"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Visualize a sample from the training data\n",
"sample_index = 0\n",
"plt.imshow(X_train[sample_index, 0, :, :, 2], cmap='viridis')\n",
"plt.title('Sample Input')\n",
"plt.show()\n",
"\n",
"plt.imshow(y_train[sample_index, 0, :, :, 0], cmap='viridis')\n",
"plt.title('Sample Target')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"np.save(\"X_val.npy\", X_val)\n",
"np.save(\"Y_val.npy\", y_val)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"Model: \"smogseer\"\n",
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"│ conv_lstm2d (\u001b[38;5;33mConvLSTM2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m291\u001b[0m, \u001b[38;5;34m512\u001b[0m, │ \u001b[38;5;34m12,736\u001b[0m │\n",
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"text": [
"Epoch 1/100\n"
]
},
{
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"text": [
"c:\\Users\\khant\\Documents\\smogseer\\venv\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 472ms/step - loss: 0.7290 - mean_squared_error: 0.1448 - val_loss: 0.6891 - val_mean_squared_error: 0.1320 - learning_rate: 1.0000e-05\n",
"Epoch 2/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.7017 - mean_squared_error: 0.1329 - val_loss: 0.6959 - val_mean_squared_error: 0.1347 - learning_rate: 1.0000e-05\n",
"Epoch 3/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.6911 - mean_squared_error: 0.1275 - val_loss: 0.6945 - val_mean_squared_error: 0.1335 - learning_rate: 1.0000e-05\n",
"Epoch 4/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.6827 - mean_squared_error: 0.1240 - val_loss: 0.6839 - val_mean_squared_error: 0.1284 - learning_rate: 1.0000e-05\n",
"Epoch 5/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.6741 - mean_squared_error: 0.1198 - val_loss: 0.6764 - val_mean_squared_error: 0.1248 - learning_rate: 1.0000e-05\n",
"Epoch 6/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 482ms/step - loss: 0.6639 - mean_squared_error: 0.1150 - val_loss: 0.6625 - val_mean_squared_error: 0.1180 - learning_rate: 1.0000e-05\n",
"Epoch 7/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 513ms/step - loss: 0.6518 - mean_squared_error: 0.1083 - val_loss: 0.6491 - val_mean_squared_error: 0.1114 - learning_rate: 1.0000e-05\n",
"Epoch 8/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 523ms/step - loss: 0.6380 - mean_squared_error: 0.1018 - val_loss: 0.6330 - val_mean_squared_error: 0.1035 - learning_rate: 1.0000e-05\n",
"Epoch 9/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 488ms/step - loss: 0.6223 - mean_squared_error: 0.0940 - val_loss: 0.6140 - val_mean_squared_error: 0.0941 - learning_rate: 1.0000e-05\n",
"Epoch 10/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.6055 - mean_squared_error: 0.0856 - val_loss: 0.5990 - val_mean_squared_error: 0.0868 - learning_rate: 1.0000e-05\n",
"Epoch 11/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.5873 - mean_squared_error: 0.0776 - val_loss: 0.5783 - val_mean_squared_error: 0.0768 - learning_rate: 1.0000e-05\n",
"Epoch 12/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.5693 - mean_squared_error: 0.0687 - val_loss: 0.5591 - val_mean_squared_error: 0.0677 - learning_rate: 1.0000e-05\n",
"Epoch 13/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 473ms/step - loss: 0.5517 - mean_squared_error: 0.0597 - val_loss: 0.5410 - val_mean_squared_error: 0.0591 - learning_rate: 1.0000e-05\n",
"Epoch 14/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 497ms/step - loss: 0.5337 - mean_squared_error: 0.0515 - val_loss: 0.5243 - val_mean_squared_error: 0.0514 - learning_rate: 1.0000e-05\n",
"Epoch 15/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 488ms/step - loss: 0.5173 - mean_squared_error: 0.0436 - val_loss: 0.5077 - val_mean_squared_error: 0.0438 - learning_rate: 1.0000e-05\n",
"Epoch 16/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 496ms/step - loss: 0.5011 - mean_squared_error: 0.0367 - val_loss: 0.4921 - val_mean_squared_error: 0.0369 - learning_rate: 1.0000e-05\n",
"Epoch 17/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 501ms/step - loss: 0.4868 - mean_squared_error: 0.0304 - val_loss: 0.4785 - val_mean_squared_error: 0.0311 - learning_rate: 1.0000e-05\n",
"Epoch 18/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 485ms/step - loss: 0.4738 - mean_squared_error: 0.0247 - val_loss: 0.4654 - val_mean_squared_error: 0.0256 - learning_rate: 1.0000e-05\n",
"Epoch 19/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4612 - mean_squared_error: 0.0201 - val_loss: 0.4515 - val_mean_squared_error: 0.0200 - learning_rate: 1.0000e-05\n",
"Epoch 20/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 473ms/step - loss: 0.4499 - mean_squared_error: 0.0160 - val_loss: 0.4439 - val_mean_squared_error: 0.0171 - learning_rate: 1.0000e-05\n",
"Epoch 21/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4414 - mean_squared_error: 0.0124 - val_loss: 0.4351 - val_mean_squared_error: 0.0137 - learning_rate: 1.0000e-05\n",
