notebooks/8_classes_EfficientNetV2B0_96x96_bs_256_weighted.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "c3SqkReH4WQe"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, confusion_matrix, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "notebook_name = '8_classes_EfficientNetV2B0_96x96_bs_256_weighted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "zy1ftuOe4lFK"
   },
   "outputs": [],
   "source": [
    "train_dir = '../data/images/train'\n",
    "val_dir = '../data/images/val'\n",
    "test_dir = '../data/images/test'\n",
    "\n",
    "train_df = pd.read_csv('../data/majority_train.csv')\n",
    "val_df = pd.read_csv('../data/majority_val.csv')\n",
    "test_df = pd.read_csv('../data/majority_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fer0000001.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fer0000002.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fer0000004.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fer0000005.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fer0000006.png</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25040</th>\n",
       "      <td>fer0028633.png</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25041</th>\n",
       "      <td>fer0028634.png</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25042</th>\n",
       "      <td>fer0028635.png</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25043</th>\n",
       "      <td>fer0028636.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25044</th>\n",
       "      <td>fer0028637.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             filename    class\n",
       "0      fer0000001.png  neutral\n",
       "1      fer0000002.png  neutral\n",
       "2      fer0000004.png  neutral\n",
       "3      fer0000005.png  neutral\n",
       "4      fer0000006.png  sadness\n",
       "...               ...      ...\n",
       "25040  fer0028633.png     fear\n",
       "25041  fer0028634.png    anger\n",
       "25042  fer0028635.png  sadness\n",
       "25043  fer0028636.png  neutral\n",
       "25044  fer0028637.png  neutral\n",
       "\n",
       "[25045 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_jL-3C814lqo",
    "outputId": "5e9ce65b-bb3d-4864-9ede-f93edc545b4a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25045 validated image filenames belonging to 8 classes.\n",
      "Found 3191 validated image filenames belonging to 8 classes.\n",
      "Found 3137 validated image filenames belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.08,\n",
    "    height_shift_range=0.08,\n",
    "    zoom_range=0.05,\n",
    "    shear_range=np.rad2deg(np.arctan(0.05)),  # used to set the value by fraction instead of an angle\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='reflect',\n",
    ")\n",
    "\n",
    "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator()\n",
    "\n",
    "target_size = (96, 96)\n",
    "interpolation = 'bilinear'\n",
    "color_mode = 'grayscale'\n",
    "batch_size = 256\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(\n",
    "    dataframe=train_df,\n",
    "    directory=train_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")\n",
    "\n",
    "val_generator = test_datagen.flow_from_dataframe(\n",
    "    dataframe=val_df,\n",
    "    directory=val_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_dataframe(\n",
    "    dataframe=test_df,\n",
    "    directory=test_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = list(train_generator.class_indices.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger',\n",
       " 'contempt',\n",
       " 'disgust',\n",
       " 'fear',\n",
       " 'happiness',\n",
       " 'neutral',\n",
       " 'sadness',\n",
       " 'surprise']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = next(train_generator)\n",
    "\n",
    "plt.figure(figsize=[20, 20])\n",
    "\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plt.title(f'{data[1][i]} = {labels[np.argmax(data[1][i])]}')\n",
    "    plt.imshow(data[0][i], cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "nz51JAUD7vrT"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.applications.efficientnet_v2.EfficientNetV2B0(\n",
    "    input_shape=(96, 96, 1),\n",
    "    weights=None,\n",
    "    classes=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"efficientnetv2-b0\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 96, 96, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " rescaling (Rescaling)          (None, 96, 96, 1)    0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)             (None, 48, 48, 32)   288         ['rescaling[0][0]']              \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalization)   (None, 48, 48, 32)   128         ['stem_conv[0][0]']              \n",
      "                                                                                                  \n",
      " stem_activation (Activation)   (None, 48, 48, 32)   0           ['stem_bn[0][0]']                \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv2D)  (None, 48, 48, 16)   4608        ['stem_activation[0][0]']        \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchNorma  (None, 48, 48, 16)  64          ['block1a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1a_project_activation (Ac  (None, 48, 48, 16)  0           ['block1a_project_bn[0][0]']     \n",
      " tivation)                                                                                        \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2D)   (None, 24, 24, 64)   9216        ['block1a_project_activation[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNormal  (None, 24, 24, 64)  256         ['block2a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_expand_activation (Act  (None, 24, 24, 64)  0           ['block2a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv2D)  (None, 24, 24, 32)   2048        ['block2a_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchNorma  (None, 24, 24, 32)  128         ['block2a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2D)   (None, 24, 24, 128)  36864       ['block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNormal  (None, 24, 24, 128)  512        ['block2b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_expand_activation (Act  (None, 24, 24, 128)  0          ['block2b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv2D)  (None, 24, 24, 32)   4096        ['block2b_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchNorma  (None, 24, 24, 32)  128         ['block2b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_add (Add)              (None, 24, 24, 32)   0           ['block2b_project_bn[0][0]',     \n",
      "                                                                  'block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2D)   (None, 12, 12, 128)  36864       ['block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNormal  (None, 12, 12, 128)  512        ['block3a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_expand_activation (Act  (None, 12, 12, 128)  0          ['block3a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv2D)  (None, 12, 12, 48)   6144        ['block3a_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchNorma  (None, 12, 12, 48)  192         ['block3a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2D)   (None, 12, 12, 192)  82944       ['block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNormal  (None, 12, 12, 192)  768        ['block3b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_expand_activation (Act  (None, 12, 12, 192)  0          ['block3b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv2D)  (None, 12, 12, 48)   9216        ['block3b_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchNorma  (None, 12, 12, 48)  192         ['block3b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_add (Add)              (None, 12, 12, 48)   0           ['block3b_project_bn[0][0]',     \n",
      "                                                                  'block3a_project_bn[0][0]']     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2D)   (None, 12, 12, 192)  9216        ['block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNormal  (None, 12, 12, 192)  768        ['block4a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_expand_activation (Act  (None, 12, 12, 192)  0          ['block4a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_dwconv2 (DepthwiseConv  (None, 6, 6, 192)   1728        ['block4a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormalization  (None, 6, 6, 192)   768         ['block4a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_activation (Activation  (None, 6, 6, 192)   0           ['block4a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (GlobalAver  (None, 192)         0           ['block4a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block4a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)     (None, 1, 1, 12)     2316        ['block4a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)     (None, 1, 1, 192)    2496        ['block4a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multiply)   (None, 6, 6, 192)    0           ['block4a_activation[0][0]',     \n",
      "                                                                  'block4a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv2D)  (None, 6, 6, 96)     18432       ['block4a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchNorma  (None, 6, 6, 96)    384         ['block4a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2D)   (None, 6, 6, 384)    36864       ['block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNormal  (None, 6, 6, 384)   1536        ['block4b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_expand_activation (Act  (None, 6, 6, 384)   0           ['block4b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_dwconv2 (DepthwiseConv  (None, 6, 6, 384)   3456        ['block4b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormalization  (None, 6, 6, 384)   1536        ['block4b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_activation (Activation  (None, 6, 6, 384)   0           ['block4b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (GlobalAver  (None, 384)         0           ['block4b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block4b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)     (None, 1, 1, 24)     9240        ['block4b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)     (None, 1, 1, 384)    9600        ['block4b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multiply)   (None, 6, 6, 384)    0           ['block4b_activation[0][0]',     \n",
      "                                                                  'block4b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv2D)  (None, 6, 6, 96)     36864       ['block4b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchNorma  (None, 6, 6, 96)    384         ['block4b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_add (Add)              (None, 6, 6, 96)     0           ['block4b_project_bn[0][0]',     \n",
      "                                                                  'block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2D)   (None, 6, 6, 384)    36864       ['block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNormal  (None, 6, 6, 384)   1536        ['block4c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_expand_activation (Act  (None, 6, 6, 384)   0           ['block4c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_dwconv2 (DepthwiseConv  (None, 6, 6, 384)   3456        ['block4c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block4c_bn (BatchNormalization  (None, 6, 6, 384)   1536        ['block4c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_activation (Activation  (None, 6, 6, 384)   0           ['block4c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (GlobalAver  (None, 384)         0           ['block4c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block4c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)     (None, 1, 1, 24)     9240        ['block4c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)     (None, 1, 1, 384)    9600        ['block4c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multiply)   (None, 6, 6, 384)    0           ['block4c_activation[0][0]',     \n",
      "                                                                  'block4c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv2D)  (None, 6, 6, 96)     36864       ['block4c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchNorma  (None, 6, 6, 96)    384         ['block4c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4c_add (Add)              (None, 6, 6, 96)     0           ['block4c_project_bn[0][0]',     \n",
