main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from my_metrics import *\n",
    "from sub_network import *\n",
    "from data_loader import *\n",
    "from plotting_utils import *\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dir1 = r\"D:\\ib811_database\\S1_IEEE\"\n",
    "image_dir2 = r\"D:\\ib811_database\\S2_IEEE\"\n",
    "\n",
    "images1 = load_images(image_dir1)\n",
    "images2 = load_images(image_dir2)\n",
    "\n",
    "pairs_1, pairs_2, labels = create_labels(images1, images2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_pairs_1.shape=(512, 256, 256)\n",
      "test_pairs_1.shape=(128, 256, 256)\n",
      "train_labels.shape=(512,)\n",
      "test_labels.shape=(128,)\n"
     ]
    }
   ],
   "source": [
    "train_pairs_1, test_pairs_1, train_pairs_2, test_pairs_2, train_labels, test_labels = train_test_split(\n",
    "    pairs_1, pairs_2, labels, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "print(\"train_pairs_1.shape={}\".format(train_pairs_1.shape))\n",
    "print(\"test_pairs_1.shape={}\".format(test_pairs_1.shape))\n",
    "print(\"train_labels.shape={}\".format(train_labels.shape))\n",
    "print(\"test_labels.shape={}\".format(test_labels.shape))\n",
    "\n",
    "trainPairs_0 = np.array(train_pairs_1).reshape(-1, 256, 256, 1)\n",
    "trainPairs_1 = np.array(train_pairs_2).reshape(-1, 256, 256, 1)\n",
    "testPairs_0 = np.array(test_pairs_1).reshape(-1, 256, 256, 1)\n",
    "testPairs_1 = np.array(test_pairs_2).reshape(-1, 256, 256, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 256, 256, 1)]     0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 256, 256, 32)      320       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 256, 256, 32)     128       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 128, 128, 32)     0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128, 128, 32)      0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 128, 128, 64)      18496     \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 128, 128, 64)     256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 64, 64, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64, 64, 64)        0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 64, 64, 128)       73856     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 64, 64, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 32, 32, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 32, 32, 128)       0         \n",
      "                                                                 \n",
      " global_average_pooling2d (G  (None, 128)              0         \n",
      " lobalAveragePooling2D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               16512     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 110,080\n",
      "Trainable params: 109,632\n",
      "Non-trainable params: 448\n",
      "_________________________________________________________________\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 256, 256, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)           [(None, 256, 256, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " model (Functional)             (None, 128)          110080      ['input_1[0][0]',                \n",
      "                                                                  'input_2[0][0]']                \n",
      "                                                                                                  \n",
      " euclidean_distance (EuclideanD  (None, 1)           0           ['model[0][0]',                  \n",
      " istance)                                                         'model[1][0]']                  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 1)            2           ['euclidean_distance[0][0]']     \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 110,082\n",
      "Trainable params: 109,634\n",
      "Non-trainable params: 448\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#主網路架構\n",
    "\n",
    "inputShape = (256, 256, 1)\n",
    "\n",
    "inputA = Input(inputShape)\n",
    "inputB = Input(inputShape)\n",
    "\n",
    "featureExtractor = sub_network(inputShape)\n",
    "\n",
    "featsA = featureExtractor(inputA)\n",
    "featsB = featureExtractor(inputB)\n",
    "\n",
    "distance = EuclideanDistance()([featsA, featsB])\n",
    "\n",
    "outputs = Dense(1, activation=\"sigmoid\")(distance)\n",
    "\n",
    "model = Model(inputs=[inputA, inputB], outputs=outputs)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6722\n",
      "Epoch 1: val_loss improved from inf to 0.12507, saving model to snn.hdf5\n",
      "32/32 [==============================] - 8s 132ms/step - loss: 0.1206 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6722 - val_loss: 0.1251 - val_accuracy: 0.4375 - val_precision: 0.4375 - val_recall: 1.0000 - val_f1_score: 0.5905\n",
      "Epoch 2/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.1129 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6718\n",
      "Epoch 2: val_loss improved from 0.12507 to 0.11849, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.1129 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6718 - val_loss: 0.1185 - val_accuracy: 0.5156 - val_precision: 0.4760 - val_recall: 1.0000 - val_f1_score: 0.6276\n",
      "Epoch 3/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.1027 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6673\n",
      "Epoch 3: val_loss improved from 0.11849 to 0.11467, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.1027 - accuracy: 0.5156 - precision: 0.5156 - recall: 1.0000 - f1_score: 0.6673 - val_loss: 0.1147 - val_accuracy: 0.5859 - val_precision: 0.5076 - val_recall: 1.0000 - val_f1_score: 0.6567\n",
      "Epoch 4/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.1001 - accuracy: 0.5195 - precision: 0.5176 - recall: 1.0000 - f1_score: 0.6741\n",
      "Epoch 4: val_loss improved from 0.11467 to 0.11013, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 122ms/step - loss: 0.1001 - accuracy: 0.5195 - precision: 0.5176 - recall: 1.0000 - f1_score: 0.6741 - val_loss: 0.1101 - val_accuracy: 0.6562 - val_precision: 0.5524 - val_recall: 0.9886 - val_f1_score: 0.6934\n",
      "Epoch 5/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.5488 - precision: 0.5327 - recall: 1.0000 - f1_score: 0.6866\n",
