyfcuHEDixcvhpeXF1566SXV8dQD6qaOE2xvLlLsk4l4WY03vEUkLty+j+zcfGQ+NO4Z+bOhjTHy8yM6/34MBQAtVTXIB1UCvTC0eaRNz2Nt3nKZ2ZO3OAtj/q60Aolu0uMXNsY9P6gHtD3c7fOXHOHEgX7Sz+KS5sMPP8SkSZNQUFBg9psUe/P398e8efMwb948o/fZs2ePzuUNGzbERx99ZKWUlX7KQIErVRyddZBmcn3zU+LwzdE0jGtX3eh9/Dw9jK5oOqsKfgrcziqaxEC9LHCVYkH9Id1Vgp+2IGBaWf5a1zr4/HHgcNv4Vjrf9ne0sHurrSY+CCvnjX6NvFWBxBC/YmMkGvE9mDJRkr1FBfvg0h3jJ9EZ2zYabWpWULUgUFL/28i5cBiFudnwrt5EY5v1LzRH/2UHAABe1Zsg8/AmZP/xC4DHrRILHuGf7/+NMd9rn/fSpUslBhKtKT4+Hjt27MC0adMwceJE+Pn5YcSIERjyr+kYsPJEifueOFG0/ty5cxgyZIjW+t27d2sEEmfOnKmxfsWKFar/KwOJALB582bMnz8f69atw48//gi5XI5WrVphzpw5qFnzyXAV6oHxXS87dxcx5/trIEfKznPt+o054sID8d24RPRfegAXjJzMzOPxRG+OePT2ksuwb7r5473ag66YxNbxrdB2/h69+4T4229IB3MZU30o3sPCTYJJBW2B2vjbXlZuaan0bMtIrNyXqvpd7sCJC8l8Fl21Q4cOYcKECSgoKMCYMWOwbds2AEBQUBB27NiBL7/8EsOHD4dcLkdwcDDWrl2LXbt2WSXh5JrYIpHoidBAL/yrfYxJwah1o5rh42fibZgq29s1uY3OwfxdpViQuUkY3iLS0clQWf9Cc5vP2Kw0LVlt5j4BGFM7/frF5gA0K4r6Zki0Bl35aGa3OtY5tonL1SkfbJT3QWfK7tvGt8LJEga6Hz58OIQQaNy4qMWou8xN5yDvN9KuIKTfGwAA75otUHXa9/Ctr3ncuPAn3ZY8I2JRddr38G9cNIFIcNeJqDrte1Sd9j2EEFo/kZGRFn5S0yQmJmLfvn3IycnB33//jQ8//BA+vn4a23hG1EfVad+rWiMCwKxZs3SmX/mTlJSkcYyStlXn5eWFmTNn4syZM3jw4AHu3buHLVu2oEGDBhrbqd9TLBkv0R7K8suYsmh+ypNZfnVdelvXuZ8rNr6tM/lseGNEV/DRGMNaX32PfzUl05WNooJ98EIb/cNajEyMwuvd6mDNyKYWnXuVGWOYG0vfdX+pQ4zq/z7FxqOWSRKSalTQud+WcYlay9RbNFqzfFb/236je12NdRwr1zVZVLtYtGgRhBCYMGECFi9erBqzRS6Xo127dhg0aBBWrFiBgwcPQpIkzJw5E/Hxrv0ATJZRjoXDeiORecKDvPGUFca+cSQ/Tw+dg/m70lhZM7vVwYk3rDfDoCWaRAWhmRUH+i6J+gzlMjftcXd0SbBgnM1gX+u0MOypY0bgwc0iVP839p6UVFN3ZdwUzpjPveQy1cDotiZz8QqAqyT/27EtkVSzgtMH6pw8eWRl+maPtVXLp+KcOb/5e3pg5+QkeLo/+S4cWd9ztrKjb3zJkyCqTxamLyD9r3ZPAm7VKmi2bneXueG5xCizh0hRSqoZYtH+JdHXEKey2vjGBcU+vCRJWDyoodakLIDu8QjP/Z1lYSo1GZOPzM1rr3erg6Vqk9mRfVkUSNy3bx8kSdIaRLr4G9QGDRpg8eLFuHDhAt5//31LTkkurlDVIsO5bk5kH+Vt1O2QdHONvzL1bsIOTIaJZG6SWTMsliZvdK9jch7bMak1fprY2ujtVz/XBJ8MsbySWPyeU7OiH97qFav63dhWMNOfqq0zgGxSIxpli0Q9GV5XSz9LDGte1arHs5S7k7eQM1bTKNtORGWpBuGBWPVsE8MbOpgLFftkBfomxPpjTrLO5dbmCr2MCi1MpLX+pqqWd65x68a0jUbbEl7mNdIzC7E6Sc//XUXxeoNymBX1LFM8+7hJgMJdpjUpiz4xIUWt7pcMcv7GX88lRiG5XiVHJ6PMsqg2d+vWLSgUClSt+qSS6ubmhocPH2pt27t3b3h4eGDjxo2WnJJcnJeHDPWq+GNixxqOTorRXGXsT1cwo0ttRyeBnIwrBQ/tbfmwxthuQtDN3sr7KvQG0F7upLuMrx7ihxoVn3QNjQnxLfEcFf090amu5ZXE4tksvtgDh7EVZn0B5JJuE8qudHn5RQMPKR538R7cNEJnd55nW0YalRZjvV6sC5EjWDs46kjKFqXO9pDtqkrZ/GFkgHqLRGdsne0MTHnssOWfT0V/T5tP3GIKN0ky+Hk/05qwrYh6DwQla7e4/L/nm2l03bcF9SQffrUDIh7fh9S/l8Jihaq+4L0+zaPLY/PYluha3zqtYZXDa7D7culjUSDR29sb3t6aFSk/Pz9kZmYiNzdXY7mHhwe8vb1x+bLmDI1Utri5Sfj+X62MemvkLFjJtR53Ge8iRMZqX7siYir6Gd7QgYrPDvhM0wikvtMVEeWNmxBj24RWOPuWdVui6CplirdILP78EBls4QQeJdwnXu9eND7j1OSaaFy1nCqQ2LRaeVyc11XvflUCvfB0kwiLByF3hsq7cmIdSyfTcSYMgljH8at3HZ0EsiP1oEZ5Kw1dYQpXeHlZvEVin/gqeK9vfY1lJX2O0vrYou9eFhceiFOPx/jV973oCs5aOys0jy6Pfo1K7n4NAJ/qCXYaQz3N6i0M1T+frq7Nxlg+7Em64qz48m9M22hMS66F+AjXefYn41hUO61SpQoyMzORn/9khq3o6GgAwOHDhzW2vX79OjIyMti6i1wOs6z1ONt4K+R4Gt1MmD1cjq9Cs7WCcgZyYy+lh8wNCnfrjo1VvJyZ1yfWpn2Ynm8VBX8vze/hnT6xeLFNtMbYjI2qBmHD6BZGl4P7prfDvD6x2P1yEr4d29Ls9Nm73NXVylTZQsLZJ/4whvLrZHllHfkFrGSVRvr+Pv669WT8tde6WmcSrNJGGQhSDuuxoH8D9E8IN3r/HnHaYwJbqkus47uPFn95o2zp7iOXwe/xGL/6hs4Sxf4taVtraBgRqHddTQteEOtLsnrw2dwWiV5y24xT6i13x+ikaLjpSce5uU/Z5LxkexbV6GrXro2CggKcOnVKtSwpKQlCCMyePVvVxTkvLw/jx48HAMTGxuo8FpGzEqX23R6Vdq6Qc9WDHGzh43pCA72wdfyTWf+Ug8SrD/xtjCY2HG/u6SYRWpVva7wg+v5fifh2bEu82rWOVrBuYJMITH+qFhYObGjxeaoEerlU1+CvX2yO2T01u1PnFxZ16zb0QPORi89IH8nuzkQA9Jex6mWlrQIXJXGFxgH9GoWhebXyaGHmpB9zetazcoqAD592fNksSZrXT9lav31ttZbuprRItGGV86tRzfWuy3lUYPZxleMXFqfeO6R4i0Rn6JWgT4CXBzxKwQvGssqigQ86deqEr7/+Glu2bEHDhkWV5bFjx2LJkiXYuXMnwsLCULNmTfz1119IT0+HJEkYN26cVRJORESlC1v4uCZlxdZDJqHc4wmVGps4U/PnzzVB7qNCq6RHPRtVCfTSWlakqKL9y5S2yM03r1Jfr4r2bIfmqhLohWv3copS5gIPuiUJ9JYjKlh7Nkyg5ECiBAldXGBGeuX1kSTNfPXZ0MaoW8XfIWlyZa6e38k09pqduTihmuzRIac3SYifJ/5vVDPjNtbxB6Sv5ZclbHFMS8llbjj7VrJGS3f9rQyLvieNMTptWOksaUiS4rNFm2JEYhS+OZaGtrU0Z4ZWb5FYUKwq5awTnG4Zl4gQf+MmgCHnZFEgsW/fvkhLS0No6JMm1FFRUVi7di2effZZpKen48CBAwCKJmGZMmUKnnnmGctSTGRnrORaH1ttkJKrz6BHTwLAdUI1A2seMgmPjOy26Okhg6eVHjDVz7hvejvd2zzeKIJlkU0E+2o+HIxuE42tJ28YGNfLNW62T9KpOfB/h1I0/iORpTrWqYjfrt7TWu7tgFaIAFT3otIya7y9enA4U71M1/2j+NAohuKd6vUMR302SwJ7bm4Sfnyp5En4ig8jZ/T57HwLjg2z3stYcgyLAomBgYF44403tJb37t0bbdq0wbZt23D16lUEBASgU6dOqF69uiWnI3IIjutpPXUqF7Vc+nf/Bo5NSBmRYsSgz86EY2i6hm9GN4fM7cnDmLNdteID1QPaeUvXNo72fr/6mLLhpEO6+1lb7cqaLfOUE8zIjPgbd3eTEOht/0kYTKX+UYoHTsl4XvLSEdghTWOSojEiMQq1Zv6osbykQF58RCD62qjeomwl3aF2iIEtXUPxFy+Dm0Xgy4NXrH6efCeacbJ4i3ZdtxN9AdaSujZvHNPC0qSZxNZ1pn0X7mj8buwYic5zpclV2GxO96CgIAwePNhWhyciF1Q9xA+X5nVhwMgOBiaEY0jzSEcnwyBmBdfTqKpmt2V9f89FFXr7V02NmZ3R2eKIkgT0iQ+DJEnoVEpatvVpWAUbj18D8KSbV0ndmH6/nolu9UNx+s3OTl0u6Mo7z7aMtHs6SotnW0Y5OglkA5Ik6WxlXlJMY+MY8yeVMqSivydS3+lqs+OXVu1rV8SWE9cdnQwAQCV/T4M1Cn35S1e5rWyp5+9ps3CITra+v52+lqnxu7G90p2tXkTOj68BiQxoWJXT1VsTg4j24Spfs5cTdDMhy+i9bg66oLpahxX/e3CiRhYAitInc5PQr1GY3jKyTmXXGn/v/ZQ41f+rlvfB8mGNMaF9Db3b+zyeAdzTQ2b1mbxtQQLLLGvgQPtli6vUTZxd8ZZ3tgoC/TslDr+93tE2BzeRUc8PejaRybRXOCov2uI5qKTrz+cushXevYkMaFuzdHSDoLJlfPsYRyfBKDEV/VCrUlGXd9Z1XJOzXbdNj7spqc8EXTww5Uzj8XWJrYT3+sYZ3G7bhFYYmeg6rbeKd6dqX7tiiQPQ+3t52DpJVqE+2QoRmcZZJ35wVcXvZFvHJ1r1+HJ3N5cYakJJX/6a1rmW6v/9HneffxJgY54EnKteRK7Bora87drpHsS8JJIkYefOnZacloiIDKgc4OXoJBht2/hWyHz4iG9NXZTyujWK0Gy9XcnfE1fSH9g9PeFB3tjzchIq+D1pmShzkxAXFoATaRlFC5yovvzRM40cnQSbignxNWo7VxuPWCo22QoRmW7ZkEa4nZXr6GS4nLJWXapv5MQcGTmPdC4P8H7yokp5qyks1J7J2VV1rFMRs7//3dHJoDLGokDinj17jNpO+ZAhhOCDIrkUYx+AiMh8bi4yuQLpt396O4T4aXYp/uqFZrh0J9sh6Yl8PLC+BrX6h60CQNvGt3KqiVwcfQ/7c06yzhYiU5Nr4r0fz2osqxLoGi8/lK02WJ0lMl25Yvf6znUrOSglpYvz3HVs47txRS0t1V846YopZD3MN3gsZRneN74KTl3LQEV/Tyul0nHCg7wtPoYTVV3IRVgUSNQ1Y7O6jIwMHDp0CAcOHED58uUxevRoyGTOP+4NEQCcmtWJY/eQy3m6STjS7uY4OhlUxoTqCAJVDvByqpax6o8ctmr9VifUtuMYmhq8GuHgrtC6JlsAgDFJ1XE3Ow+f/noJAPDfl1qj5uMhDpydqmuzY5Ph8pYNaaSaSZfKjlYxwY5OQqmgfEmUVGz4JX2zFpdGuj6pUa0LH5fhbWqGILUUTfY0o0stvL3tT7P3ZxyRTGXTQKLSrl270KdPH/z+++/YsGGDJackshs/T9cYr4lI3bw+9R2dBCKnpB6EKysVZmcej0w9lusqQUTgSd6RJInjJVqALdHKJvZMs46QYrNQl8XWZLqykjHZK/9xl2aZA/Ki3IYNVEa1jrYokEhkKrs0t2rXrh0WLlyITZs24bPPPrPHKYmIiIhU1B+0nG3WZmPJ3EystjnxM7urXgNNRR/CmQO2RFR2lKWiSNdHNaYsfvioAADgKbdtGCTQW7tByl9zn7LpOdVtGZeoEWw2xNXGKSbHs1u/zQEDBkAmkzGQSERERHanXkl21Qrz2LbRRm2n7PbmzAEuZxpL0hSa+ajoX+f9lomoLFCWS05c5FudrvubMZ8/yKdonE5vuUUdMw3aOr4VpnSuadNzlKQs5QVyDNv+Banx9PSEj48P/vjjD3udkoiIiAiAZgs41wxhGT/khty96D2xMz9HuGowt1CtO7NrfgIiKm3yCgoB2LbrrDNQv23o7NpsxF1vZrc66FyvEnwVtg2DVAn0QrNqQTY9hzXxfkamsltpc+3aNWRkZLhsxZGIiIhcl4Drt0g0xgcD4jCqdTUAQHlf550N3dW7NkvgGIlE5Bzy8osCiaV9kkihEe4yr0Wij8IdbYtNUmMrrjAm6OBmEUX/cfF7MtmfXVok5uTkYMyYMQCA2NhYe5ySiIiISKWw0NEpsI/eDcNQWCgQ6C1HayeeITXfZSOJagHpx/8vSzOlEpHzUQYSFe6lPJBo4Laha9LmYc2r2iYxRnDknUFmzAzWACr6eQIoHqQlMsyiQOLs2bNLXP/w4UNcvXoV//3vf/HPP/9AkiSMHTvWklMSERERmaxQx9h2pZWbm4Q2NSo4OhklKnTRQKJ6K0S2SCQiZ/BKl9rwlssQ7KtwdFJsql+jMOy/8A8AfeWu5sI3utfBsy2jbJ8wPRzZItHYMZIrBhQFEgO8nLcHAzkniwKJs2bNMuoPRAgBNzc3vPbaaxg0aJAlpyQiIiIymeasza4ZxCpNpiTXxFdHrjo6GSZT5hwJQGL1YFQL9kHX+pUdmSQil9A0ynXGi3M1UcE++M/Aho5Ohs31iQ/DpPUnAOhu7Vc8LOHoW31UeR/V/8e3q+7AlOiX0igM4eW80ahqOUcnhVyMRYHE1q1blxhIdHd3R7ly5RAXF4f+/fsjJibGktMRERERmaUstUh0Ba7eckaSgHI+cux6OcnRSSFyCQ3CAx2dBCoF1o1qhoGfHNTZIrF4KzxHvzQM8PZAWDkvpN3Nwb/a2zcOYmxXZUmS0Dy6vI1TQ6WRRYHEPXv2WCkZRERERLZTKcAT5/6+D4BjipP5GIQmMhOHACAriAouauWna2za4sMCBno7vruu8p5hbFdja5+XyFZK94isRERERAAWP/2k21dpnrWZbEuZd1xhNk4iZ8JJicgaShqbtviyPg2r2D5BRrJ37rd34JLKHosCibNnz8aCBQuM3n7RokUGJ2ghIiIisjb1lgmMI5K51MdIJCLjMa5B1hDg5QEA6NlAO0hYPFjtZuTMxfZg7/zPvzeyNYsCibNmzcL8+fON3v6DDz7Am2++ackpLbZt2zbMmjUL3bt3R2hoKCRJgiRJSEtLs+i4eXl5ePfddxEXFwcfHx+UK1cOSUlJ2LBhg5VSTkREREROgQ9pRCbhnwxZg5dchtR3uiK5XiVHJ8Uk9m7FzhaJZGsWjZHoigYNGoSMjAyrHvPBgwfo2LEj9u/fj8DAQCQnJ+P+/fvYtWsXfv75Z0yePNmkgCsRERHZDuvXzmHl8ARk5+U7OhkmUXWrY1iEiMiphAd5OzoJTiO6go/hjYgsYNdAYnp6Ojw9Pe15Si19+vRBTEwM4uPjER8fj5CQEIuPOWPGDOzfvx+xsbHYtWsXgoODAQBHjx5FUlIS/v3vfyMpKQndunWz+FxERERkKQaBnEHbWpbXwexNORMmg9FEpuHfDNla9RBfRydBiyPGZK5a3pvj+JLN2S2Q+PXXXyMrKws1a9a01yl1WrFihVWPd/fuXXz88ccAgI8//lgVRASARo0aYdq0aZg5cybmzp3LQCIRERGRK+P4mkRmYSteKoscccvgXxrZg0mBxIULF2LhwoUay27fvo1q1arp3UcIgXv37iEzMxOSJKFr167mpdRJbdu2DXl5eYiIiEDLli211g8aNAgzZ87EwYMHcf36dYSGhjoglURERKRUGl/U1w8LwMk06w7dQto42QoRERGVdSYFEu/du4fU1FSNZQUFBVrL9Gnfvj1ef/11U07p9I4fPw4AaNy4sc711apVQ1BQENLT0/Hbb78xkEhERORgfeO1Z3t0dWufb4aMnEeOTkap16hqObSpUQHDWkQ6OilELqU0vsAhMsQBPZuJ7MKkQGKvXr0QGRkJoKil4XPPPYeAgAD85z//0buPm5sb/P39Ua9ePURHR1uSVqd06dIlAEBERITebcLCwpCenq7aVpfc3Fzk5uaqfs/MzLReIomIiEgluV5lRyfB6nwV7vBVlLk59OzO00OG1c81cXQyiFwO44hkqhoVffHXrfsm7ePuJiG/0Hmid4LjYVApZVKNMy4uDnFxcarfn3vuOXh5eWHYsGFWT5iryMrKAgD4+OifGcnXt2jg15KCg/PmzcObb75p3cQRERERERE5wKKnG+K3K/ewYt8lNkkkk20Y3QIZD0xraV89xBd/3syyUYpcA0OXZA8WvbouLCy0VjoMmjp1Kr777juT9/vss8+QmJhogxRZ1yuvvIJJkyapfs/MzER4eLgDU0RERERERGSeHnGhCPDywIp9l9gikUzm7+kBf08PRyfDIq92rYP/bP/Lrudkd2qyB5fpA3P9+nWcPXvW5P3u3zetObSp/Pz8AADZ2dkG0+Dv7693G4VCAYVCYd3EERERUam36tkERyeBiKhEbJBIZVGPuFD0iLPvHAmFjCSSHbhZsvPBgwcRHx+PsWPHGtx25MiRiI+Px5EjR8w615dffgkhhMk/ycnJZp3PWMoxI69cuaJ3m7S0NI1tiYiIiKyheogvkmqGODoZREQ6icdBDYltEskOlN2aPx2qeyLU0izEr6hREuOIZA8WBRLXrl2LEydOoFWrVga3bdasGX777TesXbvWklM6nfj4eADQGyC9ePEi0tPTAQANGza0W7qIiIio9OOjORE5M2VMgy0SyZ4igrwdnQS7C/KROzoJVIZYFEj8+eefAQCdOnUyuG3v3r0BALt377bklE6nS5cukMvluHLlCvbt26e1Xhk4bdasGUJD7dusmYiIiIiIyGEeRxIZRyR7Kovde2f3rAcA8PN0mdHryIVZFEhMS0tDQEAAgoKCDG5bvnx5BAQE4Nq1a5ac0mHat2+PWrVqYdOmTRrLy5Urh9GjRwMAxowZg3/++Ue17tixY3j33XcBAK+++qr9EktERESl0lejmjk6CURERhOPI4lskUj2FODl2pO0mMPfiwFEsh+LcltOTg7kcuOb0AohkJXl2OnY58yZg61bt2ot79Gjh+qzxMfH46OPPtJYf+HCBVy+fBkZGRla+7799tv43//+hwMHDiAmJgbt2rVDdnY2du7ciUePHmHSpEno1q2