"Epoch 22/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4354 - mean_squared_error: 0.0095 - val_loss: 0.4283 - val_mean_squared_error: 0.0112 - learning_rate: 1.0000e-05\n",
"Epoch 23/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4290 - mean_squared_error: 0.0071 - val_loss: 0.4227 - val_mean_squared_error: 0.0093 - learning_rate: 1.0000e-05\n",
"Epoch 24/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 477ms/step - loss: 0.4218 - mean_squared_error: 0.0056 - val_loss: 0.4174 - val_mean_squared_error: 0.0075 - learning_rate: 1.0000e-05\n",
"Epoch 25/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.4188 - mean_squared_error: 0.0041 - val_loss: 0.4146 - val_mean_squared_error: 0.0065 - learning_rate: 1.0000e-05\n",
"Epoch 26/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 467ms/step - loss: 0.4148 - mean_squared_error: 0.0032 - val_loss: 0.4108 - val_mean_squared_error: 0.0053 - learning_rate: 1.0000e-05\n",
"Epoch 27/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 496ms/step - loss: 0.4136 - mean_squared_error: 0.0025 - val_loss: 0.4082 - val_mean_squared_error: 0.0044 - learning_rate: 1.0000e-05\n",
"Epoch 28/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 527ms/step - loss: 0.4110 - mean_squared_error: 0.0019 - val_loss: 0.4063 - val_mean_squared_error: 0.0038 - learning_rate: 1.0000e-05\n",
"Epoch 29/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 487ms/step - loss: 0.4096 - mean_squared_error: 0.0016 - val_loss: 0.4058 - val_mean_squared_error: 0.0037 - learning_rate: 1.0000e-05\n",
"Epoch 30/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4096 - mean_squared_error: 0.0013 - val_loss: 0.4050 - val_mean_squared_error: 0.0034 - learning_rate: 1.0000e-05\n",
"Epoch 31/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 473ms/step - loss: 0.4102 - mean_squared_error: 0.0012 - val_loss: 0.4044 - val_mean_squared_error: 0.0033 - learning_rate: 1.0000e-05\n",
"Epoch 32/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.4090 - mean_squared_error: 0.0011 - val_loss: 0.4040 - val_mean_squared_error: 0.0031 - learning_rate: 1.0000e-05\n",
"Epoch 33/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4087 - mean_squared_error: 0.0010 - val_loss: 0.4033 - val_mean_squared_error: 0.0029 - learning_rate: 1.0000e-05\n",
"Epoch 34/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4079 - mean_squared_error: 0.0010 - val_loss: 0.4037 - val_mean_squared_error: 0.0030 - learning_rate: 1.0000e-05\n",
"Epoch 35/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 485ms/step - loss: 0.4109 - mean_squared_error: 9.6707e-04 - val_loss: 0.4032 - val_mean_squared_error: 0.0029 - learning_rate: 1.0000e-05\n",
"Epoch 36/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m153s\u001b[0m 525ms/step - loss: 0.4092 - mean_squared_error: 9.3389e-04 - val_loss: 0.4035 - val_mean_squared_error: 0.0030 - learning_rate: 1.0000e-05\n",
"Epoch 37/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m154s\u001b[0m 529ms/step - loss: 0.4071 - mean_squared_error: 9.4938e-04 - val_loss: 0.4025 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 38/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 490ms/step - loss: 0.4065 - mean_squared_error: 9.0768e-04 - val_loss: 0.4027 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 39/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4091 - mean_squared_error: 8.5881e-04 - val_loss: 0.4029 - val_mean_squared_error: 0.0028 - learning_rate: 1.0000e-05\n",
"Epoch 40/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 471ms/step - loss: 0.4084 - mean_squared_error: 8.5700e-04 - val_loss: 0.4027 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 41/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4080 - mean_squared_error: 8.3285e-04 - val_loss: 0.4026 - val_mean_squared_error: 0.0028 - learning_rate: 1.0000e-05\n",
"Epoch 42/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4066 - mean_squared_error: 8.5810e-04 - val_loss: 0.4024 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 43/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 471ms/step - loss: 0.4102 - mean_squared_error: 8.3282e-04 - val_loss: 0.4024 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 44/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 490ms/step - loss: 0.4078 - mean_squared_error: 7.6995e-04 - val_loss: 0.4025 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 45/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 515ms/step - loss: 0.4082 - mean_squared_error: 7.7627e-04 - val_loss: 0.4023 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 46/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 507ms/step - loss: 0.4092 - mean_squared_error: 7.8152e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 47/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 492ms/step - loss: 0.4082 - mean_squared_error: 7.3046e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 48/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4087 - mean_squared_error: 7.4382e-04 - val_loss: 0.4021 