      "Epoch 5: val_loss improved from 0.11013 to 0.10511, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0982 - accuracy: 0.5488 - precision: 0.5327 - recall: 1.0000 - f1_score: 0.6866 - val_loss: 0.1051 - val_accuracy: 0.7344 - val_precision: 0.6068 - val_recall: 0.9792 - val_f1_score: 0.7388\n",
      "Epoch 6/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0958 - accuracy: 0.5762 - precision: 0.5471 - recall: 1.0000 - f1_score: 0.6987\n",
      "Epoch 6: val_loss improved from 0.10511 to 0.09983, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 122ms/step - loss: 0.0958 - accuracy: 0.5762 - precision: 0.5471 - recall: 1.0000 - f1_score: 0.6987 - val_loss: 0.0998 - val_accuracy: 0.6953 - val_precision: 0.5795 - val_recall: 0.9792 - val_f1_score: 0.7154\n",
      "Epoch 7/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0946 - accuracy: 0.6543 - precision: 0.6002 - recall: 0.9969 - f1_score: 0.7408\n",
      "Epoch 7: val_loss did not improve from 0.09983\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0946 - accuracy: 0.6543 - precision: 0.6002 - recall: 0.9969 - f1_score: 0.7408 - val_loss: 0.1039 - val_accuracy: 0.6641 - val_precision: 0.5623 - val_recall: 0.9636 - val_f1_score: 0.6914\n",
      "Epoch 8/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0897 - accuracy: 0.6250 - precision: 0.5817 - recall: 0.9924 - f1_score: 0.7240\n",
      "Epoch 8: val_loss improved from 0.09983 to 0.09893, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 125ms/step - loss: 0.0897 - accuracy: 0.6250 - precision: 0.5817 - recall: 0.9924 - f1_score: 0.7240 - val_loss: 0.0989 - val_accuracy: 0.7344 - val_precision: 0.6068 - val_recall: 1.0000 - val_f1_score: 0.7423\n",
      "Epoch 9/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.6602 - precision: 0.6028 - recall: 0.9898 - f1_score: 0.7435\n",
      "Epoch 9: val_loss improved from 0.09893 to 0.09341, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 136ms/step - loss: 0.0919 - accuracy: 0.6602 - precision: 0.6028 - recall: 0.9898 - f1_score: 0.7435 - val_loss: 0.0934 - val_accuracy: 0.8828 - val_precision: 0.7850 - val_recall: 0.9792 - val_f1_score: 0.8593\n",
      "Epoch 10/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0904 - accuracy: 0.6816 - precision: 0.6291 - recall: 0.9636 - f1_score: 0.7509\n",
      "Epoch 10: val_loss improved from 0.09341 to 0.09087, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0904 - accuracy: 0.6816 - precision: 0.6291 - recall: 0.9636 - f1_score: 0.7509 - val_loss: 0.0909 - val_accuracy: 0.7734 - val_precision: 0.6437 - val_recall: 0.9792 - val_f1_score: 0.7651\n",
      "Epoch 11/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.7051 - precision: 0.6521 - recall: 0.9738 - f1_score: 0.7694\n",
      "Epoch 11: val_loss improved from 0.09087 to 0.08788, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 122ms/step - loss: 0.0894 - accuracy: 0.7051 - precision: 0.6521 - recall: 0.9738 - f1_score: 0.7694 - val_loss: 0.0879 - val_accuracy: 0.8750 - val_precision: 0.7722 - val_recall: 1.0000 - val_f1_score: 0.8588\n",
      "Epoch 12/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.7031 - precision: 0.6515 - recall: 0.9861 - f1_score: 0.7715\n",
      "Epoch 12: val_loss did not improve from 0.08788\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0860 - accuracy: 0.7031 - precision: 0.6515 - recall: 0.9861 - f1_score: 0.7715 - val_loss: 0.0900 - val_accuracy: 0.7188 - val_precision: 0.6039 - val_recall: 1.0000 - val_f1_score: 0.7365\n",
      "Epoch 13/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.7617 - precision: 0.6969 - recall: 0.9880 - f1_score: 0.8065\n",
      "Epoch 13: val_loss improved from 0.08788 to 0.08241, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 142ms/step - loss: 0.0813 - accuracy: 0.7617 - precision: 0.6969 - recall: 0.9880 - f1_score: 0.8065 - val_loss: 0.0824 - val_accuracy: 0.8203 - val_precision: 0.6889 - val_recall: 1.0000 - val_f1_score: 0.8085\n",
      "Epoch 14/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0845 - accuracy: 0.7559 - precision: 0.6952 - recall: 0.9801 - f1_score: 0.8027\n",
      "Epoch 14: val_loss did not improve from 0.08241\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0845 - accuracy: 0.7559 - precision: 0.6952 - recall: 0.9801 - f1_score: 0.8027 - val_loss: 0.0832 - val_accuracy: 0.8594 - val_precision: 0.7525 - val_recall: 1.0000 - val_f1_score: 0.8496\n",
      "Epoch 15/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0799 - accuracy: 0.7734 - precision: 0.7066 - recall: 0.9696 - f1_score: 0.8074\n",
      "Epoch 15: val_loss improved from 0.08241 to 0.08215, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0799 - accuracy: 0.7734 - precision: 0.7066 - recall: 0.9696 - f1_score: 0.8074 - val_loss: 0.0822 - val_accuracy: 0.8281 - val_precision: 0.7178 - val_recall: 1.0000 - val_f1_score: 0.8206\n",
      "Epoch 16/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0808 - accuracy: 0.7793 - precision: 0.7129 - recall: 0.9845 - f1_score: 0.8145\n",
      "Epoch 16: val_loss improved from 0.08215 to 0.07726, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0808 - accuracy: 0.7793 - precision: 0.7129 - recall: 0.9845 - f1_score: 0.8145 - val_loss: 0.0773 - val_accuracy: 0.8828 - val_precision: 0.7814 - val_recall: 1.0000 - val_f1_score: 0.8684\n",
      "Epoch 17/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.7812 - precision: 0.7143 - recall: 0.9778 - f1_score: 0.8160\n",
      "Epoch 17: val_loss improved from 0.07726 to 0.07669, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0779 - accuracy: 0.7812 - precision: 0.7143 - recall: 0.9778 - f1_score: 0.8160 - val_loss: 0.0767 - val_accuracy: 0.8828 - val_precision: 0.7848 - val_recall: 1.0000 - val_f1_score: 0.8689\n",
      "Epoch 18/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0769 - accuracy: 0.8184 - precision: 0.7527 - recall: 0.9794 - f1_score: 0.8447\n",
      "Epoch 18: val_loss improved from 0.07669 to 0.07531, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0769 - accuracy: 0.8184 - precision: 0.7527 - recall: 0.9794 - f1_score: 0.8447 - val_loss: 0.0753 - val_accuracy: 0.8750 - val_precision: 0.7636 - val_recall: 0.9792 - val_f1_score: 0.8508\n",
      "Epoch 19/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0730 - accuracy: 0.8340 - precision: 0.7733 - recall: 0.9774 - f1_score: 0.8559\n",
