diff --git a/README.odt b/README.odt
new file mode 100644
index 0000000..c9cf808
Binary files /dev/null and b/README.odt differ
diff --git a/notebooks/v2_DenseNet_dataaug.ipynb b/notebooks/v2_DenseNet_dataaug.ipynb
new file mode 100644
index 0000000..b8f11d3
--- /dev/null
+++ b/notebooks/v2_DenseNet_dataaug.ipynb
@@ -0,0 +1,1065 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "35472663-a1e6-4228-a178-5006b2effe76",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d43896a6-ec52-44e5-a44e-cd7fabd18f18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DATA_RAW_PATH = \"../data/RAW/RT-BENE.zip\" \n",
+ "DATA_INTER_PATH = \"/opt/data/Ubuntu/projects/RT-BENE/data/intermediate\"\n",
+ "DATA_PATH = f\"{DATA_INTER_PATH}/RT-BENE\"\n",
+ "if not os.path.exists(DATA_PATH):\n",
+ " !unzip -q $DATA_RAW_PATH -d $DATA_INTER_PATH"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "da68634c-20ed-4ada-967b-a10f34029522",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import tensorflow as tf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3cb3c96d-687c-483c-a0d3-1144962d32b9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " blink_id | \n",
+ " left_eye | \n",
+ " right_eye | \n",
+ " video | \n",
+ " blink | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0 | \n",
+ " 0_left_000001_rgb.png | \n",
+ " 0_right_000001_rgb.png | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 1 | \n",
+ " 0_left_000002_rgb.png | \n",
+ " 0_right_000002_rgb.png | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2 | \n",
+ " 0_left_000003_rgb.png | \n",
+ " 0_right_000003_rgb.png | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 3 | \n",
+ " 0_left_000004_rgb.png | \n",
+ " 0_right_000004_rgb.png | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " 0_left_000005_rgb.png | \n",
+ " 0_right_000005_rgb.png | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 107345 | \n",
+ " 107345 | \n",
+ " 16_left_009059_rgb.png | \n",
+ " 16_right_009059_rgb.png | \n",
+ " 16 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 107346 | \n",
+ " 107346 | \n",
+ " 16_left_009060_rgb.png | \n",
+ " 16_right_009060_rgb.png | \n",
+ " 16 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 107347 | \n",
+ " 107347 | \n",
+ " 16_left_009061_rgb.png | \n",
+ " 16_right_009061_rgb.png | \n",
+ " 16 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 107348 | \n",
+ " 107348 | \n",
+ " 16_left_009062_rgb.png | \n",
+ " 16_right_009062_rgb.png | \n",
+ " 16 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 107349 | \n",
+ " 107349 | \n",
+ " 16_left_009063_rgb.png | \n",
+ " 16_right_009063_rgb.png | \n",
+ " 16 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
107350 rows × 5 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Images in video | \n",
+ " % blink frames | \n",
+ "
\n",
+ " \n",
+ " | video | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 12865 | \n",
+ " 7.236689 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 8671 | \n",
+ " 1.476185 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 8702 | \n",
+ " 9.066881 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 3205 | \n",
+ " 5.210608 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4750 | \n",
+ " 2.736842 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 5355 | \n",
+ " 2.054155 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 1857 | \n",
+ " 8.023694 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 6108 | \n",
+ " 7.514735 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 4210 | \n",
+ " 1.068884 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 16559 | \n",
+ " 2.131771 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 12817 | \n",