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 49/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4066 - mean_squared_error: 7.5448e-04 - val_loss: 0.4021 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 50/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4072 - mean_squared_error: 7.7316e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 51/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 473ms/step - loss: 0.4084 - mean_squared_error: 7.0784e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 52/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 471ms/step - loss: 0.4087 - mean_squared_error: 7.3632e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 53/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 486ms/step - loss: 0.4063 - mean_squared_error: 7.1279e-04 - val_loss: 0.4020 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 54/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 497ms/step - loss: 0.4082 - mean_squared_error: 6.9338e-04 - val_loss: 0.4017 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 55/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 511ms/step - loss: 0.4068 - mean_squared_error: 6.9151e-04 - val_loss: 0.4017 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 56/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 484ms/step - loss: 0.4070 - mean_squared_error: 6.7310e-04 - val_loss: 0.4021 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 57/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4069 - mean_squared_error: 6.8701e-04 - val_loss: 0.4019 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 58/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4067 - mean_squared_error: 7.1130e-04 - val_loss: 0.4019 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 59/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4079 - mean_squared_error: 6.7344e-04 - val_loss: 0.4021 - val_mean_squared_error: 0.0026 - learning_rate: 1.0000e-05\n",
"Epoch 60/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4085 - mean_squared_error: 6.8823e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 61/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4087 - mean_squared_error: 6.4379e-04 - val_loss: 0.4018 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 62/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 476ms/step - loss: 0.4060 - mean_squared_error: 6.5046e-04 - val_loss: 0.4017 - val_mean_squared_error: 0.0025 - learning_rate: 1.0000e-05\n",
"Epoch 63/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4051 - mean_squared_error: 6.1816e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 64/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 465ms/step - loss: 0.4089 - mean_squared_error: 6.1312e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 65/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 486ms/step - loss: 0.4068 - mean_squared_error: 6.4825e-04 - val_loss: 0.4014 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 66/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m151s\u001b[0m 517ms/step - loss: 0.4057 - mean_squared_error: 6.1512e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 67/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m155s\u001b[0m 532ms/step - loss: 0.4075 - mean_squared_error: 6.2446e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 68/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 465ms/step - loss: 0.4074 - mean_squared_error: 5.9204e-04 - val_loss: 0.4014 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 69/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.4068 - mean_squared_error: 6.0754e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 70/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4056 - mean_squared_error: 6.0360e-04 - val_loss: 0.4023 - val_mean_squared_error: 0.0027 - learning_rate: 1.0000e-05\n",
"Epoch 71/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 475ms/step - loss: 0.4064 - mean_squared_error: 6.0216e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 72/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 467ms/step - loss: 0.4074 - mean_squared_error: 5.6818e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-05\n",
"Epoch 73/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4077 - mean_squared_error: 5.8116e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 74/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 500ms/step - loss: 0.4056 - mean_squared_error: 5.9187e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-05\n",
"Epoch 75/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 515ms/step - loss: 0.4066 - mean_squared_error: 5.6511e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 76/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 504ms/step - loss: 0.4078 - mean_squared_error: 5.5301e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 77/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 472ms/step - loss: 0.4076 - mean_squared_error: 5.5795e-04 - val_loss: 0.4017 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 78/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4045 - mean_squared_error: 5.5711e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-05\n",
