diff --git a/docs/getting_started/results/example.h5 b/docs/getting_started/results/example.h5 new file mode 100644 index 0000000..ecc1b45 Binary files /dev/null and b/docs/getting_started/results/example.h5 differ diff --git a/docs/getting_started/training_data/example.npy b/docs/getting_started/training_data/example.npy new file mode 100644 index 0000000..b86c789 Binary files /dev/null and b/docs/getting_started/training_data/example.npy differ diff --git a/docs/getting_started/tutorial.ipynb b/docs/getting_started/tutorial.ipynb index f7b1bb6..11fc56e 100644 --- a/docs/getting_started/tutorial.ipynb +++ b/docs/getting_started/tutorial.ipynb @@ -16,17 +16,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os, sys\n", - "sys.path.insert(1, '/Users/arcticfox/Documents/GitHub/stella/')\n", - "import stella\n", + "\n", "import numpy as np\n", "from tqdm import tqdm_notebook\n", "import matplotlib.pyplot as plt\n", "\n", + "sys.path.insert(0, '../../')\n", + "\n", + "import stella\n", + "\n", "plt.rcParams['font.size'] = 20" ] }, @@ -46,21 +49,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: ./Guenther_2020_flare_catalog.txt already exists. Automatically overwriting ASCII files is deprecated. Use the argument 'overwrite=True' in the future. [astropy.io.ascii.ui]\n", - "WARNING: Logging before flag parsing goes to stderr.\n", - "W0714 08:45:08.602910 4409996736 logger.py:204] AstropyDeprecationWarning: ./Guenther_2020_flare_catalog.txt already exists. Automatically overwriting ASCII files is deprecated. Use the argument 'overwrite=True' in the future.\n" - ] - } - ], + "outputs": [], "source": [ - "download = stella.DownloadSets(fn_dir='.')\n", + "download = stella.DownloadSets(fn_dir='training_data')\n", "download.download_catalog()" ] }, @@ -75,24 +68,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/5 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -249,11 +230,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "OUT_DIR = '/Users/arcticfox/Desktop/results/'" + "OUT_DIR = 'results'" ] }, { @@ -265,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -288,538 +269,870 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "conv1d (Conv1D) (None, 200, 16) 128 \n", - "_________________________________________________________________\n", - "max_pooling1d (MaxPooling1D) (None, 100, 16) 0 \n", - "_________________________________________________________________\n", - "dropout (Dropout) (None, 100, 16) 0 \n", - "_________________________________________________________________\n", - "conv1d_1 (Conv1D) (None, 100, 64) 3136 \n", - "_________________________________________________________________\n", - "max_pooling1d_1 (MaxPooling1 (None, 50, 64) 0 \n", - "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 50, 64) 0 \n", - "_________________________________________________________________\n", - "flatten (Flatten) (None, 3200) 0 \n", - "_________________________________________________________________\n", - "dense (Dense) (None, 32) 102432 \n", - "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 32) 0 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 1) 33 \n", - "=================================================================\n", - "Total params: 105,729\n", - "Trainable params: 105,729\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Train on 18458 samples, validate on 2307 samples\n", - "Epoch 1/200\n", - "18458/18458 [==============================] - 3s 174us/sample - loss: 0.5494 - accuracy: 0.7645 - precision: 0.2500 - recall: 2.3020e-04 - val_loss: 0.5289 - val_accuracy: 0.7707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n", - "Epoch 2/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.5324 - accuracy: 0.7647 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_loss: 0.4919 - val_accuracy: 0.7707 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n", - "Epoch 3/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.4737 - accuracy: 0.7863 - precision: 0.9761 - recall: 0.0942 - val_loss: 0.3863 - val_accuracy: 0.8466 - val_precision: 0.9944 - val_recall: 0.3327\n", - "Epoch 4/200\n", - "18458/18458 [==============================] - 3s 139us/sample - loss: 0.3604 - accuracy: 0.8620 - precision: 0.9653 - recall: 0.4291 - val_loss: 0.3166 - val_accuracy: 0.8643 - val_precision: 0.9865 - val_recall: 0.4140\n", - "Epoch 5/200\n", - "18458/18458 [==============================] - 3s 154us/sample - loss: 0.3120 - accuracy: 0.8820 - precision: 0.9577 - recall: 0.5216 - val_loss: 0.2419 - val_accuracy: 0.9016 - val_precision: 0.9809 - val_recall: 0.5822\n", - "Epoch 6/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.2938 - accuracy: 0.8948 - precision: 0.9475 - recall: 0.5854 - val_loss: 0.2647 - val_accuracy: 0.8882 - val_precision: 0.9892 - val_recall: 0.5180\n", - "Epoch 7/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.2628 - accuracy: 0.9055 - precision: 0.9514 - recall: 0.6308 - val_loss: 0.2178 - val_accuracy: 0.9137 - val_precision: 0.9797 - val_recall: 0.6371\n", - "Epoch 8/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.2715 - accuracy: 0.9031 - precision: 0.9422 - recall: 0.6268 - val_loss: 0.2429 - val_accuracy: 0.9068 - val_precision: 0.9816 - val_recall: 0.6049\n", - "Epoch 9/200\n", - "18458/18458 [==============================] - 3s 147us/sample - loss: 0.2567 - accuracy: 0.9083 - precision: 0.9311 - recall: 0.6593 - val_loss: 0.2139 - val_accuracy: 0.9272 - val_precision: 0.9640 - val_recall: 0.7089\n", - "Epoch 10/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.2447 - accuracy: 0.9140 - precision: 0.9230 - recall: 0.6924 - val_loss: 0.2234 - val_accuracy: 0.9272 - val_precision: 0.9593 - val_recall: 0.7127\n", - "Epoch 11/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.2286 - accuracy: 0.9192 - precision: 0.9242 - recall: 0.7155 - val_loss: 0.1910 - val_accuracy: 0.9332 - val_precision: 0.9518 - val_recall: 0.7467\n", - "Epoch 12/200\n", - "18458/18458 [==============================] - 2s 127us/sample - loss: 0.2241 - accuracy: 0.9227 - precision: 0.9244 - recall: 0.7316 - val_loss: 0.1881 - val_accuracy: 0.9276 - val_precision: 0.9525 - val_recall: 0.7202\n", - "Epoch 13/200\n", - "18458/18458 [==============================] - 2s 132us/sample - loss: 0.2025 - accuracy: 0.9306 - precision: 0.9327 - recall: 0.7599 - val_loss: 0.1686 - val_accuracy: 0.9371 - val_precision: 0.9444 - val_recall: 0.7713\n", - "Epoch 14/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.2141 - accuracy: 0.9288 - precision: 0.9314 - recall: 0.7530 - val_loss: 0.1662 - val_accuracy: 0.9371 - val_precision: 0.9528 - val_recall: 0.7637\n", - "Epoch 15/200\n", - "18458/18458 [==============================] - 3s 142us/sample - loss: 0.2016 - accuracy: 0.9320 - precision: 0.9282 - recall: 0.7705 - val_loss: 0.2023 - val_accuracy: 0.9224 - val_precision: 0.9730 - val_recall: 0.6805\n", - "Epoch 16/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.2043 - accuracy: 0.9303 - precision: 0.9245 - recall: 0.7666 - val_loss: 0.1942 - val_accuracy: 0.9306 - val_precision: 0.9792 - val_recall: 0.7127\n", - "Epoch 17/200\n", - "18458/18458 [==============================] - 2s 132us/sample - loss: 0.1916 - accuracy: 0.9362 - precision: 0.9366 - recall: 0.7820 - val_loss: 0.1506 - val_accuracy: 0.9458 - val_precision: 0.9633 - val_recall: 0.7940\n", - "Epoch 18/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.1894 - accuracy: 0.9370 - precision: 0.9364 - recall: 0.7857 - val_loss: 0.1658 - val_accuracy: 0.9363 - val_precision: 0.9848 - val_recall: 0.7335\n", - "Epoch 19/200\n", - "18458/18458 [==============================] - 3s 144us/sample - loss: 0.1748 - accuracy: 0.9426 - precision: 0.9460 - recall: 0.8020 - val_loss: 0.1541 - val_accuracy: 0.9450 - val_precision: 0.9855 - val_recall: 0.7713\n", - "Epoch 20/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.1772 - accuracy: 0.9430 - precision: 0.9487 - recall: 0.8011 - val_loss: 0.1432 - val_accuracy: 0.9484 - val_precision: 0.9724 - val_recall: 0.7977\n", - "Epoch 21/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.1881 - accuracy: 0.9385 - precision: 0.9378 - recall: 0.7912 - val_loss: 0.1620 - val_accuracy: 0.9402 - val_precision: 0.9780 - val_recall: 0.7561\n", - "Epoch 22/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.1757 - accuracy: 0.9428 - precision: 0.9489 - recall: 0.8002 - val_loss: 0.1311 - val_accuracy: 0.9567 - val_precision: 0.9673 - val_recall: 0.8393\n", - "Epoch 23/200\n", - "18458/18458 [==============================] - 3s 142us/sample - loss: 0.1633 - accuracy: 0.9491 - precision: 0.9568 - recall: 0.8207 - val_loss: 0.1282 - val_accuracy: 0.9575 - val_precision: 0.9615 - val_recall: 0.8488\n", - "Epoch 24/200\n", - "18458/18458 [==============================] - 2s 129us/sample - loss: 0.1653 - accuracy: 0.9467 - precision: 0.9546 - recall: 0.8124 - val_loss: 0.1277 - val_accuracy: 0.9645 - val_precision: 0.9786 - val_recall: 0.8639\n", - "Epoch 25/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.1575 - accuracy: 0.9516 - precision: 0.9600 - recall: 0.8290 - val_loss: 0.1345 - val_accuracy: 0.9714 - val_precision: 0.9715 - val_recall: 0.9017\n", - "Epoch 26/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.1517 - accuracy: 0.9518 - precision: 0.9569 - recall: 0.8326 - val_loss: 0.1194 - val_accuracy: 0.9718 - val_precision: 0.9696 - val_recall: 0.9055\n", - "Epoch 27/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.1546 - accuracy: 0.9519 - precision: 0.9567 - recall: 0.8336 - val_loss: 0.1546 - val_accuracy: 0.9714 - val_precision: 0.9530 - val_recall: 0.9206\n", - "Epoch 28/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.1399 - accuracy: 0.9561 - precision: 0.9614 - recall: 0.8476 - val_loss: 0.1711 - val_accuracy: 0.9710 - val_precision: 0.9262 - val_recall: 0.9490\n", - "Epoch 29/200\n", - "18458/18458 [==============================] - 2s 132us/sample - loss: 0.1476 - accuracy: 0.9546 - precision: 0.9599 - recall: 0.8423 - val_loss: 0.1175 - val_accuracy: 0.9632 - val_precision: 0.9723 - val_recall: 0.8639\n", - "Epoch 30/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.1420 - accuracy: 0.9562 - precision: 0.9585 - recall: 0.8506 - val_loss: 0.1119 - val_accuracy: 0.9697 - val_precision: 0.9713 - val_recall: 0.8941\n", - "Epoch 31/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.1488 - accuracy: 0.9539 - precision: 0.9561 - recall: 0.8430 - val_loss: 0.1121 - val_accuracy: 0.9736 - val_precision: 0.9680 - val_recall: 0.9149\n", - "Epoch 32/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.1413 - accuracy: 0.9567 - precision: 0.9624 - recall: 0.8492 - val_loss: 0.1030 - val_accuracy: 0.9701 - val_precision: 0.9733 - val_recall: 0.8941\n", - "Epoch 33/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.1398 - accuracy: 0.9547 - precision: 0.9580 - recall: 0.8444 - val_loss: 0.1127 - val_accuracy: 0.9775 - val_precision: 0.9649 - val_recall: 0.9357\n", - "Epoch 34/200\n", - "18458/18458 [==============================] - 3s 150us/sample - loss: 0.1326 - accuracy: 0.9582 - precision: 0.9625 - recall: 0.8559 - val_loss: 0.1092 - val_accuracy: 0.9775 - val_precision: 0.9704 - val_recall: 0.9301\n", - "Epoch 35/200\n", - "18458/18458 [==============================] - 3s 186us/sample - loss: 0.1385 - accuracy: 0.9577 - precision: 0.9643 - recall: 0.8517 - val_loss: 0.1370 - val_accuracy: 0.9740 - val_precision: 0.9383 - val_recall: 0.9490\n", - "Epoch 36/200\n", - "18458/18458 [==============================] - 4s 207us/sample - loss: 0.1301 - accuracy: 0.9604 - precision: 0.9672 - recall: 0.8610 - val_loss: 0.1323 - val_accuracy: 0.9567 - val_precision: 0.9633 - val_recall: 0.8431\n", - "Epoch 37/200\n", - "18458/18458 [==============================] - 4s 192us/sample - loss: 0.1275 - accuracy: 0.9605 - precision: 0.9626 - recall: 0.8658 - val_loss: 0.1484 - val_accuracy: 0.9749 - val_precision: 0.9305 - val_recall: 0.9622\n", - "Epoch 38/200\n", - "18458/18458 [==============================] - 3s 168us/sample - loss: 0.1423 - accuracy: 0.9550 - precision: 0.9549 - recall: 0.8490 - val_loss: 0.1096 - val_accuracy: 0.9684 - val_precision: 0.9653 - val_recall: 0.8941\n", - "Epoch 39/200\n", - "18458/18458 [==============================] - 3s 146us/sample - loss: 0.1412 - accuracy: 0.9543 - precision: 0.9548 - recall: 0.8460 - val_loss: 0.1397 - val_accuracy: 0.9701 - val_precision: 0.9212 - val_recall: 0.9509\n", - "Epoch 40/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.1285 - accuracy: 0.9598 - precision: 0.9649 - recall: 0.8605 - val_loss: 0.1038 - val_accuracy: 0.9679 - val_precision: 0.9577 - val_recall: 0.8998\n", - "Epoch 41/200\n", - "18458/18458 [==============================] - 3s 144us/sample - loss: 0.1324 - accuracy: 0.9584 - precision: 0.9625 - recall: 0.8568 - val_loss: 0.1238 - val_accuracy: 0.9757 - val_precision: 0.9355 - val_recall: 0.9603\n", - "Epoch 42/200\n", - "18458/18458 [==============================] - 3s 142us/sample - loss: 0.1273 - accuracy: 0.9613 - precision: 0.9635 - recall: 0.8686 - val_loss: 0.2219 - val_accuracy: 0.9376 - val_precision: 0.7966 - val_recall: 0.9773\n", - "Epoch 43/200\n", - "18458/18458 [==============================] - 3s 145us/sample - loss: 0.1220 - accuracy: 0.9625 - precision: 0.9670 - recall: 0.8702 - val_loss: 0.0966 - val_accuracy: 0.9701 - val_precision: 0.9832 - val_recall: 0.8847\n", - "Epoch 44/200\n", - "18458/18458 [==============================] - 3s 152us/sample - loss: 0.1265 - accuracy: 0.9610 - precision: 0.9658 - recall: 0.8651 - val_loss: 0.0997 - val_accuracy: 0.9697 - val_precision: 0.9752 - val_recall: 0.8904\n", - "Epoch 45/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.1268 - accuracy: 0.9615 - precision: 0.9633 - recall: 0.8695 - val_loss: 0.0941 - val_accuracy: 0.9710 - val_precision: 0.9676 - val_recall: 0.9036\n", - "Epoch 46/200\n", - "18458/18458 [==============================] - 3s 149us/sample - loss: 0.1285 - accuracy: 0.9601 - precision: 0.9635 - recall: 0.8633 - val_loss: 0.1479 - val_accuracy: 0.9684 - val_precision: 0.9028 - val_recall: 0.9660\n", - "Epoch 47/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.1328 - accuracy: 0.9585 - precision: 0.9571 - recall: 0.8623 - val_loss: 0.1346 - val_accuracy: 0.9658 - val_precision: 0.9151 - val_recall: 