      "Epoch 19: val_loss did not improve from 0.07531\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0730 - accuracy: 0.8340 - precision: 0.7733 - recall: 0.9774 - f1_score: 0.8559 - val_loss: 0.0768 - val_accuracy: 0.8594 - val_precision: 0.7616 - val_recall: 0.9886 - val_f1_score: 0.8475\n",
      "Epoch 20/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.8203 - precision: 0.7555 - recall: 0.9859 - f1_score: 0.8461\n",
      "Epoch 20: val_loss did not improve from 0.07531\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0762 - accuracy: 0.8203 - precision: 0.7555 - recall: 0.9859 - f1_score: 0.8461 - val_loss: 0.0759 - val_accuracy: 0.9062 - val_precision: 0.8205 - val_recall: 0.9792 - val_f1_score: 0.8901\n",
      "Epoch 21/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0753 - accuracy: 0.8359 - precision: 0.7817 - recall: 0.9569 - f1_score: 0.8523\n",
      "Epoch 21: val_loss improved from 0.07531 to 0.07252, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0753 - accuracy: 0.8359 - precision: 0.7817 - recall: 0.9569 - f1_score: 0.8523 - val_loss: 0.0725 - val_accuracy: 0.9297 - val_precision: 0.8535 - val_recall: 1.0000 - val_f1_score: 0.9148\n",
      "Epoch 22/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0704 - accuracy: 0.8672 - precision: 0.8172 - recall: 0.9671 - f1_score: 0.8790\n",
      "Epoch 22: val_loss improved from 0.07252 to 0.06986, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0704 - accuracy: 0.8672 - precision: 0.8172 - recall: 0.9671 - f1_score: 0.8790 - val_loss: 0.0699 - val_accuracy: 0.8906 - val_precision: 0.7998 - val_recall: 0.9792 - val_f1_score: 0.8723\n",
      "Epoch 23/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0664 - accuracy: 0.8789 - precision: 0.8263 - recall: 0.9822 - f1_score: 0.8900\n",
      "Epoch 23: val_loss improved from 0.06986 to 0.06929, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0664 - accuracy: 0.8789 - precision: 0.8263 - recall: 0.9822 - f1_score: 0.8900 - val_loss: 0.0693 - val_accuracy: 0.9297 - val_precision: 0.8475 - val_recall: 1.0000 - val_f1_score: 0.9144\n",
      "Epoch 24/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0689 - accuracy: 0.8574 - precision: 0.8110 - recall: 0.9688 - f1_score: 0.8748\n",
      "Epoch 24: val_loss did not improve from 0.06929\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0689 - accuracy: 0.8574 - precision: 0.8110 - recall: 0.9688 - f1_score: 0.8748 - val_loss: 0.0699 - val_accuracy: 0.8516 - val_precision: 0.7396 - val_recall: 0.9792 - val_f1_score: 0.8308\n",
      "Epoch 25/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0686 - accuracy: 0.8887 - precision: 0.8431 - recall: 0.9652 - f1_score: 0.8930\n",
      "Epoch 25: val_loss improved from 0.06929 to 0.06642, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 129ms/step - loss: 0.0686 - accuracy: 0.8887 - precision: 0.8431 - recall: 0.9652 - f1_score: 0.8930 - val_loss: 0.0664 - val_accuracy: 0.9453 - val_precision: 0.9004 - val_recall: 0.9792 - val_f1_score: 0.9322\n",
      "Epoch 26/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0651 - accuracy: 0.8945 - precision: 0.8536 - recall: 0.9659 - f1_score: 0.9032\n",
      "Epoch 26: val_loss did not improve from 0.06642\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0651 - accuracy: 0.8945 - precision: 0.8536 - recall: 0.9659 - f1_score: 0.9032 - val_loss: 0.0671 - val_accuracy: 0.8828 - val_precision: 0.7816 - val_recall: 0.9792 - val_f1_score: 0.8616\n",
      "Epoch 27/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0658 - accuracy: 0.8750 - precision: 0.8254 - recall: 0.9629 - f1_score: 0.8828\n",
      "Epoch 27: val_loss improved from 0.06642 to 0.06508, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 122ms/step - loss: 0.0658 - accuracy: 0.8750 - precision: 0.8254 - recall: 0.9629 - f1_score: 0.8828 - val_loss: 0.0651 - val_accuracy: 0.9062 - val_precision: 0.8126 - val_recall: 1.0000 - val_f1_score: 0.8879\n",
      "Epoch 28/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0651 - accuracy: 0.9043 - precision: 0.8710 - recall: 0.9691 - f1_score: 0.9094\n",
      "Epoch 28: val_loss improved from 0.06508 to 0.06420, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0651 - accuracy: 0.9043 - precision: 0.8710 - recall: 0.9691 - f1_score: 0.9094 - val_loss: 0.0642 - val_accuracy: 0.9531 - val_precision: 0.9375 - val_recall: 0.9437 - val_f1_score: 0.9348\n",
      "Epoch 29/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0624 - accuracy: 0.9316 - precision: 0.9056 - recall: 0.9816 - f1_score: 0.9390\n",
      "Epoch 29: val_loss improved from 0.06420 to 0.06242, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 131ms/step - loss: 0.0624 - accuracy: 0.9316 - precision: 0.9056 - recall: 0.9816 - f1_score: 0.9390 - val_loss: 0.0624 - val_accuracy: 0.9062 - val_precision: 0.8036 - val_recall: 1.0000 - val_f1_score: 0.8883\n",
      "Epoch 30/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0606 - accuracy: 0.9004 - precision: 0.8676 - recall: 0.9685 - f1_score: 0.9105\n",
      "Epoch 30: val_loss did not improve from 0.06242\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0606 - accuracy: 0.9004 - precision: 0.8676 - recall: 0.9685 - f1_score: 0.9105 - val_loss: 0.0661 - val_accuracy: 0.8672 - val_precision: 0.7533 - val_recall: 1.0000 - val_f1_score: 0.8485\n",
      "Epoch 31/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0590 - accuracy: 0.9277 - precision: 0.9004 - recall: 0.9733 - f1_score: 0.9307\n",
      "Epoch 31: val_loss improved from 0.06242 to 0.05927, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0590 - accuracy: 0.9277 - precision: 0.9004 - recall: 0.9733 - f1_score: 0.9307 - val_loss: 0.0593 - val_accuracy: 0.9531 - val_precision: 0.9063 - val_recall: 0.9792 - val_f1_score: 0.9355\n",
      "Epoch 32/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0595 - accuracy: 0.9414 - precision: 0.9302 - recall: 0.9608 - f1_score: 0.9411\n",
      "Epoch 32: val_loss improved from 0.05927 to 0.05806, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0595 - accuracy: 0.9414 - precision: 0.9302 - recall: 0.9608 - f1_score: 0.9411 - val_loss: 0.0581 - val_accuracy: 0.9531 - val_precision: 0.8884 - val_recall: 1.0000 - val_f1_score: 0.9372\n",
      "Epoch 33/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0576 - accuracy: 0.9160 - precision: 0.8944 - recall: 0.9636 - f1_score: 0.9239\n",