"Epoch 79/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 465ms/step - loss: 0.4070 - mean_squared_error: 5.5179e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 80/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 465ms/step - loss: 0.4070 - mean_squared_error: 5.6309e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 81/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.4083 - mean_squared_error: 5.5748e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-05\n",
"Epoch 82/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 480ms/step - loss: 0.4079 - mean_squared_error: 5.1798e-04 - val_loss: 0.4014 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 83/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 494ms/step - loss: 0.4055 - mean_squared_error: 5.5006e-04 - val_loss: 0.4015 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 84/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 508ms/step - loss: 0.4075 - mean_squared_error: 5.0081e-04\n",
"Epoch 84: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m150s\u001b[0m 513ms/step - loss: 0.4075 - mean_squared_error: 5.0092e-04 - val_loss: 0.4016 - val_mean_squared_error: 0.0024 - learning_rate: 1.0000e-05\n",
"Epoch 85/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 493ms/step - loss: 0.4071 - mean_squared_error: 5.4880e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 86/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.4065 - mean_squared_error: 5.2907e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 87/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.4068 - mean_squared_error: 5.1186e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 88/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 465ms/step - loss: 0.4057 - mean_squared_error: 5.3199e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 89/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 466ms/step - loss: 0.4072 - mean_squared_error: 5.4235e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 90/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 469ms/step - loss: 0.4053 - mean_squared_error: 5.0030e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 91/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 480ms/step - loss: 0.4060 - mean_squared_error: 5.1027e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 92/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 493ms/step - loss: 0.4064 - mean_squared_error: 5.2571e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 93/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 493ms/step - loss: 0.4065 - mean_squared_error: 5.3916e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 94/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 497ms/step - loss: 0.4077 - mean_squared_error: 5.1107e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 95/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 490ms/step - loss: 0.4085 - mean_squared_error: 5.0312e-04 - val_loss: 0.4013 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 96/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 470ms/step - loss: 0.4053 - mean_squared_error: 4.9508e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 97/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 467ms/step - loss: 0.4073 - mean_squared_error: 4.9562e-04\n",
"Epoch 97: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.4073 - mean_squared_error: 4.9570e-04 - val_loss: 0.4010 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-06\n",
"Epoch 98/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 474ms/step - loss: 0.4071 - mean_squared_error: 5.2952e-04 - val_loss: 0.4010 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-07\n",
"Epoch 99/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.4060 - mean_squared_error: 4.8378e-04 - val_loss: 0.4012 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-07\n",
"Epoch 100/100\n",
"\u001b[1m292/292\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 474ms/step - loss: 0.4075 - mean_squared_error: 5.2382e-04 - val_loss: 0.4011 - val_mean_squared_error: 0.0023 - learning_rate: 1.0000e-07\n",
"Restoring model weights from the end of the best epoch: 97.\n"
]
}
],
"source": [
"# Define the model with correct input shape\n",
"inp = layers.Input(shape=(1, X_data_clean.shape[2], X_data_clean.shape[3], X_data_clean.shape[4]))\n",
"\n",
"x = layers.BatchNormalization()(inp)\n",
"x = layers.ConvLSTM2D(\n",
" filters=16,\n",
" kernel_size=(3, 3),\n",
" padding=\"same\",\n",
" return_sequences=True,\n",
" activation=\"tanh\",\n",
" recurrent_activation=\"sigmoid\",\n",
" kernel_initializer=\"glorot_uniform\"\n",
")(x)\n",
"x = layers.BatchNormalization()(x)\n",
"x = layers.ConvLSTM2D(\n",
" filters=32,\n",
" kernel_size=(3, 3),\n",
" padding=\"same\",\n",
" return_sequences=True,\n",
" activation=\"tanh\",\n",
" recurrent_activation=\"sigmoid\",\n",