0.9376\n", - "Epoch 48/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.1177 - accuracy: 0.9627 - precision: 0.9668 - recall: 0.8713 - val_loss: 0.1043 - val_accuracy: 0.9770 - val_precision: 0.9440 - val_recall: 0.9565\n", - "Epoch 49/200\n", - "18458/18458 [==============================] - 3s 144us/sample - loss: 0.1247 - accuracy: 0.9613 - precision: 0.9649 - recall: 0.8669 - val_loss: 0.0888 - val_accuracy: 0.9705 - val_precision: 0.9792 - val_recall: 0.8904\n", - "Epoch 50/200\n", - "18458/18458 [==============================] - 3s 159us/sample - loss: 0.1171 - accuracy: 0.9642 - precision: 0.9668 - recall: 0.8780 - val_loss: 0.0991 - val_accuracy: 0.9701 - val_precision: 0.9812 - val_recall: 0.8866\n", - "Epoch 51/200\n", - "18458/18458 [==============================] - 3s 169us/sample - loss: 0.1190 - accuracy: 0.9638 - precision: 0.9674 - recall: 0.8755 - val_loss: 0.1182 - val_accuracy: 0.9723 - val_precision: 0.9204 - val_recall: 0.9622\n", - "Epoch 52/200\n", - "18458/18458 [==============================] - 3s 161us/sample - loss: 0.1179 - accuracy: 0.9628 - precision: 0.9635 - recall: 0.8752 - val_loss: 0.1217 - val_accuracy: 0.9627 - val_precision: 0.9743 - val_recall: 0.8601\n", - "Epoch 53/200\n", - "18458/18458 [==============================] - 3s 150us/sample - loss: 0.1166 - accuracy: 0.9632 - precision: 0.9671 - recall: 0.8734 - val_loss: 0.1692 - val_accuracy: 0.9649 - val_precision: 0.8836 - val_recall: 0.9754\n", - "Epoch 54/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.1164 - accuracy: 0.9627 - precision: 0.9661 - recall: 0.8720 - val_loss: 0.0974 - val_accuracy: 0.9783 - val_precision: 0.9460 - val_recall: 0.9603\n", - "Epoch 55/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.1215 - accuracy: 0.9627 - precision: 0.9659 - recall: 0.8725 - val_loss: 0.1093 - val_accuracy: 0.9766 - val_precision: 0.9406 - val_recall: 0.9584\n", - "Epoch 56/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.1217 - accuracy: 0.9619 - precision: 0.9636 - recall: 0.8709 - val_loss: 0.0962 - val_accuracy: 0.9783 - val_precision: 0.9460 - val_recall: 0.9603\n", - "Epoch 57/200\n", - "18458/18458 [==============================] - 3s 144us/sample - loss: 0.1147 - accuracy: 0.9635 - precision: 0.9672 - recall: 0.8748 - val_loss: 0.1161 - val_accuracy: 0.9744 - val_precision: 0.9288 - val_recall: 0.9622\n", - "Epoch 58/200\n", - "18458/18458 [==============================] - 3s 141us/sample - loss: 0.1266 - accuracy: 0.9596 - precision: 0.9610 - recall: 0.8633 - val_loss: 0.1003 - val_accuracy: 0.9697 - val_precision: 0.9674 - val_recall: 0.8979\n", - "Epoch 59/200\n", - "18458/18458 [==============================] - 3s 145us/sample - loss: 0.1082 - accuracy: 0.9647 - precision: 0.9641 - recall: 0.8831 - val_loss: 0.0879 - val_accuracy: 0.9710 - val_precision: 0.9833 - val_recall: 0.8885\n", - "Epoch 60/200\n", - "18458/18458 [==============================] - 3s 142us/sample - loss: 0.1127 - accuracy: 0.9633 - precision: 0.9636 - recall: 0.8773 - val_loss: 0.0904 - val_accuracy: 0.9727 - val_precision: 0.9854 - val_recall: 0.8941\n", - "Epoch 61/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.1044 - accuracy: 0.9679 - precision: 0.9688 - recall: 0.8923 - val_loss: 0.1206 - val_accuracy: 0.9688 - val_precision: 0.9044 - val_recall: 0.9660\n", - "Epoch 62/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.1095 - accuracy: 0.9641 - precision: 0.9619 - recall: 0.8824 - val_loss: 0.1610 - val_accuracy: 0.9545 - val_precision: 0.8522 - val_recall: 0.9698\n", - "Epoch 63/200\n" + "/Users/bella/anaconda3/envs/stella_ENV/lib/python3.12/site-packages/keras-3.3.3-py3.12.egg/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] }, + { + "data": { + "text/html": [ + "
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+       "│ conv1d_1 (Conv1D)               │ (None, 100, 64)        │         3,136 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_1 (MaxPooling1D)  │ (None, 50, 64)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (Dropout)             │ (None, 50, 64)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten (Flatten)               │ (None, 3200)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 32)             │       102,432 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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3s 138us/sample - loss: 0.0896 - accuracy: 0.9719 - precision: 0.9712 - recall: 0.9077 - val_loss: 0.0853 - val_accuracy: 0.9775 - val_precision: 0.9328 - val_recall: 0.9716\n", - "Epoch 112/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0909 - accuracy: 0.9724 - precision: 0.9710 - recall: 0.9098 - val_loss: 0.1586 - val_accuracy: 0.9523 - val_precision: 0.8429 - val_recall: 0.9735\n", - "Epoch 113/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0875 - accuracy: 0.9717 - precision: 0.9719 - recall: 0.9061 - val_loss: 0.0689 - val_accuracy: 0.9809 - val_precision: 0.9602 - val_recall: 0.9565\n", - "Epoch 114/200\n", - "18458/18458 [==============================] - 3s 153us/sample - loss: 0.0836 - accuracy: 0.9737 - precision: 0.9758 - recall: 0.9107 - val_loss: 0.0657 - val_accuracy: 0.9818 - val_precision: 0.9518 - val_recall: 0.9698\n", - "Epoch 115/200\n", - "18458/18458 [==============================] - 3s 142us/sample - 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accuracy: 0.9729 - precision: 0.9729 - recall: 0.9100 - val_loss: 0.2024 - val_accuracy: 0.9315 - val_precision: 0.7732 - val_recall: 0.9924\n", - "Epoch 120/200\n", - "18458/18458 [==============================] - 3s 155us/sample - loss: 0.1031 - accuracy: 0.9690 - precision: 0.9694 - recall: 0.8966 - val_loss: 0.0815 - val_accuracy: 0.9705 - val_precision: 0.9458 - val_recall: 0.9244\n", - "Epoch 121/200\n", - "18458/18458 [==============================] - 3s 155us/sample - loss: 0.0880 - accuracy: 0.9724 - precision: 0.9712 - recall: 0.9098 - val_loss: 0.1191 - val_accuracy: 0.9662 - val_precision: 0.9005 - val_recall: 0.9584\n", - "Epoch 122/200\n", - "18458/18458 [==============================] - 3s 145us/sample - loss: 0.0942 - accuracy: 0.9706 - precision: 0.9684 - recall: 0.9045 - val_loss: 0.1026 - val_accuracy: 0.9697 - val_precision: 0.9005 - val_recall: 0.9754\n", - "Epoch 123/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0876 - accuracy: 0.9722 - precision: 0.9735 - recall: 0.9065 - val_loss: 0.0634 - val_accuracy: 0.9814 - val_precision: 0.9620 - val_recall: 0.9565\n", - "Epoch 124/200\n", - "18458/18458 [==============================] - 3s 145us/sample - loss: 0.0854 - accuracy: 0.9725 - precision: 0.9715 - recall: 0.9098 - val_loss: 0.0870 - val_accuracy: 0.9736 - val_precision: 0.9179 - val_recall: 0.9716\n", - "Epoch 125/200\n", - "18458/18458 [==============================] - 3s 147us/sample - loss: 0.0901 - accuracy: 0.9721 - precision: 0.9717 - recall: 0.9079 - val_loss: 0.1402 - val_accuracy: 0.9541 - val_precision: 0.8484 - val_recall: 0.9735\n", - "Epoch 126/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0903 - accuracy: 0.9718 - precision: 0.9712 - recall: 0.9072 - val_loss: 0.1050 - val_accuracy: 0.9666 - val_precision: 0.8979 - val_recall: 0.9641\n", - "Epoch 127/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0899 - accuracy: 0.9718 - precision: 0.9721 - recall: 0.9063 - val_loss: 0.0627 - val_accuracy: 0.9775 - val_precision: 0.9818 - val_recall: 0.9187\n", - "Epoch 128/200\n", - "18458/18458 [==============================] - 3s 148us/sample - loss: 0.0909 - accuracy: 0.9714 - precision: 0.9709 - recall: 0.9056 - val_loss: 0.1331 - val_accuracy: 0.9619 - val_precision: 0.8693 - val_recall: 0.9811\n", - "Epoch 129/200\n", - "18458/18458 [==============================] - 3s 140us/sample - loss: 0.0987 - accuracy: 0.9680 - precision: 0.9681 - recall: 0.8936 - val_loss: 0.1068 - val_accuracy: 0.9666 - val_precision: 0.8897 - val_recall: 0.9754\n", - "Epoch 130/200\n", - "18458/18458 [==============================] - 3s 140us/sample - loss: 0.0808 - accuracy: 0.9746 - precision: 0.9745 - recall: 0.9160 - val_loss: 0.0795 - val_accuracy: 0.9753 - val_precision: 0.9260 - val_recall: 0.9698\n", - "Epoch 131/200\n", - "18458/18458 [==============================] - 3s 140us/sample - loss: 0.0899 - accuracy: 0.9722 - precision: 0.9698 - recall: 0.9100 - val_loss: 0.1077 - val_accuracy: 0.9623 - val_precision: 0.8932 - val_recall: 0.9490\n", - "Epoch 132/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0853 - accuracy: 0.9739 - precision: 0.9731 - recall: 0.9146 - val_loss: 0.1118 - val_accuracy: 0.9627 - val_precision: 0.8799 - val_recall: 0.9698\n" + "Epoch 1/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8003 - loss: 0.5036 - precision: 0.2702 - recall: 0.0087 - val_accuracy: 0.8130 - val_loss: 0.4545 - val_precision: 1.0000 - val_recall: 0.0565\n", + "Epoch 2/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8346 - loss: 0.4200 - precision: 0.9765 - recall: 0.1629 - val_accuracy: 0.8845 - val_loss: 0.3165 - val_precision: 0.9372 - val_recall: 0.4475\n", + "Epoch 3/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8874 - loss: 0.3172 - precision: 0.9290 - recall: 0.4617 - val_accuracy: 0.9118 - val_loss: 0.2575 - val_precision: 0.9397 - val_recall: 0.5928\n", + "Epoch 4/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9094 - loss: 0.2677 - precision: 0.9151 - recall: 0.5934 - val_accuracy: 0.9163 - val_loss: 0.2377 - val_precision: 0.9154 - val_recall: 0.6367\n", + "Epoch 5/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9168 - loss: 0.2450 - precision: 0.9081 - recall: 0.6408 - val_accuracy: 0.9113 - val_loss: 0.2967 - val_precision: 0.9239 - val_recall: 0.6021\n", + "Epoch 6/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9127 - loss: 0.2512 - precision: 0.8974 - recall: 0.6270 - val_accuracy: 0.9060 - val_loss: 0.2696 - val_precision: 0.9335 - val_recall: 0.5663\n", + "Epoch 7/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9128 - loss: 0.2519 - precision: 0.9141 - recall: 0.6138 - val_accuracy: 0.9031 - val_loss: 0.2435 - val_precision: 0.9530 - val_recall: 0.5375\n", + "Epoch 8/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9172 - loss: 0.2382 - precision: 0.8926 - recall: 0.6577 - val_accuracy: 0.9200 - val_loss: 0.2392 - val_precision: 0.9273 - val_recall: 0.6471\n", + "Epoch 9/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9249 - loss: 0.2176 - precision: 0.8993 - recall: 0.6955 - val_accuracy: 0.9118 - val_loss: 0.2744 - val_precision: 0.9227 - val_recall: 0.6055\n", + "Epoch 10/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9286 - loss: 0.2082 - precision: 0.8993 - recall: 0.7169 - val_accuracy: 0.9332 - val_loss: 0.1894 - val_precision: 0.9297 - val_recall: 0.7174\n", + "Epoch 11/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9348 - loss: 0.1875 - precision: 0.9110 - recall: 0.7402 - val_accuracy: 0.9431 - val_loss: 0.1736 - val_precision: 0.9280 - val_recall: 0.7728\n", + "Epoch 12/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9402 - loss: 0.1761 - precision: 0.9188 - recall: 0.7627 - val_accuracy: 0.9527 - val_loss: 0.1568 - val_precision: 0.9400 - val_recall: 0.8131\n", + "Epoch 13/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9447 - loss: 0.1684 - precision: 0.9315 - recall: 0.7752 - val_accuracy: 0.9550 - val_loss: 0.1692 - val_precision: 0.8998 - val_recall: 0.8697\n", + "Epoch 14/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9471 - loss: 0.1623 - precision: 0.9339 - recall: 0.7864 - val_accuracy: 0.9520 - val_loss: 0.2052 - val_precision: 0.8598 - val_recall: 0.9054\n", + "Epoch 15/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9486 - loss: 0.1601 - precision: 0.9274 - recall: 0.8009 - val_accuracy: 0.9563 - val_loss: 0.1316 - val_precision: 0.9471 - val_recall: 0.8258\n", + "Epoch 16/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9477 - loss: 0.1575 - precision: 0.9286 - recall: 0.7952 - val_accuracy: 0.9490 - val_loss: 0.1477 - val_precision: 0.8676 - val_recall: 0.8766\n", + "Epoch 17/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9509 - loss: 0.1526 - precision: 0.9343 - recall: 0.8065 - val_accuracy: 0.9079 - val_loss: 0.3133 - val_precision: 0.6924 - val_recall: 0.9631\n", + "Epoch 18/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9485 - loss: 0.1605 - precision: 0.9294 - recall: 0.7984 - val_accuracy: 0.9504 - val_loss: 0.1680 - val_precision: 0.9392 - val_recall: 0.8016\n", + "Epoch 19/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9537 - loss: 0.1445 - precision: 0.9375 - recall: 0.8186 - val_accuracy: 0.9508 - val_loss: 0.2002 - val_precision: 0.8280 - val_recall: 0.9493\n", + "Epoch 20/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9521 - loss: 0.1507 - precision: 0.9389 - recall: 0.8086 - val_accuracy: 0.9518 - val_loss: 0.1838 - val_precision: 0.8361 - val_recall: 0.9412\n", + "Epoch 21/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9555 - loss: 0.1376 - precision: 0.9445 - recall: 0.8217 - val_accuracy: 0.9593 - val_loss: 0.1891 - val_precision: 0.8577 - val_recall: 0.9527\n", + "Epoch 22/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9528 - loss: 0.1422 - precision: 0.9368 - recall: 0.8150 - val_accuracy: 0.9579 - val_loss: 0.1359 - val_precision: 0.9306 - val_recall: 0.8512\n", + "Epoch 23/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9563 - loss: 0.1359 - precision: 0.9473 - recall: 0.8235 - val_accuracy: 0.9627 - val_loss: 0.1504 - val_precision: 0.8920 - val_recall: 0.9239\n", + "Epoch 24/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9588 - loss: 0.1318 - precision: 0.9501 - recall: 0.8339 - val_accuracy: 0.9627 - val_loss: 0.1344 - val_precision: 0.9151 - val_recall: 0.8950\n", + "Epoch 25/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9564 - loss: 0.1351 - precision: 0.9462 - recall: 0.8249 - val_accuracy: 0.9595 - val_loss: 0.1261 - val_precision: 0.9002 - val_recall: 0.8950\n", + "Epoch 26/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9587 - loss: 0.1295 - precision: 0.9492 - recall: 0.8344 - val_accuracy: 0.9620 - val_loss: 0.1201 - val_precision: 0.9158 - val_recall: 0.8904\n", + "Epoch 27/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9596 - loss: 0.1323 - precision: 0.9475 - recall: 0.8408 - val_accuracy: 0.9643 - val_loss: 0.1119 - val_precision: 0.9362 - val_recall: 0.8800\n", + "Epoch 28/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9584 - loss: 0.1356 - precision: 0.9524 - recall: 0.8298 - val_accuracy: 0.9630 - val_loss: 0.1545 - val_precision: 0.8869 - val_recall: 0.9319\n", + "Epoch 29/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9589 - loss: 0.1307 - precision: 0.9512 - recall: 0.8335 - val_accuracy: 0.9675 - val_loss: 0.1049 - val_precision: 0.9405 - val_recall: 0.8927\n", + "Epoch 30/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9601 - loss: 0.1307 - precision: 0.9523 - recall: 0.8389 - val_accuracy: 0.9659 - val_loss: 0.1100 - val_precision: 0.9264 - val_recall: 0.8997\n", + "Epoch 31/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9628 - loss: 0.1216 - precision: 0.9559 - recall: 0.8499 - val_accuracy: 0.9650 - val_loss: 0.1049 - val_precision: 0.9530 - val_recall: 0.8662\n", + "Epoch 32/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9616 - loss: 0.1274 - precision: 0.9570 - recall: 0.8421 - val_accuracy: 0.9639 - val_loss: 0.1053 - val_precision: 0.9527 - val_recall: 0.8604\n", + "Epoch 33/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9642 - loss: 0.1212 - precision: 0.9576 - recall: 0.8554 - val_accuracy: 0.9604 - val_loss: 0.1237 - val_precision: 0.9026 - val_recall: 0.8973\n", + "Epoch 34/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9629 - loss: 0.1231 - precision: 0.9567 - recall: 0.8497 - val_accuracy: 0.9643 - val_loss: 0.1103 - val_precision: 0.9394 - val_recall: 0.8766\n", + "Epoch 35/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9627 - loss: 0.1190 - precision: 0.9567 - recall: 0.8485 - val_accuracy: 0.9531 - val_loss: 0.1485 - val_precision: 0.9559 - val_recall: 0.8005\n", + "Epoch 36/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9623 - loss: 0.1226 - precision: 0.9592 - recall: 0.8439 - val_accuracy: 0.9678 - val_loss: 0.1057 - val_precision: 0.9363 - val_recall: 0.8985\n", + "Epoch 37/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9638 - loss: 0.1169 - precision: 0.9570 - recall: 0.8540 - val_accuracy: 0.9641 - val_loss: 0.1023 - val_precision: 0.9575 - val_recall: 0.8570\n", + "Epoch 38/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9637 - loss: 0.1116 - precision: 0.9607 - recall: 0.8497 - val_accuracy: 0.9588 - val_loss: 0.1343 - val_precision: 0.9387 - val_recall: 0.8478\n", + "Epoch 39/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9614 - loss: 0.1234 - precision: 0.9583 - recall: 0.8401 - val_accuracy: 0.9632 - val_loss: 0.1298 - val_precision: 0.9526 - val_recall: 0.8570\n", + "Epoch 40/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9632 - loss: 0.1215 - precision: 0.9599 - recall: 0.8481 - val_accuracy: 0.9682 - val_loss: 0.1160 - val_precision: 0.9242 - val_recall: 0.9146\n", + "Epoch 41/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9659 - loss: 0.1123 - precision: 0.9641 - recall: 0.8584 - val_accuracy: 0.9655 - val_loss: 0.1195 - val_precision: 0.9087 - val_recall: 0.9181\n", + "Epoch 42/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9624 - loss: 0.1247 - precision: 0.9542 - recall: 0.8492 - val_accuracy: 0.9467 - val_loss: 0.1731 - val_precision: 0.9594 - val_recall: 0.7636\n", + "Epoch 43/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9654 - loss: 0.1146 - precision: 0.9621 - recall: 0.8579 - val_accuracy: 0.9648 - val_loss: 0.1134 - val_precision: 0.9199 - val_recall: 0.9008\n", + "Epoch 44/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9639 - loss: 0.1157 - precision: 0.9592 - recall: 0.8523 - val_accuracy: 0.9668 - val_loss: 0.1128 - val_precision: 0.9446 - val_recall: 0.8847\n", + "Epoch 45/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9629 - loss: 0.1201 - precision: 0.9566 - recall: 0.8497 - val_accuracy: 0.9527 - val_loss: 0.1596 - val_precision: 0.8416 - val_recall: 0.9377\n", + "Epoch 46/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9628 - loss: 0.1155 - precision: 0.9516 - recall: 0.8540 - val_accuracy: 0.9625 - val_loss: 0.1291 - val_precision: 0.9524 - val_recall: 0.8535\n", + "Epoch 47/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9618 - loss: 0.1182 - precision: 0.9629 - recall: 0.8377 - val_accuracy: 0.9588 - val_loss: 0.1270 - val_precision: 0.9526 - val_recall: 0.8339\n", + "Epoch 48/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9626 - loss: 0.1152 - precision: 0.9587 - recall: 0.8459 - val_accuracy: 0.9710 - val_loss: 0.0887 - val_precision: 0.9405 - val_recall: 0.9112\n", + "Epoch 49/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9680 - loss: 0.1074 - precision: 0.9618 - recall: 0.8715 - val_accuracy: 0.9678 - val_loss: 0.0928 - val_precision: 0.9504 - val_recall: 0.8835\n", + "Epoch 50/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9666 - loss: 0.1060 - precision: 0.9595 - recall: 0.8669 - val_accuracy: 0.9696 - val_loss: 0.0952 - val_precision: 0.9553 - val_recall: 0.8881\n", + "Epoch 51/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9651 - loss: 0.1094 - precision: 0.9560 - recall: 0.8618 - val_accuracy: 0.9607 - val_loss: 0.1326 - val_precision: 0.9328 - val_recall: 0.8639\n", + "Epoch 52/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9663 - loss: 0.1052 - precision: 0.9621 - recall: 0.8625 - val_accuracy: 0.9675 - val_loss: 0.0988 - val_precision: 0.9514 - val_recall: 0.8812\n", + "Epoch 53/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9662 - loss: 0.1070 - precision: 0.9631 - recall: 0.8607 - val_accuracy: 0.9666 - val_loss: 0.1134 - val_precision: 0.9467 - val_recall: 0.8812\n", + "Epoch 54/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9644 - loss: 0.1153 - precision: 0.9579 - recall: 0.8564 - val_accuracy: 0.9618 - val_loss: 0.1426 - val_precision: 0.9569 - val_recall: 0.8454\n", + "Epoch 55/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9602 - loss: 0.1245 - precision: 0.9524 - recall: 0.8395 - val_accuracy: 0.9673 - val_loss: 0.1006 - val_precision: 0.9571 - val_recall: 0.8743\n", + "Epoch 56/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9641 - loss: 0.1144 - precision: 0.9604 - recall: 0.8523 - val_accuracy: 0.9698 - val_loss: 0.0946 - val_precision: 0.9487 - val_recall: 0.8962\n", + "Epoch 57/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9657 - loss: 0.1073 - precision: 0.9601 - recall: 0.8611 - val_accuracy: 0.9712 - val_loss: 0.0926 - val_precision: 0.9458 - val_recall: 0.9066\n", + "Epoch 58/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9665 - loss: 0.1134 - precision: 0.9631 - recall: 0.8625 - val_accuracy: 0.9682 - val_loss: 0.0956 - val_precision: 0.9282 - val_recall: 0.9100\n", + "Epoch 59/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1068 - precision: 0.9603 - recall: 0.8724 - val_accuracy: 0.9657 - val_loss: 0.1051 - val_precision: 0.9324 - val_recall: 0.8916\n", + "Epoch 60/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1050 - precision: 0.9637 - recall: 0.8696 - val_accuracy: 0.9739 - val_loss: 0.0820 - val_precision: 0.9564 - val_recall: 0.9100\n", + "Epoch 61/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9674 - loss: 0.1005 - precision: 0.9594 - recall: 0.8707 - val_accuracy: 0.9680 - val_loss: 0.0936 - val_precision: 0.9353 - val_recall: 0.9008\n", + "Epoch 62/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9676 - loss: 0.1025 - precision: 0.9605 - recall: 0.8706 - val_accuracy: 0.9701 - val_loss: 0.0810 - val_precision: 0.9635 - val_recall: 0.8824\n", + "Epoch 63/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9697 - loss: 0.1012 - precision: 0.9651 - recall: 0.8776 - val_accuracy: 0.9701 - val_loss: 0.0904 - val_precision: 0.9466 - val_recall: 0.8997\n", + "Epoch 64/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9672 - loss: 0.1006 - precision: 0.9606 - recall: 0.8687 - val_accuracy: 0.9682 - val_loss: 0.0976 - val_precision: 0.9643 - val_recall: 0.8720\n", + "Epoch 65/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9622 - loss: 0.1167 - precision: 0.9578 - recall: 0.8447 - val_accuracy: 0.9678 - val_loss: 0.0925 - val_precision: 0.9427 - val_recall: 0.8916\n", + "Epoch 66/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.0970 - precision: 0.9633 - recall: 0.8698 - val_accuracy: 0.9675 - val_loss: 0.1055 - val_precision: 0.9341 - val_recall: 0.8997\n", + "Epoch 67/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.1085 - precision: 0.9642 - recall: 0.8655 - val_accuracy: 0.9696 - val_loss: 0.0937 - val_precision: 0.9287 - val_recall: 0.9170\n", + "Epoch 68/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9688 - loss: 0.0963 - precision: 0.9587 - recall: 0.8788 - val_accuracy: 0.9694 - val_loss: 0.0981 - val_precision: 0.9633 - val_recall: 0.8789\n", + "Epoch 69/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9704 - loss: 0.0974 - precision: 0.9652 - recall: 0.8810 - val_accuracy: 0.9730 - val_loss: 0.0833 - val_precision: 0.9550 - val_recall: 0.9066\n", + "Epoch 70/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9681 - loss: 0.1044 - precision: 0.9636 - recall: 0.8702 - val_accuracy: 0.9705 - val_loss: 0.1026 - val_precision: 0.9624 - val_recall: 0.8858\n", + "Epoch 71/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.1069 - precision: 0.9633 - recall: 0.8665 - val_accuracy: 0.9668 - val_loss: 0.1031 - val_precision: 0.9468 - val_recall: 0.8824\n", + "Epoch 72/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9716 - loss: 0.0893 - precision: 0.9677 - recall: 0.8852 - val_accuracy: 0.9705 - val_loss: 0.0916 - val_precision: 0.9489 - val_recall: 0.8997\n", + "Epoch 73/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9692 - loss: 0.0988 - precision: 0.9611 - recall: 0.8786 - val_accuracy: 0.9687 - val_loss: 0.0965 - val_precision: 0.9244 - val_recall: 0.9170\n", + "Epoch 74/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9677 - loss: 0.1008 - precision: 0.9644 - recall: 0.8674 - val_accuracy: 0.9723 - val_loss: 0.0915 - val_precision: 0.9628 - val_recall: 0.8950\n", + "Epoch 75/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9699 - loss: 0.0983 - precision: 0.9663 - recall: 0.8770 - val_accuracy: 0.9710 - val_loss: 0.0853 - val_precision: 0.9458 - val_recall: 0.9054\n", + "Epoch 76/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.1048 - precision: 0.9619 - recall: 0.8710 - val_accuracy: 0.9733 - val_loss: 0.0933 - val_precision: 0.9486 - val_recall: 0.9146\n", + "Epoch 77/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9695 - loss: 0.0944 - precision: 0.9600 - recall: 0.8811 - val_accuracy: 0.9733 - val_loss: 0.0811 - val_precision: 0.9596 - val_recall: 0.9031\n", + "Epoch 78/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9701 - loss: 0.0939 - precision: 0.9626 - recall: 0.8819 - val_accuracy: 0.9755 - val_loss: 0.0778 - val_precision: 0.9578 - val_recall: 0.9170\n", + "Epoch 79/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.1015 - precision: 0.9650 - recall: 0.8740 - val_accuracy: 0.9714 - val_loss: 0.0877 - val_precision: 0.9592 - val_recall: 0.8939\n", + "Epoch 80/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9704 - loss: 0.0960 - precision: 0.9661 - recall: 0.8805 - val_accuracy: 0.9714 - val_loss: 0.0892 - val_precision: 0.9417 - val_recall: 0.9123\n", + "Epoch 81/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.0948 - precision: 0.9647 - recall: 0.8790 - val_accuracy: 0.9728 - val_loss: 0.0855 - val_precision: 0.9369 - val_recall: 0.9250\n", + "Epoch 82/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9702 - loss: 0.0972 - precision: 0.9635 - recall: 0.8816 - val_accuracy: 0.9664 - val_loss: 0.1284 - val_precision: 0.9615 - val_recall: 0.8651\n", + "Epoch 83/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9654 - loss: 0.1096 - precision: 0.9600 - recall: 0.8599 - val_accuracy: 0.9657 - val_loss: 0.1157 - val_precision: 0.9638 - val_recall: 0.8593\n", + "Epoch 84/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9704 - loss: 0.0934 - precision: 0.9649 - recall: 0.8817 - val_accuracy: 0.9730 - val_loss: 0.0859 - val_precision: 0.9595 - val_recall: 0.9020\n", + "Epoch 85/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.0975 - precision: 0.9621 - recall: 0.8814 - val_accuracy: 0.9755 - val_loss: 0.0757 - val_precision: 0.9703 - val_recall: 0.9043\n", + "Epoch 86/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9698 - loss: 0.0956 - precision: 0.9631 - recall: 0.8798 - val_accuracy: 0.9671 - val_loss: 0.1056 - val_precision: 0.9513 - val_recall: 0.8789\n", + "Epoch 87/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9693 - loss: 0.0933 - precision: 0.9605 - recall: 0.8797 - val_accuracy: 0.9719 - val_loss: 0.0941 - val_precision: 0.9615 - val_recall: 0.8939\n", + "Epoch 88/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0951 - precision: 0.9650 - recall: 0.8863 - val_accuracy: 0.9755 - val_loss: 0.0811 - val_precision: 0.9786 - val_recall: 0.8962\n", + "Epoch 89/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9701 - loss: 0.0938 - precision: 0.9640 - recall: 0.8808 - val_accuracy: 0.9776 - val_loss: 0.0866 - val_precision: 0.9695 - val_recall: 0.9158\n", + "Epoch 90/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9700 - loss: 0.0952 - precision: 0.9653 - recall: 0.8788 - val_accuracy: 0.9728 - val_loss: 0.0883 - val_precision: 0.9474 - val_recall: 0.9135\n", + "Epoch 91/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9700 - loss: 0.0951 - precision: 0.9650 - recall: 0.8788 - val_accuracy: 0.9739 - val_loss: 0.0852 - val_precision: 0.9631 - val_recall: 0.9031\n", + "Epoch 92/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9710 - loss: 0.0914 - precision: 0.9639 - recall: 0.8855 - val_accuracy: 0.9733 - val_loss: 0.0948 - val_precision: 0.9711 - val_recall: 0.8916\n", + "Epoch 93/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9694 - loss: 0.0983 - precision: 0.9678 - recall: 0.8733 - val_accuracy: 0.9753 - val_loss: 0.0784 - val_precision: 0.9578 - val_recall: 0.9158\n", + "Epoch 94/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9707 - loss: 0.0938 - precision: 0.9654 - recall: 0.8823 - val_accuracy: 0.9758 - val_loss: 0.0867 - val_precision: 0.9762 - val_recall: 0.8997\n", + "Epoch 95/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9732 - loss: 0.0874 - precision: 0.9692 - recall: 0.8916 - val_accuracy: 