+ " 5.399079 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 935 | \n",
+ " 2.459893 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 9586 | \n",
+ " 3.077405 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 5371 | \n",
+ " 4.002979 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 1810 | \n",
+ " 1.602210 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 4549 | \n",
+ " 0.923280 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for data in it:\n",
+ " #print(data[1])\n",
+ " fig,axs = plt.subplots(10,2, figsize=(4,20))\n",
+ " for i in range(10):\n",
+ " axs[i,0].imshow(data[0][0][i])\n",
+ " axs[i,1].imshow(data[0][1][i])\n",
+ " fig.tight_layout()\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "94104655-132f-4517-b98e-5c36107623c9",
+ "metadata": {},
+ "source": [
+ "### Create model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "0ccc5e52-356e-41cc-9434-448d84f4189c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow import keras\n",
+ "from tensorflow.keras import regularizers, initializers, layers\n",
+ "from tensorflow.keras.applications import DenseNet121"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "c7dd581f-c27d-4bc3-9084-d45cc726e623",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"densenet_all_data_rebalanced_aug_dropout\"\n",
+ "__________________________________________________________________________________________________\n",
+ " Layer (type) Output Shape Param # Connected to \n",
+ "==================================================================================================\n",
+ " input_1 (InputLayer) [(None, 36, 60, 3)] 0 [] \n",
+ " \n",
+ " input_2 (InputLayer) [(None, 36, 60, 3)] 0 [] \n",
+ " \n",
+ " featext_left (Functional) (None, 1, 1, 1024) 7037504 ['input_1[0][0]'] \n",
+ " \n",
+ " featext_right (Functional) (None, 1, 1, 1024) 7037504 ['input_2[0][0]'] \n",
+ " \n",
+ " concatenate (Concatenate) (None, 1, 1, 2048) 0 ['featext_left[0][0]', \n",
+ " 'featext_right[0][0]'] \n",
+ " \n",
+ " flatten (Flatten) (None, 2048) 0 ['concatenate[0][0]'] \n",
+ " \n",
+ " dense (Dense) (None, 256) 524544 ['flatten[0][0]'] \n",
+ " \n",
+ " dropout (Dropout) (None, 256) 0 ['dense[0][0]'] \n",
+ " \n",
+ " dense_1 (Dense) (None, 128) 32896 ['dropout[0][0]'] \n",
+ " \n",
+ " dropout_1 (Dropout) (None, 128) 0 ['dense_1[0][0]'] \n",
+ " \n",
+ " dense_2 (Dense) (None, 2) 258 ['dropout_1[0][0]'] \n",
+ " \n",
+ "==================================================================================================\n",
+ "Total params: 14,632,706\n",
+ "Trainable params: 14,465,410\n",
+ "Non-trainable params: 167,296\n",
+ "__________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "left_eye = keras.Input(shape=(36, 60, 3))\n",
+ "right_eye = keras.Input(shape=(36, 60, 3))\n",
+ "featext_left = DenseNet121(weights=\"imagenet\", include_top=False, input_shape=(36, 60, 3))\n",
+ "featext_left._name = \"featext_left\"\n",
+ "for layer in featext_left.layers:\n",
+ " layer.kernel_regularizer=regularizers.l1(1e-3),\n",
+ "featext_right = DenseNet121(weights=\"imagenet\", include_top=False, input_shape=(36, 60, 3))\n",
+ "featext_right._name = \"featext_right\"\n",
+ "for layer in featext_right.layers:\n",
+ " layer.kernel_regularizer=regularizers.l1(1e-3),\n",
+ "left_feat_extractor = featext_left(left_eye)\n",
+ "right_feat_extractor = featext_right(right_eye)\n",
+ "concat = layers.Concatenate()([left_feat_extractor, right_feat_extractor])\n",
+ "flat = layers.Flatten()(concat)\n",
+ "dense_1 = layers.Dense(256, activation=\"relu\",\n",
+ " kernel_regularizer=regularizers.l2(1e-5),\n",
+ " kernel_initializer=initializers.GlorotNormal)(flat)\n",