bbT4QERERlRkNI8pp/M6HcyIioiJeHjLkPCpAaKCXo5Nid26sEJAdWRRIDAkJwdWrV3H9+nWD3XavXbuGzMxMVKlSxZJTWuzChQs4dOiQ1vLjx4+r/u/p6WnSMb29vbFnzx4sWLAAa9aswbZt2yCXy9G8eXOMGzcOKSkpFqebiIiIiM8JRORKlD1MJRZeZAduZTibleXPTvZnUSCxWbNmuHr1KpYsWYK5c+eWuO2SJUsAAE2bNrXklBZbtWoVVq1aZfJ+qampJa6Xy+WYPn06pk+fbl7CiIiIyKZ+ndoWjwoKHZ0Mi7DFARG5kshgHwBAg/BAxyaEyoSyNzKiuqL6QRkcHpIcwKIxEkeMGAEhBN577z188sknerdbtmwZ3nvvPUiShBEjRlhySiIiIiKzhAd5o1oFX0cnwyLFw4gSpzAgIicWXcEXf85JRsvqwY5OClGppmyRKMp4OJXsw6IWiR07dkS/fv2wYcMGjB49GkuWLEG3bt1QtWpVAMDly5exZcsWnDlzBkII9O3bF0899ZRVEk5ERERU1rBBIhG5Gk8PmaOTQGVEWW6Npxw+oCx/B2Q/Fk/ts3r1akiShK+//hqnTp3C6dOnNdaLxzl54MCBWL58uaWnIyIiIiqzlA8KoQGeuJ7xkIFFIiIiUmuRSGR7FnVtBgAvLy989dVX2LFjBwYNGoSqVatCoVDA09MTkZGReOaZZ7Br1y6sXbsWXl5lb/YkIiIiImvjgwIREZGmstytVzmGciGbJJIdWNwiUaldu3Zo166dtQ5HRERERHrwOYGIiIiUFO5FbcRcfVI5cg0Wt0g0VmFhIbZs2YJevXrZ65REREREpZIbuzQTERHRYwr3orFI8/IZSCTbs1qLRH3OnTuH5cuX4/PPP8etW7dsfToiIiKiUk+SJMRWCcCUzjUdnRQiIiKn0CW2MjYeu+boZDiEwqOojRgDiWQPNgkkPnjwAOvXr8fy5cuxf/9+AE8mXaldu7YtTklERERUZkgSsOVfiY5OBhERkdN4v18c5vSs5+hkOITscVeFgkKOfUK2Z9VA4sGDB7F8+XKsX78e9+/fB1AUQKxVqxZSUlKQkpKCevXK5h82ERERkbVwtmYiIiJNMjcJPgqbd7p0SqwWkD1Z/Fd2+/ZtfP7551ixYgX+/PNPAE9aH0qShMOHD6NRo0aWnoaIiIiIAHSsUxHPtoh0dDKIiIjISUh8w0h2ZFYgUQiBbdu2YcWKFfj++++Rn58PIQS8vLzQq1cvDBs2DMnJyQDYlZmIiIjImj4d2tjRSSAiIiInopyEjR2byR5MCiReuHABK1aswOrVq3Hjxg0IISBJEhITEzF06FD0798ffn5+tkorERERERERERGpYYtEsieTAokxMTGQJAlCCERFRWHo0KEYOnQooqKibJU+IiIiIiIiIiIicgJmdW0eP3483nvvPcjlcmunh4iIiIiIiIiIiJyQmykbKxQKCCGwePFihIaGYuzYsTh48KCt0kZERERERERERMbgIIlkByYFEm/cuIFFixahfv36SE9Px8cff4yWLVuiZs2aePvtt3HlyhVbpZOIiIiIiIiIiIgcyKRAYmBgIMaNG4fjx4/j6NGjGD16NAICAnDu3DnMnDkT1apVQ7t27bBy5UpbpZeIiIiIiIiIiIgcwKRAorqGDRtiyZIluHHjBr744gu0adMGQgjs2bMHI0eOVG33008/IT8/3yqJJSIiIiIiIiIiIscwO5CopFAo8Mwzz2DXrl04f/48Xn31VVSpUgUAIIRA3759ERISgmeffRbbtm1jUJGIiIiIiIiIyMo4RCLZg8WBRHVRUVGYM2cOLl++jG3btqFPnz5wd3fHvXv38Pnnn6N79+6oWLGiNU9JREREREREREREdmDVQKKSJElITk7Ghg0bcO3aNcyfPx+1a9eGEAL37t2zxSmJiIiIiIiIiIjIhmwSSFQXHByMSZMm4fTp09i/fz9GjBhh61MSERERERERERGRlbnb82TNmjVDs2bN7HlKIiIiIiIiIqJSTwiOkki2Z/MWiUREREREREREROT6GEgkIiIiIiIiInJxbI9I9sBAIhERERERERERERnEQCIRERERERERkYvjEIlkD2UukLht2zbMmjUL3bt3R2hoKCRJgiRJSEtLM/uYkZGRquPo+uEEM0RERERERERE5OrsOmuzMxg0aBAyMjJscuy+ffvC19dXa3l0dLRNzkdERERERERERGQvZS6Q2KdPH8TExCA+Ph7x8fEICQmx2rHnz5+PyMhIqx2PiIiIiIiIiMiQasE+GNmqmqOTQWVAmQskrlixwtFJICIiIiIiIiKyml0vJzk6CVRGlLkxEomIiIiIiIiIiMh0Za5Foi2tXLkS6enpyM/PR2hoKNq0aYPWrVs7OllEREREREREREQWYyDRimbPnq21LCEhAWvXrkX16tVL3Dc3Nxe5ubmq3zMzM62ePiIiIiIiIiIiInOxa7MVdO3aFWvXrsX58+eRk5ODS5cu4fPPP0dERAQOHz6MpKQk/P333yUeY968eQgICFD9hIeH2yn1REREREREREREhklCCOHoRBhj6tSp+O6770ze77PPPkNiYqLe9ZIkAQCuXr2KsLAws9OnS3p6Oho1aoTU1FRMmDAB//nPf/Ruq6tFYnh4ODIyMuDv72/VdBERERERERERkWvJzMxEQECAQ2NFLtO1+fr16zh79qzJ+92/f98GqTFOUFAQXnrpJbz00kvYsmVLiYFEhUIBhUJhv8QRERERERERERGZwGW6Nn/55ZcQQpj8k5yc7NB0165dGwCQlpbm0HQQERERERERERFZwmUCia7qn3/+AQD4+fk5OCVERERERERERETmc5muza5q3bp1AIAmTZqYtJ9y6ErO3kxERERERERERMoYkSOnO2Eg0Ujt27fHtWvXMG/ePPTu3Vu1fPPmzQgLC0OjRo00ts/KysLMmTNVE8RMmjTJpPNlZWUBAGdvJiIiIiIiIiIilaysLAQEBDjk3GUukDhnzhxs3bpVa3mPHj0gl8sBAPHx8fjoo4801l+4cAGXL19GRkaGxvLdu3dj4cKFiIiIQGxsLAIDA3H9+nX89ttvuHv3Ltzd3TF