      "                                                                  'block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2D)   (None, 6, 6, 576)    55296       ['block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNormal  (None, 6, 6, 576)   2304        ['block5a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_expand_activation (Act  (None, 6, 6, 576)   0           ['block5a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_dwconv2 (DepthwiseConv  (None, 6, 6, 576)   5184        ['block5a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormalization  (None, 6, 6, 576)   2304        ['block5a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_activation (Activation  (None, 6, 6, 576)   0           ['block5a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (GlobalAver  (None, 576)         0           ['block5a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshape)   (None, 1, 1, 576)    0           ['block5a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)     (None, 1, 1, 24)     13848       ['block5a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)     (None, 1, 1, 576)    14400       ['block5a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multiply)   (None, 6, 6, 576)    0           ['block5a_activation[0][0]',     \n",
      "                                                                  'block5a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_conv (Conv2D)  (None, 6, 6, 112)    64512       ['block5a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchNorma  (None, 6, 6, 112)   448         ['block5a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2D)   (None, 6, 6, 672)    75264       ['block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNormal  (None, 6, 6, 672)   2688        ['block5b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_expand_activation (Act  (None, 6, 6, 672)   0           ['block5b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_dwconv2 (DepthwiseConv  (None, 6, 6, 672)   6048        ['block5b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormalization  (None, 6, 6, 672)   2688        ['block5b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_activation (Activation  (None, 6, 6, 672)   0           ['block5b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (GlobalAver  (None, 672)         0           ['block5b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5b_se_reshape[0][0]']     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multiply)   (None, 6, 6, 672)    0           ['block5b_activation[0][0]',     \n",
      "                                                                  'block5b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv2D)  (None, 6, 6, 112)    75264       ['block5b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchNorma  (None, 6, 6, 112)   448         ['block5b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_add (Add)              (None, 6, 6, 112)    0           ['block5b_project_bn[0][0]',     \n",
      "                                                                  'block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2D)   (None, 6, 6, 672)    75264       ['block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNormal  (None, 6, 6, 672)   2688        ['block5c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_expand_activation (Act  (None, 6, 6, 672)   0           ['block5c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_dwconv2 (DepthwiseConv  (None, 6, 6, 672)   6048        ['block5c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormalization  (None, 6, 6, 672)   2688        ['block5c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_activation (Activation  (None, 6, 6, 672)   0           ['block5c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (GlobalAver  (None, 672)         0           ['block5c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multiply)   (None, 6, 6, 672)    0           ['block5c_activation[0][0]',     \n",
      "                                                                  'block5c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv2D)  (None, 6, 6, 112)    75264       ['block5c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchNorma  (None, 6, 6, 112)   448         ['block5c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5c_add (Add)              (None, 6, 6, 112)    0           ['block5c_project_bn[0][0]',     \n",
      "                                                                  'block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_conv (Conv2D)   (None, 6, 6, 672)    75264       ['block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_bn (BatchNormal  (None, 6, 6, 672)   2688        ['block5d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5d_expand_activation (Act  (None, 6, 6, 672)   0           ['block5d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5d_dwconv2 (DepthwiseConv  (None, 6, 6, 672)   6048        ['block5d_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5d_bn (BatchNormalization  (None, 6, 6, 672)   2688        ['block5d_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_activation (Activation  (None, 6, 6, 672)   0           ['block5d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_se_squeeze (GlobalAver  (None, 672)         0           ['block5d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_se_excite (Multiply)   (None, 6, 6, 672)    0           ['block5d_activation[0][0]',     \n",
      "                                                                  'block5d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_conv (Conv2D)  (None, 6, 6, 112)    75264       ['block5d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_bn (BatchNorma  (None, 6, 6, 112)   448         ['block5d_project_conv[0][0]']   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5d_add (Add)              (None, 6, 6, 112)    0           ['block5d_project_bn[0][0]',     \n",
      "                                                                  'block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_conv (Conv2D)   (None, 6, 6, 672)    75264       ['block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_bn (BatchNormal  (None, 6, 6, 672)   2688        ['block5e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_expand_activation (Act  (None, 6, 6, 672)   0           ['block5e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_dwconv2 (DepthwiseConv  (None, 6, 6, 672)   6048        ['block5e_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5e_bn (BatchNormalization  (None, 6, 6, 672)   2688        ['block5e_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_activation (Activation  (None, 6, 6, 672)   0           ['block5e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_se_squeeze (GlobalAver  (None, 672)         0           ['block5e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5e_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_se_excite (Multiply)   (None, 6, 6, 672)    0           ['block5e_activation[0][0]',     \n",
      "                                                                  'block5e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_conv (Conv2D)  (None, 6, 6, 112)    75264       ['block5e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_bn (BatchNorma  (None, 6, 6, 112)   448         ['block5e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5e_add (Add)              (None, 6, 6, 112)    0           ['block5e_project_bn[0][0]',     \n",
      "                                                                  'block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2D)   (None, 6, 6, 672)    75264       ['block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNormal  (None, 6, 6, 672)   2688        ['block6a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_expand_activation (Act  (None, 6, 6, 672)   0           ['block6a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_dwconv2 (DepthwiseConv  (None, 3, 3, 672)   6048        ['block6a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormalization  (None, 3, 3, 672)   2688        ['block6a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_activation (Activation  (None, 3, 3, 672)   0           ['block6a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (GlobalAver  (None, 672)         0           ['block6a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block6a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block6a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block6a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multiply)   (None, 3, 3, 672)    0           ['block6a_activation[0][0]',     \n",
      "                                                                  'block6a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv2D)  (None, 3, 3, 192)    129024      ['block6a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6b_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block6b_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_activation (Activation  (None, 3, 3, 1152)  0           ['block6b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (GlobalAver  (None, 1152)        0           ['block6b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6b_activation[0][0]',     \n",
      "                                                                  'block6b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_add (Add)              (None, 3, 3, 192)    0           ['block6b_project_bn[0][0]',     \n",
      "                                                                  'block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_activation (Activation  (None, 3, 3, 1152)  0           ['block6c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (GlobalAver  (None, 1152)        0           ['block6c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6c_activation[0][0]',     \n",
      "                                                                  'block6c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6c_add (Add)              (None, 3, 3, 192)    0           ['block6c_project_bn[0][0]',     \n",
      "                                                                  'block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6d_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6d_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6d_activation (Activation  (None, 3, 3, 1152)  0           ['block6d_bn[0][0]']             \n",
      " )                                                                                                \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block6d_se_squeeze (GlobalAver  (None, 1152)        0           ['block6d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6d_activation[0][0]',     \n",
      "                                                                  'block6d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6d_add (Add)              (None, 3, 3, 192)    0           ['block6d_project_bn[0][0]',     \n",
      "                                                                  'block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6e_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6e_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6e_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_activation (Activation  (None, 3, 3, 1152)  0           ['block6e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_se_squeeze (GlobalAver  (None, 1152)        0           ['block6e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6e_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6e_activation[0][0]',     \n",
      "                                                                  'block6e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6e_add (Add)              (None, 3, 3, 192)    0           ['block6e_project_bn[0][0]',     \n",
      "                                                                  'block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6f_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6f_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6f_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_activation (Activation  (None, 3, 3, 1152)  0           ['block6f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_se_squeeze (GlobalAver  (None, 1152)        0           ['block6f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6f_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6f_se_reduce[0][0]']      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block6f_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6f_activation[0][0]',     \n",
      "                                                                  'block6f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6f_add (Add)              (None, 3, 3, 192)    0           ['block6f_project_bn[0][0]',     \n",
      "                                                                  'block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6g_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6g_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6g_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_activation (Activation  (None, 3, 3, 1152)  0           ['block6g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_se_squeeze (GlobalAver  (None, 1152)        0           ['block6g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6g_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6g_activation[0][0]',     \n",
      "                                                                  'block6g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6g_add (Add)              (None, 3, 3, 192)    0           ['block6g_project_bn[0][0]',     \n",