      "Epoch 33: val_loss did not improve from 0.05806\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0576 - accuracy: 0.9160 - precision: 0.8944 - recall: 0.9636 - f1_score: 0.9239 - val_loss: 0.0590 - val_accuracy: 0.9531 - val_precision: 0.8884 - val_recall: 1.0000 - val_f1_score: 0.9372\n",
      "Epoch 34/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0573 - accuracy: 0.9258 - precision: 0.9042 - recall: 0.9626 - f1_score: 0.9290\n",
      "Epoch 34: val_loss did not improve from 0.05806\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0573 - accuracy: 0.9258 - precision: 0.9042 - recall: 0.9626 - f1_score: 0.9290 - val_loss: 0.0592 - val_accuracy: 0.9531 - val_precision: 0.9542 - val_recall: 0.9187 - val_f1_score: 0.9321\n",
      "Epoch 35/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0544 - accuracy: 0.9453 - precision: 0.9467 - recall: 0.9660 - f1_score: 0.9534\n",
      "Epoch 35: val_loss did not improve from 0.05806\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0544 - accuracy: 0.9453 - precision: 0.9467 - recall: 0.9660 - f1_score: 0.9534 - val_loss: 0.0596 - val_accuracy: 0.9219 - val_precision: 0.8524 - val_recall: 1.0000 - val_f1_score: 0.9165\n",
      "Epoch 36/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0529 - accuracy: 0.9434 - precision: 0.9280 - recall: 0.9696 - f1_score: 0.9461\n",
      "Epoch 36: val_loss improved from 0.05806 to 0.05692, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0529 - accuracy: 0.9434 - precision: 0.9280 - recall: 0.9696 - f1_score: 0.9461 - val_loss: 0.0569 - val_accuracy: 0.9531 - val_precision: 0.9271 - val_recall: 0.9542 - val_f1_score: 0.9348\n",
      "Epoch 37/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9512 - precision: 0.9560 - recall: 0.9551 - f1_score: 0.9527\n",
      "Epoch 37: val_loss improved from 0.05692 to 0.05414, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0534 - accuracy: 0.9512 - precision: 0.9560 - recall: 0.9551 - f1_score: 0.9527 - val_loss: 0.0541 - val_accuracy: 0.9453 - val_precision: 0.8884 - val_recall: 0.9792 - val_f1_score: 0.9259\n",
      "Epoch 38/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9453 - precision: 0.9236 - recall: 0.9800 - f1_score: 0.9466\n",
      "Epoch 38: val_loss improved from 0.05414 to 0.05219, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0536 - accuracy: 0.9453 - precision: 0.9236 - recall: 0.9800 - f1_score: 0.9466 - val_loss: 0.0522 - val_accuracy: 0.9766 - val_precision: 0.9583 - val_recall: 0.9792 - val_f1_score: 0.9636\n",
      "Epoch 39/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9551 - precision: 0.9563 - recall: 0.9576 - f1_score: 0.9549\n",
      "Epoch 39: val_loss improved from 0.05219 to 0.05133, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 127ms/step - loss: 0.0511 - accuracy: 0.9551 - precision: 0.9563 - recall: 0.9576 - f1_score: 0.9549 - val_loss: 0.0513 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 40/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0499 - accuracy: 0.9570 - precision: 0.9549 - recall: 0.9735 - f1_score: 0.9609\n",
      "Epoch 40: val_loss did not improve from 0.05133\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0499 - accuracy: 0.9570 - precision: 0.9549 - recall: 0.9735 - f1_score: 0.9609 - val_loss: 0.0516 - val_accuracy: 0.9766 - val_precision: 0.9479 - val_recall: 1.0000 - val_f1_score: 0.9696\n",
      "Epoch 41/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0509 - accuracy: 0.9375 - precision: 0.9325 - recall: 0.9574 - f1_score: 0.9411\n",
      "Epoch 41: val_loss did not improve from 0.05133\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0509 - accuracy: 0.9375 - precision: 0.9325 - recall: 0.9574 - f1_score: 0.9411 - val_loss: 0.0555 - val_accuracy: 0.9219 - val_precision: 0.8415 - val_recall: 1.0000 - val_f1_score: 0.9031\n",
      "Epoch 42/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9453 - precision: 0.9585 - recall: 0.9338 - f1_score: 0.9431\n",
      "Epoch 42: val_loss improved from 0.05133 to 0.05128, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0511 - accuracy: 0.9453 - precision: 0.9585 - recall: 0.9338 - f1_score: 0.9431 - val_loss: 0.0513 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9750 - val_f1_score: 0.9557\n",
      "Epoch 43/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0506 - accuracy: 0.9355 - precision: 0.9305 - recall: 0.9509 - f1_score: 0.9355\n",
      "Epoch 43: val_loss did not improve from 0.05128\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0506 - accuracy: 0.9355 - precision: 0.9305 - recall: 0.9509 - f1_score: 0.9355 - val_loss: 0.0528 - val_accuracy: 0.9453 - val_precision: 1.0000 - val_recall: 0.8687 - val_f1_score: 0.9274\n",
      "Epoch 44/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0463 - accuracy: 0.9551 - precision: 0.9574 - recall: 0.9600 - f1_score: 0.9557\n",
      "Epoch 44: val_loss improved from 0.05128 to 0.04913, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0463 - accuracy: 0.9551 - precision: 0.9574 - recall: 0.9600 - f1_score: 0.9557 - val_loss: 0.0491 - val_accuracy: 0.9375 - val_precision: 0.8875 - val_recall: 0.9479 - val_f1_score: 0.9123\n",
      "Epoch 45/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0462 - accuracy: 0.9512 - precision: 0.9575 - recall: 0.9436 - f1_score: 0.9467\n",
      "Epoch 45: val_loss did not improve from 0.04913\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0462 - accuracy: 0.9512 - precision: 0.9575 - recall: 0.9436 - f1_score: 0.9467 - val_loss: 0.0553 - val_accuracy: 0.8828 - val_precision: 0.7917 - val_recall: 0.9792 - val_f1_score: 0.8612\n",
      "Epoch 46/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0471 - accuracy: 0.9609 - precision: 0.9707 - recall: 0.9618 - f1_score: 0.9632\n",
      "Epoch 46: val_loss improved from 0.04913 to 0.04831, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 131ms/step - loss: 0.0471 - accuracy: 0.9609 - precision: 0.9707 - recall: 0.9618 - f1_score: 0.9632 - val_loss: 0.0483 - val_accuracy: 0.9609 - val_precision: 0.9383 - val_recall: 0.9792 - val_f1_score: 0.9532\n",
      "Epoch 47/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0441 - accuracy: 0.9551 - precision: 0.9436 - recall: 0.9769 - f1_score: 0.9574\n",
      "Epoch 47: val_loss improved from 0.04831 to 0.04787, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0441 - accuracy: 0.9551 - precision: 0.9436 - recall: 0.9769 - f1_score: 0.9574 - val_loss: 0.0479 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 48/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0453 - accuracy: 0.9590 - precision: 0.9753 - recall: 0.9501 - f1_score: 0.9601\n",