0.9744 - val_loss: 0.0867 - val_precision: 0.9374 - val_recall: 0.9331\n", + "Epoch 96/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9725 - loss: 0.0859 - precision: 0.9653 - recall: 0.8920 - val_accuracy: 0.9765 - val_loss: 0.0739 - val_precision: 0.9614 - val_recall: 0.9181\n", + "Epoch 97/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9706 - loss: 0.0887 - precision: 0.9623 - recall: 0.8848 - val_accuracy: 0.9749 - val_loss: 0.0814 - val_precision: 0.9458 - val_recall: 0.9262\n", + "Epoch 98/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9695 - loss: 0.0931 - precision: 0.9611 - recall: 0.8804 - val_accuracy: 0.9760 - val_loss: 0.0779 - val_precision: 0.9681 - val_recall: 0.9089\n", + "Epoch 99/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9723 - loss: 0.0859 - precision: 0.9666 - recall: 0.8898 - val_accuracy: 0.9762 - val_loss: 0.0848 - val_precision: 0.9681 - val_recall: 0.9100\n", + "Epoch 100/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9679 - loss: 0.1002 - precision: 0.9572 - recall: 0.8756 - val_accuracy: 0.9749 - val_loss: 0.0824 - val_precision: 0.9714 - val_recall: 0.8997\n", + "Epoch 101/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9693 - loss: 0.0978 - precision: 0.9597 - recall: 0.8806 - val_accuracy: 0.9765 - val_loss: 0.0988 - val_precision: 0.9693 - val_recall: 0.9100\n", + "Epoch 102/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9705 - loss: 0.0946 - precision: 0.9604 - recall: 0.8861 - val_accuracy: 0.9751 - val_loss: 0.0813 - val_precision: 0.9749 - val_recall: 0.8973\n", + "Epoch 103/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9709 - loss: 0.0921 - precision: 0.9680 - recall: 0.8812 - val_accuracy: 0.9785 - val_loss: 0.0818 - val_precision: 0.9489 - val_recall: 0.9423\n", + "Epoch 104/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0865 - precision: 0.9668 - recall: 0.9003 - val_accuracy: 0.9765 - val_loss: 0.0929 - val_precision: 0.9569 - val_recall: 0.9227\n", + "Epoch 105/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9733 - loss: 0.0829 - precision: 0.9678 - recall: 0.8938 - val_accuracy: 0.9746 - val_loss: 0.0921 - val_precision: 0.9701 - val_recall: 0.8997\n", + "Epoch 106/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9706 - loss: 0.0931 - precision: 0.9663 - recall: 0.8813 - val_accuracy: 0.9751 - val_loss: 0.0859 - val_precision: 0.9810 - val_recall: 0.8916\n", + "Epoch 107/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9722 - loss: 0.0879 - precision: 0.9636 - recall: 0.8921 - val_accuracy: 0.9710 - val_loss: 0.1080 - val_precision: 0.9501 - val_recall: 0.9008\n", + "Epoch 108/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9729 - loss: 0.0874 - precision: 0.9687 - recall: 0.8907 - val_accuracy: 0.9781 - val_loss: 0.0778 - val_precision: 0.9695 - val_recall: 0.9181\n", + "Epoch 109/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9732 - loss: 0.0891 - precision: 0.9661 - recall: 0.8945 - val_accuracy: 0.9742 - val_loss: 0.0932 - val_precision: 0.9435 - val_recall: 0.9250\n", + "Epoch 110/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9728 - loss: 0.0876 - precision: 0.9683 - recall: 0.8907 - val_accuracy: 0.9760 - val_loss: 0.0848 - val_precision: 0.9669 - val_recall: 0.9100\n", + "Epoch 111/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9723 - loss: 0.0892 - precision: 0.9666 - recall: 0.8895 - val_accuracy: 0.9762 - val_loss: 0.0893 - val_precision: 0.9704 - val_recall: 0.9077\n", + "Epoch 112/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9708 - loss: 0.0913 - precision: 0.9586 - recall: 0.8896 - val_accuracy: 0.9762 - val_loss: 0.0840 - val_precision: 0.9727 - val_recall: 0.9054\n", + "Epoch 113/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.0931 - precision: 0.9656 - recall: 0.8849 - val_accuracy: 0.9751 - val_loss: 0.0909 - val_precision: 0.9656 - val_recall: 0.9066\n", + "Epoch 114/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9710 - loss: 0.0891 - precision: 0.9659 - recall: 0.8835 - val_accuracy: 0.9762 - val_loss: 0.0812 - val_precision: 0.9569 - val_recall: 0.9216\n", + "Epoch 115/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9724 - loss: 0.0865 - precision: 0.9662 - recall: 0.8902 - val_accuracy: 0.9744 - val_loss: 0.1025 - val_precision: 0.9643 - val_recall: 0.9043\n", + "Epoch 116/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9727 - loss: 0.0899 - precision: 0.9674 - recall: 0.8910 - val_accuracy: 0.9744 - val_loss: 0.0943 - val_precision: 0.9543 - val_recall: 0.9146\n", + "Epoch 117/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9725 - loss: 0.0877 - precision: 0.9638 - recall: 0.8933 - val_accuracy: 0.9790 - val_loss: 0.0755 - val_precision: 0.9686 - val_recall: 0.9239\n", + "Epoch 118/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9747 - loss: 0.0797 - precision: 0.9670 - recall: 0.9018 - val_accuracy: 0.9742 - val_loss: 0.0807 - val_precision: 0.9520 - val_recall: 0.9158\n", + "Epoch 119/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9723 - loss: 0.0879 - precision: 0.9618 - recall: 0.8944 - val_accuracy: 0.9739 - val_loss: 0.1077 - val_precision: 0.9631 - val_recall: 0.9031\n", + "Epoch 120/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9727 - loss: 0.0857 - precision: 0.9647 - recall: 0.8938 - val_accuracy: 0.9737 - val_loss: 0.0916 - val_precision: 0.9653 - val_recall: 0.8997\n", + "Epoch 121/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0835 - precision: 0.9689 - recall: 0.8956 - val_accuracy: 0.9749 - val_loss: 0.0839 - val_precision: 0.9501 - val_recall: 0.9216\n", + "Epoch 122/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9717 - loss: 0.0899 - precision: 0.9625 - recall: 0.8906 - val_accuracy: 0.9755 - val_loss: 0.0911 - val_precision: 0.9612 - val_recall: 0.9135\n", + "Epoch 123/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9717 - loss: 0.0868 - precision: 0.9677 - recall: 0.8856 - val_accuracy: 0.9783 - val_loss: 0.0849 - val_precision: 0.9684 - val_recall: 0.9204\n", + "Epoch 124/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9706 - loss: 0.0907 - precision: 0.9615 - recall: 0.8858 - val_accuracy: 0.9771 - val_loss: 0.0828 - val_precision: 0.9717 - val_recall: 0.9112\n", + "Epoch 125/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9730 - loss: 0.0870 - precision: 0.9631 - recall: 0.8970 - val_accuracy: 0.9797 - val_loss: 0.0717 - val_precision: 0.9756 - val_recall: 0.9204\n", + "Epoch 126/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9722 - loss: 0.0863 - precision: 0.9648 - recall: 0.8908 - val_accuracy: 0.9783 - val_loss: 0.0707 - val_precision: 0.9573 - val_recall: 0.9319\n", + "Epoch 127/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0830 - precision: 0.9702 - recall: 0.8989 - val_accuracy: 0.9723 - val_loss: 0.0934 - val_precision: 0.9462 - val_recall: 0.9123\n", + "Epoch 128/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9724 - loss: 0.0868 - precision: 0.9661 - recall: 0.8908 - val_accuracy: 0.9742 - val_loss: 0.0809 - val_precision: 0.9274 - val_recall: 0.9435\n", + "Epoch 129/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9716 - loss: 0.0889 - precision: 0.9617 - recall: 0.8909 - val_accuracy: 0.9694 - val_loss: 0.0984 - val_precision: 0.9693 - val_recall: 0.8731\n", + "Epoch 130/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9725 - loss: 0.0856 - precision: 0.9656 - recall: 0.8917 - val_accuracy: 0.9641 - val_loss: 0.1274 - val_precision: 0.9494 - val_recall: 0.8651\n", + "Epoch 131/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9711 - loss: 0.0957 - precision: 0.9655 - recall: 0.8843 - val_accuracy: 0.9739 - val_loss: 0.0969 - val_precision: 0.9748 - val_recall: 0.8916\n", + "Epoch 132/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9720 - loss: 0.0867 - precision: 0.9683 - recall: 0.8866 - val_accuracy: 0.9726 - val_loss: 0.0943 - val_precision: 0.9844 - val_recall: 0.8754\n", + "Epoch 133/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9716 - loss: 0.0909 - precision: 0.9671 - recall: 0.8855 - val_accuracy: 0.9790 - val_loss: 0.0715 - val_precision: 0.9652 - val_recall: 0.9273\n", + "Epoch 134/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9735 - loss: 0.0859 - precision: 0.9640 - recall: 0.8986 - val_accuracy: 0.9675 - val_loss: 0.1213 - val_precision: 0.9630 - val_recall: 0.8697\n", + "Epoch 135/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9714 - loss: 0.0885 - precision: 0.9633 - recall: 0.8882 - val_accuracy: 0.9744 - val_loss: 0.1060 - val_precision: 0.9713 - val_recall: 0.8973\n", + "Epoch 136/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9720 - loss: 0.0893 - precision: 0.9641 - recall: 0.8909 - val_accuracy: 0.9783 - val_loss: 0.0708 - val_precision: 0.9541 - val_recall: 0.9354\n", + "Epoch 137/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9729 - loss: 0.0840 - precision: 0.9651 - recall: 0.8941 - val_accuracy: 0.9753 - val_loss: 0.0867 - val_precision: 0.9567 - val_recall: 0.9170\n", + "Epoch 138/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9741 - loss: 0.0836 - precision: 0.9682 - recall: 0.8974 - val_accuracy: 0.9739 - val_loss: 0.0896 - val_precision: 0.9575 - val_recall: 0.9089\n", + "Epoch 139/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9750 - loss: 0.0820 - precision: 0.9679 - recall: 0.9023 - val_accuracy: 0.9776 - val_loss: 0.0807 - val_precision: 0.9649 - val_recall: 0.9204\n", + "Epoch 140/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9744 - loss: 0.0810 - precision: 0.9681 - recall: 0.8989 - val_accuracy: 0.9790 - val_loss: 0.0789 - val_precision: 0.9743 - val_recall: 0.9181\n", + "Epoch 141/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9720 - loss: 0.0886 - precision: 0.9655 - recall: 0.8893 - val_accuracy: 0.9817 - val_loss: 0.0745 - val_precision: 0.9679 - val_recall: 0.9389\n", + "Epoch 142/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9751 - loss: 0.0793 - precision: 0.9669 - recall: 0.9040 - val_accuracy: 0.9755 - val_loss: 0.0843 - val_precision: 0.9691 - val_recall: 0.9054\n", + "Epoch 143/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9740 - loss: 0.0855 - precision: 0.9671 - recall: 0.8983 - val_accuracy: 0.9687 - val_loss: 0.1159 - val_precision: 0.9679 - val_recall: 0.8708\n", + "Epoch 144/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9737 - loss: 0.0839 - precision: 0.9705 - recall: 0.8933 - val_accuracy: 0.9790 - val_loss: 0.0800 - val_precision: 0.9532 - val_recall: 0.9400\n", + "Epoch 145/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9749 - loss: 0.0819 - precision: 0.9677 - recall: 0.9022 - val_accuracy: 0.9513 - val_loss: 0.1848 - val_precision: 0.9593 - val_recall: 0.7878\n", + "Epoch 146/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9716 - loss: 0.0891 - precision: 0.9644 - recall: 0.8886 - val_accuracy: 0.9778 - val_loss: 0.0839 - val_precision: 0.9718 - val_recall: 0.9146\n", + "Epoch 147/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0803 - precision: 0.9672 - recall: 0.9057 - val_accuracy: 0.9767 - val_loss: 0.0955 - val_precision: 0.9775 - val_recall: 0.9031\n", + "Epoch 148/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9743 - loss: 0.0817 - precision: 0.9684 - recall: 0.8985 - val_accuracy: 0.9781 - val_loss: 0.0843 - val_precision: 0.9661 - val_recall: 0.9216\n", + "Epoch 149/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9740 - loss: 0.0811 - precision: 0.9668 - recall: 0.8982 - val_accuracy: 0.9794 - val_loss: 0.0706 - val_precision: 0.9698 - val_recall: 0.9250\n", + "Epoch 150/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9732 - loss: 0.0834 - precision: 0.9687 - recall: 0.8925 - val_accuracy: 0.9790 - val_loss: 0.0776 - val_precision: 0.9586 - val_recall: 0.9343\n", + "Epoch 151/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9758 - loss: 0.0772 - precision: 0.9702 - recall: 0.9043 - val_accuracy: 0.9799 - val_loss: 0.0756 - val_precision: 0.9756 - val_recall: 0.9216\n", + "Epoch 152/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0780 - precision: 0.9671 - recall: 0.8997 - val_accuracy: 0.9636 - val_loss: 0.1511 - val_precision: 0.9515 - val_recall: 0.8604\n", + "Epoch 153/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9726 - loss: 0.0852 - precision: 0.9637 - recall: 0.8945 - val_accuracy: 0.9778 - val_loss: 0.0754 - val_precision: 0.9508 - val_recall: 0.9366\n", + "Epoch 154/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0773 - precision: 0.9689 - recall: 0.9030 - val_accuracy: 0.9739 - val_loss: 0.0997 - val_precision: 0.9642 - val_recall: 0.9020\n", + "Epoch 155/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9762 - loss: 0.0762 - precision: 0.9688 - recall: 0.9082 - val_accuracy: 0.9739 - val_loss: 0.0910 - val_precision: 0.9724 - val_recall: 0.8939\n", + "Epoch 156/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9711 - loss: 0.0891 - precision: 0.9638 - recall: 0.8862 - val_accuracy: 0.9767 - val_loss: 0.0817 - val_precision: 0.9873 - val_recall: 0.8939\n", + "Epoch 157/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9717 - loss: 0.0872 - precision: 0.9640 - recall: 0.8891 - val_accuracy: 0.9753 - val_loss: 0.0933 - val_precision: 0.9691 - val_recall: 0.9043\n", + "Epoch 158/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.0829 - precision: 0.9673 - recall: 0.8983 - val_accuracy: 0.9762 - val_loss: 0.0847 - val_precision: 0.9647 - val_recall: 0.9135\n", + "Epoch 159/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9738 - loss: 0.0827 - precision: 0.9684 - recall: 0.8960 - val_accuracy: 0.9778 - val_loss: 0.0888 - val_precision: 0.9672 - val_recall: 0.9193\n", + "Epoch 160/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9747 - loss: 0.0831 - precision: 0.9665 - recall: 0.9022 - val_accuracy: 0.9735 - val_loss: 0.1000 - val_precision: 0.9619 - val_recall: 0.9020\n", + "Epoch 161/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9744 - loss: 0.0762 - precision: 0.9682 - recall: 0.8994 - val_accuracy: 0.9737 - val_loss: 0.1081 - val_precision: 0.9772 - val_recall: 0.8881\n", + "Epoch 162/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9706 - loss: 0.0904 - precision: 0.9652 - recall: 0.8822 - val_accuracy: 0.9723 - val_loss: 0.1042 - val_precision: 0.9605 - val_recall: 0.8973\n", + "Epoch 