      "                                                                  'block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_conv (Conv2D)   (None, 3, 3, 1152)   221184      ['block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_bn (BatchNormal  (None, 3, 3, 1152)  4608        ['block6h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_expand_activation (Act  (None, 3, 3, 1152)  0           ['block6h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_dwconv2 (DepthwiseConv  (None, 3, 3, 1152)  10368       ['block6h_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6h_bn (BatchNormalization  (None, 3, 3, 1152)  4608        ['block6h_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_activation (Activation  (None, 3, 3, 1152)  0           ['block6h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_se_squeeze (GlobalAver  (None, 1152)        0           ['block6h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6h_se_reshape (Reshape)   (None, 1, 1, 1152)   0           ['block6h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_reduce (Conv2D)     (None, 1, 1, 48)     55344       ['block6h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_expand (Conv2D)     (None, 1, 1, 1152)   56448       ['block6h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_se_excite (Multiply)   (None, 3, 3, 1152)   0           ['block6h_activation[0][0]',     \n",
      "                                                                  'block6h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_conv (Conv2D)  (None, 3, 3, 192)    221184      ['block6h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_bn (BatchNorma  (None, 3, 3, 192)   768         ['block6h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block6h_add (Add)              (None, 3, 3, 192)    0           ['block6h_project_bn[0][0]',     \n",
      "                                                                  'block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)              (None, 3, 3, 1280)   245760      ['block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization)    (None, 3, 3, 1280)   5120        ['top_conv[0][0]']               \n",
      "                                                                                                  \n",
      " top_activation (Activation)    (None, 3, 3, 1280)   0           ['top_bn[0][0]']                 \n",
      "                                                                                                  \n",
      " avg_pool (GlobalAveragePooling  (None, 1280)        0           ['top_activation[0][0]']         \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " top_dropout (Dropout)          (None, 1280)         0           ['avg_pool[0][0]']               \n",
      "                                                                                                  \n",
      " predictions (Dense)            (None, 8)            10248       ['top_dropout[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5,928,984\n",
      "Trainable params: 5,868,376\n",
      "Non-trainable params: 60,608\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "VAksXWJv7yZ3"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.01),\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_callback = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    factor=0.1,\n",
    "    patience=10,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop_callback = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    min_delta=0,\n",
    "    patience=20,\n",
    "    restore_best_weights=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_loss_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/min_loss.h5',\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_accuracy_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_accuracy.h5',\n",
    "    monitor='val_accuracy',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_precision_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_precision.h5',\n",
    "    monitor='val_precision',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_recall_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_recall.h5',\n",
    "    monitor='val_recall',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger',\n",
       " 'contempt',\n",
       " 'disgust',\n",
       " 'fear',\n",
       " 'happiness',\n",
       " 'neutral',\n",
       " 'sadness',\n",
       " 'surprise']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights = {\n",
    "    0: 1.0,\n",
    "    1: 5.0,\n",
    "    2: 4.0,\n",
    "    3: 2.0,\n",
    "    4: 1.0,\n",
    "    5: 0.75,\n",
    "    6: 1.0,\n",
    "    7: 1.0,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "_yx8z1758eIJ",
    "outputId": "86b9d417-18b6-40c0-f7ad-494ac6f9a5aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 2.36009, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 1: val_accuracy improved from -inf to 0.27672, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 1: val_precision improved from -inf to 0.26644, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 1: val_recall improved from -inf to 0.02413, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 55s - loss: 2.7101 - accuracy: 0.3104 - precision: 0.2839 - recall: 0.0443 - val_loss: 2.3601 - val_accuracy: 0.2767 - val_precision: 0.2664 - val_recall: 0.0241 - lr: 0.0100 - 55s/epoch - 561ms/step\n",
      "Epoch 2/300\n",
      "\n",
      "Epoch 2: val_loss improved from 2.36009 to 1.72009, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 2: val_accuracy improved from 0.27672 to 0.36979, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 2: val_precision improved from 0.26644 to 0.33333, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 2: val_recall improved from 0.02413 to 0.16045, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 40s - loss: 1.8883 - accuracy: 0.3163 - precision: 0.3858 - recall: 0.0098 - val_loss: 1.7201 - val_accuracy: 0.3698 - val_precision: 0.3333 - val_recall: 0.1605 - lr: 0.0100 - 40s/epoch - 405ms/step\n",
      "Epoch 3/300\n",
      "\n",
      "Epoch 3: val_loss improved from 1.72009 to 1.63013, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 3: val_accuracy did not improve from 0.36979\n",
      "\n",
      "Epoch 3: val_precision did not improve from 0.33333\n",
      "\n",
      "Epoch 3: val_recall did not improve from 0.16045\n",
      "98/98 - 37s - loss: 1.8405 - accuracy: 0.3240 - precision: 0.3738 - recall: 0.0140 - val_loss: 1.6301 - val_accuracy: 0.3334 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - lr: 0.0100 - 37s/epoch - 379ms/step\n",
      "Epoch 4/300\n",
      "\n",
      "Epoch 4: val_loss did not improve from 1.63013\n",
      "\n",
      "Epoch 4: val_accuracy did not improve from 0.36979\n",
      "\n",
      "Epoch 4: val_precision did not improve from 0.33333\n",
      "\n",
      "Epoch 4: val_recall did not improve from 0.16045\n",
      "98/98 - 37s - loss: 1.8079 - accuracy: 0.3372 - precision: 0.4052 - recall: 0.0167 - val_loss: 4.0773 - val_accuracy: 0.3419 - val_precision: 0.2847 - val_recall: 0.0244 - lr: 0.0100 - 37s/epoch - 376ms/step\n",
      "Epoch 5/300\n",
      "\n",
      "Epoch 5: val_loss improved from 1.63013 to 1.58955, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 5: val_accuracy improved from 0.36979 to 0.39768, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 5: val_precision improved from 0.33333 to 0.55556, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 5: val_recall did not improve from 0.16045\n",
      "98/98 - 38s - loss: 1.7266 - accuracy: 0.3596 - precision: 0.4502 - recall: 0.0258 - val_loss: 1.5895 - val_accuracy: 0.3977 - val_precision: 0.5556 - val_recall: 0.0345 - lr: 0.0100 - 38s/epoch - 387ms/step\n",
      "Epoch 6/300\n",
      "\n",
      "Epoch 6: val_loss did not improve from 1.58955\n",
      "\n",
      "Epoch 6: val_accuracy did not improve from 0.39768\n",
      "\n",
      "Epoch 6: val_precision did not improve from 0.55556\n",
      "\n",
      "Epoch 6: val_recall improved from 0.16045 to 0.20840, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 1.6469 - accuracy: 0.3922 - precision: 0.4869 - recall: 0.0592 - val_loss: 1.5933 - val_accuracy: 0.3475 - val_precision: 0.4398 - val_recall: 0.2084 - lr: 0.0100 - 37s/epoch - 380ms/step\n",
      "Epoch 7/300\n",
      "\n",
      "Epoch 7: val_loss did not improve from 1.58955\n",
      "\n",
      "Epoch 7: val_accuracy did not improve from 0.39768\n",
      "\n",
      "Epoch 7: val_precision did not improve from 0.55556\n",
      "\n",
      "Epoch 7: val_recall improved from 0.20840 to 0.26073, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 1.5211 - accuracy: 0.4755 - precision: 0.6253 - recall: 0.1847 - val_loss: 1.5971 - val_accuracy: 0.3638 - val_precision: 0.4514 - val_recall: 0.2607 - lr: 0.0100 - 37s/epoch - 380ms/step\n",
      "Epoch 8/300\n",
      "\n",
      "Epoch 8: val_loss improved from 1.58955 to 1.12177, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 8: val_accuracy improved from 0.39768 to 0.61391, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 8: val_precision improved from 0.55556 to 0.72458, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 8: val_recall improved from 0.26073 to 0.48229, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 39s - loss: 1.3972 - accuracy: 0.5437 - precision: 0.6970 - recall: 0.3067 - val_loss: 1.1218 - val_accuracy: 0.6139 - val_precision: 0.7246 - val_recall: 0.4823 - lr: 0.0100 - 39s/epoch - 394ms/step\n",
      "Epoch 9/300\n",
      "\n",
      "Epoch 9: val_loss did not improve from 1.12177\n",
      "\n",
      "Epoch 9: val_accuracy did not improve from 0.61391\n",
      "\n",
      "Epoch 9: val_precision did not improve from 0.72458\n",
      "\n",
      "Epoch 9: val_recall improved from 0.48229 to 0.49608, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 1.2993 - accuracy: 0.5746 - precision: 0.7319 - recall: 0.3639 - val_loss: 1.1481 - val_accuracy: 0.6089 - val_precision: 0.7051 - val_recall: 0.4961 - lr: 0.0100 - 37s/epoch - 382ms/step\n",
      "Epoch 10/300\n",
      "\n",
      "Epoch 10: val_loss did not improve from 1.12177\n",
      "\n",
      "Epoch 10: val_accuracy did not improve from 0.61391\n",
      "\n",
      "Epoch 10: val_precision improved from 0.72458 to 0.73823, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 10: val_recall did not improve from 0.49608\n",
      "98/98 - 37s - loss: 1.2249 - accuracy: 0.6024 - precision: 0.7515 - recall: 0.4131 - val_loss: 1.1764 - val_accuracy: 0.6092 - val_precision: 0.7382 - val_recall: 0.4569 - lr: 0.0100 - 37s/epoch - 380ms/step\n",
      "Epoch 11/300\n",
      "\n",
      "Epoch 11: val_loss improved from 1.12177 to 1.08778, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 11: val_accuracy improved from 0.61391 to 0.62739, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 11: val_precision improved from 0.73823 to 0.74678, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 11: val_recall did not improve from 0.49608\n",
      "98/98 - 38s - loss: 1.1469 - accuracy: 0.6301 - precision: 0.7683 - recall: 0.4618 - val_loss: 1.0878 - val_accuracy: 0.6274 - val_precision: 0.7468 - val_recall: 0.4538 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 12/300\n",
      "\n",
      "Epoch 12: val_loss improved from 1.08778 to 1.01796, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 12: val_accuracy improved from 0.62739 to 0.63178, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 12: val_precision did not improve from 0.74678\n",
      "\n",
      "Epoch 12: val_recall did not improve from 0.49608\n",
      "98/98 - 38s - loss: 1.0760 - accuracy: 0.6550 - precision: 0.7776 - recall: 0.5068 - val_loss: 1.0180 - val_accuracy: 0.6318 - val_precision: 0.7443 - val_recall: 0.4688 - lr: 0.0100 - 38s/epoch - 386ms/step\n",
      "Epoch 13/300\n",
      "\n",
      "Epoch 13: val_loss did not improve from 1.01796\n",
      "\n",
      "Epoch 13: val_accuracy improved from 0.63178 to 0.64651, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 13: val_precision did not improve from 0.74678\n",
      "\n",
      "Epoch 13: val_recall improved from 0.49608 to 0.55186, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 1.0360 - accuracy: 0.6697 - precision: 0.7810 - recall: 0.5287 - val_loss: 1.0943 - val_accuracy: 0.6465 - val_precision: 0.7368 - val_recall: 0.5519 - lr: 0.0100 - 38s/epoch - 384ms/step\n",
      "Epoch 14/300\n",
      "\n",
      "Epoch 14: val_loss improved from 1.01796 to 1.00814, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 14: val_accuracy improved from 0.64651 to 0.65622, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 14: val_precision did not improve from 0.74678\n",
      "\n",
      "Epoch 14: val_recall improved from 0.55186 to 0.55374, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.9912 - accuracy: 0.6809 - precision: 0.7855 - recall: 0.5550 - val_loss: 1.0081 - val_accuracy: 0.6562 - val_precision: 0.7449 - val_recall: 0.5537 - lr: 0.0100 - 38s/epoch - 391ms/step\n",
      "Epoch 15/300\n",
      "\n",
      "Epoch 15: val_loss improved from 1.00814 to 0.86217, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 15: val_accuracy improved from 0.65622 to 0.70229, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 15: val_precision improved from 0.74678 to 0.78889, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 15: val_recall improved from 0.55374 to 0.60075, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 39s - loss: 0.9536 - accuracy: 0.6936 - precision: 0.7929 - recall: 0.5761 - val_loss: 0.8622 - val_accuracy: 0.7023 - val_precision: 0.7889 - val_recall: 0.6008 - lr: 0.0100 - 39s/epoch - 394ms/step\n",