      "Epoch 48: val_loss improved from 0.04787 to 0.04565, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 119ms/step - loss: 0.0453 - accuracy: 0.9590 - precision: 0.9753 - recall: 0.9501 - f1_score: 0.9601 - val_loss: 0.0456 - val_accuracy: 0.9609 - val_precision: 0.9271 - val_recall: 0.9792 - val_f1_score: 0.9468\n",
      "Epoch 49/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0423 - accuracy: 0.9668 - precision: 0.9535 - recall: 0.9782 - f1_score: 0.9621\n",
      "Epoch 49: val_loss improved from 0.04565 to 0.04422, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0423 - accuracy: 0.9668 - precision: 0.9535 - recall: 0.9782 - f1_score: 0.9621 - val_loss: 0.0442 - val_accuracy: 0.9688 - val_precision: 0.9542 - val_recall: 0.9687 - val_f1_score: 0.9579\n",
      "Epoch 50/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0426 - accuracy: 0.9688 - precision: 0.9710 - recall: 0.9668 - f1_score: 0.9669\n",
      "Epoch 50: val_loss did not improve from 0.04422\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0426 - accuracy: 0.9688 - precision: 0.9710 - recall: 0.9668 - f1_score: 0.9669 - val_loss: 0.0443 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 51/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0430 - accuracy: 0.9570 - precision: 0.9563 - recall: 0.9639 - f1_score: 0.9552\n",
      "Epoch 51: val_loss improved from 0.04422 to 0.04257, saving model to snn.hdf5\n",
      "32/32 [==============================] - 5s 146ms/step - loss: 0.0430 - accuracy: 0.9570 - precision: 0.9563 - recall: 0.9639 - f1_score: 0.9552 - val_loss: 0.0426 - val_accuracy: 0.9688 - val_precision: 0.9438 - val_recall: 0.9792 - val_f1_score: 0.9579\n",
      "Epoch 52/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0424 - accuracy: 0.9531 - precision: 0.9467 - recall: 0.9567 - f1_score: 0.9488\n",
      "Epoch 52: val_loss did not improve from 0.04257\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0424 - accuracy: 0.9531 - precision: 0.9467 - recall: 0.9567 - f1_score: 0.9488 - val_loss: 0.0439 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 53/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0412 - accuracy: 0.9512 - precision: 0.9502 - recall: 0.9615 - f1_score: 0.9536\n",
      "Epoch 53: val_loss did not improve from 0.04257\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0412 - accuracy: 0.9512 - precision: 0.9502 - recall: 0.9615 - f1_score: 0.9536 - val_loss: 0.0444 - val_accuracy: 0.9688 - val_precision: 0.9646 - val_recall: 0.9542 - val_f1_score: 0.9554\n",
      "Epoch 54/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0394 - accuracy: 0.9688 - precision: 0.9816 - recall: 0.9631 - f1_score: 0.9711\n",
      "Epoch 54: val_loss improved from 0.04257 to 0.04249, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 119ms/step - loss: 0.0394 - accuracy: 0.9688 - precision: 0.9816 - recall: 0.9631 - f1_score: 0.9711 - val_loss: 0.0425 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 55/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0385 - accuracy: 0.9590 - precision: 0.9730 - recall: 0.9543 - f1_score: 0.9613\n",
      "Epoch 55: val_loss did not improve from 0.04249\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0385 - accuracy: 0.9590 - precision: 0.9730 - recall: 0.9543 - f1_score: 0.9613 - val_loss: 0.0446 - val_accuracy: 0.9453 - val_precision: 0.8943 - val_recall: 0.9792 - val_f1_score: 0.9264\n",
      "Epoch 56/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0380 - accuracy: 0.9688 - precision: 0.9717 - recall: 0.9652 - f1_score: 0.9672\n",
      "Epoch 56: val_loss improved from 0.04249 to 0.03993, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0380 - accuracy: 0.9688 - precision: 0.9717 - recall: 0.9652 - f1_score: 0.9672 - val_loss: 0.0399 - val_accuracy: 0.9766 - val_precision: 0.9750 - val_recall: 0.9542 - val_f1_score: 0.9609\n",
      "Epoch 57/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0385 - accuracy: 0.9590 - precision: 0.9631 - recall: 0.9587 - f1_score: 0.9572\n",
      "Epoch 57: val_loss improved from 0.03993 to 0.03931, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0385 - accuracy: 0.9590 - precision: 0.9631 - recall: 0.9587 - f1_score: 0.9572 - val_loss: 0.0393 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 58/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0375 - accuracy: 0.9629 - precision: 0.9779 - recall: 0.9488 - f1_score: 0.9607\n",
      "Epoch 58: val_loss improved from 0.03931 to 0.03734, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0375 - accuracy: 0.9629 - precision: 0.9779 - recall: 0.9488 - f1_score: 0.9607 - val_loss: 0.0373 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 59/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0386 - accuracy: 0.9609 - precision: 0.9718 - recall: 0.9521 - f1_score: 0.9600\n",
      "Epoch 59: val_loss did not improve from 0.03734\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0386 - accuracy: 0.9609 - precision: 0.9718 - recall: 0.9521 - f1_score: 0.9600 - val_loss: 0.0376 - val_accuracy: 0.9688 - val_precision: 0.9646 - val_recall: 0.9542 - val_f1_score: 0.9554\n",
      "Epoch 60/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0368 - accuracy: 0.9707 - precision: 0.9783 - recall: 0.9595 - f1_score: 0.9665\n",
      "Epoch 60: val_loss did not improve from 0.03734\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0368 - accuracy: 0.9707 - precision: 0.9783 - recall: 0.9595 - f1_score: 0.9665 - val_loss: 0.0416 - val_accuracy: 0.9688 - val_precision: 0.9646 - val_recall: 0.9542 - val_f1_score: 0.9554\n",
      "Epoch 61/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0349 - accuracy: 0.9844 - precision: 1.0000 - recall: 0.9713 - f1_score: 0.9845\n",
      "Epoch 61: val_loss did not improve from 0.03734\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0349 - accuracy: 0.9844 - precision: 1.0000 - recall: 0.9713 - f1_score: 0.9845 - val_loss: 0.0378 - val_accuracy: 0.9766 - val_precision: 0.9750 - val_recall: 0.9542 - val_f1_score: 0.9609\n",
      "Epoch 62/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0337 - accuracy: 0.9766 - precision: 0.9817 - recall: 0.9739 - f1_score: 0.9760\n",