//nx06NDBpHSGhobi6tWr8PPzU80s7eqUM1FfvXqVM1GTTswjZAjzCBmD+YQMYR4hQ5hHyBDmETKEeYQMMSePCCGQlZWF0NBQG6dOvzIXSLxw4QIOHTqktfz48eOq/3t6ehp9vF69euH+/fs4duwYjhw5gvT0dMjlckRERGDAgAEYM2YMYmNjTU6nm5sbwsLCTN7PFfj7+7MgpRIxj5AhzCNkDOYTMoR5hAxhHiFDmEfIEOYRMsTUPOKolohKZS6QuGrVKqxatcrk/VJTU3UuT0pKQlJSkkVpIiIiIiIiIiIicnactZmIiIiIiIiIiIgMYiCR7EahUOCNN96AQqFwdFLISTGPkCHMI2QM5hMyhHmEDGEeIUOYR8gQ5hEyxFXziCQcOWc0ERERERERERERuQS2SCQiIiIiIiIiIiKDGEgkIiIiIiIiIiIigxhIJCIiIiIiIiIiIoMYSCS7+Prrr5GUlIRy5crBx8cHcXFxeO+99/Do0SNHJ42M9OjRI+zcuRNTpkxBQkICAgMD4eHhgUqVKqFHjx7YunWrzv1mzZoFSZJK/Pnzzz/1nvf8+fMYPnw4wsLCoFAoEBYWhuHDh+PixYslpjcrKwszZsxAzZo14eXlheDgYHTt2hW7du2y6Hugkg0fPtzg9X748KHOfY8ePYqUlBRUrFgRnp6eiIqKwr/+9S/8/fffJZ7z1q1bGDduHKKioqBQKFCxYkWkpKTg2LFjJe6Xl5eHd999F3FxcfDx8UG5cuWQlJSEDRs2mP35qWSpqakG84fy55dfflHtx3Kk9Dl79iwWL16M4cOHIzY2Fu7u7pAkCW+99ZbBfXfs2IEuXbogODgYXl5eqFWrFl599VXcv3+/xP3snQ8KCwuxbNkyNG3aFH5+fvDz80PTpk3xySefgEOUG2ZqHiksLMT+/fvx+uuvIzExEeXLl4eHhweCg4PRsWNHrFmzRu/3vmrVKoNlzI8//qg3rbwPOYY55Yir3U9YjljGnDxibD3l888/19iP5YjrMff5VqlM10cEkY1NmDBBABDu7u6iU6dOok+fPiIwMFAAEImJieLBgweOTiIZYfv27QKAACAqVaokunbtKvr37y/q1aunWj5q1ChRWFiosd8bb7whAIi4uDgxbNgwnT/Xr1/Xec69e/cKb29vAUDUrVtXDBgwQNStW1cAED4+PuLAgQM697t165aoUaOGACAqV64sUlJSROvWrYUkSUKSJLFo0SKrfz9UZNiwYQKAaNmypd7rnZeXp7Xf119/Ldzd3QUAkZCQIPr37y+qVasmAIiKFSuKc+fO6Tzf2bNnRUhIiAAgqlWrJvr37y8SEhJUZc7GjRt17pednS1atGghAIjAwEDRp08f0alTJ1UaJk+ebNXvhYrcvn1bb74YNmyY6tr5+fmJ+/fvq/ZjOVL6KOsGxX/mzJlT4n4LFiwQAIQkSaJ169YiJSVFVKpUSQAQNWvWFLdv39a5n73zQX5+vujTp48AILy9vUX37t1F9+7dhZeXlwAgUlJSREFBgWlfWhljah45d+6capugoCDRqVMnMWDAAFW5AkB069ZN5Obmau27cuVKAUBER0frLWNOnjyp87y8DzmOOeWIK91PWI5Yzpw8UlI9pW3btqp7UGpqqsZ+LEdcj7nPt0KwPsJAItnUpk2bBADh6+srjh49qlp++/ZtERsbywLOhezcuVP07dtX/PLLL1rr1q1bJ2QymQAgVq9erbFOWWF74403TDpfdna2CA0NFQDEK6+8orHulVdeEQBEeHi4zkB0z549BQDRvn17kZ2drVq+detWIZPJhJubmzhx4oRJ6SHjKAOJK1euNHqfa9euqW6oy5YtUy3Pz88XgwcPVgUXi9/ECwsLRcOGDQUAMWTIEJGfn69at2zZMlXZc+PGDa1zKiuWsbGxGjf6I0eOCF9fXwFAbNmyxYRPTtbw1FNPCQDi+eef11jOcqT0+fTTT8XLL78s1qxZI/744w8xZMgQgw93x44dE5IkCZlMJrZt26Zanp2dLdq3by8AiL59+2rt54h88MEHHwgAokqVKuLixYuq5RcvXlSlZfHixSV/SWWcqXnk/Pnzol27duKHH37QuB8IIcSePXuEj4+PACDefPNNrX2VAYBhw4aZlEbehxzLnHLEle4nLEcsZ04eKcno0aMFANGxY0etdSxHXI+5z7esjzCQSDamfJPy1ltvaa379ddfBQChUCjEvXv3HJA6sqYRI0aoCjd15lbYlixZIgCIGjVqaL0lKSgoUL2RWbp0qca6M2fOCABCJpNpvSlUT+fAgQNNSg8Zx5xA4pQpUwQA0aFDB611WVlZIiAgQAAQP/74o8a6rVu3qt7AZmVlae2rvJFPnz5dY3l6erqQy+UCgNi7d6/WfnPmzBEARLNmzYz+DGS5tLQ04ebmJgCIgwcPaqxjOVL6KcuOkh7uUlJSBAAxcuRIrXWpqamq/PPHH39orLN3PigoKFC1Svjyyy+19vviiy8EABEaGsrWRCYwJo+URFm2R0dHa60zNwDA+5BzMSaPuMr9hOWIbVhSjuTk5Kh61a1bt05rPcuR0kff8y3rI0JwjESymWvXruHw4cMAgEGDBmmtT0xMRHh4OHJzc7Ft2zZ7J4+srGHDhgCAq1evWuV4mzZtAgAMHDgQbm6aRZWbmxsGDBgAANi4caPO/Vq2bImqVatqHVeZF7ds2cIxOp2E8prpKid8fX3Ro0cPAPqvdY8ePeDr66u1r/J4xffbtm0b8vLyEBERgZYtW+rd7+DBg7h+/bqpH4fMtGrVKhQWFqJu3bpo2rSpVY7JcqT0yMvLU41VpKusqFq1qurvWXn9lOydDw4cOICbN29CoVCgb9++Wvv17dsXcrkc169fx6FDh0r41GRN1q6nALwPlSUsR+ibb77BvXv3EBQUhF69elntuCxHnJeu+wbrI4/Ta9LWRCY4fvw4ACAoKAhRUVE6t2ncuLHGtuS6zp07BwCoXLmyzvXHjh3D9OnTMWrUKEyZMgVr165FVlaW3uMp84QyjxSnL+8Yu192drYqzWR9u3fvxuTJkzFq1Ci88sor2LRpE3Jzc7W2y8rKwvnz5wHY7lqfO3cO2dnZRu9XrVo1BAUFAQB+++03nduQ9a1atQoAMGLECL3bsBwpu/766y88ePAAgO2up7XygXK/unXrwtPTU2s/Ly8v1K1bV+c5yXYM1VOAogHwX3vtNYwaNQqTJk3CihUrcOfOHb3b8z7kupz9fsJyxPmsWLECADB48GAoFAq927EcKT103TdYHynibtLWRCa4dOkSACAiIkLvNuHh4Rrbkmu6efOmKgig620HUPSGZMuWLRrLAgICsGjRIgwdOlRjeVZWFv755x8A+vOPMu/cvn0b2dnZ8PHxAWA43/n7+8Pf3x+ZmZm4dOkS6tSpY8QnJFMVn8kOKLoJr1ixAsnJyaplqampqv8butbFywlD11q5nxACqampqhulMWVTWFgY0tPTWTbZyc8//4zz589DLpdjyJAherdjOVJ2Ka9JYGAg/Pz8dG6jq6xwRD4wtv5z/PhxljF28uDBAyxatAiA/noKAOzbtw/79u3TWObp6YlZs2Zh2rRpWtvzPuS6nP1+wnLEuaSmpmL37t0ASn7hCbAcKS30Pd+yPlKELRLJZpRv9ZR/ALoom29nZmbaJU1kffn5+Rg8eDAyMjIQGxuLF154QWN9dHQ03n77bRw/fhzp6elIT0/H3r170a1bN2RkZGDYsGFYs2aNxj7qb4T15R/1pv/q+Yf5zrHi4uKwcOFCnD59GpmZmbh16xZ++ukntGjRAjdu3ECPHj2wZ88e1famXOvi18vQtWYecR3Kt/w9evRAcHCw1nqWI2TuNXFEPmD+cT5jxozBpUuXEBoaihkzZmitr1SpEl599VUcOnQIt2/fRmZmJg4fPoyhQ4ciNzcX06dPx9tvv621H+9DrsdV7ifMI85l5cqVEEKgcePGqF+/vs5tWI6UHiU937I+UoQtEonIIi+++CJ27tyJ8uXLY8OGDZDL5RrrdbUuatmyJbZs2YLx48dj8eLFmDhxIlJSUrT2JdczceJEjd/9/PzQsWNHdOjQAb1798bmzZvx0ksvsYsFqWRmZmLDhg0AgOeee07nNixHiMhcc+bMwerVq+Hp6Yn169ejfPnyWtskJydrtJYHirqKrV69GnFxcZg8eTJmz56NESNGoGLFivZKOtkA7ydkqsLCQlXLNH31FIDlSGli6PmW2CKRbEjZ1Fd9LIfi7t+/D6CoOS65ngkTJmD58uUoV64ctm/fjho1api0/6xZsyCTyXD79m2NAV7Vm4nryz/KvANo5h/mO+ckSRLefPNNAMCJEydUgxabcq2LXy9D15p5xDWsW7cODx48QFhYGDp37mzy/ixHygZzr4kj8gHzj/NYsGABXn/9dSgUCmzatEnnhASGTJgwAcHBwcjNzcVPP/2ksY73odLFme4nzCPOY8eOHbhy5Qq8vLx0Tq5hDJYjrsPQ8y3rI0UYSCSbiYyMBFDy7HjKdcptyXVMnjwZixYtQmBgIH766SfVrFamCAoKQkhICAAgLS1NtdzPz081OPCVK1d07qvMO8HBwRrNtZV5Sd9+mZmZqqbbzHf2Vbt2bdX/lddbfeYxQ9e6+PUydK2V+0mSpHEeQ/upp495xPaU3ZqHDx+uNYOdMViOlA3K7/nevXt6J0TQVVY4Ih8YU8aw/mN7ixcvxuTJkyGXy/HNN99otRQylkwmQ0xMDADNMgbgfai0cab7CcsR56Gsp/Tt2xcBAQFmHYPliGsw5vmW9ZEiDCSSzSj/8P755x+9g3ceOXIEABAfH2+3dJHlpk6digULFiAgIAA//fST3hmkDCkoKEBGRgYAaA1Wq8wTyjxSnL68Y+x+Pj4+JregJMsoBxgGnlxvf39/VK9eHYDtrnVMTIzGmCOG9rt48SLS09MBwKwAORnv999/x6FDhyBJEp599lmzjsFypGyoWbMmvL29AdjuelorHyj3O3PmDB4+fKi1X05ODs6cOaPznGQdS5Yswfjx41VBxK5du1p0POX9y9wyhvch1+BM9xOWI84hPT0d3377LQDDk6wYwnLEuRn7fMv6yGOCyIYSEhIEAPHWW29prfv1118FAKFQKMS9e/cckDoyx7Rp0wQAERAQIP73v/9ZdKyNGzcKAEKSJHHjxg2NdUuWLBEARI0aNURBQYHGuoKCAlGjRg0BQCxdulRj3enTpwUAIZPJxOXLl7XOOWLECAFADBw40KK0k+kWLFggAAh/f3+Rl5enWj5lyhQBQHTo0EFrn6ysLBEYGCgAiB9//FFj3datWwUAERgYKO7fv6+1b/v27QUAMX36dI3l6enpQi6XCwBi7969WvvNmTNHABDNmjUz96OSkSZNmiQAiHbt2pl9DJYjpcOwYcMEADFnzhy926SkpAgAYuTIkVrrUlNThUwmEwDEH3/8obHO3vmgoKBAVKpUSQAQX375pdZ+X3zxhQAgQkNDtdJD+hmTR4QQ4uOPPxYAhFwuF1u2bLH4vEePHhUABABx6NAhjXW8DzkXY/OIPs50P2E5Yhum5pFFixYJACI6OloUFhaafV6WI87N1Odb1keEYCCRbGrTpk0CgPD19RVHjx5VLb9z546IjY0VAMTkyZMdmEIyxauvvqq60RlTyF6+fFl88cUXIicnR2vdpk2bRFBQkAAgBg8erLU+OztbhIaGCgBixowZGutmzJghAIiwsDDx4MEDrX179uypCkypr9+2bZuQyWTCzc1NnDhxwpiPTCY4fvy42Lx5s3j06JHG8oKCAvHZZ58JT09PAUC89tprGuuvXbsmvL29BQDxySefqJbn5+eLIUOGCAAiISFBqwJXWFgoGjZsKACIoUOHivz8fNW6ZcuWqcqe4g8DQggxYcIEAUDUr19f3LlzR7X86NGjwtfXVwCwygMo6ZeXlydCQkIEALFmzRq927EcKRuMebg7evSokCRJyGQy8cMPP6iWZ2dnqx60+vbtq7WfI/LBBx98IACIKlWqiIsXL6qWX7x4UVSpUkUAEIsXLy75SyENxuSRTz75REiSZFIQMTs7W3z44YciMzNTa93PP/8sIiMjBQCRmJiotZ73IediKI+42v2E5Yj1mRpIbNCggQAg5s6dW+J2LEdcl6nPt0KwPiIEA4lkB+PHjxcAhIeHh0hOThZ9+/ZVtTBq2bKlzj8Ucj6bN29WvUlr3LixGDZsmM4f9