      "Epoch 16/300\n",
      "\n",
      "Epoch 16: val_loss did not improve from 0.86217\n",
      "\n",
      "Epoch 16: val_accuracy did not improve from 0.70229\n",
      "\n",
      "Epoch 16: val_precision did not improve from 0.78889\n",
      "\n",
      "Epoch 16: val_recall did not improve from 0.60075\n",
      "98/98 - 37s - loss: 0.9289 - accuracy: 0.7033 - precision: 0.7941 - recall: 0.5878 - val_loss: 0.8876 - val_accuracy: 0.6854 - val_precision: 0.7745 - val_recall: 0.5835 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 17/300\n",
      "\n",
      "Epoch 17: val_loss did not improve from 0.86217\n",
      "\n",
      "Epoch 17: val_accuracy did not improve from 0.70229\n",
      "\n",
      "Epoch 17: val_precision did not improve from 0.78889\n",
      "\n",
      "Epoch 17: val_recall did not improve from 0.60075\n",
      "98/98 - 37s - loss: 0.8984 - accuracy: 0.7107 - precision: 0.8015 - recall: 0.6028 - val_loss: 0.8924 - val_accuracy: 0.6659 - val_precision: 0.7212 - val_recall: 0.5788 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 18/300\n",
      "\n",
      "Epoch 18: val_loss did not improve from 0.86217\n",
      "\n",
      "Epoch 18: val_accuracy did not improve from 0.70229\n",
      "\n",
      "Epoch 18: val_precision improved from 0.78889 to 0.79575, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 18: val_recall did not improve from 0.60075\n",
      "98/98 - 37s - loss: 0.8711 - accuracy: 0.7143 - precision: 0.8019 - recall: 0.6163 - val_loss: 0.8773 - val_accuracy: 0.6876 - val_precision: 0.7958 - val_recall: 0.5873 - lr: 0.0100 - 37s/epoch - 380ms/step\n",
      "Epoch 19/300\n",
      "\n",
      "Epoch 19: val_loss improved from 0.86217 to 0.84517, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 19: val_accuracy improved from 0.70229 to 0.70354, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 19: val_precision did not improve from 0.79575\n",
      "\n",
      "Epoch 19: val_recall improved from 0.60075 to 0.61297, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.8432 - accuracy: 0.7250 - precision: 0.8071 - recall: 0.6290 - val_loss: 0.8452 - val_accuracy: 0.7035 - val_precision: 0.7903 - val_recall: 0.6130 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 20/300\n",
      "\n",
      "Epoch 20: val_loss improved from 0.84517 to 0.76569, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 20: val_accuracy improved from 0.70354 to 0.73676, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 20: val_precision did not improve from 0.79575\n",
      "\n",
      "Epoch 20: val_recall improved from 0.61297 to 0.68129, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.8249 - accuracy: 0.7312 - precision: 0.8081 - recall: 0.6376 - val_loss: 0.7657 - val_accuracy: 0.7368 - val_precision: 0.7871 - val_recall: 0.6813 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 21/300\n",
      "\n",
      "Epoch 21: val_loss did not improve from 0.76569\n",
      "\n",
      "Epoch 21: val_accuracy did not improve from 0.73676\n",
      "\n",
      "Epoch 21: val_precision did not improve from 0.79575\n",
      "\n",
      "Epoch 21: val_recall did not improve from 0.68129\n",
      "98/98 - 37s - loss: 0.8046 - accuracy: 0.7366 - precision: 0.8102 - recall: 0.6494 - val_loss: 1.0043 - val_accuracy: 0.6500 - val_precision: 0.7571 - val_recall: 0.5274 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 22/300\n",
      "\n",
      "Epoch 22: val_loss did not improve from 0.76569\n",
      "\n",
      "Epoch 22: val_accuracy did not improve from 0.73676\n",
      "\n",
      "Epoch 22: val_precision did not improve from 0.79575\n",
      "\n",
      "Epoch 22: val_recall did not improve from 0.68129\n",
      "98/98 - 37s - loss: 0.7847 - accuracy: 0.7442 - precision: 0.8150 - recall: 0.6593 - val_loss: 0.7991 - val_accuracy: 0.7274 - val_precision: 0.7831 - val_recall: 0.6688 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 23/300\n",
      "\n",
      "Epoch 23: val_loss improved from 0.76569 to 0.76484, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 23: val_accuracy did not improve from 0.73676\n",
      "\n",
      "Epoch 23: val_precision did not improve from 0.79575\n",
      "\n",
      "Epoch 23: val_recall did not improve from 0.68129\n",
      "98/98 - 37s - loss: 0.7692 - accuracy: 0.7491 - precision: 0.8181 - recall: 0.6710 - val_loss: 0.7648 - val_accuracy: 0.7336 - val_precision: 0.7914 - val_recall: 0.6753 - lr: 0.0100 - 37s/epoch - 381ms/step\n",
      "Epoch 24/300\n",
      "\n",
      "Epoch 24: val_loss did not improve from 0.76484\n",
      "\n",
      "Epoch 24: val_accuracy did not improve from 0.73676\n",
      "\n",
      "Epoch 24: val_precision improved from 0.79575 to 0.79867, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 24: val_recall did not improve from 0.68129\n",
      "98/98 - 37s - loss: 0.7427 - accuracy: 0.7578 - precision: 0.8236 - recall: 0.6809 - val_loss: 0.7846 - val_accuracy: 0.7283 - val_precision: 0.7987 - val_recall: 0.6415 - lr: 0.0100 - 37s/epoch - 381ms/step\n",
      "Epoch 25/300\n",
      "\n",
      "Epoch 25: val_loss improved from 0.76484 to 0.75317, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 25: val_accuracy improved from 0.73676 to 0.74209, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 25: val_precision improved from 0.79867 to 0.80130, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 25: val_recall did not improve from 0.68129\n",
      "98/98 - 38s - loss: 0.7292 - accuracy: 0.7581 - precision: 0.8261 - recall: 0.6867 - val_loss: 0.7532 - val_accuracy: 0.7421 - val_precision: 0.8013 - val_recall: 0.6584 - lr: 0.0100 - 38s/epoch - 389ms/step\n",
      "Epoch 26/300\n",
      "\n",
      "Epoch 26: val_loss did not improve from 0.75317\n",
      "\n",
      "Epoch 26: val_accuracy did not improve from 0.74209\n",
      "\n",
      "Epoch 26: val_precision did not improve from 0.80130\n",
      "\n",
      "Epoch 26: val_recall did not improve from 0.68129\n",
      "98/98 - 37s - loss: 0.7213 - accuracy: 0.7598 - precision: 0.8243 - recall: 0.6896 - val_loss: 0.8225 - val_accuracy: 0.7098 - val_precision: 0.7536 - val_recall: 0.6537 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 27/300\n",
      "\n",
      "Epoch 27: val_loss improved from 0.75317 to 0.73176, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 27: val_accuracy improved from 0.74209 to 0.75337, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 27: val_precision improved from 0.80130 to 0.81829, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 27: val_recall did not improve from 0.68129\n",
      "98/98 - 38s - loss: 0.6994 - accuracy: 0.7656 - precision: 0.8253 - recall: 0.6972 - val_loss: 0.7318 - val_accuracy: 0.7534 - val_precision: 0.8183 - val_recall: 0.6760 - lr: 0.0100 - 38s/epoch - 390ms/step\n",
      "Epoch 28/300\n",
      "\n",
      "Epoch 28: val_loss improved from 0.73176 to 0.66613, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 28: val_accuracy improved from 0.75337 to 0.76559, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 28: val_precision did not improve from 0.81829\n",
      "\n",
      "Epoch 28: val_recall improved from 0.68129 to 0.70291, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98/98 - 38s - loss: 0.6868 - accuracy: 0.7683 - precision: 0.8278 - recall: 0.7019 - val_loss: 0.6661 - val_accuracy: 0.7656 - val_precision: 0.8121 - val_recall: 0.7029 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 29/300\n",
      "\n",
      "Epoch 29: val_loss improved from 0.66613 to 0.63807, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 29: val_accuracy improved from 0.76559 to 0.77311, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 29: val_precision improved from 0.81829 to 0.81844, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 29: val_recall improved from 0.70291 to 0.72046, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.6703 - accuracy: 0.7794 - precision: 0.8353 - recall: 0.7172 - val_loss: 0.6381 - val_accuracy: 0.7731 - val_precision: 0.8184 - val_recall: 0.7205 - lr: 0.0100 - 38s/epoch - 392ms/step\n",
      "Epoch 30/300\n",
      "\n",
      "Epoch 30: val_loss did not improve from 0.63807\n",
      "\n",
      "Epoch 30: val_accuracy did not improve from 0.77311\n",
      "\n",
      "Epoch 30: val_precision did not improve from 0.81844\n",
      "\n",
      "Epoch 30: val_recall did not improve from 0.72046\n",
      "98/98 - 37s - loss: 0.6582 - accuracy: 0.7808 - precision: 0.8365 - recall: 0.7200 - val_loss: 0.6922 - val_accuracy: 0.7590 - val_precision: 0.8114 - val_recall: 0.7010 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 31/300\n",
      "\n",
      "Epoch 31: val_loss did not improve from 0.63807\n",
      "\n",
      "Epoch 31: val_accuracy improved from 0.77311 to 0.77907, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 31: val_precision improved from 0.81844 to 0.82745, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 31: val_recall improved from 0.72046 to 0.72736, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.6439 - accuracy: 0.7822 - precision: 0.8359 - recall: 0.7255 - val_loss: 0.6556 - val_accuracy: 0.7791 - val_precision: 0.8275 - val_recall: 0.7274 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 32/300\n",
      "\n",
      "Epoch 32: val_loss did not improve from 0.63807\n",
      "\n",
      "Epoch 32: val_accuracy did not improve from 0.77907\n",
      "\n",
      "Epoch 32: val_precision did not improve from 0.82745\n",
      "\n",
      "Epoch 32: val_recall did not improve from 0.72736\n",
      "98/98 - 37s - loss: 0.6411 - accuracy: 0.7843 - precision: 0.8367 - recall: 0.7264 - val_loss: 0.7662 - val_accuracy: 0.7505 - val_precision: 0.7874 - val_recall: 0.7092 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 33/300\n",
      "\n",
      "Epoch 33: val_loss improved from 0.63807 to 0.61176, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 33: val_accuracy improved from 0.77907 to 0.78502, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 33: val_precision improved from 0.82745 to 0.83428, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 33: val_recall improved from 0.72736 to 0.73833, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.6256 - accuracy: 0.7854 - precision: 0.8365 - recall: 0.7328 - val_loss: 0.6118 - val_accuracy: 0.7850 - val_precision: 0.8343 - val_recall: 0.7383 - lr: 0.0100 - 38s/epoch - 392ms/step\n",
      "Epoch 34/300\n",
      "\n",
      "Epoch 34: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 34: val_accuracy improved from 0.78502 to 0.78753, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 34: val_precision improved from 0.83428 to 0.83493, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 34: val_recall did not improve from 0.73833\n",
      "98/98 - 38s - loss: 0.6241 - accuracy: 0.7890 - precision: 0.8380 - recall: 0.7326 - val_loss: 0.6221 - val_accuracy: 0.7875 - val_precision: 0.8349 - val_recall: 0.7355 - lr: 0.0100 - 38s/epoch - 385ms/step\n",
      "Epoch 35/300\n",
      "\n",
      "Epoch 35: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 35: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 35: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 35: val_recall improved from 0.73833 to 0.74741, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 0.6094 - accuracy: 0.7899 - precision: 0.8378 - recall: 0.7380 - val_loss: 0.6140 - val_accuracy: 0.7831 - val_precision: 0.8270 - val_recall: 0.7474 - lr: 0.0100 - 37s/epoch - 381ms/step\n",
      "Epoch 36/300\n",
      "\n",
      "Epoch 36: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 36: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 36: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 36: val_recall did not improve from 0.74741\n",
      "98/98 - 37s - loss: 0.6023 - accuracy: 0.7948 - precision: 0.8403 - recall: 0.7436 - val_loss: 0.6882 - val_accuracy: 0.7455 - val_precision: 0.7805 - val_recall: 0.7020 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 37/300\n",
      "\n",
      "Epoch 37: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 37: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 37: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 37: val_recall did not improve from 0.74741\n",
      "98/98 - 37s - loss: 0.5956 - accuracy: 0.7954 - precision: 0.8413 - recall: 0.7461 - val_loss: 0.7629 - val_accuracy: 0.7039 - val_precision: 0.7548 - val_recall: 0.6443 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 38/300\n",
      "\n",
      "Epoch 38: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 38: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 38: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 38: val_recall did not improve from 0.74741\n",
      "98/98 - 37s - loss: 0.5916 - accuracy: 0.7968 - precision: 0.8402 - recall: 0.7517 - val_loss: 0.6648 - val_accuracy: 0.7531 - val_precision: 0.7928 - val_recall: 0.6979 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 39/300\n",
      "\n",
      "Epoch 39: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 39: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 39: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 39: val_recall did not improve from 0.74741\n",