+ "do_1 = layers.Dropout(0.2)(dense_1)\n",
+ "dense_2 = layers.Dense(128, activation=\"relu\",\n",
+ " kernel_regularizer=regularizers.l2(1e-5),\n",
+ " kernel_initializer=initializers.GlorotNormal)(do_1)\n",
+ "do_2 = layers.Dropout(0.2)(dense_2)\n",
+ "out = layers.Dense(2, activation=\"softmax\")(do_2)\n",
+ "\n",
+ "model = keras.Model(inputs=([left_eye, right_eye]), outputs=out, name=\"densenet_all_data_rebalanced_aug_dropout\")\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "5246c770-00a1-45de-a158-e019f4743882",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from keras import backend as K\n",
+ "\n",
+ "def recall_m(y_true, y_pred):\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
+ " recall = true_positives / (possible_positives + K.epsilon())\n",
+ " return recall\n",
+ "\n",
+ "def precision_m(y_true, y_pred):\n",
+ " true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
+ " predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
+ " precision = true_positives / (predicted_positives + K.epsilon())\n",
+ " return precision\n",
+ "\n",
+ "def F1_score(y_true, y_pred):\n",
+ " precision = precision_m(y_true, y_pred)\n",
+ " recall = recall_m(y_true, y_pred)\n",
+ " return 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
+ " \n",
+ "model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-3), \n",
+ " loss=keras.losses.CategoricalCrossentropy(), \n",
+ " metrics=[keras.metrics.Accuracy(), keras.metrics.Precision(), keras.metrics.Recall(), F1_score])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "2ccf3233-fa62-48fe-b367-6135623d9f86",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Model failed to serialize as JSON. Ignoring... Cannot serialize (,) since it doesn't implement `get_config()`, and also doesn\t have `__name__`\n",
+ "Epoch 1/40\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-02-06 16:56:49.902876: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8204\n",
+ "2022-02-06 16:56:51.517747: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+ "2022-02-06 16:56:55.352128: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "134/134 [==============================] - 100s 575ms/step - loss: 0.1430 - accuracy: 0.0050 - precision: 0.9458 - recall: 0.9458 - F1_score: 0.9460 - val_loss: 3.1240 - val_accuracy: 0.0205 - val_precision: 0.6792 - val_recall: 0.6792 - val_F1_score: 0.6792 - lr: 0.0010\n",
+ "Epoch 2/40\n",
+ "134/134 [==============================] - 79s 594ms/step - loss: 0.0686 - accuracy: 0.0167 - precision: 0.9779 - recall: 0.9779 - F1_score: 0.9776 - val_loss: 0.5844 - val_accuracy: 0.1583 - val_precision: 0.8812 - val_recall: 0.8812 - val_F1_score: 0.8812 - lr: 0.0010\n",
+ "Epoch 3/40\n",
+ "134/134 [==============================] - 76s 570ms/step - loss: 0.0519 - accuracy: 0.0310 - precision: 0.9837 - recall: 0.9837 - F1_score: 0.9839 - val_loss: 0.0364 - val_accuracy: 0.0968 - val_precision: 0.9879 - val_recall: 0.9879 - val_F1_score: 0.9879 - lr: 0.0010\n",
+ "Epoch 4/40\n",
+ "134/134 [==============================] - 74s 556ms/step - loss: 0.0466 - accuracy: 0.0349 - precision: 0.9840 - recall: 0.9840 - F1_score: 0.9843 - val_loss: 0.0610 - val_accuracy: 0.1652 - val_precision: 0.9833 - val_recall: 0.9833 - val_F1_score: 0.9833 - lr: 0.0010\n",
+ "Epoch 5/40\n",
+ "134/134 [==============================] - 83s 626ms/step - loss: 0.0548 - accuracy: 0.0356 - precision: 0.9829 - recall: 0.9829 - F1_score: 0.9830 - val_loss: 199.9079 - val_accuracy: 0.0387 - val_precision: 0.0427 - val_recall: 0.0427 - val_F1_score: 0.0427 - lr: 0.0010\n",
+ "Epoch 6/40\n",