" kernel_initializer=\"glorot_uniform\"\n",
")(x)\n",
"x = layers.BatchNormalization()(x)\n",
"x = layers.Conv3D(\n",
" filters=1, kernel_size=(3, 3, 3), activation=\"sigmoid\", padding=\"same\"\n",
")(x)\n",
"\n",
"model = keras.models.Model(inp, x, name=\"smogseer\")\n",
"\n",
"# Use a reduced learning rate and gradient clipping\n",
"optimizer = keras.optimizers.Adam(learning_rate=1e-5, clipnorm=1.0)\n",
"model.compile(\n",
" loss=keras.losses.binary_crossentropy,\n",
" optimizer=optimizer,\n",
" metrics=['mean_squared_error']\n",
")\n",
"\n",
"# Print the model summary\n",
"model.summary()\n",
"\n",
"# Data Generator Class\n",
"\n",
"class DataGenerator(Sequence):\n",
" def __init__(self, X_data, y_data, batch_size):\n",
" self.X_data = X_data\n",
" self.y_data = y_data\n",
" self.batch_size = batch_size\n",
" self.indices = np.arange(X_data.shape[0])\n",
" \n",
" def __len__(self):\n",
" return int(np.ceil(len(self.indices) / self.batch_size))\n",
" \n",
" def __getitem__(self, index):\n",
" batch_indices = self.indices[index * self.batch_size:(index + 1) * self.batch_size]\n",
" batch_X = self.X_data[batch_indices]\n",
" batch_y = self.y_data[batch_indices]\n",
" return batch_X, batch_y\n",
"\n",
" def on_epoch_end(self):\n",
" np.random.shuffle(self.indices)\n",
"\n",
"batch_size = 1\n",
"train_generator = DataGenerator(X_train, y_train, batch_size)\n",
"val_generator = DataGenerator(X_val, y_val, batch_size)\n",
"\n",
"# Define callbacks for monitoring and adjusting learning rate\n",
"callbacks = [\n",
" keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.1, patience=10, verbose=1, min_lr=1e-7\n",
" ),\n",
" keras.callbacks.EarlyStopping(\n",
" monitor='val_loss', patience=15, verbose=1, restore_best_weights=True\n",
" ),\n",
" keras.callbacks.TensorBoard(log_dir='./logs')\n",
"]\n",
"\n",
"# Train the model using data generators\n",
"history = model.fit(train_generator, epochs=100, validation_data=val_generator, callbacks=callbacks)\n",
"# Save the model\n",
"model.save('smogseer.keras')\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 814ms/step\n",
"\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 456ms/step - loss: 0.4003 - mean_squared_error: 0.0019\n",
"Validation Loss: 0.40102294087409973\n",
"Validation Accuracy: 0.0022550118155777454\n"
]
}
],
"source": [
"## LOAD CHECKPOINTS IF NEEDED\n",
"# \n",
"\n",
"# Load the model\n",
"model = load_model('smogseer100.keras')\n",
"\n",
"# Run predictions on validation data\n",
"predictions = model.predict(X_val)\n",
"\n",
"# Evaluate the model on validation data\n",
"val_loss, val_accuracy = model.evaluate(X_val, y_val)\n",
"print(f\"Validation Loss: {val_loss}\")\n",
"print(f\"Validation Accuracy: {val_accuracy}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Plot comparisons and training history\n",
"\n",
"def plot_comparison(y_true, y_pred, index, save_path):\n",
" \"\"\"\n",
" Plots the ground truth and the predicted output for a given index.\n",
" \n",
" Parameters:\n",
" - y_true: Ground truth data\n",
" - y_pred: Predicted data\n",
" - index: Index of the sample to plot\n",
" - save_path: Path to save the plot\n",
" \"\"\"\n",
" fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
"\n",
" # Plot ground truth\n",
" ax = axes[0]\n",
" ax.imshow(y_true[index, 0, :, :, 0], cmap='viridis')\n",
" ax.set_title('Ground Truth')\n",
" ax.axis('off')\n",
"\n",
" # Plot prediction\n",
" ax = axes[1]\n",
" ax.imshow(y_pred[index, 0, :, :, 0], cmap='viridis')\n",
" ax.set_title('Prediction')\n",
" ax.axis('off')\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(save_path)\n",
" plt.close()\n",
"\n",
"# Visualize a few samples\n",
"num_samples_to_plot = 5\n",
"for i in range(num_samples_to_plot):\n",
" plot_comparison(y_val, predictions, i, f'comparison_plot_100_{i}.png')\n",
"\n",
"# Plot training history\n",
"def plot_training_history(history, save_path):\n",
" \"\"\"\n",
" Plots the training and validation loss and accuracy over epochs.\n",
"\n",
" Parameters:\n",
" - history: Keras History object\n",
" - save_path: Path to save the plot\n",
" \"\"\"\n",
" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
"\n",
" # Plot loss\n",
" ax1.plot(history.history['loss'], label='Training Loss')\n",
" ax1.plot(history.history['val_loss'], label='Validation Loss')\n",
" ax1.set_title('Loss over epochs')\n",
" ax1.set_xlabel('Epoch')\n",
" ax1.set_ylabel('Loss')\n",
" ax1.legend()\n",
"\n",
" # Plot accuracy\n",
" ax2.plot(history.history['mean_squared_error'], label='Training MSE')\n",
" ax2.plot(history.history['val_mean_squared_error'], label='Validation MSE')\n",
" ax2.set_title('MSE over epochs')\n",
" ax2.set_xlabel('Epoch')\n",
" ax2.set_ylabel('MSE')\n",
" ax2.legend()\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(save_path)\n",
" plt.close()\n",
"\n",
"# Plot training history\n",
"plot_training_history(history, 'training_history_epoch100.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
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