      "98/98 - 37s - loss: 0.5778 - accuracy: 0.8016 - precision: 0.8450 - recall: 0.7527 - val_loss: 0.6985 - val_accuracy: 0.7543 - val_precision: 0.8057 - val_recall: 0.7133 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 40/300\n",
      "\n",
      "Epoch 40: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 40: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 40: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 40: val_recall did not improve from 0.74741\n",
      "98/98 - 37s - loss: 0.5746 - accuracy: 0.8038 - precision: 0.8455 - recall: 0.7564 - val_loss: 0.7609 - val_accuracy: 0.7317 - val_precision: 0.7782 - val_recall: 0.6829 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 41/300\n",
      "\n",
      "Epoch 41: val_loss did not improve from 0.61176\n",
      "\n",
      "Epoch 41: val_accuracy did not improve from 0.78753\n",
      "\n",
      "Epoch 41: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 41: val_recall improved from 0.74741 to 0.75086, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 0.5714 - accuracy: 0.8040 - precision: 0.8467 - recall: 0.7560 - val_loss: 0.6537 - val_accuracy: 0.7856 - val_precision: 0.8147 - val_recall: 0.7509 - lr: 0.0100 - 37s/epoch - 382ms/step\n",
      "Epoch 42/300\n",
      "\n",
      "Epoch 42: val_loss improved from 0.61176 to 0.58197, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 42: val_accuracy improved from 0.78753 to 0.80069, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 42: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 42: val_recall improved from 0.75086 to 0.77029, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.5592 - accuracy: 0.8057 - precision: 0.8463 - recall: 0.7623 - val_loss: 0.5820 - val_accuracy: 0.8007 - val_precision: 0.8298 - val_recall: 0.7703 - lr: 0.0100 - 38s/epoch - 389ms/step\n",
      "Epoch 43/300\n",
      "\n",
      "Epoch 43: val_loss improved from 0.58197 to 0.57990, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 43: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 43: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 43: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5476 - accuracy: 0.8101 - precision: 0.8481 - recall: 0.7657 - val_loss: 0.5799 - val_accuracy: 0.7913 - val_precision: 0.8285 - val_recall: 0.7493 - lr: 0.0100 - 37s/epoch - 381ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44/300\n",
      "\n",
      "Epoch 44: val_loss improved from 0.57990 to 0.57778, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 44: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 44: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 44: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5443 - accuracy: 0.8097 - precision: 0.8510 - recall: 0.7665 - val_loss: 0.5778 - val_accuracy: 0.7800 - val_precision: 0.8168 - val_recall: 0.7543 - lr: 0.0100 - 37s/epoch - 381ms/step\n",
      "Epoch 45/300\n",
      "\n",
      "Epoch 45: val_loss did not improve from 0.57778\n",
      "\n",
      "Epoch 45: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 45: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 45: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5473 - accuracy: 0.8107 - precision: 0.8487 - recall: 0.7687 - val_loss: 0.6096 - val_accuracy: 0.7910 - val_precision: 0.8282 - val_recall: 0.7568 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 46/300\n",
      "\n",
      "Epoch 46: val_loss did not improve from 0.57778\n",
      "\n",
      "Epoch 46: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 46: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 46: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5401 - accuracy: 0.8138 - precision: 0.8520 - recall: 0.7696 - val_loss: 0.6125 - val_accuracy: 0.7759 - val_precision: 0.8230 - val_recall: 0.7386 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 47/300\n",
      "\n",
      "Epoch 47: val_loss did not improve from 0.57778\n",
      "\n",
      "Epoch 47: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 47: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 47: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5215 - accuracy: 0.8139 - precision: 0.8523 - recall: 0.7746 - val_loss: 0.5897 - val_accuracy: 0.7938 - val_precision: 0.8269 - val_recall: 0.7578 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 48/300\n",
      "\n",
      "Epoch 48: val_loss did not improve from 0.57778\n",
      "\n",
      "Epoch 48: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 48: val_precision did not improve from 0.83493\n",
      "\n",
      "Epoch 48: val_recall did not improve from 0.77029\n",
      "98/98 - 37s - loss: 0.5200 - accuracy: 0.8163 - precision: 0.8519 - recall: 0.7777 - val_loss: 0.6126 - val_accuracy: 0.7769 - val_precision: 0.8191 - val_recall: 0.7308 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 49/300\n",
      "\n",
      "Epoch 49: val_loss improved from 0.57778 to 0.56844, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 49: val_accuracy did not improve from 0.80069\n",
      "\n",
      "Epoch 49: val_precision improved from 0.83493 to 0.83736, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 49: val_recall did not improve from 0.77029\n",
      "98/98 - 38s - loss: 0.5136 - accuracy: 0.8184 - precision: 0.8547 - recall: 0.7795 - val_loss: 0.5684 - val_accuracy: 0.7950 - val_precision: 0.8374 - val_recall: 0.7599 - lr: 0.0100 - 38s/epoch - 384ms/step\n",
      "Epoch 50/300\n",
      "\n",
      "Epoch 50: val_loss improved from 0.56844 to 0.56294, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 50: val_accuracy improved from 0.80069 to 0.81040, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 50: val_precision improved from 0.83736 to 0.83863, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 50: val_recall improved from 0.77029 to 0.77687, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.5054 - accuracy: 0.8182 - precision: 0.8546 - recall: 0.7830 - val_loss: 0.5629 - val_accuracy: 0.8104 - val_precision: 0.8386 - val_recall: 0.7769 - lr: 0.0100 - 38s/epoch - 392ms/step\n",
      "Epoch 51/300\n",
      "\n",
      "Epoch 51: val_loss did not improve from 0.56294\n",
      "\n",
      "Epoch 51: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 51: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 51: val_recall did not improve from 0.77687\n",
      "98/98 - 37s - loss: 0.5080 - accuracy: 0.8190 - precision: 0.8550 - recall: 0.7818 - val_loss: 0.7078 - val_accuracy: 0.7659 - val_precision: 0.8032 - val_recall: 0.7264 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 52/300\n",
      "\n",
      "Epoch 52: val_loss did not improve from 0.56294\n",
      "\n",
      "Epoch 52: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 52: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 52: val_recall did not improve from 0.77687\n",
      "98/98 - 37s - loss: 0.4976 - accuracy: 0.8238 - precision: 0.8579 - recall: 0.7877 - val_loss: 0.5879 - val_accuracy: 0.7972 - val_precision: 0.8250 - val_recall: 0.7637 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 53/300\n",
      "\n",
      "Epoch 53: val_loss did not improve from 0.56294\n",
      "\n",
      "Epoch 53: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 53: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 53: val_recall did not improve from 0.77687\n",
      "98/98 - 37s - loss: 0.4969 - accuracy: 0.8257 - precision: 0.8570 - recall: 0.7877 - val_loss: 0.5798 - val_accuracy: 0.7944 - val_precision: 0.8259 - val_recall: 0.7628 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 54/300\n",
      "\n",
      "Epoch 54: val_loss improved from 0.56294 to 0.56188, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 54: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 54: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 54: val_recall improved from 0.77687 to 0.77719, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.4874 - accuracy: 0.8265 - precision: 0.8589 - recall: 0.7919 - val_loss: 0.5619 - val_accuracy: 0.8007 - val_precision: 0.8300 - val_recall: 0.7772 - lr: 0.0100 - 38s/epoch - 384ms/step\n",
      "Epoch 55/300\n",
      "\n",
      "Epoch 55: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 55: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 55: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 55: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4833 - accuracy: 0.8281 - precision: 0.8587 - recall: 0.7944 - val_loss: 0.6432 - val_accuracy: 0.7850 - val_precision: 0.8171 - val_recall: 0.7603 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 56/300\n",
      "\n",
      "Epoch 56: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 56: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 56: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 56: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4873 - accuracy: 0.8270 - precision: 0.8597 - recall: 0.7927 - val_loss: 0.5768 - val_accuracy: 0.7941 - val_precision: 0.8221 - val_recall: 0.7634 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 57/300\n",
      "\n",
      "Epoch 57: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 57: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 57: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 57: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4724 - accuracy: 0.8289 - precision: 0.8574 - recall: 0.7973 - val_loss: 0.5821 - val_accuracy: 0.7976 - val_precision: 0.8263 - val_recall: 0.7737 - lr: 0.0100 - 37s/epoch - 379ms/step\n",
      "Epoch 58/300\n",
      "\n",
      "Epoch 58: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 58: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 58: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 58: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4725 - accuracy: 0.8303 - precision: 0.8606 - recall: 0.7972 - val_loss: 0.5836 - val_accuracy: 0.8001 - val_precision: 0.8228 - val_recall: 0.7769 - lr: 0.0100 - 37s/epoch - 380ms/step\n",
      "Epoch 59/300\n",
      "\n",
      "Epoch 59: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 59: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 59: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 59: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4625 - accuracy: 0.8336 - precision: 0.8633 - recall: 0.8032 - val_loss: 0.5874 - val_accuracy: 0.7882 - val_precision: 0.8180 - val_recall: 0.7606 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 60/300\n",
      "\n",
      "Epoch 60: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 60: val_accuracy did not improve from 0.81040\n",
      "\n",
      "Epoch 60: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 60: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4593 - accuracy: 0.8349 - precision: 0.8653 - recall: 0.8049 - val_loss: 0.6119 - val_accuracy: 0.7950 - val_precision: 0.8205 - val_recall: 0.7709 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 61/300\n",
      "\n",
      "Epoch 61: val_loss did not improve from 0.56188\n",
      "\n",
      "Epoch 61: val_accuracy did not improve from 0.81040\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 61: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 61: val_recall did not improve from 0.77719\n",
      "98/98 - 37s - loss: 0.4579 - accuracy: 0.8347 - precision: 0.8623 - recall: 0.8073 - val_loss: 0.7966 - val_accuracy: 0.7277 - val_precision: 0.7718 - val_recall: 0.6838 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 62/300\n",
      "\n",
      "Epoch 62: val_loss improved from 0.56188 to 0.54965, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 62: val_accuracy improved from 0.81040 to 0.81134, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 62: val_precision did not improve from 0.83863\n",
      "\n",
      "Epoch 62: val_recall improved from 0.77719 to 0.78283, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.4562 - accuracy: 0.8362 - precision: 0.8649 - recall: 0.8051 - val_loss: 0.5497 - val_accuracy: 0.8113 - val_precision: 0.8385 - val_recall: 0.7828 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 63/300\n",
      "\n",
      "Epoch 63: val_loss improved from 0.54965 to 0.51524, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 63: val_accuracy improved from 0.81134 to 0.81855, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 63: val_precision improved from 0.83863 to 0.84800, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 63: val_recall improved from 0.78283 to 0.78502, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.4511 - accuracy: 0.8369 - precision: 0.8640 - recall: 0.8073 - val_loss: 0.5152 - val_accuracy: 0.8186 - val_precision: 0.8480 - val_recall: 0.7850 - lr: 0.0100 - 38s/epoch - 392ms/step\n",
      "Epoch 64/300\n",
      "\n",
      "Epoch 64: val_loss did not improve from 0.51524\n",
      "\n",
      "Epoch 64: val_accuracy did not improve from 0.81855\n",
      "\n",
      "Epoch 64: val_precision did not improve from 0.84800\n",
      "\n",
      "Epoch 64: val_recall did not improve from 0.78502\n",
      "98/98 - 37s - loss: 0.4486 - accuracy: 0.8364 - precision: 0.8640 - recall: 0.8084 - val_loss: 0.6008 - val_accuracy: 0.7872 - val_precision: 0.8205 - val_recall: 0.7590 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 65/300\n",
      "\n",