      "Epoch 62: val_loss did not improve from 0.03734\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0337 - accuracy: 0.9766 - precision: 0.9817 - recall: 0.9739 - f1_score: 0.9760 - val_loss: 0.0386 - val_accuracy: 0.9609 - val_precision: 0.9229 - val_recall: 0.9792 - val_f1_score: 0.9485\n",
      "Epoch 63/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0330 - accuracy: 0.9746 - precision: 0.9764 - recall: 0.9796 - f1_score: 0.9765\n",
      "Epoch 63: val_loss improved from 0.03734 to 0.03473, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0330 - accuracy: 0.9746 - precision: 0.9764 - recall: 0.9796 - f1_score: 0.9765 - val_loss: 0.0347 - val_accuracy: 0.9766 - val_precision: 0.9583 - val_recall: 0.9792 - val_f1_score: 0.9636\n",
      "Epoch 64/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0340 - accuracy: 0.9707 - precision: 0.9805 - recall: 0.9599 - f1_score: 0.9682\n",
      "Epoch 64: val_loss did not improve from 0.03473\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0340 - accuracy: 0.9707 - precision: 0.9805 - recall: 0.9599 - f1_score: 0.9682 - val_loss: 0.0380 - val_accuracy: 0.9688 - val_precision: 0.9438 - val_recall: 0.9792 - val_f1_score: 0.9598\n",
      "Epoch 65/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0354 - accuracy: 0.9746 - precision: 0.9926 - recall: 0.9614 - f1_score: 0.9752\n",
      "Epoch 65: val_loss improved from 0.03473 to 0.03422, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0354 - accuracy: 0.9746 - precision: 0.9926 - recall: 0.9614 - f1_score: 0.9752 - val_loss: 0.0342 - val_accuracy: 0.9688 - val_precision: 0.9646 - val_recall: 0.9542 - val_f1_score: 0.9554\n",
      "Epoch 66/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0338 - accuracy: 0.9707 - precision: 0.9847 - recall: 0.9612 - f1_score: 0.9711\n",
      "Epoch 66: val_loss did not improve from 0.03422\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0338 - accuracy: 0.9707 - precision: 0.9847 - recall: 0.9612 - f1_score: 0.9711 - val_loss: 0.0346 - val_accuracy: 0.9766 - val_precision: 0.9646 - val_recall: 0.9792 - val_f1_score: 0.9693\n",
      "Epoch 67/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0343 - accuracy: 0.9629 - precision: 0.9812 - recall: 0.9400 - f1_score: 0.9580\n",
      "Epoch 67: val_loss did not improve from 0.03422\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0343 - accuracy: 0.9629 - precision: 0.9812 - recall: 0.9400 - f1_score: 0.9580 - val_loss: 0.0353 - val_accuracy: 0.9688 - val_precision: 0.9542 - val_recall: 0.9542 - val_f1_score: 0.9514\n",
      "Epoch 68/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0307 - accuracy: 0.9766 - precision: 0.9901 - recall: 0.9649 - f1_score: 0.9759\n",
      "Epoch 68: val_loss did not improve from 0.03422\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0307 - accuracy: 0.9766 - precision: 0.9901 - recall: 0.9649 - f1_score: 0.9759 - val_loss: 0.0349 - val_accuracy: 0.9531 - val_precision: 0.9321 - val_recall: 0.9437 - val_f1_score: 0.9347\n",
      "Epoch 69/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0317 - accuracy: 0.9746 - precision: 0.9882 - recall: 0.9625 - f1_score: 0.9742\n",
      "Epoch 69: val_loss did not improve from 0.03422\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0317 - accuracy: 0.9746 - precision: 0.9882 - recall: 0.9625 - f1_score: 0.9742 - val_loss: 0.0345 - val_accuracy: 0.9609 - val_precision: 0.9636 - val_recall: 0.9532 - val_f1_score: 0.9554\n",
      "Epoch 70/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0316 - accuracy: 0.9668 - precision: 0.9760 - recall: 0.9680 - f1_score: 0.9701\n",
      "Epoch 70: val_loss improved from 0.03422 to 0.03411, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 119ms/step - loss: 0.0316 - accuracy: 0.9668 - precision: 0.9760 - recall: 0.9680 - f1_score: 0.9701 - val_loss: 0.0341 - val_accuracy: 0.9531 - val_precision: 0.9479 - val_recall: 0.9437 - val_f1_score: 0.9389\n",
      "Epoch 71/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0295 - accuracy: 0.9727 - precision: 0.9768 - recall: 0.9689 - f1_score: 0.9708\n",
      "Epoch 71: val_loss did not improve from 0.03411\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0295 - accuracy: 0.9727 - precision: 0.9768 - recall: 0.9689 - f1_score: 0.9708 - val_loss: 0.0342 - val_accuracy: 0.9609 - val_precision: 0.9646 - val_recall: 0.9437 - val_f1_score: 0.9500\n",
      "Epoch 72/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0297 - accuracy: 0.9746 - precision: 0.9859 - recall: 0.9679 - f1_score: 0.9750\n",
      "Epoch 72: val_loss improved from 0.03411 to 0.03218, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0297 - accuracy: 0.9746 - precision: 0.9859 - recall: 0.9679 - f1_score: 0.9750 - val_loss: 0.0322 - val_accuracy: 0.9688 - val_precision: 0.9583 - val_recall: 0.9542 - val_f1_score: 0.9497\n",
      "Epoch 73/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0287 - accuracy: 0.9824 - precision: 0.9887 - recall: 0.9791 - f1_score: 0.9818\n",
      "Epoch 73: val_loss improved from 0.03218 to 0.03158, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 122ms/step - loss: 0.0287 - accuracy: 0.9824 - precision: 0.9887 - recall: 0.9791 - f1_score: 0.9818 - val_loss: 0.0316 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 74/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0290 - accuracy: 0.9766 - precision: 0.9853 - recall: 0.9689 - f1_score: 0.9758\n",
      "Epoch 74: val_loss improved from 0.03158 to 0.03129, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 119ms/step - loss: 0.0290 - accuracy: 0.9766 - precision: 0.9853 - recall: 0.9689 - f1_score: 0.9758 - val_loss: 0.0313 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 75/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0293 - accuracy: 0.9824 - precision: 0.9908 - recall: 0.9786 - f1_score: 0.9838\n",
      "Epoch 75: val_loss improved from 0.03129 to 0.02960, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0293 - accuracy: 0.9824 - precision: 0.9908 - recall: 0.9786 - f1_score: 0.9838 - val_loss: 0.0296 - val_accuracy: 0.9766 - val_precision: 1.0000 - val_recall: 0.9437 - val_f1_score: 0.9693\n",
      "Epoch 76/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0293 - accuracy: 0.9766 - precision: 0.9860 - recall: 0.9722 - f1_score: 0.9774\n",
      "Epoch 76: val_loss did not improve from 0.02960\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0293 - accuracy: 0.9766 - precision: 0.9860 - recall: 0.9722 - f1_score: 0.9774 - val_loss: 0.0310 - val_accuracy: 0.9609 - val_precision: 0.9688 - val_recall: 0.9125 - val_f1_score: 0.9381\n",