163/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9749 - loss: 0.0794 - precision: 0.9687 - recall: 0.9014 - val_accuracy: 0.9813 - val_loss: 0.0663 - val_precision: 0.9758 - val_recall: 0.9285\n", + "Epoch 164/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9715 - loss: 0.0928 - precision: 0.9623 - recall: 0.8894 - val_accuracy: 0.9792 - val_loss: 0.0731 - val_precision: 0.9652 - val_recall: 0.9285\n", + "Epoch 165/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9722 - loss: 0.0895 - precision: 0.9643 - recall: 0.8916 - val_accuracy: 0.9797 - val_loss: 0.0704 - val_precision: 0.9687 - val_recall: 0.9273\n", + "Epoch 166/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0816 - precision: 0.9722 - recall: 0.9003 - val_accuracy: 0.9755 - val_loss: 0.0924 - val_precision: 0.9668 - val_recall: 0.9077\n", + "Epoch 167/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0799 - precision: 0.9651 - recall: 0.8997 - val_accuracy: 0.9765 - val_loss: 0.0877 - val_precision: 0.9484 - val_recall: 0.9319\n", + "Epoch 168/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9735 - loss: 0.0877 - precision: 0.9664 - recall: 0.8961 - val_accuracy: 0.9753 - val_loss: 0.1041 - val_precision: 0.9622 - val_recall: 0.9112\n", + "Epoch 169/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0808 - precision: 0.9714 - recall: 0.9036 - val_accuracy: 0.9806 - val_loss: 0.0740 - val_precision: 0.9633 - val_recall: 0.9377\n", + "Epoch 170/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.0849 - precision: 0.9662 - recall: 0.8975 - val_accuracy: 0.9765 - val_loss: 0.0798 - val_precision: 0.9670 - val_recall: 0.9123\n", + "Epoch 171/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9729 - loss: 0.0809 - precision: 0.9642 - recall: 0.8953 - val_accuracy: 0.9749 - val_loss: 0.1025 - val_precision: 0.9761 - val_recall: 0.8950\n", + "Epoch 172/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9724 - loss: 0.0823 - precision: 0.9654 - recall: 0.8915 - val_accuracy: 0.9769 - val_loss: 0.0859 - val_precision: 0.9776 - val_recall: 0.9043\n", + "Epoch 173/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9751 - loss: 0.0749 - precision: 0.9725 - recall: 0.8984 - val_accuracy: 0.9753 - val_loss: 0.0989 - val_precision: 0.9656 - val_recall: 0.9077\n", + "Epoch 174/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.0821 - precision: 0.9663 - recall: 0.8916 - val_accuracy: 0.9742 - val_loss: 0.1012 - val_precision: 0.9724 - val_recall: 0.8950\n", + "Epoch 175/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9732 - loss: 0.0818 - precision: 0.9643 - recall: 0.8964 - val_accuracy: 0.9819 - val_loss: 0.0921 - val_precision: 0.9702 - val_recall: 0.9377\n", + "Epoch 176/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9735 - loss: 0.0817 - precision: 0.9674 - recall: 0.8954 - val_accuracy: 0.9751 - val_loss: 0.1096 - val_precision: 0.9726 - val_recall: 0.8997\n", + "Epoch 177/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0749 - precision: 0.9701 - recall: 0.9053 - val_accuracy: 0.9781 - val_loss: 0.0883 - val_precision: 0.9595 - val_recall: 0.9285\n", + "Epoch 178/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9762 - loss: 0.0759 - precision: 0.9707 - recall: 0.9063 - val_accuracy: 0.9803 - val_loss: 0.0854 - val_precision: 0.9733 - val_recall: 0.9262\n", + "Epoch 179/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9728 - loss: 0.0840 - precision: 0.9645 - recall: 0.8943 - val_accuracy: 0.9817 - val_loss: 0.0730 - val_precision: 0.9747 - val_recall: 0.9319\n", + "Epoch 180/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9743 - loss: 0.0830 - precision: 0.9686 - recall: 0.8984 - val_accuracy: 0.9696 - val_loss: 0.1279 - val_precision: 0.9542 - val_recall: 0.8893\n", + "Epoch 181/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9721 - loss: 0.0891 - precision: 0.9592 - recall: 0.8962 - val_accuracy: 0.9771 - val_loss: 0.0982 - val_precision: 0.9752 - val_recall: 0.9077\n", + "Epoch 182/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0746 - precision: 0.9689 - recall: 0.9083 - val_accuracy: 0.9765 - val_loss: 0.1085 - val_precision: 0.9716 - val_recall: 0.9077\n", + "Epoch 183/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0776 - precision: 0.9701 - recall: 0.9006 - val_accuracy: 0.9765 - val_loss: 0.0818 - val_precision: 0.9636 - val_recall: 0.9158\n", + "Epoch 184/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9738 - loss: 0.0816 - precision: 0.9641 - recall: 0.9001 - val_accuracy: 0.9797 - val_loss: 0.0844 - val_precision: 0.9779 - val_recall: 0.9181\n", + "Epoch 185/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0760 - precision: 0.9668 - recall: 0.9057 - val_accuracy: 0.9760 - val_loss: 0.0951 - val_precision: 0.9547 - val_recall: 0.9227\n", + "Epoch 186/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0802 - precision: 0.9670 - recall: 0.8999 - val_accuracy: 0.9744 - val_loss: 0.0871 - val_precision: 0.9643 - val_recall: 0.9043\n", + "Epoch 187/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9742 - loss: 0.0822 - precision: 0.9692 - recall: 0.8974 - val_accuracy: 0.9652 - val_loss: 0.1622 - val_precision: 0.9430 - val_recall: 0.8777\n", + "Epoch 188/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9727 - loss: 0.0889 - precision: 0.9680 - recall: 0.8905 - val_accuracy: 0.9723 - val_loss: 0.1364 - val_precision: 0.9698 - val_recall: 0.8881\n", + "Epoch 189/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.0827 - precision: 0.9697 - recall: 0.8963 - val_accuracy: 0.9753 - val_loss: 0.1119 - val_precision: 0.9810 - val_recall: 0.8927\n", + "Epoch 190/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9724 - loss: 0.0838 - precision: 0.9664 - recall: 0.8906 - val_accuracy: 0.9797 - val_loss: 0.0821 - val_precision: 0.9631 - val_recall: 0.9331\n", + "Epoch 191/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9747 - loss: 0.0773 - precision: 0.9667 - recall: 0.9024 - val_accuracy: 0.9826 - val_loss: 0.0766 - val_precision: 0.9714 - val_recall: 0.9400\n", + "Epoch 192/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9758 - loss: 0.0760 - precision: 0.9662 - recall: 0.9084 - val_accuracy: 0.9819 - val_loss: 0.0716 - val_precision: 0.9747 - val_recall: 0.9331\n", + "Epoch 193/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.0772 - precision: 0.9693 - recall: 0.8993 - val_accuracy: 0.9826 - val_loss: 0.0758 - val_precision: 0.9737 - val_recall: 0.9377\n", + "Epoch 194/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.0780 - precision: 0.9690 - recall: 0.8989 - val_accuracy: 0.9771 - val_loss: 0.1015 - val_precision: 0.9604 - val_recall: 0.9227\n", + "Epoch 195/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9732 - loss: 0.0881 - precision: 0.9641 - recall: 0.8970 - val_accuracy: 0.9847 - val_loss: 0.0705 - val_precision: 0.9796 - val_recall: 0.9423\n", + "Epoch 196/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0745 - precision: 0.9675 - recall: 0.9047 - val_accuracy: 0.9815 - val_loss: 0.0818 - val_precision: 0.9656 - val_recall: 0.9400\n", + "Epoch 197/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9734 - loss: 0.0832 - precision: 0.9647 - recall: 0.8974 - val_accuracy: 0.9817 - val_loss: 0.0750 - val_precision: 0.9701 - val_recall: 0.9366\n", + "Epoch 198/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9763 - loss: 0.0756 - precision: 0.9736 - recall: 0.9038 - val_accuracy: 0.9778 - val_loss: 0.0922 - val_precision: 0.9765 - val_recall: 0.9100\n", + "Epoch 199/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9747 - loss: 0.0761 - precision: 0.9675 - recall: 0.9012 - val_accuracy: 0.9781 - val_loss: 0.0955 - val_precision: 0.9707 - val_recall: 0.9170\n", + "Epoch 200/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9747 - loss: 0.0797 - precision: 0.9659 - recall: 0.9032 - val_accuracy: 0.9799 - val_loss: 0.0982 - val_precision: 0.9756 - val_recall: 0.9216\n", + "Epoch 201/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9756 - loss: 0.0781 - precision: 0.9704 - recall: 0.9033 - val_accuracy: 0.9762 - val_loss: 0.0810 - val_precision: 0.9602 - val_recall: 0.9181\n", + "Epoch 202/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9738 - loss: 0.0799 - precision: 0.9645 - recall: 0.8998 - val_accuracy: 0.9781 - val_loss: 0.0898 - val_precision: 0.9707 - val_recall: 0.9170\n", + "Epoch 203/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9757 - loss: 0.0750 - precision: 0.9694 - recall: 0.9049 - val_accuracy: 0.9803 - val_loss: 0.0769 - val_precision: 0.9557 - val_recall: 0.9446\n", + "Epoch 204/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9736 - loss: 0.0849 - precision: 0.9658 - recall: 0.8971 - val_accuracy: 0.9803 - val_loss: 0.0821 - val_precision: 0.9525 - val_recall: 0.9481\n", + "Epoch 205/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9745 - loss: 0.0753 - precision: 0.9639 - recall: 0.9044 - val_accuracy: 0.9790 - val_loss: 0.0932 - val_precision: 0.9652 - val_recall: 0.9273\n", + "Epoch 206/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9772 - loss: 0.0762 - precision: 0.9732 - recall: 0.9088 - val_accuracy: 0.9790 - val_loss: 0.0892 - val_precision: 0.9511 - val_recall: 0.9423\n", + "Epoch 207/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9760 - loss: 0.0750 - precision: 0.9684 - recall: 0.9076 - val_accuracy: 0.9781 - val_loss: 0.0909 - val_precision: 0.9541 - val_recall: 0.9343\n", + "Epoch 208/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0805 - precision: 0.9690 - recall: 0.8978 - val_accuracy: 0.9801 - val_loss: 0.0962 - val_precision: 0.9699 - val_recall: 0.9285\n", + "Epoch 209/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9759 - loss: 0.0755 - precision: 0.9711 - recall: 0.9043 - val_accuracy: 0.9771 - val_loss: 0.1053 - val_precision: 0.9776 - val_recall: 0.9054\n", + "Epoch 210/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9762 - loss: 0.0730 - precision: 0.9699 - recall: 0.9068 - val_accuracy: 0.9835 - val_loss: 0.0762 - val_precision: 0.9830 - val_recall: 0.9331\n", + "Epoch 211/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9768 - loss: 0.0762 - precision: 0.9722 - recall: 0.9079 - val_accuracy: 0.9831 - val_loss: 0.0741 - val_precision: 0.9760 - val_recall: 0.9377\n", + "Epoch 212/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9736 - loss: 0.0827 - precision: 0.9690 - recall: 0.8945 - val_accuracy: 0.9797 - val_loss: 0.0850 - val_precision: 0.9687 - val_recall: 0.9273\n", + "Epoch 213/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0706 - precision: 0.9711 - recall: 0.9153 - val_accuracy: 0.9776 - val_loss: 0.0815 - val_precision: 0.9683 - val_recall: 0.9170\n", + "Epoch 214/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9774 - loss: 0.0746 - precision: 0.9727 - recall: 0.9105 - val_accuracy: 0.9744 - val_loss: 0.1113 - val_precision: 0.9598 - val_recall: 0.9089\n", + "Epoch 215/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9770 - loss: 0.0758 - precision: 0.9690 - recall: 0.9119 - val_accuracy: 0.9801 - val_loss: 0.0856 - val_precision: 0.9688 - val_recall: 0.9296\n", + "Epoch 216/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9764 - loss: 0.0734 - precision: 0.9691 - recall: 0.9089 - val_accuracy: 0.9817 - val_loss: 0.0687 - val_precision: 0.9570 - val_recall: 0.9504\n", + "Epoch 217/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0736 - precision: 0.9700 - recall: 0.9061 - val_accuracy: 0.9797 - val_loss: 0.0862 - val_precision: 0.9653 - val_recall: 0.9308\n", + "Epoch 218/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0751 - precision: 0.9688 - recall: 0.9036 - val_accuracy: 0.9778 - val_loss: 0.0948 - val_precision: 0.9718 - val_recall: 0.9146\n", + "Epoch 219/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9726 - loss: 0.0871 - precision: 0.9642 - recall: 0.8936 - val_accuracy: 0.9783 - val_loss: 0.0947 - val_precision: 0.9662 - val_recall: 0.9227\n", + "Epoch 220/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9748 - loss: 0.0762 - precision: 0.9728 - recall: 0.8965 - val_accuracy: 0.9792 - val_loss: 0.0790 - val_precision: 0.9608 - val_recall: 0.9331\n", + "Epoch 221/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0819 - precision: 0.9690 - recall: 0.8979 - val_accuracy: 0.9831 - val_loss: 0.0667 - val_precision: 0.9760 - val_recall: 0.9377\n", + "Epoch 222/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9743 - loss: 0.0783 - precision: 0.9671 - recall: 0.8997 - val_accuracy: 0.9794 - val_loss: 0.0864 - val_precision: 0.9779 - val_recall: 0.9170\n", + "Epoch 223/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9760 - loss: 0.0784 - precision: 0.9702 - recall: 0.9055 - val_accuracy: 0.9744 - val_loss: 0.1078 - val_precision: 0.9713 - val_recall: 0.8973\n", + "Epoch 224/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9748 - loss: 0.0754 - precision: 0.9688 - recall: 0.9009 - val_accuracy: 0.9742 - val_loss: 0.1068 - val_precision: 0.9598 - val_recall: 0.9077\n", + "Epoch 225/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9745 - loss: 0.0797 - precision: 0.9672 - recall: 0.9009 - val_accuracy: 0.9813 - val_loss: 0.0856 - val_precision: 0.9548 - val_recall: 0.9504\n", + "Epoch 226/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9716 - loss: 0.0876 - precision: 0.9663 - recall: 0.8866 - val_accuracy: 0.9792 - val_loss: 0.0745 - val_precision: 0.9565 - val_recall: 0.9377\n", + "Epoch 227/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9742 - loss: 0.0770 - precision: 0.9674 - recall: 0.8992 - val_accuracy: 0.9803 - val_loss: 0.0732 - val_precision: 0.9827 - val_recall: 0.9170\n", + "Epoch 228/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9769 - loss: 0.0726 - precision: 0.9707 - recall: 0.9099 - val_accuracy: 0.9815 - val_loss: 0.0745 - val_precision: 0.9656 - val_recall: 0.9400\n", + "Epoch 229/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9754 - loss: 0.0782 - precision: 0.9722 - recall: 0.9006 - val_accuracy: 0.9819 - val_loss: 0.0775 - val_precision: 0.9724 - val_recall: 0.9354\n", + "Epoch 230/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9778 - loss: 0.0685 - precision: 0.9695 - recall: 0.9156 - val_accuracy: 0.9691 - val_loss: 0.1269 - val_precision: 0.9729 - val_recall: 0.8685\n", + "Epoch 231/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9740 - loss: 0.0832 - precision: 0.9683 - recall: 0.8969 - val_accuracy: 0.9358 - val_loss: 0.2244 - val_precision: 0.9522 - val_recall: 0.7116\n", + "Epoch 232/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9565 - loss: 0.1319 - precision: 0.9605 - recall: 0.8117 - val_accuracy: 0.9815 - val_loss: 0.0782 - val_precision: 0.9746 - val_recall: 0.9308\n", + "Epoch 233/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9762 - loss: 0.0746 - precision: 0.9707 - recall: 0.9064 - val_accuracy: 0.9767 - val_loss: 0.0937 - val_precision: 0.9740 - val_recall: 0.9066\n", + "Epoch 234/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0746 - precision: 0.9676 - recall: 0.9028 - val_accuracy: 0.9822 - val_loss: 0.0709 - val_precision: 0.9829 - val_recall: 0.9262\n", + "Epoch 235/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0681 - precision: 0.9725 - recall: 0.9097 - val_accuracy: 0.9833 - val_loss: 0.0731 - val_precision: 0.9772 - val_recall: 0.9377\n", + "Epoch 236/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0709 - precision: 0.9713 - recall: 0.9167 - val_accuracy: 0.9781 - val_loss: 0.0717 - val_precision: 0.9684 - val_recall: 0.9193\n", + "Epoch 237/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0774 - precision: 0.9709 - recall: 0.9011 - val_accuracy: 0.9792 - val_loss: 0.0682 - val_precision: 0.9439 - val_recall: 0.9516\n", + "Epoch 238/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0768 - precision: 0.9685 - recall: 0.9032 - val_accuracy: 0.9790 - val_loss: 0.0887 - val_precision: 0.9674 - val_recall: 0.9250\n", + "Epoch 239/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9751 - loss: 0.0757 - precision: 0.9672 - recall: 0.9038 - val_accuracy: 0.9803 - val_loss: 0.0752 - val_precision: 0.9632 - val_recall: 0.9366\n", + "Epoch 240/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0719 - precision: 0.9665 - recall: 0.9155 - val_accuracy: 0.9714 - val_loss: 0.1179 - val_precision: 0.9756 - val_recall: 0.8777\n", + "Epoch 241/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9755 - loss: 0.0797 - precision: 0.9701 - recall: 0.9029 - val_accuracy: 0.9835 - val_loss: 0.0644 - val_precision: 0.9704 - val_recall: 0.9458\n", + "Epoch 242/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9758 - loss: 0.0771 - precision: 0.9720 - recall: 0.9026 - val_accuracy: 0.9689 - val_loss: 0.1048 - val_precision: 0.9867 - val_recall: 0.8547\n", + "Epoch 243/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9726 - loss: 0.0846 - precision: 0.9621 - recall: 0.8960 - val_accuracy: 0.9813 - val_loss: 0.0736 - val_precision: 0.9623 - val_recall: 0.9423\n", + "Epoch 244/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0723 - precision: 0.9721 - recall: 0.9098 - val_accuracy: 0.9790 - val_loss: 0.0972 - val_precision: 0.9652 - val_recall: 0.9273\n", + "Epoch 245/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9768 - loss: 0.0734 - precision: 0.9679 - recall: 0.9118 - val_accuracy: 0.9797 - val_loss: 0.0733 - val_precision: 0.9513 - val_recall: 0.9458\n", + "Epoch 246/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9757 - loss: 0.0731 - precision: 0.9705 - recall: 0.9036 - val_accuracy: 0.9769 - val_loss: 0.0954 - val_precision: 0.9705 - val_recall: 0.9112\n", + "Epoch 247/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9771 - loss: 0.0734 - precision: 0.9680 - recall: 0.9136 - val_accuracy: 0.9801 - val_loss: 0.0802 - val_precision: 0.9688 - val_recall: 0.9296\n", + "Epoch 248/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9727 - loss: 0.0829 - precision: 0.9634 - recall: 0.8945 - val_accuracy: 0.9769 - val_loss: 0.0797 - val_precision: 0.9637 - val_recall: 0.9181\n", + "Epoch 249/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9753 - loss: 0.0777 - precision: 0.9686 - recall: 0.9034 - val_accuracy: 0.9758 - val_loss: 0.0928 - val_precision: 0.9738 - val_recall: 0.9020\n", + "Epoch 250/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0727 - precision: 0.9740 - recall: 0.9115 - val_accuracy: 0.9684 - val_loss: 0.1290 - val_precision: 0.9620 - val_recall: 0.8754\n", + "Epoch 251/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9746 - loss: 0.0835 - precision: 0.9681 - recall: 0.9005 - val_accuracy: 0.9819 - val_loss: 0.0708 - val_precision: 0.9713 - val_recall: 0.9366\n", + "Epoch 252/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9768 - loss: 0.0763 - precision: 0.9726 - recall: 0.9076 - val_accuracy: 0.9824 - val_loss: 0.0752 - val_precision: 0.9625 - val_recall: 0.9481\n", + "Epoch 253/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9748 - loss: 0.0755 - precision: 0.9679 - recall: 0.9014 - val_accuracy: 0.9787 - val_loss: 0.0815 - val_precision: 0.9743 - val_recall: 0.9170\n", + "Epoch 254/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0756 - precision: 0.9693 - recall: 0.9072 - val_accuracy: 0.9765 - val_loss: 0.1001 - val_precision: 0.9739 - val_recall: 0.9054\n", + "Epoch 255/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9776 - loss: 0.0696 - precision: 0.9728 - recall: 0.9115 - val_accuracy: 0.9815 - val_loss: 0.0700 - val_precision: 0.9667 - val_recall: 0.9389\n", + "Epoch 256/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9751 - loss: 0.0801 - precision: 0.9667 - recall: 0.9046 - val_accuracy: 0.9783 - val_loss: 0.0760 - val_precision: 0.9696 - val_recall: 0.9193\n", + "Epoch 257/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9754 - loss: 0.0791 - precision: 0.9686 - recall: 0.9042 - val_accuracy: 0.9822 - val_loss: 0.0717 - val_precision: 0.9736 - val_recall: 0.9354\n", + "Epoch 258/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9752 - loss: 0.0727 - precision: 0.9673 - recall: 0.9042 - val_accuracy: 0.9696 - val_loss: 0.1215 - val_precision: 0.9705 - val_recall: 0.8731\n", + "Epoch 259/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9715 - loss: 0.0857 - precision: 0.9632 - recall: 0.8891 - val_accuracy: 0.9771 - val_loss: 0.1011 - val_precision: 0.9648 - val_recall: 0.9181\n", + "Epoch 260/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0732 - precision: 0.9743 - recall: 0.9076 - val_accuracy: 0.9751 - val_loss: 0.0991 - val_precision: 0.9702 - val_recall: 0.9020\n", + "Epoch 261/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9757 - loss: 0.0756 - precision: 0.9690 - recall: 0.9051 - val_accuracy: 0.9790 - val_loss: 0.0872 - val_precision: 0.9708 - val_recall: 0.9216\n", + "Epoch 262/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9774 - loss: 0.0697 - precision: 0.9751 - recall: 0.9078 - val_accuracy: 0.9840 - val_loss: 0.0715 - val_precision: 0.9830 - val_recall: 0.9354\n", + "Epoch 263/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0658 - precision: 0.9729 - recall: 0.9152 - val_accuracy: 0.9668 - val_loss: 0.1389 - val_precision: 0.9570 - val_recall: 0.8720\n", + "Epoch 264/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9720 - loss: 0.0888 - precision: 0.9673 - recall: 0.8872 - val_accuracy: 0.9598 - val_loss: 0.1605 - val_precision: 0.9638 - val_recall: 0.8281\n", + "Epoch 265/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0746 - precision: 0.9661 - recall: 0.9044 - val_accuracy: 0.9815 - val_loss: 0.0654 - val_precision: 0.9634 - val_recall: 0.9423\n", + "Epoch 266/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9737 - loss: 0.0800 - precision: 0.9688 - recall: 0.8952 - val_accuracy: 0.9833 - val_loss: 0.0710 - val_precision: 0.9704 - val_recall: 0.9446\n", + "Epoch 267/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9772 - loss: 0.0717 - precision: 0.9691 - recall: 0.9129 - val_accuracy: 0.9815 - val_loss: 0.0822 - val_precision: 0.9735 - val_recall: 0.9319\n", + "Epoch 268/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9750 - loss: 0.0781 - precision: 0.9691 - recall: 0.9012 - val_accuracy: 0.9792 - val_loss: 0.0868 - val_precision: 0.9814 - val_recall: 0.9123\n", + "Epoch 269/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9770 - loss: 0.0684 - precision: 0.9713 - recall: 0.9098 - val_accuracy: 0.9755 - val_loss: 0.1062 - val_precision: 0.9774 - val_recall: 0.8973\n", + "Epoch 270/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9733 - loss: 0.0818 - precision: 0.9662 - recall: 0.8952 - val_accuracy: 0.9794 - val_loss: 0.0805 - val_precision: 0.9802 - val_recall: 0.9146\n", + "Epoch 271/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9767 - loss: 0.0702 - precision: 0.9726 - recall: 0.9067 - val_accuracy: 0.9797 - val_loss: 0.0931 - val_precision: 0.9709 - val_recall: 0.9250\n", + "Epoch 272/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0664 - precision: 0.9739 - recall: 0.9145 - val_accuracy: 0.9835 - val_loss: 0.0694 - val_precision: 0.9865 - val_recall: 0.9296\n", + "Epoch 273/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9760 - loss: 0.0718 - precision: 0.9694 - recall: 0.9066 - val_accuracy: 0.9801 - val_loss: 0.0853 - val_precision: 0.9779 - val_recall: 0.9204\n", + "Epoch 274/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9768 - loss: 0.0715 - precision: 0.9691 - recall: 0.9109 - val_accuracy: 0.9819 - val_loss: 0.0770 - val_precision: 0.9713 - val_recall: 0.9366\n", + "Epoch 275/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9786 - loss: 0.0729 - precision: 0.9745 - recall: 0.9151 - val_accuracy: 0.9666 - val_loss: 0.1384 - val_precision: 0.9652 - val_recall: 0.8627\n", + "Epoch 276/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0729 - precision: 0.9704 - recall: 0.9058 - val_accuracy: 0.9831 - val_loss: 0.0803 - val_precision: 0.9511 - val_recall: 0.9642\n", + "Epoch 277/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0719 - precision: 0.9775 - recall: 0.9107 - val_accuracy: 0.9829 - val_loss: 0.0649 - val_precision: 0.9703 - val_recall: 0.9423\n", + "Epoch 278/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9770 - loss: 0.0741 - precision: 0.9701 - recall: 0.9106 - val_accuracy: 0.9822 - val_loss: 0.0677 - val_precision: 0.9669 - val_recall: 0.9423\n", + "Epoch 279/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0684 - precision: 0.9742 - recall: 0.9138 - val_accuracy: 0.9778 - val_loss: 0.0883 - val_precision: 0.9789 - val_recall: 0.9077\n", + "Epoch 280/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9773 - loss: 0.0755 - precision: 0.9700 - recall: 0.9125 - val_accuracy: 0.9824 - val_loss: 0.0717 - val_precision: 0.9714 - val_recall: 0.9389\n", + "Epoch 281/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0675 - precision: 0.9703 - recall: 0.9157 - val_accuracy: 0.9849 - val_loss: 0.0684 - val_precision: 0.9831 - val_recall: 0.9400\n", + "Epoch 282/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9780 - loss: 0.0693 - precision: 0.9724 - recall: 0.9137 - val_accuracy: 0.9790 - val_loss: 0.0793 - val_precision: 0.9522 - val_recall: 0.9412\n", + "Epoch 283/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9752 - loss: 0.0757 - precision: 0.9670 - recall: 0.9045 - val_accuracy: 0.9797 - val_loss: 0.0749 - val_precision: 0.9631 - val_recall: 0.9331\n", + "Epoch 284/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9756 - loss: 0.0730 - precision: 0.9692 - recall: 0.9045 - val_accuracy: 0.9803 - val_loss: 0.0763 - val_precision: 0.9546 - val_recall: 0.9458\n", + "Epoch 285/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.0768 - precision: 0.9696 - recall: 0.8988 - val_accuracy: 0.9831 - val_loss: 0.0692 - val_precision: 0.9715 - val_recall: 0.9423\n", + "Epoch 286/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9766 - loss: 0.0746 - precision: 0.9692 - recall: 0.9102 - val_accuracy: 0.9726 - val_loss: 0.1000 - val_precision: 0.9807 - val_recall: 0.8789\n", + "Epoch 287/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9759 - loss: 0.0755 - precision: 0.9716 - recall: 0.9035 - val_accuracy: 0.9817 - val_loss: 0.0721 - val_precision: 0.9701 - val_recall: 0.9366\n", + "Epoch 288/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9770 - loss: 0.0725 - precision: 0.9723 - recall: 0.9087 - val_accuracy: 0.9787 - val_loss: 0.0824 - val_precision: 0.9564 - val_recall: 0.9354\n", + "Epoch 289/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9771 - loss: 0.0716 - precision: 0.9699 - recall: 0.9116 - val_accuracy: 0.9808 - val_loss: 0.0853 - val_precision: 0.9711 - val_recall: 0.9308\n", + "Epoch 290/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9769 - loss: 0.0726 - precision: 0.9712 - recall: 0.9092 - val_accuracy: 0.9792 - val_loss: 0.0868 - val_precision: 0.9697 - val_recall: 0.9239\n", + "Epoch 291/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9761 - loss: 0.0735 - precision: 0.9713 - recall: 0.9049 - val_accuracy: 0.9833 - val_loss: 0.0681 - val_precision: 0.9693 - val_recall: 0.9458\n", + "Epoch 292/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0660 - precision: 0.9740 - recall: 0.9140 - val_accuracy: 0.9822 - val_loss: 0.0702 - val_precision: 0.9793 - val_recall: 0.9296\n", + "Epoch 293/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9787 - loss: 0.0678 - precision: 0.9714 - recall: 0.9184 - val_accuracy: 0.9822 - val_loss: 0.0774 - val_precision: 0.9603 - val_recall: 0.9493\n", + "Epoch 294/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9778 - loss: 0.0723 - precision: 0.9711 - recall: 0.9141 - val_accuracy: 0.9824 - val_loss: 0.0741 - val_precision: 0.9714 - val_recall: 0.9389\n", + "Epoch 295/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0740 - precision: 0.9690 - recall: 0.9085 - val_accuracy: 0.9721 - val_loss: 0.1149 - val_precision: 0.9757 - val_recall: 0.8812\n", + "Epoch 296/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9763 - loss: 0.0797 - precision: 0.9694 - recall: 0.9078 - val_accuracy: 0.9803 - val_loss: 0.0760 - val_precision: 0.9654 - val_recall: 0.9343\n", + "Epoch 297/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9782 - loss: 0.0710 - precision: 0.9701 - recall: 0.9169 - val_accuracy: 0.9785 - val_loss: 0.0756 - val_precision: 0.9552 - val_recall: 0.9354\n", + "Epoch 298/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9789 - loss: 0.0702 - precision: 0.9704 - recall: 0.9207 - val_accuracy: 0.9829 - val_loss: 0.0693 - val_precision: 0.9726 - val_recall: 0.9400\n", + "Epoch 299/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9780 - loss: 0.0678 - precision: 0.9714 - recall: 0.9148 - val_accuracy: 