      "Epoch 65: val_loss did not improve from 0.51524\n",
      "\n",
      "Epoch 65: val_accuracy improved from 0.81855 to 0.82419, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 65: val_precision did not improve from 0.84800\n",
      "\n",
      "Epoch 65: val_recall improved from 0.78502 to 0.80445, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.4305 - accuracy: 0.8432 - precision: 0.8683 - recall: 0.8161 - val_loss: 0.5264 - val_accuracy: 0.8242 - val_precision: 0.8433 - val_recall: 0.8045 - lr: 0.0100 - 38s/epoch - 384ms/step\n",
      "Epoch 66/300\n",
      "\n",
      "Epoch 66: val_loss did not improve from 0.51524\n",
      "\n",
      "Epoch 66: val_accuracy did not improve from 0.82419\n",
      "\n",
      "Epoch 66: val_precision did not improve from 0.84800\n",
      "\n",
      "Epoch 66: val_recall did not improve from 0.80445\n",
      "98/98 - 37s - loss: 0.4334 - accuracy: 0.8410 - precision: 0.8690 - recall: 0.8161 - val_loss: 0.6295 - val_accuracy: 0.7803 - val_precision: 0.8055 - val_recall: 0.7581 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 67/300\n",
      "\n",
      "Epoch 67: val_loss improved from 0.51524 to 0.49517, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 67: val_accuracy improved from 0.82419 to 0.82983, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 67: val_precision improved from 0.84800 to 0.84962, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 67: val_recall improved from 0.80445 to 0.80915, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.4297 - accuracy: 0.8446 - precision: 0.8696 - recall: 0.8187 - val_loss: 0.4952 - val_accuracy: 0.8298 - val_precision: 0.8496 - val_recall: 0.8092 - lr: 0.0100 - 38s/epoch - 392ms/step\n",
      "Epoch 68/300\n",
      "\n",
      "Epoch 68: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 68: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 68: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 68: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4333 - accuracy: 0.8427 - precision: 0.8682 - recall: 0.8176 - val_loss: 0.5653 - val_accuracy: 0.8048 - val_precision: 0.8291 - val_recall: 0.7831 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 69/300\n",
      "\n",
      "Epoch 69: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 69: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 69: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 69: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4314 - accuracy: 0.8467 - precision: 0.8710 - recall: 0.8193 - val_loss: 0.5743 - val_accuracy: 0.8063 - val_precision: 0.8282 - val_recall: 0.7825 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 70/300\n",
      "\n",
      "Epoch 70: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 70: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 70: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 70: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4202 - accuracy: 0.8462 - precision: 0.8716 - recall: 0.8225 - val_loss: 0.5542 - val_accuracy: 0.8063 - val_precision: 0.8333 - val_recall: 0.7788 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 71/300\n",
      "\n",
      "Epoch 71: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 71: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 71: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 71: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4167 - accuracy: 0.8464 - precision: 0.8710 - recall: 0.8213 - val_loss: 0.5175 - val_accuracy: 0.8248 - val_precision: 0.8470 - val_recall: 0.8048 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 72/300\n",
      "\n",
      "Epoch 72: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 72: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 72: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 72: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4131 - accuracy: 0.8485 - precision: 0.8700 - recall: 0.8226 - val_loss: 0.5589 - val_accuracy: 0.8001 - val_precision: 0.8329 - val_recall: 0.7750 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 73/300\n",
      "\n",
      "Epoch 73: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 73: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 73: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 73: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4093 - accuracy: 0.8477 - precision: 0.8712 - recall: 0.8241 - val_loss: 0.5196 - val_accuracy: 0.8229 - val_precision: 0.8427 - val_recall: 0.8057 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 74/300\n",
      "\n",
      "Epoch 74: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 74: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 74: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 74: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4030 - accuracy: 0.8515 - precision: 0.8736 - recall: 0.8279 - val_loss: 0.5412 - val_accuracy: 0.8120 - val_precision: 0.8363 - val_recall: 0.7907 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 75/300\n",
      "\n",
      "Epoch 75: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 75: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 75: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 75: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4020 - accuracy: 0.8501 - precision: 0.8718 - recall: 0.8260 - val_loss: 0.6060 - val_accuracy: 0.7994 - val_precision: 0.8206 - val_recall: 0.7813 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 76/300\n",
      "\n",
      "Epoch 76: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 76: val_accuracy did not improve from 0.82983\n",
      "\n",
      "Epoch 76: val_precision did not improve from 0.84962\n",
      "\n",
      "Epoch 76: val_recall did not improve from 0.80915\n",
      "98/98 - 37s - loss: 0.4046 - accuracy: 0.8542 - precision: 0.8755 - recall: 0.8307 - val_loss: 0.5369 - val_accuracy: 0.8157 - val_precision: 0.8397 - val_recall: 0.8013 - lr: 0.0100 - 37s/epoch - 377ms/step\n",
      "Epoch 77/300\n",
      "\n",
      "Epoch 77: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
      "\n",
      "Epoch 77: val_loss did not improve from 0.49517\n",
      "\n",
      "Epoch 77: val_accuracy improved from 0.82983 to 0.83892, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 77: val_precision improved from 0.84962 to 0.85500, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 77: val_recall improved from 0.80915 to 0.82043, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.3955 - accuracy: 0.8533 - precision: 0.8744 - recall: 0.8314 - val_loss: 0.5021 - val_accuracy: 0.8389 - val_precision: 0.8550 - val_recall: 0.8204 - lr: 0.0100 - 38s/epoch - 388ms/step\n",
      "Epoch 78/300\n",
      "\n",
      "Epoch 78: val_loss improved from 0.49517 to 0.47963, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 78: val_accuracy did not improve from 0.83892\n",
      "\n",
      "Epoch 78: val_precision did not improve from 0.85500\n",
      "\n",
      "Epoch 78: val_recall did not improve from 0.82043\n",
      "98/98 - 37s - loss: 0.3301 - accuracy: 0.8787 - precision: 0.8950 - recall: 0.8595 - val_loss: 0.4796 - val_accuracy: 0.8367 - val_precision: 0.8524 - val_recall: 0.8201 - lr: 1.0000e-03 - 37s/epoch - 381ms/step\n",
      "Epoch 79/300\n",
      "\n",
      "Epoch 79: val_loss improved from 0.47963 to 0.47691, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 79: val_accuracy did not improve from 0.83892\n",
      "\n",
      "Epoch 79: val_precision did not improve from 0.85500\n",
      "\n",
      "Epoch 79: val_recall improved from 0.82043 to 0.82764, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.2992 - accuracy: 0.8847 - precision: 0.9003 - recall: 0.8689 - val_loss: 0.4769 - val_accuracy: 0.8377 - val_precision: 0.8547 - val_recall: 0.8276 - lr: 1.0000e-03 - 38s/epoch - 384ms/step\n",
      "Epoch 80/300\n",
      "\n",
      "Epoch 80: val_loss improved from 0.47691 to 0.47609, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\min_loss.h5\n",
      "\n",
      "Epoch 80: val_accuracy improved from 0.83892 to 0.84112, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 80: val_precision improved from 0.85500 to 0.85511, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 80: val_recall improved from 0.82764 to 0.82858, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.2918 - accuracy: 0.8874 - precision: 0.9000 - recall: 0.8735 - val_loss: 0.4761 - val_accuracy: 0.8411 - val_precision: 0.8551 - val_recall: 0.8286 - lr: 1.0000e-03 - 38s/epoch - 391ms/step\n",
      "Epoch 81/300\n",
      "\n",
      "Epoch 81: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 81: val_accuracy did not improve from 0.84112\n",
      "\n",
      "Epoch 81: val_precision did not improve from 0.85511\n",
      "\n",
      "Epoch 81: val_recall did not improve from 0.82858\n",
      "98/98 - 37s - loss: 0.2965 - accuracy: 0.8841 - precision: 0.8990 - recall: 0.8697 - val_loss: 0.4785 - val_accuracy: 0.8361 - val_precision: 0.8550 - val_recall: 0.8258 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 82/300\n",
      "\n",
      "Epoch 82: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 82: val_accuracy did not improve from 0.84112\n",
      "\n",
      "Epoch 82: val_precision improved from 0.85511 to 0.85562, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 82: val_recall improved from 0.82858 to 0.83015, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.2842 - accuracy: 0.8860 - precision: 0.9003 - recall: 0.8724 - val_loss: 0.4830 - val_accuracy: 0.8411 - val_precision: 0.8556 - val_recall: 0.8301 - lr: 1.0000e-03 - 38s/epoch - 384ms/step\n",
      "Epoch 83/300\n",
      "\n",
      "Epoch 83: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 83: val_accuracy did not improve from 0.84112\n",
      "\n",
      "Epoch 83: val_precision did not improve from 0.85562\n",
      "\n",
      "Epoch 83: val_recall did not improve from 0.83015\n",
      "98/98 - 37s - loss: 0.2769 - accuracy: 0.8891 - precision: 0.9024 - recall: 0.8772 - val_loss: 0.4788 - val_accuracy: 0.8395 - val_precision: 0.8534 - val_recall: 0.8283 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 84/300\n",
      "\n",
      "Epoch 84: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 84: val_accuracy did not improve from 0.84112\n",
      "\n",
      "Epoch 84: val_precision did not improve from 0.85562\n",
      "\n",
      "Epoch 84: val_recall did not improve from 0.83015\n",
      "98/98 - 37s - loss: 0.2758 - accuracy: 0.8902 - precision: 0.9019 - recall: 0.8780 - val_loss: 0.4889 - val_accuracy: 0.8364 - val_precision: 0.8501 - val_recall: 0.8267 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 85/300\n",
      "\n",
      "Epoch 85: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 85: val_accuracy did not improve from 0.84112\n",
      "\n",
      "Epoch 85: val_precision did not improve from 0.85562\n",
      "\n",
      "Epoch 85: val_recall did not improve from 0.83015\n",
      "98/98 - 37s - loss: 0.2687 - accuracy: 0.8961 - precision: 0.9079 - recall: 0.8839 - val_loss: 0.4865 - val_accuracy: 0.8352 - val_precision: 0.8512 - val_recall: 0.8280 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 86/300\n",
      "\n",
      "Epoch 86: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 86: val_accuracy improved from 0.84112 to 0.84331, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 86: val_precision improved from 0.85562 to 0.85899, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_precision.h5\n",
      "\n",
      "Epoch 86: val_recall improved from 0.83015 to 0.83234, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.2652 - accuracy: 0.8958 - precision: 0.9076 - recall: 0.8850 - val_loss: 0.4832 - val_accuracy: 0.8433 - val_precision: 0.8590 - val_recall: 0.8323 - lr: 1.0000e-03 - 38s/epoch - 387ms/step\n",
      "Epoch 87/300\n",
      "\n",
      "Epoch 87: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 87: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 87: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 87: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2584 - accuracy: 0.8962 - precision: 0.9078 - recall: 0.8849 - val_loss: 0.4976 - val_accuracy: 0.8361 - val_precision: 0.8480 - val_recall: 0.8254 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 88/300\n",
      "\n",
      "Epoch 88: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 88: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 88: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 88: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2623 - accuracy: 0.8962 - precision: 0.9080 - recall: 0.8863 - val_loss: 0.4889 - val_accuracy: 0.8339 - val_precision: 0.8449 - val_recall: 0.8248 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 89/300\n",
      "\n",
      "Epoch 89: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 89: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 89: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 89: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2559 - accuracy: 0.8997 - precision: 0.9103 - recall: 0.8890 - val_loss: 0.4867 - val_accuracy: 0.8399 - val_precision: 0.8526 - val_recall: 0.8320 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 90/300\n",
      "\n",
      "Epoch 90: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n",
      "\n",