cDw8ePHVTe/Vq1aiYEDB4qePXuKmJgY1bHatm0rsrKydJ5z7969qgBTvXr1xMCBA0W9evUEAOHj4yMOHDigc79bt26pzlG5cmXRv39/kZSUJCRJEgDEwoULbfIdlXXKFwflypUT7du3F4MGDRJdunQRERERquv99NNPawUahRBi/fr1qrd3TZs2FQMGDBDVqlUTAETFihXFuXPndJ7zzz//FBUqVBAARLVq1cSAAQNEkyZNBADh7u4uNm7cqHO/7Oxs0bx5c1V6+/btK5KTk4WHh4cAICZNmmTV74a0KVt+BAYG6nyoU2I5UjodPXpUNG3aVPUTHBysqkCrL79+/brGfsqWzZIkiaSkJNG/f39RuXJlAUDUrFlT3L59W+f57J0P8vPzRe/evQUA4e3tLXr06CF69OihSkO/fv3YisgAU/PI8ePHVdelVq1aeuspw4YN0zjP3bt3BVDUQ6ZZs2aif//+ok+fPqr8AUDExsZq5UUl3occx5w84kr3E5YjljP3XiOEEMeOHVO1Art27VqJ52E54prMeb5VKuv1EQYSyS6++uorjG96GAAADf9JREFU0bp1a+Hv7y+8vLxEvXr1xDvvvCNyc3MdnTQy0sqVK1UFbUk/VatWVe1z584dMW3aNNGuXTsREREhfHx8hIeHh6hcubLo1q2bWLt2rcGC69y5c2Lo0KEiNDRUeHh4iNDQUDF06FBx/vz5EvfLyMgQ06dPFzExMUKhUIigoCCRnJwsduzYYY2vg3S4ePGieOmll0RiYqKoUqWK8PT0FAqFQkRERIh+/fqJrVu3lrj/kSNHRJ8+fUSFChWEXC4XVatWFWPHjhU3b94scb8bN26IsWPHiqpVqwq5XC4qVKgg+vTpo9EKWpfc3Fwxb948Ua9ePeHl5SUCAgJE69atxfr1603+7GS6bt26CQBizJgxJW7HcqR02r17t1H3lEuXLmntu337dpGcnCyCgoKEQqEQMTEx4pVXXtHZEkSdvfNBQUGBWLp0qWjcuLHw8fERPj4+IiEhQSxdutSiLnJlhal5xNjtAc3Hn9zcXDFz5kzx1FNPiaioKOHn5yfc3d1FhQoVRIcOHcSyZcsM1ld5H3IMU/OIK95PWI5YxpJ7zbhx4wQA0aVLF4PnYTnimsx5vlVXlusjkhBCgIiIiIiIiIiIiKgEnLWZiIiIiIiIiIiIDGIgkYiIiIiIiIiIiAxiIJGIiIiIiIiIiIgMYiCRiIiIiIiIiIiIDGIgkYiIiIiIiIiIiAxiIJGIiIiIiIiIiIgMYiCRiIiIiIiIiIiIDGIgkYiIiIiIiIiIiAxiIJGIiIiIiIiIiIgMYiCRiIiIqBSSJAmSJGHPnj2OTgoRERERlRIMJBIRERE5GWUQ0JyfVatWOTr5Tic1NRWzZs3CrFmzHJ0UIiIiIpfm7ugEEBEREZGmihUr6lx+//59ZGdnl7iNl5cXAKBmzZoAAG9vbxuk0LWkpqbizTffBAAGE4mIiIgswEAiERERkZO5efOmzuWzZs1SBcT0baP0559/Wj1dRERERFS2sWszERERERERERERGcRAIhEREVEppG+yldTUVNW61NRUXL58Gc8//zwiIiLg6emJ6OhovPbaa6ou1ABw+vRpDB48GOHh4fD09ERMTAzeeustPHr0qMQ0pKam4qWXXkLdunXh6+sLb29v1KpVCxMmTMCVK1fM/mx//vknRo0ahRo1asDb2xuenp4IDw9Hs2bNMGPGDI3WmJGRkWjbtq3W96L8GT58uNbxs7Ky8M4776B58+YICgqCQqFAeHg4Bg4ciAMHDuj9rOrf67lz5zB8+HCEhYVBoVAgIiICL774Iq5fv673c6WlpWHixImoW7cufHx8oFAoEBoaikaNGmHixIk4fPiw2d8ZERERkTWwazMRERFRGXXs2DGMGDEC9+7dg7+/P/Lz83Hx4kXMnTsXv/zyC3bu3ImffvoJ/fv3x4MHDxAQEIC8vDycP38eM2fOxOnTp7Fu3Tqdx16zZg1GjBiB3NxcAIBCoYCbmxvOnj2Ls2fPYuXKldiwYQM6depkUpq3b9+O7t27q47r4eEBHx8fpKWlIS0tDYcOHYJcLleNhVihQgVkZmbi7t27ALTHlgwICND4/bfffkP37t2RlpYGAJDJZPD29kZaWhq++uorrF+/HnPnzsUrr7yiN42HDh3C888/j6ysLPj6+kImk+Hq1atYtmwZvv76a2zfvh3x8fEa+5w4cQJt27ZVpVMmk8Hf3x83b97EjRs3cOzYMdy9e5eT6RAREZFDsUUiERERURk1YsQINGrUCGfOnEFGRgaysrKwaNEiyGQy/Prrr5g9ezaeeeYZdO/eHampqbh37x4yMzPx6quvAgC++uor7NixQ+u427dvx9ChQ1FQUICpU6fi0qVLyMnJQXZ2Nv7880+kpKQgKysLKSkpJrdMHD16NHJzc9GpUyecOnUKeXl5uHv3LnJycnD69Gm8+eabiIyMVG1/+PBhbNy4UfX7zZs3NX4WLlyoWnfjxg107twZaWlp6NOnD44cOYKcnBxkZmbi1q1bmDlzJmQyGWbMmIFvv/1WbxpfeOEFREVF4dChQ8jKykJ2djb++9//IiIiAunp6ejduzeysrI09pk8eTLu3r2L+Ph4HDhwAI8ePUJ6ejoePnyIv/76C/Pnz0fdunVN+q6IiIiIrE4QERERkUt44403BABhTBVOud3u3bs1ll+6dEm1rm7duuLhw4da+w4ZMkS1TceOHUVhYaHWNq1atRIAxIgRIzSWFxQUiJiYGAFALFu2TG/6evToIQCICRMmGPwsSrdu3VKl6/r160bvt3v3bqO+t+eee04AEIMGDdK7zYIFCwQAERcXp7Fc/XstX768uHXrlta+v//+u5DL5QKAeO+99zTWeXl5CQBi//79Rn8uIiIiIntji0QiIiKiMmrixIlQKBRayzt37qz6//Tp0yFJkt5tTp48qbH8l19+wblz5xAcHIyRI0fqPffQoUMBAP/973+NTq+fnx/c3Iqqrzdu3DB6P2M8fPgQa9euBQBMmzZN73bKdJ84cQK3bt3Suc2LL76IkJAQreW1a9dGv379AECrS3hgYCAA638uIiIiImviGIlEREREZVSTJk10LlcfRzAhIaHEbZRj+int27cPAJCRkYHQ0FC9587LywMAXL582ej0enl5oX379ti+fTuSk5Px4osvomvXrmjYsCHkcrnRx9Hl6NGjePjwIQAYPW7j5cuXtcZcBIB27drp3addu3ZYu3YtTp48iUePHsHDwwMA0K1bN3z66acYNmwY9u3bhx49eiAhIQHe3t5mfBoiIiIi22AgkYiIiKiM8vPz07nc3d3d6G2Kz9ysnJX40aNHelvsqcvJyTEqrUqfffYZevTogRMnTmDOnDmYM2cO5HI5EhIS0LNnT4wYMQJBQUEmHVM93QCMSjcAPHjwQOfyKlWq6N1HuS4/Px/p6emqQOR7772H8+fPY/fu3ViwYAEWLFgAmUyGBg0aoGvXrhg1alSJxyUiIiKyB3ZtJiIiIiKrKSgoAAA0bdoUQgijfkwRERGBY8eO4ccff8T48ePRqFEjFBYWYt++fZg6dSqqV6+OXbt2mZ1uoCi4aUy6k5KSTD6PPoGBgdi1axd+/fVXTJ06FS1btoS7uzuOHj2K2bNnIyYmBv/3f/9ntfMRERERmYOBRCIiIiKymkqVKgEwrcuyqdzc3NC5c2csXLgQR44cQXp6OtasWYOIiAjcvXsXgwYNUnWdNpYy3YDlab927ZrBde7u7jpbTiYmJuLdd9/F3r17ce/ePWzevBmxsbHIycnBc889Z3RrSSIiIiJbYCCRiIiIiKymZcuWAICbN2/iyJEjdjmnn58fBg0ahOXLlwMo6pp86tQp1XrlBC0A9LaATEhIUI2zuGXLFovSs3v3boPr6tevrxofUR9PT0/06NEDGzduBFA0IczevXstShsRERGRJRhIJCIiIiKradu2LapXrw6gaFZoQy0D09PTjT62oWN5eXmp/q8ePPT391f9/969ezr39fHxwaBBgwAA7777Lq5cuVLiuUpK99KlS3Hnzh2t5WfPnsWGDRsAAAMGDFAtz8/PR2Fhod7j6ftcRERERPbGmggRERERWY27uzuWLl0Kd3d37N27F61bt8bOnTs1JmW5ePEili5dioSEBHz00UdGH3v//v2oX78+PvjgA/zxxx+q4JsQAvv378fo0aMBAGFhYahfv75qvxo1aqhaG3722Wd6WyW+/fbbCA0NxZ07d9C8eXN88cUXyMrKUq2/ffs2vvnmG/Tu3RtPP/203nQ+evQIHTt2xOHDh1Xp27FjBzp37ozc3FyEh4fjxRdfVG2flpaGmJgYvPXWWzh+/Djy8/NV606ePInBgwcDKAp2tmnTxujvi4iIiMjaGEgkIiIiIqtq3749vv76a/j5+eHQoUPo0KEDfHx8EBwcDE9PT0RHR2P06NE4cuQIJEky6dinTp3CpEmTUKdOHXh6eiI4OBhyuRwtW7bEqVOn4O/vj7Vr10Imk6n28fb2xpAhQwAAU6dOha+vL6pWrYrIyEi8/PLLqu0qV66MHTt2oEaNGrh+/TqGDh2KwMBAlC9fHr6+vggJCUG/fv3w7bffltiCcNmyZbhw4QKaNGkCPz8/+Pr6omPHjrh8+TICAwOxceNGjVaSQFFwdebMmYiPj4enpyfKly8PhUKBuLg47NmzB3K5HKtWrTJrRmoiIiIia3F3dAKIiIiIqPTp1asXzp8/j48++gg//PADzp07h3v37sHHxwe1atVCQkICunbtii5duhh9zISEBKxfvx67d+/G//73P1y/fh137tyBp6cnqlevjk6dOmHChAkIDQ3V2nfJkiUIDw/HN998gwsXLqi6Lhfvgly7dm2cPHkSq1evxjfffIPffvsN6enpkMvlqF69Oho2bIiOHTuiX79+etPZtGlTHDlyBHPnzsXOnTtx+/ZtVKlSBV26dMHrr7+OsLAwje2rVKmC7777Drt378aBAweQlpaGv//+G+7u7qhevTratm2LCRMmICYmxujvioiIiMgWJKGvbwcRERERERklNTUVUVFRAIBLly4hMjLSsQkiIiIisgF2bSYiIiIiIiIiIiKDGEgkIiIiIiIiIiIigxhIJCIiIiIiIiIiIoMYSCQiIiIiIiIiIiKDONkKERERERERERERGcQWiURERERERERERGQQA4lERERERERERERkEAOJREREREREREREZBADiURERERERERERGQQA4lERERERERERERkEAOJREREREREREREZBADiURERERERERERGQQA4lERERERERERERk0P8DkiNri1GA3wMAAAAASUVORK5CYII=",
      "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": 15,
   "id": "d0f5ae72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "1478\n",
      "(19268,)\n",
      "1495\n",
      "(19268,)\n",
      "1973\n",
      "(19268,)\n",
      "931\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": 16,
   "id": "88da85fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "92\n",
      "(19268,)\n",
      "114\n",
      "(19268,)\n",
      "168\n",
      "(19268,)\n",
      "659\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": 17,
   "id": "43865dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19268,)\n",
      "1386\n",
      "(19268,)\n",
      "1381\n",
      "(19268,)\n",
      "1805\n",
      "(19268,)\n",
      "272\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",
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