+ "134/134 [==============================] - 76s 570ms/step - loss: 0.0937 - accuracy: 0.0080 - precision: 0.9684 - recall: 0.9684 - F1_score: 0.9673 - val_loss: 0.0965 - val_accuracy: 0.0573 - val_precision: 0.9887 - val_recall: 0.9887 - val_F1_score: 0.9887 - lr: 0.0010\n",
+ "Epoch 7/40\n",
+ "134/134 [==============================] - 81s 606ms/step - loss: 0.0482 - accuracy: 0.0268 - precision: 0.9838 - recall: 0.9838 - F1_score: 0.9840 - val_loss: 0.0941 - val_accuracy: 0.0665 - val_precision: 0.9767 - val_recall: 0.9767 - val_F1_score: 0.9767 - lr: 0.0010\n",
+ "Epoch 8/40\n",
+ "134/134 [==============================] - 77s 577ms/step - loss: 0.0556 - accuracy: 0.0315 - precision: 0.9821 - recall: 0.9821 - F1_score: 0.9801 - val_loss: 5.4431 - val_accuracy: 0.0000e+00 - val_precision: 0.2193 - val_recall: 0.2193 - val_F1_score: 0.2193 - lr: 0.0010\n",
+ "Epoch 9/40\n",
+ "134/134 [==============================] - 77s 575ms/step - loss: 0.0441 - accuracy: 0.0081 - precision: 0.9859 - recall: 0.9859 - F1_score: 0.9860 - val_loss: 0.0297 - val_accuracy: 0.0000e+00 - val_precision: 0.9917 - val_recall: 0.9917 - val_F1_score: 0.9917 - lr: 2.0000e-04\n",
+ "Epoch 10/40\n",
+ "134/134 [==============================] - 74s 558ms/step - loss: 0.0307 - accuracy: 0.0197 - precision: 0.9907 - recall: 0.9907 - F1_score: 0.9907 - val_loss: 0.0308 - val_accuracy: 3.2066e-04 - val_precision: 0.9920 - val_recall: 0.9920 - val_F1_score: 0.9920 - lr: 2.0000e-04\n",
+ "Epoch 11/40\n",
+ "134/134 [==============================] - 67s 502ms/step - loss: 0.0276 - accuracy: 0.0321 - precision: 0.9917 - recall: 0.9917 - F1_score: 0.9917 - val_loss: 0.0436 - val_accuracy: 0.0022 - val_precision: 0.9896 - val_recall: 0.9896 - val_F1_score: 0.9896 - lr: 2.0000e-04\n",
+ "Epoch 12/40\n",
+ "134/134 [==============================] - 77s 581ms/step - loss: 0.0229 - accuracy: 0.0488 - precision: 0.9928 - recall: 0.9928 - F1_score: 0.9927 - val_loss: 0.0382 - val_accuracy: 0.0394 - val_precision: 0.9925 - val_recall: 0.9925 - val_F1_score: 0.9925 - lr: 2.0000e-04\n",
+ "Epoch 13/40\n",
+ "134/134 [==============================] - 75s 566ms/step - loss: 0.0223 - accuracy: 0.0612 - precision: 0.9931 - recall: 0.9931 - F1_score: 0.9932 - val_loss: 0.0574 - val_accuracy: 0.0099 - val_precision: 0.9866 - val_recall: 0.9866 - val_F1_score: 0.9866 - lr: 2.0000e-04\n",
+ "Epoch 14/40\n",
+ "134/134 [==============================] - 74s 557ms/step - loss: 0.0214 - accuracy: 0.0760 - precision: 0.9937 - recall: 0.9937 - F1_score: 0.9937 - val_loss: 0.0436 - val_accuracy: 0.0194 - val_precision: 0.9907 - val_recall: 0.9907 - val_F1_score: 0.9907 - lr: 2.0000e-04\n",
+ "Epoch 15/40\n",
+ "134/134 [==============================] - 76s 572ms/step - loss: 0.0157 - accuracy: 0.0646 - precision: 0.9957 - recall: 0.9957 - F1_score: 0.9958 - val_loss: 0.0462 - val_accuracy: 0.0025 - val_precision: 0.9908 - val_recall: 0.9908 - val_F1_score: 0.9908 - lr: 4.0000e-05\n",
+ "Epoch 16/40\n",
+ "134/134 [==============================] - 68s 512ms/step - loss: 0.0136 - accuracy: 0.0779 - precision: 0.9964 - recall: 0.9964 - F1_score: 0.9964 - val_loss: 0.0436 - val_accuracy: 0.0045 - val_precision: 0.9915 - val_recall: 0.9915 - val_F1_score: 0.9915 - lr: 4.0000e-05\n",
+ "Epoch 17/40\n",
+ "134/134 [==============================] - 77s 576ms/step - loss: 0.0128 - accuracy: 0.0890 - precision: 0.9967 - recall: 0.9967 - F1_score: 0.9968 - val_loss: 0.0461 - val_accuracy: 0.0099 - val_precision: 0.9916 - val_recall: 0.9916 - val_F1_score: 0.9916 - lr: 4.0000e-05\n",
+ "Epoch 18/40\n",
+ "134/134 [==============================] - 74s 554ms/step - loss: 0.0115 - accuracy: 0.0974 - precision: 0.9971 - recall: 0.9971 - F1_score: 0.9971 - val_loss: 0.0454 - val_accuracy: 0.0170 - val_precision: 0.9919 - val_recall: 0.9919 - val_F1_score: 0.9919 - lr: 4.0000e-05\n",