      "Epoch 77/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0280 - accuracy: 0.9746 - precision: 0.9778 - recall: 0.9715 - f1_score: 0.9736\n",
      "Epoch 77: val_loss improved from 0.02960 to 0.02853, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 131ms/step - loss: 0.0280 - accuracy: 0.9746 - precision: 0.9778 - recall: 0.9715 - f1_score: 0.9736 - val_loss: 0.0285 - val_accuracy: 0.9844 - val_precision: 1.0000 - val_recall: 0.9687 - val_f1_score: 0.9832\n",
      "Epoch 78/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0295 - accuracy: 0.9746 - precision: 0.9937 - recall: 0.9584 - f1_score: 0.9737\n",
      "Epoch 78: val_loss did not improve from 0.02853\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0295 - accuracy: 0.9746 - precision: 0.9937 - recall: 0.9584 - f1_score: 0.9737 - val_loss: 0.0288 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 79/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0265 - accuracy: 0.9785 - precision: 0.9829 - recall: 0.9762 - f1_score: 0.9780\n",
      "Epoch 79: val_loss did not improve from 0.02853\n",
      "32/32 [==============================] - 4s 116ms/step - loss: 0.0265 - accuracy: 0.9785 - precision: 0.9829 - recall: 0.9762 - f1_score: 0.9780 - val_loss: 0.0291 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 80/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0277 - accuracy: 0.9805 - precision: 0.9965 - recall: 0.9706 - f1_score: 0.9824\n",
      "Epoch 80: val_loss improved from 0.02853 to 0.02754, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0277 - accuracy: 0.9805 - precision: 0.9965 - recall: 0.9706 - f1_score: 0.9824 - val_loss: 0.0275 - val_accuracy: 0.9766 - val_precision: 0.9750 - val_recall: 0.9687 - val_f1_score: 0.9693\n",
      "Epoch 81/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0298 - accuracy: 0.9668 - precision: 0.9736 - recall: 0.9611 - f1_score: 0.9652\n",
      "Epoch 81: val_loss did not improve from 0.02754\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0298 - accuracy: 0.9668 - precision: 0.9736 - recall: 0.9611 - f1_score: 0.9652 - val_loss: 0.0288 - val_accuracy: 0.9609 - val_precision: 0.9271 - val_recall: 0.9792 - val_f1_score: 0.9468\n",
      "Epoch 82/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0252 - accuracy: 0.9746 - precision: 0.9924 - recall: 0.9590 - f1_score: 0.9741\n",
      "Epoch 82: val_loss did not improve from 0.02754\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0252 - accuracy: 0.9746 - precision: 0.9924 - recall: 0.9590 - f1_score: 0.9741 - val_loss: 0.0287 - val_accuracy: 0.9609 - val_precision: 0.9271 - val_recall: 0.9792 - val_f1_score: 0.9468\n",
      "Epoch 83/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0271 - accuracy: 0.9746 - precision: 0.9816 - recall: 0.9738 - f1_score: 0.9763\n",
      "Epoch 83: val_loss did not improve from 0.02754\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0271 - accuracy: 0.9746 - precision: 0.9816 - recall: 0.9738 - f1_score: 0.9763 - val_loss: 0.0337 - val_accuracy: 0.9531 - val_precision: 0.9092 - val_recall: 0.9792 - val_f1_score: 0.9372\n",
      "Epoch 84/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0277 - accuracy: 0.9668 - precision: 0.9780 - recall: 0.9624 - f1_score: 0.9678\n",
      "Epoch 84: val_loss improved from 0.02754 to 0.02742, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0277 - accuracy: 0.9668 - precision: 0.9780 - recall: 0.9624 - f1_score: 0.9678 - val_loss: 0.0274 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 85/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0232 - accuracy: 0.9883 - precision: 1.0000 - recall: 0.9761 - f1_score: 0.9872\n",
      "Epoch 85: val_loss improved from 0.02742 to 0.02586, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 121ms/step - loss: 0.0232 - accuracy: 0.9883 - precision: 1.0000 - recall: 0.9761 - f1_score: 0.9872 - val_loss: 0.0259 - val_accuracy: 0.9766 - val_precision: 0.9750 - val_recall: 0.9687 - val_f1_score: 0.9693\n",
      "Epoch 86/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0231 - accuracy: 0.9844 - precision: 0.9969 - recall: 0.9729 - f1_score: 0.9838\n",
      "Epoch 86: val_loss did not improve from 0.02586\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0231 - accuracy: 0.9844 - precision: 0.9969 - recall: 0.9729 - f1_score: 0.9838 - val_loss: 0.0272 - val_accuracy: 0.9688 - val_precision: 0.9479 - val_recall: 0.9792 - val_f1_score: 0.9582\n",
      "Epoch 87/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0249 - accuracy: 0.9766 - precision: 0.9806 - recall: 0.9720 - f1_score: 0.9741\n",
      "Epoch 87: val_loss improved from 0.02586 to 0.02560, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0249 - accuracy: 0.9766 - precision: 0.9806 - recall: 0.9720 - f1_score: 0.9741 - val_loss: 0.0256 - val_accuracy: 0.9766 - val_precision: 0.9750 - val_recall: 0.9687 - val_f1_score: 0.9693\n",
      "Epoch 88/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0244 - accuracy: 0.9766 - precision: 0.9877 - recall: 0.9758 - f1_score: 0.9804\n",
      "Epoch 88: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0244 - accuracy: 0.9766 - precision: 0.9877 - recall: 0.9758 - f1_score: 0.9804 - val_loss: 0.0268 - val_accuracy: 0.9688 - val_precision: 0.9542 - val_recall: 0.9687 - val_f1_score: 0.9579\n",
      "Epoch 89/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0245 - accuracy: 0.9805 - precision: 0.9851 - recall: 0.9784 - f1_score: 0.9807\n",
      "Epoch 89: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0245 - accuracy: 0.9805 - precision: 0.9851 - recall: 0.9784 - f1_score: 0.9807 - val_loss: 0.0278 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 90/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0255 - accuracy: 0.9785 - precision: 0.9861 - recall: 0.9719 - f1_score: 0.9779\n",
      "Epoch 90: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0255 - accuracy: 0.9785 - precision: 0.9861 - recall: 0.9719 - f1_score: 0.9779 - val_loss: 0.0265 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 91/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0243 - accuracy: 0.9785 - precision: 0.9961 - recall: 0.9629 - f1_score: 0.9778\n",