      "Epoch 90: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 90: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 90: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 90: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2554 - accuracy: 0.9000 - precision: 0.9119 - recall: 0.8897 - val_loss: 0.4903 - val_accuracy: 0.8392 - val_precision: 0.8518 - val_recall: 0.8323 - lr: 1.0000e-03 - 37s/epoch - 377ms/step\n",
      "Epoch 91/300\n",
      "\n",
      "Epoch 91: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 91: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 91: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 91: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2450 - accuracy: 0.9014 - precision: 0.9118 - recall: 0.8904 - val_loss: 0.4877 - val_accuracy: 0.8405 - val_precision: 0.8509 - val_recall: 0.8314 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 92/300\n",
      "\n",
      "Epoch 92: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 92: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 92: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 92: val_recall did not improve from 0.83234\n",
      "98/98 - 37s - loss: 0.2483 - accuracy: 0.9016 - precision: 0.9121 - recall: 0.8904 - val_loss: 0.4877 - val_accuracy: 0.8414 - val_precision: 0.8532 - val_recall: 0.8323 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 93/300\n",
      "\n",
      "Epoch 93: val_loss did not improve from 0.47609\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 93: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 93: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 93: val_recall improved from 0.83234 to 0.83359, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 0.2477 - accuracy: 0.9007 - precision: 0.9124 - recall: 0.8912 - val_loss: 0.4818 - val_accuracy: 0.8421 - val_precision: 0.8542 - val_recall: 0.8336 - lr: 1.0000e-04 - 37s/epoch - 381ms/step\n",
      "Epoch 94/300\n",
      "\n",
      "Epoch 94: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 94: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 94: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 94: val_recall did not improve from 0.83359\n",
      "98/98 - 37s - loss: 0.2449 - accuracy: 0.9018 - precision: 0.9127 - recall: 0.8924 - val_loss: 0.4856 - val_accuracy: 0.8405 - val_precision: 0.8528 - val_recall: 0.8336 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 95/300\n",
      "\n",
      "Epoch 95: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 95: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 95: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 95: val_recall did not improve from 0.83359\n",
      "98/98 - 37s - loss: 0.2438 - accuracy: 0.9037 - precision: 0.9137 - recall: 0.8928 - val_loss: 0.4850 - val_accuracy: 0.8414 - val_precision: 0.8519 - val_recall: 0.8327 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 96/300\n",
      "\n",
      "Epoch 96: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 96: val_accuracy did not improve from 0.84331\n",
      "\n",
      "Epoch 96: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 96: val_recall improved from 0.83359 to 0.83485, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 37s - loss: 0.2449 - accuracy: 0.9034 - precision: 0.9126 - recall: 0.8931 - val_loss: 0.4847 - val_accuracy: 0.8430 - val_precision: 0.8552 - val_recall: 0.8348 - lr: 1.0000e-04 - 37s/epoch - 381ms/step\n",
      "Epoch 97/300\n",
      "\n",
      "Epoch 97: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 97: val_accuracy improved from 0.84331 to 0.84394, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_accuracy.h5\n",
      "\n",
      "Epoch 97: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 97: val_recall improved from 0.83485 to 0.83579, saving model to ../models/EfficientNetV2B0_96x96_256_weighted_manually_2\\max_recall.h5\n",
      "98/98 - 38s - loss: 0.2445 - accuracy: 0.9040 - precision: 0.9143 - recall: 0.8934 - val_loss: 0.4858 - val_accuracy: 0.8439 - val_precision: 0.8540 - val_recall: 0.8358 - lr: 1.0000e-04 - 38s/epoch - 384ms/step\n",
      "Epoch 98/300\n",
      "\n",
      "Epoch 98: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 98: val_accuracy did not improve from 0.84394\n",
      "\n",
      "Epoch 98: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 98: val_recall did not improve from 0.83579\n",
      "98/98 - 37s - loss: 0.2435 - accuracy: 0.9026 - precision: 0.9138 - recall: 0.8930 - val_loss: 0.4858 - val_accuracy: 0.8417 - val_precision: 0.8531 - val_recall: 0.8320 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 99/300\n",
      "\n",
      "Epoch 99: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 99: val_accuracy did not improve from 0.84394\n",
      "\n",
      "Epoch 99: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 99: val_recall did not improve from 0.83579\n",
      "98/98 - 37s - loss: 0.2449 - accuracy: 0.9042 - precision: 0.9135 - recall: 0.8940 - val_loss: 0.4874 - val_accuracy: 0.8424 - val_precision: 0.8531 - val_recall: 0.8352 - lr: 1.0000e-04 - 37s/epoch - 377ms/step\n",
      "Epoch 100/300\n",
      "\n",
      "Epoch 100: ReduceLROnPlateau reducing learning rate to 9.999999019782991e-06.\n",
      "Restoring model weights from the end of the best epoch: 80.\n",
      "\n",
      "Epoch 100: val_loss did not improve from 0.47609\n",
      "\n",
      "Epoch 100: val_accuracy did not improve from 0.84394\n",
      "\n",
      "Epoch 100: val_precision did not improve from 0.85899\n",
      "\n",
      "Epoch 100: val_recall did not improve from 0.83579\n",
      "98/98 - 37s - loss: 0.2422 - accuracy: 0.9000 - precision: 0.9108 - recall: 0.8911 - val_loss: 0.4894 - val_accuracy: 0.8421 - val_precision: 0.8524 - val_recall: 0.8327 - lr: 1.0000e-04 - 37s/epoch - 378ms/step\n",
      "Epoch 100: early stopping\n"
     ]
    }
   ],
   "source": [
    "fit_history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=300,\n",
    "    validation_data=val_generator,\n",
    "    class_weight=class_weights,\n",
    "    callbacks=[\n",
    "        lr_callback,\n",
    "        early_stop_callback,\n",
    "        min_loss_callback,\n",
    "        max_accuracy_callback,\n",
    "        max_precision_callback,\n",
    "        max_recall_callback,\n",
    "    ],\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graphics(fit_history, metric_name):\n",
    "    history = fit_history.history\n",
    "    \n",
    "    metric = history[metric_name]\n",
    "    val_metric = history[f'val_{metric_name}']\n",
    "    \n",
    "    epochs = range(1, len(metric) + 1)\n",
    "\n",
    "    plt.plot(epochs, metric, 'b', label=f'Training {metric_name}')\n",
    "    plt.plot(epochs, val_metric, 'r', label=f'Validation {metric_name}')\n",
    "    \n",
    "    plt.title(f'Training and validation {metric_name}')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.ylabel(f'{metric_name}')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graphics(fit_history, 'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graphics(fit_history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graphics(fit_history, 'precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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66RYAypa1keG2bLEnfxELCI0bQ61amSPNHXFEZnvWffsyg0Vamo1Kd+yx0KyZdXsWsT/CwoX2h5ozx5pMdeoE7dpZtFywwF5t2oTld/Lmo0Wga9euPPTQQ/To0SNj3XPPPcfSpUsZMWJEjt8JHar53HPPzWinHyqnkUyzGzt2LM2bN6dly5YAPP7443Tp0oVu3bod/oWFQX6Gq4707+lyl5oKP/wA331no2JWr2732GrVrEPXunX2Co7gGSwhqVIFunSBAwesZGTVKivpuPRSe2V0bN+82Q5crpxlDT780G7SABddZDfZGjVg925ISrLXrl3QrZtt79TJErB8uQWGNWsgOdlu2gkJ9v169axrco8edtNfuhSeegpGjcr6tF69umVN7rjDLqCUy6v5qOcIikC/fv0YPXp0lkAwevRonnnmmXx9f+LEiYU+99ixYzn//PMzAsHgwYMLfazc+HDV0Sk52R4ywR5KN2608XrmzbOSiVq17GG4fHl7iF61KnPkzrg4OOYYG5N/40YrNSlf3kbtPPJIG+PnnHPsgfzUU+GUUw4uyTmoA9jGjTbCY+jwoCLWq2zwYHuyz041s7gmqFUrCwz51aIFvPce/OtfduE7d1pwad8+s9gmyvn/7iJwySWX8Nhjj7Fv3z7KlSvHypUr+eOPPzj99NO55ZZbmD17Nrt37+aSSy7hySefPOj7jRs3JikpiVq1ajF06FDeeecdateuTcOGDWnfvj1gfQRGjhzJvn37OOaYY3jvvfeYP38+48aNY8aMGTz11FOMGTOGIUOGZDxxT5s2jfvuu4+0tDROOukkRowYQfny5WncuDHXXHMN48ePZ//+/XzyyScce+yxWdLkw1VHr61brWXO88/bPTRUxYp2L27Txh7OV6ywh++jjoJeveDoo+2m3rFj5kOyqt03K1XKf+/eg/ZLT4f+/a3I5aOPrIxo3z67SWf7t3nQgUKDwOGoXdteMSisgUBEegLPA3HA66r6j2zbjwLeAaoH9nlIVQv/eAwRGYc6ISGBjh07MmnSJPr06cPo0aO59NJLERGGDh1KQkICBw4c4Oyzz2bBggW0bt06x+PMmTOH0aNHM3/+fNLS0jjxxBMzAsHFF1/MDYFKoscee4w33niDO+64g969e2fc+EPt2bOH/v37M23aNJo3b85f//pXRowYwd133w3YeEFz587l5ZdfZtiwYbz++usHpceHqy4dgsUtwaKa446z+1l6uj3FL11qxTQpKfbA+/77Vlx9zTVw/fVWAgNWrN28OcR9/ZWVuX/0fL7u7CKZxeqF9sILMGmSlctfeulhHswVVNgCgYjEAcOB7kAyMFtExqnqopDdHgM+VtURItISmAg0DleawilYPBQMBG+88QYAH3/8MSNHjiQtLY1169axaNGiXAPBrFmzuOiii6hUqRIAvXv3ztj266+/8thjj7F161Z27NiRpRgqJ0uXLqVJkyY0b94cgGuuuYbhw4dnBIKLL74YgPbt2/PZZ5/leAwfrrrk2bzZhmL+7Te7ya9caS0Rd+/Oul+NGvaUvndv1vWVK8Npp9kAbm2XjIYFf1olaahnn7WhPs84w4bxPJQ1a6wc/aWXsg4GtGEDnHmmtQW97bbcvz93rpXF9+lzcFpcsQhnjqAjsExVVwCIyGigDxAaCBQ4IvC5GvDHYZ81QuNQ9+nThwEDBjB37lx27dpF+/bt+f333xk2bBizZ8+mRo0a9O/fv9DDNvfv35+xY8fSpk0b3n77baZPn35Y6Q0OZZ3XMNY+XHXkqFoZ/v799sS9aZPNsPX++zZ9YsWK0KiRvbp0sWbmLVtaZe7ixbBkiRXdHHusla40amRl/hUrhpzgwocssvztb5nFK3v3WvMegEcftZvzoeqHHnkEvvjCgkCwYhfs85IlFgiOOCLnyXZ37YIrrrAszBtv+MhxERLOQFAfWBOynAycnG2fQcAUEbkDqAzkWMMjIjcCNwIcddRRRZ7QolClShW6du3K3/72t4zZwbZt20blypWpVq0aGzZsYNKkSbnOQwDQpUsX+vfvz8MPP0xaWhrjx4/PGC9o+/btHHnkkezfv58PPvggY0jrqlWrsj2080pAixYtWLlyJcuWLcuoUzjjjDMKfX3B4apffPFFRIR58+bRrl27jOGqu3btmlE0VCbQdjt0uOrsRVexbu9eu2EvWGAtbOrXt5t1fLwNq/zpp1bcE6piRbuX3n67tZzM7Z55zjn5SMCCBZalAGvfGcxhfvedZS+uu85uzG+9lXe79QULbDyIatWsHfwTT1gN886d8PLLcO65drHXXmvlVtlzhQ88YGVX06bZPJEuIiJdWdwPeFtV/yUipwLvicjxqpoeupOqjgRGgjUfjUA686Vfv35cdNFFjA7MltGmTRvatWvHscceS8OGDencuXOe3z/xxBO57LLLaNOmDbVr184yzPOQIUM4+eSTSUxM5OSTT864+V9++eXccMMNvPDCC1nmB6hQoQJvvfUWffv2zagsvvkwJj0dOHAgd999N61btyY9PZ0mTZowYcIErr/+en777Tdat25N2bJlueGGG7j99tszhquuW7duTA9XHez7BDaGzvjxdqP/8Ucr289JfLy1brz7bqszVbXWlD17FuE4++PGZVa0TpiQGQimTLHmPf/+t7VrHzQIrroqJCuRzcMP2w1+4kRruvnii9bC5+23reL3kUcsu3LWWVb2P3q05TLAip+GD7fxps86q4guzBWKqoblBZwKfBmy/DDwcLZ9FgINQ5ZXALXzOm779u01u0WLFh20zpVepf333L5ddcQI1VatVO02nvXVrp3qQw+pfvSR6qJFqtu2qS5Zovrll6qffqq6eXMRJSQ1VfWnn1Tfe0916dKs2zp0UD3lFNXevVUbNVJNT7f1J56oesYZ9nn6dEvwP/+Z8/FnzMi6/aKLVGvUUN26VbVpUzt+8LgpKXbhoNq/v+ry5apHHqnasqXq7t1FdMEuL0CS5na/zm3D4b6w3MYKoAlQDvgFaJVtn0lA/8Dn47A6AsnruB4Iol9J/z137VJduFD19ddVr7xStX591WrVVJs3Vz3tNPscvOE/8YTqkCGqTz2l+uqrqqtXF1Miu3fPGn2OO041Lc22JSfbuqefVh050j4vWKC6caN9fuqpzOP06mXrKldWrVPHotstt6h+9pnd6OvXtz+IquqPP9q+Z51l759+mjVNe/eqPvaYalycvcqWVZ07t3j+Hi7PQBC2oiFVTROR24Evsaahb6rqQhEZHEjQOOBe4DURGYBVHPcPJNi5EmHjRpsa8YcfrDhn6VJrDBNUuzYMavQW6XXrMaNCDzZsgPPPh1tvtY5UOZbjT5tmQwk89VTeFbEHDljxzPff24kffDB/I7Ht3m3H793bKoLXrrVWO+++a2X148fbfr17W/MisHVNmtjn0EqGt96ymWFSU63cf80aO06wx/xrr2UWG518srUS+vprG0fiwguzpqtcORgyxM57551w5ZU2hIKLvNwiREl95ZYjSA9mQV2plp6eXvQ5gtTUAu2+bZvq22+rduumKmIPt+XK2QPwddfZE/5776n++qtq+oejbYfWrfN38K++Ui1f3r5z550575OebtmH6tWzPtV36KC6Y0fWff/7X9V9+7Ku+/VX23/UqMzjdeyo2rChFcP06qV69NGZxTYdOqieeqoV2SQkZOYccrN3rxULvfHGwft++aWde/jw/P09XLEhEkVD4XrlFAhWrFihKSkpHgxKufT0dE1JSdEVK1YU3UEff9xuvLNn6/79qosXq/7wg92vPvxQ9cknVfv1Uz3pJNXGjTOLdcCKuQcOVP3uu1yKsZOSVCtWtBeo/vFH3mmZOdP2PeEE1RtvtO+89lrWfVJTVS+7LLOI5b33rDx93DiLShddpHrggOrOnRaVcrrpjh1r63/6KXPd11/bukGDLKrdc0/mtieftGPXrKnat29B/ro5mzPH0uhKlLwCQaRbDRWJBg0akJycTEpKSqST4g5ThQoVaBDaKelwzJiBDhmCqPJ712s5iSQ278g6HIGINdts3tza2yckWCvG7t1tKIVcm7WvX29FH4mJ1sC/Z08rjvnrXzP3+eQTGzStenVrXvn663ayqVPtRCtXWhlS48Y2Eub339uxVq6Ep5+2ppXBYZSbNrWOXgMGWHPOn36yrsRly1qHrFDLltn7Mcdkruva1Yp8Bg2y5ZDOipx/vjX73Lw5n21PD+HEEw//GK545RYhSuorpxyBcwcOqG7aZMU6e/ao/jJ9i26q1ECX0kz78pEq6IT2A/Wdd1QnTlT99lsrQdm5U+0Ly5dnFpWE+vbbg4tj0tOtZU2lSqrz5tnJExNVr7gia4IaNrTinfr1bd/WrVXXrs3cZ8sW1WbNshb/tGqlOmtWzheZnq566622X+3aqlOmqHbpotq5c9b9br7ZWu9kN3eufTchQXX//qzHrV/ftq1cmcdf2ZVmRHvRkIsdBw6orltnrXZmzrRmmpdcYqUamffTdP2IvrqPeP33FT/rqlWq+te/WkuVOXPsxj9mjOpVV6kef7xqfLx98f/+L+vJxoyx9VdfnXX9lCm2/uWXM9ddeaUFg2CRyNSpts+HH+Z9QcuWWdOiL76wVjuHsn+/6ptvZhZD3XST3dhDg1i3blbWlZNHH1V95pmD1z/yiDV5clHLA4ErHdLSci1bXrPG7pcNGmR9gAZbd801qv/+t93Lx/T7RBV01+NPZx5g82bVunXtyTdYCZuYqHr++XYT7NXLAsXMmbb/qlW2X7lyqmXKWIP/oDPPVK1XzwJK0Lvv2jHnzLHlq6+2Codg08pwee45O++GDZnrGje2ig/nQuQVCKKijsBFgVmzbICzW2+FQYP47TebveqXX2x8/O++s9v+OedYK8rERCvLb9TIisKzlOX3nwC1a1Px8fsz1yUk2JAJ115rZeJXXWXdd4PNN7dts9muLrvMJju58kprvjlzpo1tP2iQDY/8/fc2JMOzz2Yd/rh7d3ufMsVmnhozJu8euUUlOIHP4sXWlnXfPps8IKdxfZzLTW4RoqS+PEcQBXbtyvrk/9579uQNui2xiZ5+WnrG036lStZs85FHVPPdmKhVK9Xzzit4uhYssFY9tWrZyd9/39Y/9pgtz59vOYiaNQ+uN1BVbdPGcgtvvmn7f/99wdNQUKtX27lGjLDlpUtt+Z13wn9uV6rgOQJXYqxZY8NkxsXZDFC1a8Po0fzWoCujNvdkUMqDVC6/kH/+83guvNCe9nOafzxXO3bY03FhBrk74QQbKO3aa631z5VX2vp777WRNPv3t7kuhgzJeQD+c86x0W937rRcwSmnFDwNBdWggaVl8WJbzqnFkHOHUJD/Ys4dll274Jdr/sX+HXsYnX4pC77fzq6PxvEa13Pihsms727FGf+56QseeMCadBYoCICVI6WnQ2EHuuvfH2bPth6zQdWrw333WRCoWjX3sfV79LBxo2fPtuMUx5DKIjbW9JIltuyBwBWC5whcWKSl/Mn6u//BL2fcyboy9Vm+HD4fmcLcLSOZXOsqJvQYydg0OJCmdOosrLwaatU6Ek7uSJnx4+CxRwt34qQkew/M7FYoHXKY3/vOO22oheuuyxyWIbvOna1OYM+e4i2jP+64zDkEli2zYJWYWHznd6WeBwJ3+HbuzCgqWbHC6mQbPj+Um3f+i1WjvuVCppNGWUYf8zwV/9zD+TMf5ILjgl/O9tTcuzc89phNYH7kkQVPy+zZVlySnzF5CqJqVfj997yzKBUqQN++lvXJaaL1cDnuOJuxZscOCwQH1Z47lzcvGnKHZc+jQ0irXpOBZ86icWOb3Py9p5P52+6XSD3qBDrzPVtueIgda1O5LOUl5C9/QY7LYzLy4Fj1wYHRCiopqfDFQocSF3foG+w771iP4uIUbDm0ZElmIHCuADwQuELb++l4Kvz9cUhL4+bvruKsE7cybBgs6jeYcvFKtRnj4I47qPras1S+9lIbwfLhh/M+aKtWNgrmuHGHTsDPP9sUiUFbt8L//pdz0U40CwaC//7Xci0eCFwBeSBwhZK26DfS+l1FEu35dtA06uta3ix3M/de8BtVPnoTbr7ZxtAZNsyGJ54yxcbjOdQ4NCJWPDR