0.9781 - val_loss: 0.0933 - val_precision: 0.9801 - val_recall: 0.9077\n", + "Epoch 300/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9793 - loss: 0.0676 - precision: 0.9766 - recall: 0.9164 - val_accuracy: 0.9813 - val_loss: 0.0793 - val_precision: 0.9656 - val_recall: 0.9389\n", + "Epoch 301/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9775 - loss: 0.0715 - precision: 0.9713 - recall: 0.9123 - val_accuracy: 0.9803 - val_loss: 0.0801 - val_precision: 0.9815 - val_recall: 0.9181\n", + "Epoch 302/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9742 - loss: 0.0806 - precision: 0.9680 - recall: 0.8984 - val_accuracy: 0.9824 - val_loss: 0.0703 - val_precision: 0.9691 - val_recall: 0.9412\n", + "Epoch 303/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9786 - loss: 0.0661 - precision: 0.9717 - recall: 0.9181 - val_accuracy: 0.9824 - val_loss: 0.0715 - val_precision: 0.9771 - val_recall: 0.9331\n", + "Epoch 304/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9787 - loss: 0.0663 - precision: 0.9735 - recall: 0.9163 - val_accuracy: 0.9838 - val_loss: 0.0647 - val_precision: 0.9761 - val_recall: 0.9412\n", + "Epoch 305/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9771 - loss: 0.0708 - precision: 0.9730 - recall: 0.9087 - val_accuracy: 0.9829 - val_loss: 0.0673 - val_precision: 0.9726 - val_recall: 0.9400\n", + "Epoch 306/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9781 - loss: 0.0655 - precision: 0.9701 - recall: 0.9167 - val_accuracy: 0.9854 - val_loss: 0.0655 - val_precision: 0.9663 - val_recall: 0.9596\n", + "Epoch 307/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9764 - loss: 0.0687 - precision: 0.9661 - recall: 0.9118 - val_accuracy: 0.9783 - val_loss: 0.0764 - val_precision: 0.9719 - val_recall: 0.9170\n", + "Epoch 308/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9761 - loss: 0.0731 - precision: 0.9672 - recall: 0.9093 - val_accuracy: 0.9799 - val_loss: 0.0763 - val_precision: 0.9839 - val_recall: 0.9135\n", + "Epoch 309/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9765 - loss: 0.0699 - precision: 0.9720 - recall: 0.9067 - val_accuracy: 0.9803 - val_loss: 0.0823 - val_precision: 0.9710 - val_recall: 0.9285\n", + "Epoch 310/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9770 - loss: 0.0708 - precision: 0.9716 - recall: 0.9093 - val_accuracy: 0.9817 - val_loss: 0.0688 - val_precision: 0.9770 - val_recall: 0.9296\n", + "Epoch 311/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9768 - loss: 0.0689 - precision: 0.9708 - recall: 0.9095 - val_accuracy: 0.9815 - val_loss: 0.0706 - val_precision: 0.9701 - val_recall: 0.9354\n", + "Epoch 312/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9771 - loss: 0.0768 - precision: 0.9721 - recall: 0.9096 - val_accuracy: 0.9817 - val_loss: 0.0738 - val_precision: 0.9624 - val_recall: 0.9446\n", + "Epoch 313/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9730 - loss: 0.0846 - precision: 0.9665 - recall: 0.8934 - val_accuracy: 0.9794 - val_loss: 0.0838 - val_precision: 0.9709 - val_recall: 0.9239\n", + "Epoch 314/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9789 - loss: 0.0640 - precision: 0.9762 - recall: 0.9151 - val_accuracy: 0.9822 - val_loss: 0.0712 - val_precision: 0.9669 - val_recall: 0.9423\n", + "Epoch 315/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9758 - loss: 0.0758 - precision: 0.9701 - recall: 0.9046 - val_accuracy: 0.9803 - val_loss: 0.0879 - val_precision: 0.9710 - val_recall: 0.9285\n", + "Epoch 316/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9787 - loss: 0.0657 - precision: 0.9725 - recall: 0.9173 - val_accuracy: 0.9840 - val_loss: 0.0640 - val_precision: 0.9716 - val_recall: 0.9469\n", + "Epoch 317/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9763 - loss: 0.0741 - precision: 0.9720 - recall: 0.9053 - val_accuracy: 0.9831 - val_loss: 0.0668 - val_precision: 0.9692 - val_recall: 0.9446\n", + "Epoch 318/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9779 - loss: 0.0720 - precision: 0.9726 - recall: 0.9132 - val_accuracy: 0.9792 - val_loss: 0.0805 - val_precision: 0.9767 - val_recall: 0.9170\n", + "Epoch 319/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9758 - loss: 0.0754 - precision: 0.9692 - recall: 0.9057 - val_accuracy: 0.9735 - val_loss: 0.0873 - val_precision: 0.9607 - val_recall: 0.9031\n", + "Epoch 320/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9755 - loss: 0.0741 - precision: 0.9671 - recall: 0.9061 - val_accuracy: 0.9801 - val_loss: 0.0691 - val_precision: 0.9722 - val_recall: 0.9262\n", + "Epoch 321/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9758 - loss: 0.0752 - precision: 0.9656 - recall: 0.9093 - val_accuracy: 0.9856 - val_loss: 0.0581 - val_precision: 0.9741 - val_recall: 0.9527\n", + "Epoch 322/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.0670 - precision: 0.9740 - recall: 0.9187 - val_accuracy: 0.9822 - val_loss: 0.0630 - val_precision: 0.9625 - val_recall: 0.9469\n", + "Epoch 323/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9792 - loss: 0.0643 - precision: 0.9717 - recall: 0.9208 - val_accuracy: 0.9806 - val_loss: 0.0809 - val_precision: 0.9666 - val_recall: 0.9343\n", + "Epoch 324/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9783 - loss: 0.0705 - precision: 0.9731 - recall: 0.9145 - val_accuracy: 0.9819 - val_loss: 0.0722 - val_precision: 0.9770 - val_recall: 0.9308\n", + "Epoch 325/325\n", + "\u001b[1m547/547\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9791 - loss: 0.0667 - precision: 0.9741 - recall: 0.9177 - val_accuracy: 0.9829 - val_loss: 0.0664 - val_precision: 0.9737 - val_recall: 0.9389\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Epoch 133/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0965 - accuracy: 0.9697 - precision: 0.9690 - recall: 0.8999 - val_loss: 0.1128 - val_accuracy: 0.9575 - val_precision: 0.8954 - val_recall: 0.9225\n", - "Epoch 134/200\n", - "18458/18458 [==============================] - 2s 130us/sample - loss: 0.0885 - accuracy: 0.9717 - precision: 0.9682 - recall: 0.9098 - val_loss: 0.0859 - val_accuracy: 0.9701 - val_precision: 0.9064 - val_recall: 0.9698\n", - "Epoch 135/200\n", - "18458/18458 [==============================] - 2s 129us/sample - loss: 0.0915 - accuracy: 0.9706 - precision: 0.9682 - recall: 0.9049 - val_loss: 0.0814 - val_accuracy: 0.9749 - val_precision: 0.9274 - val_recall: 0.9660\n", - "Epoch 136/200\n", - "18458/18458 [==============================] - 2s 129us/sample - loss: 0.0927 - accuracy: 0.9712 - precision: 0.9699 - recall: 0.9058 - val_loss: 0.1020 - val_accuracy: 0.9688 - val_precision: 0.8988 - val_recall: 0.9735\n", - "Epoch 137/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0865 - accuracy: 0.9732 - precision: 0.9725 - recall: 0.9121 - val_loss: 0.0615 - val_accuracy: 0.9822 - val_precision: 0.9552 - val_recall: 0.9679\n", - "Epoch 138/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0812 - accuracy: 0.9752 - precision: 0.9741 - recall: 0.9192 - val_loss: 0.0655 - val_accuracy: 0.9801 - val_precision: 0.9497 - val_recall: 0.9641\n", - "Epoch 139/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0857 - accuracy: 0.9737 - precision: 0.9749 - recall: 0.9116 - val_loss: 0.1483 - val_accuracy: 0.9536 - val_precision: 0.8403 - val_recall: 0.9849\n", - "Epoch 140/200\n", - "18458/18458 [==============================] - 3s 138us/sample - loss: 0.1002 - accuracy: 0.9686 - precision: 0.9682 - recall: 0.8959 - val_loss: 0.1285 - val_accuracy: 0.9610 - val_precision: 0.8702 - val_recall: 0.9754\n", - "Epoch 141/200\n", - "18458/18458 [==============================] - 3s 143us/sample - loss: 0.0845 - accuracy: 0.9732 - precision: 0.9707 - recall: 0.9137 - val_loss: 0.0677 - val_accuracy: 0.9783 - val_precision: 0.9632 - val_recall: 0.9414\n", - "Epoch 142/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0894 - accuracy: 0.9720 - precision: 0.9707 - recall: 0.9084 - val_loss: 0.1276 - val_accuracy: 0.9614 - val_precision: 0.8691 - val_recall: 0.9792\n", - "Epoch 143/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0876 - accuracy: 0.9717 - precision: 0.9675 - recall: 0.9105 - val_loss: 0.1315 - val_accuracy: 0.9575 - val_precision: 0.8598 - val_recall: 0.9735\n", - "Epoch 144/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.0983 - accuracy: 0.9679 - precision: 0.9683 - recall: 0.8930 - val_loss: 0.0673 - val_accuracy: 0.9805 - val_precision: 0.9583 - val_recall: 0.9565\n", - "Epoch 145/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0851 - accuracy: 0.9726 - precision: 0.9692 - recall: 0.9128 - val_loss: 0.0735 - val_accuracy: 0.9762 - val_precision: 0.9309 - val_recall: 0.9679\n", - "Epoch 146/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0843 - accuracy: 0.9733 - precision: 0.9707 - recall: 0.9144 - val_loss: 0.0745 - val_accuracy: 0.9762 - val_precision: 0.9309 - val_recall: 0.9679\n", - "Epoch 147/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0854 - accuracy: 0.9731 - precision: 0.9718 - recall: 0.9121 - val_loss: 0.0767 - val_accuracy: 0.9740 - val_precision: 0.9718 - val_recall: 0.9130\n", - "Epoch 148/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0862 - accuracy: 0.9727 - precision: 0.9694 - recall: 0.9130 - val_loss: 0.0895 - val_accuracy: 0.9705 - val_precision: 0.9094 - val_recall: 0.9679\n", - "Epoch 149/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0892 - accuracy: 0.9712 - precision: 0.9683 - recall: 0.9072 - val_loss: 0.0793 - val_accuracy: 0.9779 - val_precision: 0.9377 - val_recall: 0.9679\n", - "Epoch 150/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.0947 - accuracy: 0.9705 - precision: 0.9696 - recall: 0.9031 - val_loss: 0.1545 - val_accuracy: 0.9536 - val_precision: 0.8425 - val_recall: 0.9811\n", - "Epoch 151/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0906 - accuracy: 0.9716 - precision: 0.9681 - recall: 0.9091 - val_loss: 0.0634 - val_accuracy: 0.9788 - val_precision: 0.9858 - val_recall: 0.9206\n", - "Epoch 152/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.0887 - accuracy: 0.9726 - precision: 0.9720 - recall: 0.9098 - val_loss: 0.1612 - val_accuracy: 0.9502 - val_precision: 0.8339 - val_recall: 0.9773\n", - "Epoch 153/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.0977 - accuracy: 0.9687 - precision: 0.9663 - recall: 0.8983 - val_loss: 0.1101 - val_accuracy: 0.9645 - val_precision: 0.8807 - val_recall: 0.9773\n", - "Epoch 154/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0745 - accuracy: 0.9771 - precision: 0.9750 - recall: 0.9263 - val_loss: 0.1646 - val_accuracy: 0.9571 - val_precision: 0.8525 - val_recall: 0.9830\n", - "Epoch 155/200\n", - "18458/18458 [==============================] - 3s 139us/sample - loss: 0.0822 - accuracy: 0.9737 - precision: 0.9735 - recall: 0.9130 - val_loss: 0.1084 - val_accuracy: 0.9623 - val_precision: 0.8877 - val_recall: 0.9565\n", - "Epoch 156/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0830 - accuracy: 0.9721 - precision: 0.9664 - recall: 0.9132 - val_loss: 0.0739 - val_accuracy: 0.9766 - val_precision: 0.9326 - val_recall: 0.9679\n", - "Epoch 157/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.0809 - accuracy: 0.9742 - precision: 0.9736 - recall: 0.9153 - val_loss: 0.1272 - val_accuracy: 0.9692 - val_precision: 0.8976 - val_recall: 0.9773\n", - "Epoch 158/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.0957 - accuracy: 0.9708 - precision: 0.9685 - recall: 0.9054 - val_loss: 0.0702 - val_accuracy: 0.9801 - val_precision: 0.9464 - val_recall: 0.9679\n", - "Epoch 159/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0760 - accuracy: 0.9756 - precision: 0.9739 - recall: 0.9208 - val_loss: 0.1130 - val_accuracy: 0.9736 - val_precision: 0.9224 - val_recall: 0.9660\n", - "Epoch 160/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0873 - accuracy: 0.9726 - precision: 0.9722 - recall: 0.9098 - val_loss: 0.1625 - val_accuracy: 0.9484 - val_precision: 0.8213 - val_recall: 0.9905\n", - "Epoch 161/200\n", - "18458/18458 [==============================] - 2s 130us/sample - loss: 0.0889 - accuracy: 0.9708 - precision: 0.9696 - recall: 0.9042 - val_loss: 0.1490 - val_accuracy: 0.9510 - val_precision: 0.8344 - val_recall: 0.9811\n", - "Epoch 162/200\n", - "18458/18458 [==============================] - 2s 129us/sample - loss: 0.0771 - accuracy: 0.9758 - precision: 0.9735 - recall: 0.9222 - val_loss: 0.0893 - val_accuracy: 0.9740 - val_precision: 0.9180 - val_recall: 0.9735\n", - "Epoch 163/200\n", - "18458/18458 [==============================] - 2s 127us/sample - loss: 0.0778 - accuracy: 0.9752 - precision: 0.9725 - recall: 0.9208 - val_loss: 0.1083 - val_accuracy: 0.9645 - val_precision: 0.8834 - val_recall: 0.9735\n", - "Epoch 164/200\n", - "18458/18458 [==============================] - 2s 128us/sample - loss: 0.0917 - accuracy: 0.9722 - precision: 0.9738 - recall: 0.9061 - val_loss: 0.1728 - val_accuracy: 0.9410 - val_precision: 0.8066 - val_recall: 0.9773\n", - "Epoch 165/200\n", - "18458/18458 [==============================] - 2s 130us/sample - loss: 0.0893 - accuracy: 0.9721 - precision: 0.9689 - recall: 0.9107 - val_loss: 0.0724 - val_accuracy: 0.9805 - val_precision: 0.9465 - val_recall: 0.9698\n", - "Epoch 166/200\n", - "18458/18458 [==============================] - 2s 132us/sample - loss: 0.0803 - accuracy: 0.9741 - precision: 0.9719 - recall: 0.9164 - val_loss: 0.0907 - val_accuracy: 0.9701 - val_precision: 0.9244 - val_recall: 0.9471\n", - "Epoch 167/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0843 - accuracy: 0.9752 - precision: 0.9744 - recall: 0.9187 - val_loss: 0.0675 - val_accuracy: 0.9805 - val_precision: 0.9515 - val_recall: 0.9641\n" + "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 168/200\n", - "18458/18458 [==============================] - 2s 134us/sample - loss: 0.0885 - accuracy: 0.9733 - precision: 0.9723 - recall: 0.9128 - val_loss: 0.2243 - val_accuracy: 0.9207 - val_precision: 0.7464 - val_recall: 0.9905\n", - "Epoch 169/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0760 - accuracy: 0.9772 - precision: 0.9778 - recall: 0.9240 - val_loss: 0.1398 - val_accuracy: 0.9545 - val_precision: 0.8464 - val_recall: 0.9792\n", - "Epoch 170/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0936 - accuracy: 0.9717 - precision: 0.9716 - recall: 0.9063 - val_loss: 0.1020 - val_accuracy: 0.9649 - val_precision: 0.9870 - val_recall: 0.8582\n", - "Epoch 171/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0839 - accuracy: 0.9727 - precision: 0.9724 - recall: 0.9098 - val_loss: 0.0609 - val_accuracy: 0.9818 - val_precision: 0.9728 - val_recall: 0.9471\n", - "Epoch 172/200\n", - "18458/18458 [==============================] - 2s 131us/sample - loss: 0.0874 - accuracy: 0.9721 - precision: 0.9717 - recall: 0.9079 - val_loss: 0.0986 - val_accuracy: 0.9684 - val_precision: 0.8972 - val_recall: 0.9735\n", - "Epoch 173/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0808 - accuracy: 0.9742 - precision: 0.9724 - recall: 0.9164 - val_loss: 0.0894 - val_accuracy: 0.9727 - val_precision: 0.9146 - val_recall: 0.9716\n", - "Epoch 174/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0875 - accuracy: 0.9709 - precision: 0.9685 - recall: 0.9058 - val_loss: 0.1509 - val_accuracy: 0.9471 - val_precision: 0.9880 - val_recall: 0.7788\n", - "Epoch 175/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0845 - accuracy: 0.9733 - precision: 0.9739 - recall: 0.9111 - val_loss: 0.1013 - val_accuracy: 0.9666 - val_precision: 0.8897 - val_recall: 0.9754\n", - "Epoch 176/200\n", - "18458/18458 [==============================] - 3s 137us/sample - loss: 0.0906 - accuracy: 0.9718 - precision: 0.9721 - recall: 0.9063 - val_loss: 0.0845 - val_accuracy: 0.9762 - val_precision: 0.9293 - val_recall: 0.9698\n", - "Epoch 177/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0943 - accuracy: 0.9703 - precision: 0.9691 - recall: 0.9026 - val_loss: 0.1231 - val_accuracy: 0.9580 - val_precision: 0.8636 - val_recall: 0.9698\n", - "Epoch 178/200\n", - "18458/18458 [==============================] - 2s 127us/sample - loss: 0.0820 - accuracy: 0.9752 - precision: 0.9741 - recall: 0.9190 - val_loss: 0.0791 - val_accuracy: 0.9731 - val_precision: 0.9238 - val_recall: 0.9622\n", - "Epoch 179/200\n", - "18458/18458 [==============================] - 2s 128us/sample - loss: 0.0810 - accuracy: 0.9746 - precision: 0.9736 - recall: 0.9171 - val_loss: 0.0765 - val_accuracy: 0.9757 - val_precision: 0.9308 - val_recall: 0.9660\n", - "Epoch 180/200\n", - "18458/18458 [==============================] - 2s 130us/sample - loss: 0.0872 - accuracy: 0.9720 - precision: 0.9705 - recall: 0.9086 - val_loss: 0.0805 - val_accuracy: 0.9753 - val_precision: 0.9245 - val_recall: 0.9716\n", - "Epoch 181/200\n", - "18458/18458 [==============================] - 2s 130us/sample - loss: 0.0781 - accuracy: 0.9755 - precision: 0.9739 - recall: 0.9206 - val_loss: 0.1136 - val_accuracy: 0.9606 - val_precision: 0.8712 - val_recall: 0.9716\n", - "Epoch 182/200\n", - "18458/18458 [==============================] - 2s 133us/sample - loss: 0.0909 - accuracy: 0.9705 - precision: 0.9708 - recall: 0.9017 - val_loss: 0.0902 - val_accuracy: 0.9718 - val_precision: 0.9143 - val_recall: 0.9679\n", - "Epoch 183/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.0750 - accuracy: 0.9774 - precision: 0.9776 - recall: 0.9252 - val_loss: 0.0919 - val_accuracy: 0.9701 - val_precision: 0.8993 - val_recall: 0.9792\n", - "Epoch 184/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0709 - accuracy: 0.9789 - precision: 0.9778 - recall: 0.9316 - val_loss: 0.1111 - val_accuracy: 0.9640 - val_precision: 0.8805 - val_recall: 0.9754\n", - "Epoch 185/200\n", - "18458/18458 [==============================] - 3s 136us/sample - loss: 0.0779 - accuracy: 0.9749 - precision: 0.9741 - recall: 0.9176 - val_loss: 0.0660 - val_accuracy: 0.9796 - val_precision: 0.9382 - val_recall: 0.9754\n", - "Epoch 186/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0750 - accuracy: 0.9761 - precision: 0.9735 - recall: 0.9233 - val_loss: 0.1257 - val_accuracy: 0.9610 - val_precision: 0.8714 - val_recall: 0.9735\n", - "Epoch 187/200\n", - "18458/18458 [==============================] - 2s 135us/sample - loss: 0.0818 - accuracy: 0.9738 - precision: 0.9719 - 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val_loss: 0.0579 - val_accuracy: 0.9840 - val_precision: 0.9624 - val_recall: 0.9679\n", - "Epoch 192/200\n", - "18458/18458 [==============================] - 3s 141us/sample - loss: 0.0717 - accuracy: 0.9779 - precision: 0.9742 - recall: 0.9307 - val_loss: 0.1076 - val_accuracy: 0.9640 - val_precision: 0.8845 - val_recall: 0.9698\n", - "Epoch 193/200\n", - "18458/18458 [==============================] - 3s 141us/sample - loss: 0.0916 - accuracy: 0.9706 - precision: 0.9692 - recall: 0.9040 - val_loss: 0.0836 - val_accuracy: 0.9731 - val_precision: 0.9162 - val_recall: 0.9716\n", - "Epoch 194/200\n", - "18458/18458 [==============================] - 3s 142us/sample - loss: 0.0653 - accuracy: 0.9797 - precision: 0.9806 - recall: 0.9321 - val_loss: 0.0641 - val_accuracy: 0.9805 - val_precision: 0.9449 - val_recall: 0.9716\n", - "Epoch 195/200\n", - "18458/18458 [==============================] - 3s 140us/sample - loss: 0.0856 - accuracy: 0.9733 - precision: 0.9709 - recall: 0.9139 - val_loss: 0.1037 - 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What can we learn from it? Behind the scenes, $\\texttt{stella}$ creates a table of the history output by each model run. What's in your history depends on your metrics. So, for example, the default metrics are 'accuracy', 'precision', and 'recall', so in our $\\texttt{cnn.history_table}$ we see columns for each of these values from the training set as well as from the validation set (the columns beginning with 'val_')." + "" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Table length=200\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "
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270038761.001340.9110570648570.00021912424
47424873.001375.66335484330530.11497914
271999994.001328.23632141434380.0043182336
............
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231831315.011370.68592091031931.0
33837062.001372.11180698373372.2111965e-06
231017428.011361.11662942402430.999871
114794572.001357.46907674723180.012727063
139996019.001336.50187586954480.014939568
118327563.001369.85581146993420.45524606
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\n", " tic gt tpeak pred_s0002 \n", " float64 int64 float64 float32 \n", "----------- ----- ------------------ -------------\n", - " 55269690.0 0 1332.7376590932145 0.0053598885\n", - "201795667.0 1 1373.0537959924561 1.0\n", - " 80453023.0 0 1374.3399511708512 0.00066155713\n", - "161172848.0 0 1343.1752241130807 0.020634037\n", - "231122278.0 0 1340.0770763736205 0.020502886\n", - " 25132694.0 0 1355.0857187085387 0.009274018\n", - " 31740375.0 1 1351.193163007814 0.99998176\n", - " 31852565.0 0 1332.3193510129825 0.016599169\n", - "220557560.0 1 1345.0766190177035 0.99853826\n", - " 31740375.0 0 1380.7584138286325 9.603994e-05\n", + " 12377787.0 0 1379.61330097752 0.001399845\n", + "141334293.0 0 1377.2987565676303 0.011261474\n", + "229144144.0 0 1356.784838572032 0.0010426973\n", + " 79301672.0 0 1327.6742029269476 4.8696513e-09\n", + "273853024.0 0 1366.6144046520733 0.047348995\n", + " 5640393.0 1 1360.2597480858935 1.0\n", + "270038761.0 0 1340.911057064857 0.00021912424\n", + " 47424873.0 0 1375.6633548433053 0.11497914\n", + "271999994.0 0 1328.2363214143438 0.0043182336\n", " ... ... ... ...\n", - " 5727213.0 0 1377.555473552861 0.0006570381\n", - " 25132999.0 0 1375.6855870175875 0.057495333\n", - "176955267.0 1 1335.2901855122575 1.0\n", - "231910796.0 0 1365.9642105949472 0.0011209704\n", - "231831315.0 1 1370.6859209103193 1.0\n", - " 33837062.0 0 1372.1118069837337 2.2111965e-06\n", - "231017428.0 1 1361.1166294240243 0.999871\n", - "114794572.0 0 1357.4690767472318 0.012727063\n", - "139996019.0 0 1336.5018758695448 0.014939568\n", - "118327563.0 0 1369.8558114699342 0.45524606" + "220557792.0 0 1364.483728144287 0.009777189\n", + "220417260.0 0 1375.2285361001725 6.945784e-08\n", + "142082942.0 0 1365.1179144938264 0.0031967638\n", + "382520453.0 0 1370.4204334678072 0.005997822\n", + "219998026.0 0 1359.4707012258739 0.009141052\n", + "219212899.0 0 1337.1932484817178 0.020020813\n", + "229060052.0 0 1349.766484185095 0.001780488\n", + "220429097.0 0 1350.5960927463368 2.4389468e-07\n", + " 79491341.0 0 1334.4282229804037 0.00012549189\n", + "290027057.0 0 1343.7388348027748 6.224723e-08" ] }, - "execution_count": 31, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -916,19 +1227,17 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1047,30 +1352,35 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "//anaconda3/lib/python3.7/site-packages/lightkurve/lightcurvefile.py:47: LightkurveWarning: `LightCurveFile.header` is deprecated, please use `LightCurveFile.get_header()` instead.\n", - " LightkurveWarning)\n" + "/var/folders/pq/xr8rr6kj661dlbhfxj5vw0z00000gn/T/ipykernel_12727/930336468.py:3: LightkurveDeprecationWarning: The search_lightcurvefile function is deprecated and may be removed in a future version.\n", + " Use search_lightcurve() instead.\n", + " lc = search_lightcurvefile(target='tic62124646', mission='TESS')\n", + "/Users/bella/anaconda3/envs/stella_ENV/lib/python3.12/site-packages/lightkurve-2.4.2-py3.12.egg/lightkurve/search.py:424: LightkurveWarning: Warning: 11 files available to download. Only the first file has been downloaded. Please use `download_all()` or specify additional criteria (e.g. quarter, campaign, or sector) to limit your search.\n", + " warnings.warn(\n", + "/var/folders/pq/xr8rr6kj661dlbhfxj5vw0z00000gn/T/ipykernel_12727/930336468.py:4: LightkurveDeprecationWarning: The PDCSAP_FLUX function is deprecated and may be removed in a future version.\n", + " lc = lc.download().PDCSAP_FLUX\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1096,20 +1406,48 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [00:00<00:00, 1.29it/s]\n" + "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1168,7 +1504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index f319be3..50648c7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,8 +14,8 @@ pybind11 tornado matplotlib lightkurve>=2.0.0 -numpy -sklearn +numpy<2,>=1.21 +scikit-learn more-itertools scipy!=1.4.1 poetry diff --git a/stella/download_nn_set.py b/stella/download_nn_set.py index daf36ca..d13c53f 100644 --- a/stella/download_nn_set.py +++ b/stella/download_nn_set.py @@ -63,8 +63,7 @@ def download_catalog(self): Vizier.ROW_LIMIT = -1 - catalog_list = Vizier.find_catalogs('TESS flares sectors') - catalogs = Vizier.get_catalogs(catalog_list.keys()) + catalogs = Vizier.get_catalogs('J/AJ/159/60') self.flare_table = catalogs[1] self.flare_table.rename_column('_tab2_5', 'tpeak') diff --git a/stella/neural_network.py b/stella/neural_network.py index 92b41f8..007fb7e 100755 --- a/stella/neural_network.py +++ b/stella/neural_network.py @@ -89,6 +89,40 @@ def __init__(self, output_dir, ds=None, self.output_dir = output_dir + # Clean the training and validation data + self.clean_data() + + + def clean_data(self): + """ + Removes NaN values from the traning and validation data, and replaces the values + with zeros + """ + # Clean training data + valid_indices_train = ~np.isnan(self.ds.train_data).any(axis=(1, 2)) + self.ds.train_data = self.ds.train_data[valid_indices_train] + self.ds.train_labels = self.ds.train_labels[valid_indices_train] + + # Clean validation data + valid_indices_val = ~np.isnan(self.ds.val_data).any(axis=(1, 2)) + self.ds.val_data = self.ds.val_data[valid_indices_val] + self.ds.val_labels = self.ds.val_labels[valid_indices_val] + + # Clean additional validation attributes + self.ds.val_ids = self.ds.val_ids[valid_indices_val] + self.ds.val_tpeaks = self.ds.val_tpeaks[valid_indices_val] + + # Replace NaN values with zero + self.ds.train_data = np.nan_to_num(self.ds.train_data, nan=0.0) + self.ds.val_data = np.nan_to_num(self.ds.val_data, nan=0.0) + + # Replace NaN values with the mean of the corresponding feature + col_mean_train = np.nanmean(self.ds.train_data, axis=1, keepdims=True) + self.ds.train_data = np.where(np.isnan(self.ds.train_data), col_mean_train, self.ds.train_data) + + col_mean_val = np.nanmean(self.ds.val_data, axis=1, keepdims=True) + self.ds.val_data = np.where(np.isnan(self.ds.val_data), col_mean_val, self.ds.val_data) + def create_model(self, seed): """ @@ -117,7 +151,7 @@ def create_model(self, seed): # CONVOLUTIONAL LAYERS model.add(tf.keras.layers.Conv1D(filters=filter1, kernel_size=7, activation='relu', padding='same', - input_shape=(self.cadences, 1))) + input_shape=(int(self.cadences), 1))) model.add(tf.keras.layers.MaxPooling1D(pool_size=2)) model.add(tf.keras.layers.Dropout(dropout)) model.add(tf.keras.layers.Conv1D(filters=filter2, kernel_size=3, @@ -517,30 +551,38 @@ def identify_gaps(t): model = keras.models.load_model(modelname) - self.model = model +# Create and compile the model + input_shape = model.input_shape + + input_layer = keras.layers.Input(shape=input_shape[1:]) + new_model = keras.models.Model(inputs=input_layer, outputs=model(input_layer)) + + new_model.compile(optimizer = self.optimizer, + loss = self.loss, + metrics = self.metrics) + + self.model = new_model # GETS REQUIRED INPUT SHAPE FROM MODEL cadences = model.input.shape[1] + print(cadences) cad_pad = cadences/2 # REFORMATS FOR A SINGLE LIGHT CURVE PASSED IN - try: - times[0][0] - except: - times = [times] + if not isinstance(times[0], np.ndarray): + times = [times] fluxes = [fluxes] - errs = [errs] + errs = [errs] - predictions = [] pred_t, pred_f, pred_e = [], [], [] for j in tqdm(range(len(times))): - time = times[j] + 0.0 - lc = fluxes[j] / np.nanmedian(fluxes[j]) # MUST BE NORMALIZED - err = errs[j] + 0.0 + time = np.array(times[j], dtype=float) + lc = np.array(fluxes[j], dtype=float) / np.nanmedian(fluxes[j]) # MUST BE NORMALIZED + err = np.array(errs[j], dtype=float) + 0.0 - q = ( (np.isnan(time) == False) & (np.isnan(lc) == False)) + q = (~np.isnan(time)) & (~np.isnan(lc)) time, lc, err = time[q], lc[q], err[q] # APPENDS MASKED LIGHT CURVES TO KEEP TRACK OF @@ -569,4 +611,4 @@ def identify_gaps(t): self.predict_time = np.array(pred_t) self.predict_flux = np.array(pred_f) self.predict_err = np.array(pred_e) - self.predictions = np.array(predictions) + self.predictions = np.array(predictions) \ No newline at end of file