+ "Epoch 19/40\n",
+ "134/134 [==============================] - 77s 576ms/step - loss: 0.0127 - accuracy: 0.1021 - precision: 0.9971 - recall: 0.9971 - F1_score: 0.9971 - val_loss: 0.0462 - val_accuracy: 0.0327 - val_precision: 0.9922 - val_recall: 0.9922 - val_F1_score: 0.9922 - lr: 4.0000e-05\n",
+ "Epoch 20/40\n",
+ "134/134 [==============================] - 74s 554ms/step - loss: 0.0095 - accuracy: 0.1068 - precision: 0.9980 - recall: 0.9980 - F1_score: 0.9981 - val_loss: 0.0473 - val_accuracy: 0.0347 - val_precision: 0.9920 - val_recall: 0.9920 - val_F1_score: 0.9920 - lr: 8.0000e-06\n",
+ "Epoch 21/40\n",
+ "134/134 [==============================] - 76s 569ms/step - loss: 0.0105 - accuracy: 0.1083 - precision: 0.9977 - recall: 0.9977 - F1_score: 0.9977 - val_loss: 0.0491 - val_accuracy: 0.0369 - val_precision: 0.9919 - val_recall: 0.9919 - val_F1_score: 0.9919 - lr: 8.0000e-06\n",
+ "Epoch 22/40\n",
+ "134/134 [==============================] - 74s 557ms/step - loss: 0.0104 - accuracy: 0.1124 - precision: 0.9978 - recall: 0.9978 - F1_score: 0.9978 - val_loss: 0.0478 - val_accuracy: 0.0450 - val_precision: 0.9923 - val_recall: 0.9923 - val_F1_score: 0.9923 - lr: 8.0000e-06\n",
+ "Epoch 23/40\n",
+ "134/134 [==============================] - 70s 521ms/step - loss: 0.0087 - accuracy: 0.1139 - precision: 0.9983 - recall: 0.9983 - F1_score: 0.9984 - val_loss: 0.0477 - val_accuracy: 0.0496 - val_precision: 0.9924 - val_recall: 0.9924 - val_F1_score: 0.9924 - lr: 8.0000e-06\n",
+ "Epoch 24/40\n",
+ "134/134 [==============================] - 74s 558ms/step - loss: 0.0112 - accuracy: 0.1173 - precision: 0.9974 - recall: 0.9974 - F1_score: 0.9975 - val_loss: 0.0488 - val_accuracy: 0.0558 - val_precision: 0.9921 - val_recall: 0.9921 - val_F1_score: 0.9921 - lr: 8.0000e-06\n",
+ "Epoch 25/40\n",
+ "134/134 [==============================] - 72s 533ms/step - loss: 0.0091 - accuracy: 0.1177 - precision: 0.9982 - recall: 0.9982 - F1_score: 0.9982 - val_loss: 0.0482 - val_accuracy: 0.0579 - val_precision: 0.9924 - val_recall: 0.9924 - val_F1_score: 0.9924 - lr: 1.6000e-06\n",
+ "Epoch 26/40\n",
+ "134/134 [==============================] - 68s 507ms/step - loss: 0.0093 - accuracy: 0.1177 - precision: 0.9981 - recall: 0.9981 - F1_score: 0.9982 - val_loss: 0.0482 - val_accuracy: 0.0596 - val_precision: 0.9926 - val_recall: 0.9926 - val_F1_score: 0.9926 - lr: 1.6000e-06\n",
+ "Epoch 27/40\n",
+ "125/134 [==========================>...] - ETA: 3s - loss: 0.0107 - accuracy: 0.1184 - precision: 0.9974 - recall: 0.9974 - F1_score: 0.9975"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m tensorboard_callback \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mTensorBoard(log_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./logs/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrun_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, update_freq\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,)\n\u001b[1;32m 13\u001b[0m reduce_lr_callback \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mReduceLROnPlateau(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[1;32m 14\u001b[0m patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mval_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mmodel_checkpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtensorboard_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_lr_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 66\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m 1378\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 1379\u001b[0m epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m 1380\u001b[0m step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m 1381\u001b[0m batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 1382\u001b[0m _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 1383\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m 1386\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 945\u001b[0m \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 946\u001b[0m \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 949\u001b[0m \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2956\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2953\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m 2954\u001b[0m (graph_function,\n\u001b[1;32m 2955\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2957\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1853\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1849\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1850\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1851\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1852\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1853\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1854\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1855\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1856\u001b[0m args,\n\u001b[1;32m 1857\u001b[0m possible_gradient_type,\n\u001b[1;32m 1858\u001b[0m executing_eagerly)\n\u001b[1;32m 1859\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
+ "File \u001b[0;32m~/.local/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "\n",
+ "run_id = f'{model.name}-{datetime.now().strftime(\"%m-%H%M%S\")}'\n",
+ "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
+ " filepath=f\"models/{run_id}/\" + \"{epoch:02d}-{val_F1_score:.2f}\",\n",
+ " save_weights_only=True,\n",
+ " monitor='val_F1_score',\n",
+ " mode='max',\n",
+ " save_best_only=False)\n",
+ "\n",
+ "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=f\"./logs/{run_id}\", update_freq=100,)\n",
+ "\n",
+ "reduce_lr_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
+ " patience=5)\n",
+ "\n",
+ "history = model.fit(train_dataset, \n",
+ " validation_data=val_dataset, \n",
+ " epochs=40, \n",
+ " steps_per_epoch=int(train_data.shape[0]/(2*batch_size)), \n",
+ " validation_steps=int(val_data.shape[0]/batch_size),\n",
+ " callbacks=[model_checkpoint_callback, tensorboard_callback, reduce_lr_callback])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "f8a2f845-b418-48e1-bfcc-2f96dccf675f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.load_weights(\"models/densenet_all_data_rebalanced_aug_dropout-02-165635/26-0.99\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f7a994f3-5e62-49fe-b010-cd942a7bbaa7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-02-13 20:10:50.384532: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8204\n",
+ "2022-02-13 20:10:51.969706: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
+ "2022-02-13 20:10:55.708456: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "84/84 [==============================] - 30s 228ms/step - loss: 0.0451 - accuracy: 0.0566 - precision: 0.9925 - recall: 0.9925 - F1_score: 0.9926\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = model.evaluate(test_dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f2e0a960-fdd7-459c-84cd-d6165ad917e0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/v3.ipynb b/notebooks/v3.ipynb
deleted file mode 100644
index e130b1a..0000000
--- a/notebooks/v3.ipynb
+++ /dev/null
@@ -1,1299 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "35472663-a1e6-4228-a178-5006b2effe76",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "d43896a6-ec52-44e5-a44e-cd7fabd18f18",