1qhU55eann2z8nL59bZhlgDlz7D3WAsHRR1t/iC+/tCnRPBC4AvJA4ApMt21nQ+eL2JVWjsVDxnDmE2fAk09ah6tzz7Wy8kcDlb3lysHHH0OvXjaQWn706WPz3E6ZkvP2JUvgvPNsNvYDB+D55219sKI41gJB2bJ285882ZaPPjqy6XGljgcCl2/Ll9u9/L2jB1F36xL+c9Vorn6skW186CHo0sV2GjDA+gcEHXWUzWnbtm3+TnTaadZkM6d6grVrrZlmXJxN8NK3r7Xm2bbNKoqbNi3CiX1LkeOOs6I38ByBKzAPBO6Qfv3VHsCPOQZeemQtl21+mWWn/pVr3j07c6e4OBg1ylr8PPDA4Z2wbFmb6SrYJDLU7bfbpOiTJlmC7r/fgsDIkZYjiLXcQFCwnqBixcK1tnIxzQOBy9G+fVbkfv310KaNjfUzZAgsufrvlI8/QIsPHj+4AU39+rZT1aqHn4AuXawtarD8H6yz1tSp1kY/WNfQvr3VFTzzDKxaFb4WQyVdMBAcfXQheuG5WOf9CFyGtWvtwXrSJBvsbd8+ezi/6y4r8q+5fSU0f82iQ3B+23A5/XR7nzULLr/cPs+ebW3lzz4767733291E+A5Ai8WcoXgjw7RaOxY2LQp37snJVlRe6NG9kBfsaLd/D/6yCbLevZZG+mTwYPtafPRQvb6LYi2baFKFRv9M+jrr61V0ZlnZt23Z084/njbFquzYx17rP02zZtHOiWuFPIcQbRJToaLLrJa3Yceyn2/7dtJk7IM/VcFhgyxmRTvucdafTZtmsP+v/1mnaXuusuKgMItPt6GbJg1K3PdtGkWIGrWzLqvCIwYYUNEH3FE+NNWElWubBXybdpEOiWuFPIcQbQJNqH844+ct69eDXfeSXrtOnzb6AoGDYIrrrB+SM88k0sQULVB1ypWzDu4FLUuXaymevNm2L3bbvRnnZXzvqeddviV1KVdjx5FP7SGiwmeI4g2wUCwYcPB2wYPhiFDSFdYosfSZc9Yvnjud3rfdYjy/s8/t6acw4ZlbRYabsF6gm+/tSfeffsOrh9wzh02zxFEm7wCwbBhrD26C011BXceMxEpI/RePzLv423bZiNvtmljxULF6aSTbBawWbOsfiA+3p78nXNFygNBNFHNNRDs3bwDtm/nhaXn0LJHQz6b3RC54AJ4/XXrxRu0aJGN37Nrly0PHGjFTCNHZk7rWFwqVLDhKWbOtPqBjh2Lpmmqcy4LDwTRZNUqK0+vXBnWr89YvWQJXHr6OgBa96jHuHGBOtVbbrHWRWPGZH7/jDPgwgutCOgvf4EXX7SJWDp2LP7rAasnmDvXApwXCzkXFh4IokkwN9Ctm43EuXcvb7xhLSrT11oguPK+IzMf7Lt3tw5II0ZY+/zeva3T1ocf2jSNM2bYuPpPPRWRywGsnuDAAZt1LLeKYufcYfFAEE1mz7YeYN27AzDwpo1cf721wnznHxYIsgw/UKYM3HSTVcb27GktdD7+2Dpwvfqq5SqWLLG2pZFy6qk2fEWFCvbZOVfkPBBEk6QkaN2anQk2O9akdzZw7702KGXC3hwCAdhE7eXL2xgS//qXTcAeFB9vTUYjqWpVqyA+5xxLp3OuyHnz0WiRng5z5rC+6+Xc9XAdPgKeun0DPYcFtv/xh91Is8+3W6sWDB1qrYOKu1VQfo0f7+PnOBdGHgiiROrc5VRLTeXRsSexum4dAHq2C2k5tG6ddTbKaarFe+8tplQWkrcUci6s/DErCsyeDQ+ebRXFx13dga8WWCDI0oR03TqoVy8CqXPOlXQeCEq5qVNtFOYOJJFevgL3vdGSKokV7Sk6eyDwceqdcznwQFCKffKJjb7ctClc3TKJMu3aWqshsGKgkL4EHgicc7nxQFBKvfkmXHaZ9fOa8fUByv93Ttax+OvUycwR7NkDf/7pgcA5l6OwBgIR6SkiS0VkmYjkOGyliFwqIotEZKGIjApneqLF8OFw3XXWonLKFKjx4yTYuRM6dcrcKTQQrMul6ahzzhHGQCAiccBwoBfQEugnIi2z7dMMeBjorKqtgLvDlZ5o8a9/2bS9vXvbkECVyh+Ahx+2makuuSRzRw8Ezrl8CmeOoCOwTFVXqOo+YDTQJ9s+NwDDVfVPAFXdGMb0lB5Ll8KAATYGf4jnn7dpAfr2hU8/DfSveu896xE8dGhm/QBYINiyxYaMCAYCbzXknMtBOANBfWBNyHJyYF2o5kBzEflORH4UkZ45HUhEbhSRJBFJSklJCVNyS5CXXoLnnoMbbrARRbH7/d13w8UXw6hRgXv+7t02OuhJJ1l0CFUn0IR040bPETjn8hTpDmXxQDPgTKABMFNETlDVraE7qepIYCRAhw4dtJjTWLxUYcIEG9/ngw/gxBMZ3+werr3WBt8cNSpkNOiXXrKpKd999+COYnVC+hKsW2dfqlWrWC/FOVc6hDMQrAUahiw3CKwLlQz8pKr7gd9F5DcsMMwOY7pKtsWLbcb4V16Br75C77+fV+JOoF277nz+echwO3Pnwt//Dr16WUeC7IJTFq5fb8NL1KnjwzQ453IUzjvDbKCZiDQRkXLA5cC4bPuMxXIDiEgtrKhoRRjTVPJNmACAnnc+L3d8m1/TWzI6vS8zTnmAqr/NseGl77jDioPKlbPpI3OSPUfgxULOuVyELRCoahpwO/AlsBj4WFUXishgEekd2O1LYLOILAK+Ae5X1c3hSlOpMGEC2qYttw6tz20PVuGl7uOo2K0zlV75t/UTqF0bXn4Zbr3VKpVbtsz5OB4InHP5FNY6AlWdCEzMtu7xkM8K3BN4uS1b4LvvmNrxEV55BR56CIYObUKZMv+xbZ9/bgML3XADtG+f97EqVYIqVTIDgY/l75zLRaQri12oL7+E9HQe/+l8brwRnn46ZFtCgvUiu+66/B+vTh2rTE5J8RyBcy5XHghKkANfTGBrXCJ/HHkSX/5fERywTh1YsMA+eyBwzuXCm5GUFGlp7P1iEuMPnMvI18vY5PKHq04d+N//7LMHAudcLjwQlATbt7Pi/8ZQac+f7Ox6Pj16FNFx69TJ6JDmgcA5lxsvGoqkUaOsu3BKCk2BXVTkyre6F93xg30JwAOBcy5XniOIpE8+ARGWXvsPLuETxjzxX6o3qlZ0xw82IRXJ/Oycc9l4jiCSFi1CO59Gv/kPsrUJjHqkiI8fvPnXrh0yLoVzzmXlOYJI2bMHli1jES2ZNw8GD7aOwkUqGAi8WMg5lwcPBJGydCmkp/P6D604/njo1y8M5/BA4JzLBy8viJSFCwGYur4VQ1+FuLgwnMMDgXMuHzxHECE7fl5IGnHU7tycCy4I00mqVIE2beCUU8J0AudcNPAcQYQsHrOIqjRj+OvlD5pKoEjNnx/GgzvnooHnCCJg0iSolryQ9JatOPbYSKfGORfrPBAUsx074O6b93A0y2l+YatIJ8c557xoqLj94x9QcfUS4kiHNh4InHOR5zmCYqQK778PV7W1FkO5TirjnHPFyANBUfriC/jvf3PdvGABrFoF3eottJ6+zZsXY+Kccy5nHgiKyj//CRdeaLOH5WLsWBv259j0RdCsWRi6EjvnXMHlWUcgInlOIamqzxZtckqpp5+GRx6Bhg3hp59g2TI45piDdvviC5sxssKyhdC2bfGn0znncnCoHEHVQ7zcM89YELjySpg1yx75R406aLfVq2HePPjLubth+XJo5RXFzrmSIc8cgao+WVwJKbWeegp69YJ33rFxIs44Az74AAYOJLSn2Lhx9n5xyyVWa+yBwDlXQhyqaOiFvLar6p1Fm5xSZv9+2L4dOnXKHCzoyiutnmDOHOjQIWPXL76AFi2g8c5AiyEPBM65EuJQRUNzDvGKbamp9l69eua6v/zFKoE/+CBj1datMH069OmDDTYXH2+Vxc45VwIcqmjoneJKSKm0dau9hwaCGjXg3HNh9GgYNgzi4pg4EdLS4IoWc+Du4VZjXLZsJFLsnHMHyVfzURFJFJFhIjJRRL4OvsKduBIvp0AAVjy0fj188w1g9QOnJiyl9YM9oWZN+PDDYk2mc87lJb/9CD4AFgNNgCeBlcDsMKWp9MgtEJx3HhxxBNx6K+n33k+V8R8yfm93pEwZmDIF6tcv7pQ651yu8hsIaqrqG8B+VZ2hqn8DzgpjukqH3AJBxYrw2muQmAgvvsDru67giPRUmDzZ6waccyVOfgPB/sD7OhE5T0TaAQlhSlPpkVsgALj0UvjuOwYN2MZpZb5n1+yF0K5dcabOOefyJb+jjz4lItWAe4EXgSOAAWFLVWnx55/2nlMgCJjwVXkqdzqVat5a1DlXQuUrEKjqhMDHVKBr+JJTymzdav0HKlfOcfP69dabeOjQ4k2Wc84VRH5bDb0jItVDlmuIyJthS1VpsXWr5QZymWty8mR779Wr2FLknHMFlt86gtaqujW4oKp/Al7gHQwEuZg0CerW9fHlnHMlW34DQRkRqRFcEJEEfHYzCwQ1auS4KS3NWor27JlrhsE550qE/N7M/wX8ICKfBJb7Al7ynUeO4KefbPO55xZngpxzruDyW1n8rogkkdl34GJVXRS+ZJUSW7dCvXo5bpo0yeqRu3cv3iQ551xBFWSGsgRgp6q+BKSISJNDfUFEeorIUhFZJiIP5bHfX0RERaRDbvuUSHnkCCZPtiGF8qhCcM65EiG/rYaeAB4EHg6sKgu8f4jvxAHDgV5AS6CfiBw0W7uIVAXuAn7Kf7JLiFwCwYYNNgq1txZyzpUG+c0RXAT0BnYCqOofHHqGso7AMlVdoar7gNFAnxz2GwL8E9iTz7SUDPv2wa5dOQaCKVPsvWfP4k2Sc84VRn4DwT5VVUABRCTnHlRZ1QfWhCwnB9ZlEJETgYaq+p+8DiQiN4pIkogkpaSk5DPJYZbTXAQBkyZB7drebNQ5VzocMhCIiAATRORVoLqI3ABMBV47nBOLSBngWWzYijyp6khV7aCqHRITEw/ntEUnl3GGDhzIbDZapiA1MM45FyGHbDWkqioifYF7gG1AC+BxVf3qEF9dCzQMWW4QWBdUFTgemG6xhrrAOBHprapJ+b+ECMklEMyZA5s3e7GQc670yG8/grnAVlW9vwDHng00C7QuWgtcDlwR3KiqqUCt4LKITAfuKxVBAHINBJMmWQeyc84p9hQ551yh5DcQnAxcKSKrCFQYA6hq69y+oKppInI78CUQB7ypqgtFZDCQpKrjDiPdkZdLIJg8GTp2tInInHOuNMhvIOhRmIOr6kRgYrZ1j+ey75mFOUfE5DAE9ebN1qP4iScikyTnnCuM/PYsXhXuhJQ6OeQIvvoKVL1+wDlXuni7lsLauhXi46FSpYxVkydbkVCH0tU/2jkX4zwQFFa2uQhUrdlo9+42xpBzzpUWHggKK9vwEsuXw7p10NXnb3POlTIeCAorWyCYMcPeu3SJSGqcc67QPBAUVrZAMHMmJCZCixYRS5FzzhWKB4LCyiEQdOnis5E550ofDwSFFTJN5erVsHKlFws550onDwSFFZIjmDXLVnkgcM6VRh4ICmPvXti9OyMQzJwJ1arBCSdENlnOOVcYHggKI9tcBDNmwOmne/8B51zp5IGgMEKGl9iwAZYu9WIh51zp5YGgMEICgdcPOOdKOw8EhRESCGbOtOGGTjwxoilyzrlC80BQGCFDUM+cCZ06QdmykU2Sc84VlgeCwgjkCA5Urc7ChT7aqHOudPNAUBiBQLBxX3XS0uCooyKbHOecOxweCApj61YoW5bVKRUBaNgwsslxzrnD4YGgMAK9ilevsYGFPEfgnCvNPBAURiAQrFlji54jcM6VZh4ICiMkEFSunGUQUuecK3U8EBRGSCBo2NCHnnbOlW4eCAojWyBwzrnSzANBYXggcM5FEQ8EhbF1KweqVmf9em8x5Jwr/TwQFNTatbBnD1sr1UPVcwTOudLPA0FBTZsGwMomXQEPBM650s8DQUFNnQqJiSwpa9OReSBwzpV2HggKQtUCwdlns2at/ek8EDjnSjsPBAWxeDGsWwfdurF6NSQkWIcy55wrzTwQFMTUqfberZs3HXXORQ0PBAUxdSoccww0auSBwDkXNTwQ5Nf+/TB9OnTrBuCBwDkXNTwQ5Nfs2bB9O3Trxq5dsGWLBwLnXHTwQJBfU6fa6HJdu2YMP+29ip1z0cADQX5NnQrt20NCAqtX2yrPETjnokFYA4GI9BSRpSKyTEQeymH7PSKySEQWiMg0EWkUzvQU2q5d8MMPcPbZAD4hjXMuqoQtEIhIHDAc6AW0BPqJSMtsu80DOqhqa+BT4JlwpeewrF0LaWnQqhVggUAE6tePcLqcc64IhDNH0BFYpqorVHUfMBroE7qDqn6jqrsCiz8CDcKYnsLbtMneExMBCwR16kC5chFMk3POFZFwBoL6wJqQ5eTAutxcB0zKaYOI3CgiSSKSlJKSUoRJzKfgOWvVAiwQeEWxcy5alIjKYhG5CugA/F9O21V1pKp2UNUOiYGn8mKVQ47A6wecc9EinIFgLRB6u2wQWJeFiHQDHgV6q+reMKan8IKBoFYtVGH1ag8EzrnoEc5AMBtoJiJNRKQccDkwLnQHEWkHvIoFgY1hTMvhSUmBihWhcmVSU2HnTmhQMmsznHOuwMIWCFQ1Dbgd+BJYDHysqgtFZLCI9A7s9n9AFeATEZkvIuNyOVxkbdqUpX4APEfgnIse8eE8uKpOBCZmW/d4yOdu4Tx/kUlJyQgEycm2ygOBcy5alIjK4hJv06aMiuJgIPCiIedctIjNQJCWBgsW5H//kBzBmjVQpgwceWSY0uacc8UsNgPB6NHQrp3NNpYf2XIERx4J8WEtVHPOueITm4Fg+XJIT8+s+c3L3r2wbVuWHIEXCznnoklsBoI//rD3jflosbp5s72H5Ai8otg5F008EBxKyPASqp4jcM5Fn9gMBGsDHZzzEwhChpfwzmTOuWgUm4GgkDkC70PgnItGsRcI9u/PDAAbNhx6/5BxhrwPgXMuGsVeINiwAVTtc35zBCKQkJDRyMgDgXMumsReIAgWC5Url/86gho1ID6e5GTvTOaciz6xGwiOPz7/gSBkHoK6daFs2TCmzznnilnsBoJ27azYJz097/2zDTjnxULOuWgTm4EgLs5yBAcOwJYtee+fbXgJbzHknIs2sRkI6tbNLOg/VPFQIEfgncmcc9EqNgNBvXpQu7Yt5xUIVDNyBNu2wY4dHgicc9En9gLB2rX5DwSpqTZkda1aPjOZcy5qxV4gKEiOwDuTOediQGwFgj17rHK4Xj1ISLBOAXkFguDwEomJHgicc1ErtgJBcCKaevWs5VBiYt7DTITkCNassQ7G9eqFP5nOOVecYisQBPsQ1K9v77VrFyhH4J3JnHPRKDYDQfCx/lCBIFuOwCuKnXPRyAPBoXIEFSpA5creq9g5F7ViLxCUK2cVxZC/HEGtWiDigcA5F7ViLxDUq2e1vmCBYNs2a02Uk0Bnsk2bYPt2DwTOuegUm4EgqE4de88tVxAYXmLUKFs866zwJs855yIhtgPBoTqVbdqE1qrFyy/DySdD+/bhT6JzzhW3mAkEkybBrmVr0SMLEAhSUlizO5GlS+G228KfRueci4SYCQRb12ynUtp2VuzJZyDYtw+2beO7pbWoVQv69i2edDrnXHGLmUBw0SnWq3jSgvqZK4OBIKfexYHgMGtJItdfb61InXMuGsVMIKiwxfoQjEuql5kBqFzZXtlzBIsWwbnnArBAT+Cmm4oxoc45V8xiJhAEO5OtPlCPt94KWR/al0AVXn0V2rcnfd16rqj2HxIu6EzjxsWeWuecKzYxFwiadKrHyJEhUxWHBoKPP4abb2Z9iy601gV8svNc7r8/Msl1zrniEjuBoE8fGDWKq2+tyooVMG1aYH3t2ujGjSydv5udt93P8mrtqP/LRMo3qktSEpx+ekRT7ZxzYRcf6QQUm2bNoFkz/rIX7rwLnnvOJitrsKA2LVfP4d12zzKUNdxa/j2GPh3HffdBfOz8dZxzMSysOQIR6SkiS0VkmYg8lMP28iLyUWD7TyLSOJzpAShfHvr3h4kT4dprYcmW2tRhA4PKP03q2RczYfsZPPSQBwHnXOwI2+1OROKA4UB3IBmYLSLjVHVRyG7XAX+q6jEicjnwT+CycKUp6JFHbEjpLl2g7Yw6yIADxOl+qr36DPh8A865GBPOHEFHYJmqrlDVfcBooE+2ffoA7wQ+fwqcLRIcES58EhLgrrugXTuQOoG+BHfeCUcfHe5TO+dciRPOQFAfWBOynBxYl+M+qpoGpAI1sx9IRG4UkSQRSUoJzhpWVLp1gwEDYODAoj2uc86VEqWi1ZCqjlTVDqraITExsWgPnpgIzz4LRxxRtMd1zrlSIpyBYC0QOrljg8C6HPcRkXigGrA5jGlyzjmXTTgDwWygmYg0EZFywOXAuGz7jAOuCXy+BPhaVTWMaXLOOZdN2FoNqWqaiNwOfAnEAW+q6kIRGQwkqeo44A3gPRFZBmzBgoVzzrliFNbW8qo6EZiYbd3jIZ/3AD7As3PORVCpqCx2zjkXPh4InHMuxnkgcM65GOeBwDnnYpyUttaaIpICrCrAV2oBm8KUnJIsFq87Fq8ZYvO6Y/Ga4fCuu5Gq5tgjt9QFgoISkSRV7RDpdBS3WLzuWLxmiM3rjsVrhvBdtxcNOedcjPNA4JxzMS4WAsHISCcgQmLxumPxmiE2rzsWrxnCdN1RX0fgnHMub7GQI3DOOZcHDwTOORfjojoQiEhPEVkqIstE5KFIpyccRKShiHwjIotEZKGI3BVYnyAiX4nI/wLvNSKd1qImInEiMk9EJgSWm4jIT4Hf+6PA8OdRRUSqi8inIrJERBaLyKkx8lsPCPz7/lVEPhSRCtH2e4vImyKyUUR+DVmX428r5oXAtS8QkRMP59xRGwhEJA4YDvQCWgL9RKRlZFMVFmnAvaraEjgFuC1wnQ8B01S1GTAtsBxt7gIWhyz/E/i3qh4D/AlcF5FUhdfzwGRVPRZog11/VP/WIlIfuBPooKrHY8PaX070/d5vAz2zrcvtt+0FNAu8bgRGHM6JozYQAB2BZaq6QlX3AaOBPhFOU5FT1XWqOjfweTt2Y6iPXes7gd3eAS6MSALDREQaAOcBrweWBTgL+DSwSzReczWgCzaPB6q6T1W3EuW/dUA8UDEwk2ElYB1R9nur6kxsXpZQuf22fYB31fwIVBeRIwt77mgOBPWBNSHLyYF1UUtEGgPtgJ+AOqq6LrBpPVAnUukKk+eAB4D0wHJNYKuqpgWWo/H3bgKkAG8FisReF5HKRPlvraprgWHAaiwApAJziP7fG3L/bYv0/hbNgSCmiEgVYAxwt6puC90WmP4zatoJi8j5wEZVnRPptBSzeOBEYISqtgN2kq0YKNp+a4BAuXgfLBDWAypzcBFK1AvnbxvNgWAt0DBkuUFgXdQRkbJYEPhAVT8LrN4QzCoG3jdGKn1h0BnoLSIrsSK/s7Cy8+qBogOIzt87GUhW1Z8Cy59igSGaf2uAbsDvqpqiqvuBz7B/A9H+e0Puv22R3t+iORDMBpoFWhaUwyqXxkU4TUUuUDb+BrBYVZ8N2TQOuCbw+Rrgi+JOW7io6sOq2kBVG2O/69eqeiXwDXBJYLeoumYAVV0PrBGRFoFVZwOLiOLfOmA1cIqIVAr8ew9ed1T/3gG5/bbjgL8GWg+dAqSGFCEVnKpG7Qs4F/gNWA48Gun0hOkaT8OyiwuA+YHXuViZ+TTgf8BUICHSaQ3T9Z8JTAh8bgr8DCwDPgHKRzp9YbjetkBS4PceC9SIhd8aeBJYAvwKvAeUj7bfG/gQqwPZj+X+rsvttwUEaxW5HPgv1qKq0Of2ISaccy7GRXPRkHPOuXzwQOCcczHOA4FzzsU4DwTOORfjPBA451yM80DgXJiJyJnBEVKdK4k8EDjnXIzzQOBcgIhcJSI/i8h8EXk1MN/BDhH5d2As/GkikhjYt62I/BgYC/7zkHHijxGRqSLyi4jMFZGjA4evEjKPwAeBHrKIyD8Cc0ksEJFhEbp0F+M8EDgHiMhxwGVAZ1VtCxwArsQGOEtS1VbADOCJwFfeBR5U1dZYz87g+g+A4araBuiE9RQFGxX2bmxujKZAZxGpCVwEtAoc56lwXqNzufFA4Jw5G2gPzBaR+YHlptgw1x8F9nkfOC0wL0B1VZ0RWP8O0EVEqgL1VfVzAFXdo6q7Avv8rKrJqpqODQPSGBtOeQ/whohcDAT3da5YeSBwzgjwjqq2DbxaqOqgHPYr7Jgse0M+HwDi1cbS74iNIno+MLmQx3busHggcM5MAy4RkdqQMVdsI+z/SHCEyyuAb1U1FfhTRE4PrL8amKE2Q1yyiFwYOEZ5EamU2wkDc0hUU9WJwABs6knnil38oXdxLvqp6iIReQyYIiJlsBEgb8Mmf+kY2LYRq0cAGxL4lcCNfgVwbWD91cCrIjI4cIy+eZy2KvCFiFTAciT3FPFlOZcvPvqoc3kQkR2qWiXS6XAunLxoyDnnYpznCJxzLsZ5jsA552KcBwLnnItxHgiccy7GeSBwzrkY54HAOedi3P8DpmBdqmyR6eYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graphics(fit_history, 'recall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for image_name, emotion in test_df.values:\n",
    "    image = tf.keras.preprocessing.image.load_img(\n",
    "        os.path.join(test_dir, image_name),\n",
    "        target_size=target_size,\n",
    "        interpolation=interpolation,\n",
    "        color_mode=color_mode,\n",
    "    )\n",
    "    \n",
    "    x_test.append(tf.keras.preprocessing.image.img_to_array(image))\n",
    "    y_test.append(labels.index(emotion))\n",
    "        \n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_confusion_matrix(y_true, y_pred, labels):\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
    "    display = ConfusionMatrixDisplay(cm, display_labels=labels)\n",
    "\n",
    "    _, ax = plt.subplots(figsize=(10, 10))\n",
    "    display.plot(cmap=plt.cm.Blues, ax=ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(y_true, y_pred):\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "    recall = recall_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "\n",
    "    print(f'Accuracy: {accuracy * 100:.2f}%')\n",
    "    print(f'Precision: {precision * 100:.2f}%')\n",
    "    print(f'Recall: {recall * 100:.2f}%')\n",
    "    print(f'F1 score: {2 * (precision * recall) / (precision + recall) * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 83.77%\n",
      "Precision: 71.17%\n",
      "Recall: 69.01%\n",
      "F1 score: 70.08%\n"
     ]
    }
   ],
   "source": [
    "min_loss_model = tf.keras.models.load_model(f'../models/{notebook_name}/min_loss.h5')\n",
    "y_pred = min_loss_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.25%\n",
      "Precision: 72.75%\n",
      "Recall: 68.49%\n",
      "F1 score: 70.55%\n"
     ]
    }
   ],
   "source": [
    "# best\n",
    "max_accuracy_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_accuracy.h5')\n",
    "y_pred = max_accuracy_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.32%\n",
      "Precision: 69.71%\n",
      "Recall: 67.70%\n",
      "F1 score: 68.69%\n"
     ]
    }
   ],
   "source": [
    "max_precision_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_precision.h5')\n",
    "y_pred = max_precision_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.25%\n",
      "Precision: 72.75%\n",
      "Recall: 68.49%\n",
      "F1 score: 70.55%\n"
     ]
    }
   ],
   "source": [
    "max_recall_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_recall.h5')\n",
    "y_pred = max_recall_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "celltoolbar": "Raw Cell Format",
  "colab": {
   "collapsed_sections": [],
   "name": "mobilenet_v3_augmented_data.ipynb",
   "provenance": []
  },
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}