      "Epoch 91: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0243 - accuracy: 0.9785 - precision: 0.9961 - recall: 0.9629 - f1_score: 0.9778 - val_loss: 0.0269 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 92/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0233 - accuracy: 0.9746 - precision: 0.9852 - recall: 0.9662 - f1_score: 0.9744\n",
      "Epoch 92: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0233 - accuracy: 0.9746 - precision: 0.9852 - recall: 0.9662 - f1_score: 0.9744 - val_loss: 0.0261 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 93/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9766 - precision: 0.9798 - recall: 0.9728 - f1_score: 0.9748\n",
      "Epoch 93: val_loss did not improve from 0.02560\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0234 - accuracy: 0.9766 - precision: 0.9798 - recall: 0.9728 - f1_score: 0.9748 - val_loss: 0.0273 - val_accuracy: 0.9609 - val_precision: 0.9750 - val_recall: 0.9324 - val_f1_score: 0.9495\n",
      "Epoch 94/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0227 - accuracy: 0.9766 - precision: 0.9898 - recall: 0.9652 - f1_score: 0.9758\n",
      "Epoch 94: val_loss improved from 0.02560 to 0.02460, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 120ms/step - loss: 0.0227 - accuracy: 0.9766 - precision: 0.9898 - recall: 0.9652 - f1_score: 0.9758 - val_loss: 0.0246 - val_accuracy: 0.9766 - val_precision: 1.0000 - val_recall: 0.9437 - val_f1_score: 0.9693\n",
      "Epoch 95/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0226 - accuracy: 0.9766 - precision: 0.9933 - recall: 0.9641 - f1_score: 0.9769\n",
      "Epoch 95: val_loss did not improve from 0.02460\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0226 - accuracy: 0.9766 - precision: 0.9933 - recall: 0.9641 - f1_score: 0.9769 - val_loss: 0.0274 - val_accuracy: 0.9609 - val_precision: 0.9688 - val_recall: 0.9125 - val_f1_score: 0.9381\n",
      "Epoch 96/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0243 - accuracy: 0.9668 - precision: 0.9711 - recall: 0.9670 - f1_score: 0.9676\n",
      "Epoch 96: val_loss did not improve from 0.02460\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0243 - accuracy: 0.9668 - precision: 0.9711 - recall: 0.9670 - f1_score: 0.9676 - val_loss: 0.0252 - val_accuracy: 0.9688 - val_precision: 0.9896 - val_recall: 0.9437 - val_f1_score: 0.9639\n",
      "Epoch 97/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0227 - accuracy: 0.9824 - precision: 0.9922 - recall: 0.9785 - f1_score: 0.9844\n",
      "Epoch 97: val_loss improved from 0.02460 to 0.02438, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 127ms/step - loss: 0.0227 - accuracy: 0.9824 - precision: 0.9922 - recall: 0.9785 - f1_score: 0.9844 - val_loss: 0.0244 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 98/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0211 - accuracy: 0.9785 - precision: 0.9939 - recall: 0.9670 - f1_score: 0.9789\n",
      "Epoch 98: val_loss did not improve from 0.02438\n",
      "32/32 [==============================] - 4s 117ms/step - loss: 0.0211 - accuracy: 0.9785 - precision: 0.9939 - recall: 0.9670 - f1_score: 0.9789 - val_loss: 0.0247 - val_accuracy: 0.9688 - val_precision: 0.9750 - val_recall: 0.9437 - val_f1_score: 0.9554\n",
      "Epoch 99/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0185 - accuracy: 0.9922 - precision: 0.9961 - recall: 0.9836 - f1_score: 0.9880\n",
      "Epoch 99: val_loss improved from 0.02438 to 0.02339, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 119ms/step - loss: 0.0185 - accuracy: 0.9922 - precision: 0.9961 - recall: 0.9836 - f1_score: 0.9880 - val_loss: 0.0234 - val_accuracy: 0.9766 - val_precision: 1.0000 - val_recall: 0.9437 - val_f1_score: 0.9693\n",
      "Epoch 100/100\n",
      "32/32 [==============================] - ETA: 0s - loss: 0.0204 - accuracy: 0.9863 - precision: 0.9969 - recall: 0.9786 - f1_score: 0.9870\n",
      "Epoch 100: val_loss improved from 0.02339 to 0.02318, saving model to snn.hdf5\n",
      "32/32 [==============================] - 4s 118ms/step - loss: 0.0204 - accuracy: 0.9863 - precision: 0.9969 - recall: 0.9786 - f1_score: 0.9870 - val_loss: 0.0232 - val_accuracy: 0.9766 - val_precision: 1.0000 - val_recall: 0.9437 - val_f1_score: 0.9693\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=contrastive_loss, optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics=[\"accuracy\", precision, recall, f1_score]) \n",
    "\n",
    "model_checkpoint = ModelCheckpoint(\"snn.hdf5\", monitor=\"val_loss\", verbose=1, save_best_only=True)\n",
    "\n",
    "history = model.fit(\n",
    "    [trainPairs_0, trainPairs_1], train_labels,\n",
    "    validation_data=([testPairs_0, testPairs_1], test_labels),\n",
    "    batch_size=16,\n",
    "    epochs=100,\n",
    "    callbacks=[model_checkpoint]\n",
    ")\n",
    "\n",
    "with open(\"trainHistory.txt\", \"wb\") as file_pi:\n",
    "    pickle.dump(history.history, file_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
    "\n",
    "axs[0, 0].plot(history.history[\"accuracy\"], label=\"Train\")\n",
    "axs[0, 0].plot(history.history[\"val_accuracy\"], label=\"Validation\")\n",
    "axs[0, 0].set_title(\"Accuracy\")\n",
    "axs[0, 0].set_xlabel(\"Epochs\")\n",
    "axs[0, 0].legend()\n",
    "\n",
    "axs[0, 1].plot(history.history[\"loss\"], label=\"Train\")\n",
    "axs[0, 1].plot(history.history[\"val_loss\"], label=\"Validation\")\n",
    "axs[0, 1].set_title(\"Contrastive Loss\")\n",
    "axs[0, 1].set_xlabel(\"Epochs\")\n",
    "axs[0, 1].legend()\n",
    "\n",
    "\n",
    "axs[1, 0].plot(history.history[\"precision\"], label=\"Train\")\n",
    "axs[1, 0].plot(history.history[\"val_precision\"], label=\"Validation\")\n",
    "axs[1, 0].set_title(\"Precision\")\n",
    "axs[1, 0].set_xlabel(\"Epochs\")\n",
    "axs[1, 0].legend()\n",
    "\n",
    "axs[1, 1].plot(history.history[\"recall\"], label=\"Train\")\n",
    "axs[1, 1].plot(history.history[\"val_recall\"], label=\"Validation\")\n",
    "axs[1, 1].set_title(\"Recall\")\n",
    "axs[1, 1].set_xlabel(\"Epochs\")\n",
    "axs[1, 1].legend()\n",
    "\n",
    "axs[1, 2].plot(history.history[\"f1_score\"], label=\"Train\")\n",
    "axs[1, 2].plot(history.history[\"val_f1_score\"], label=\"Validation\")\n",
    "axs[1, 2].set_title(\"F1 Score\")\n",
    "axs[1, 2].set_xlabel(\"Epochs\")\n",
    "axs[1, 2].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test1",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}