- "metadata": {},
- "outputs": [],
- "source": [
- "DATA_RAW_PATH = \"../data/RAW/RT-BENE.zip\" \n",
- "DATA_INTER_PATH = \"/opt/data/Ubuntu/projects/RT-BENE/data/intermediate\"\n",
- "DATA_PATH = f\"{DATA_INTER_PATH}/RT-BENE\"\n",
- "if not os.path.exists(DATA_PATH):\n",
- " !unzip -q $DATA_RAW_PATH -d $DATA_INTER_PATH"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "da68634c-20ed-4ada-967b-a10f34029522",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import tensorflow as tf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "3cb3c96d-687c-483c-a0d3-1144962d32b9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " blink_id | \n",
- " left_eye | \n",
- " right_eye | \n",
- " video | \n",
- " blink | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " 0 | \n",
- " 0_left_000001_rgb.png | \n",
- " 0_right_000001_rgb.png | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " 1 | \n",
- " 0_left_000002_rgb.png | \n",
- " 0_right_000002_rgb.png | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " 2 | \n",
- " 0_left_000003_rgb.png | \n",
- " 0_right_000003_rgb.png | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 3 | \n",
- " 0_left_000004_rgb.png | \n",
- " 0_right_000004_rgb.png | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 4 | \n",
- " 0_left_000005_rgb.png | \n",
- " 0_right_000005_rgb.png | \n",
- " 0 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 107345 | \n",
- " 107345 | \n",
- " 16_left_009059_rgb.png | \n",
- " 16_right_009059_rgb.png | \n",
- " 16 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 107346 | \n",
- " 107346 | \n",
- " 16_left_009060_rgb.png | \n",
- " 16_right_009060_rgb.png | \n",
- " 16 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 107347 | \n",
- " 107347 | \n",
- " 16_left_009061_rgb.png | \n",
- " 16_right_009061_rgb.png | \n",
- " 16 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 107348 | \n",
- " 107348 | \n",
- " 16_left_009062_rgb.png | \n",
- " 16_right_009062_rgb.png | \n",
- " 16 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | 107349 | \n",
- " 107349 | \n",
- " 16_left_009063_rgb.png | \n",
- " 16_right_009063_rgb.png | \n",
- " 16 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
107350 rows × 5 columns
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " Images in video | \n",
- " % blink frames | \n",
- "
\n",
- " \n",
- " | video | \n",
- " | \n",
- " | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 0 | \n",
- " 12865 | \n",
- " 7.236689 | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " 8671 | \n",
- " 1.476185 | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " 8702 | \n",
- " 9.066881 | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 3205 | \n",
- " 5.210608 | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 4750 | \n",
- " 2.736842 | \n",
- "
\n",
- " \n",
- " | 5 | \n",
- " 5355 | \n",
- " 2.054155 | \n",
- "
\n",
- " \n",
- " | 7 | \n",
- " 1857 | \n",
- " 8.023694 | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " 6108 | \n",
- " 7.514735 | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " 4210 | \n",
- " 1.068884 | \n",
- "
\n",
- " \n",
- " | 10 | \n",
- " 16559 | \n",
- " 2.131771 | \n",
- "
\n",
- " \n",
- " | 11 | \n",
- " 12817 | \n",
- " 5.399079 | \n",
- "
\n",
- " \n",
- " | 12 | \n",
- " 935 | \n",
- " 2.459893 | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " 9586 | \n",
- " 3.077405 | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " 5371 | \n",
- " 4.002979 | \n",
- "
\n",
- " \n",
- " | 15 | \n",
- " 1810 | \n",
- " 1.602210 | \n",
- "
\n",
- " \n",
- " | 16 | \n",
- " 4549 | \n",
- " 0.923280 | \n",
- "
\n",
- " \n",
- "
\n",
- "