Resnet_mayhem (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a6570a2",
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import numpy as np \n",
    "from tqdm import tqdm\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import PIL\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.python.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6ccd2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2da4458d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_dir = './ISIC_2020_Training_JPEG/train/'\n",
    "test_img_dir = './ISIC_2020_Test_JPEG\\ISIC_2020_Test_Input/'\n",
    "train = pd.read_csv('./ISIC_2020_Training_GroundTruth.csv')\n",
    "test = pd.read_csv('./sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f865bfda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "644db83e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32542\n",
       "1      584\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dffc9b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "benign = train[train['target']==0].sample(2500)\n",
    "malignant = train[train['target']==1]\n",
    "train=pd.concat([benign,malignant])\n",
    "train=train.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04b5bcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image_name    patient_id  sex     age_approx  anatom_site_general_challenge  diagnosis  benign_malignant  target\n",
       "ISIC_0149568  IP_0962375  female  55.0        upper extremity                melanoma   malignant         1         1\n",
       "ISIC_6931277  IP_9772004  male    90.0        torso                          melanoma   malignant         1         1\n",
       "ISIC_6809586  IP_1173567  male    70.0        upper extremity                melanoma   malignant         1         1\n",
       "ISIC_6829008  IP_9635781  female  50.0        lower extremity                melanoma   malignant         1         1\n",
       "ISIC_6841294  IP_2352610  male    65.0        torso                          melanoma   malignant         1         1\n",
       "                                                                                                                   ..\n",
       "ISIC_3391427  IP_4956722  male    60.0        torso                          melanoma   malignant         1         1\n",
       "ISIC_3394520  IP_1023029  female  15.0        torso                          melanoma   malignant         1         1\n",
       "ISIC_3399306  IP_3541322  male    90.0        upper extremity                melanoma   malignant         1         1\n",
       "ISIC_3408231  IP_3034671  male    65.0        head/neck                      melanoma   malignant         1         1\n",
       "ISIC_9998682  IP_2516168  male    60.0        head/neck                      melanoma   malignant         1         1\n",
       "Length: 575, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malignant.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39952994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image_name    patient_id  sex     age_approx  anatom_site_general_challenge  diagnosis  benign_malignant  target\n",
       "ISIC_0080512  IP_1870306  male    75.0        torso                          unknown    benign            0         1\n",
       "ISIC_6615000  IP_9544996  female  35.0        torso                          unknown    benign            0         1\n",
       "ISIC_6580094  IP_1251610  male    35.0        torso                          nevus      benign            0         1\n",
       "ISIC_6587310  IP_1969685  male    50.0        torso                          unknown    benign            0         1\n",
       "ISIC_6588756  IP_1730314  male    30.0        lower extremity                unknown    benign            0         1\n",
       "                                                                                                                   ..\n",
       "ISIC_3350519  IP_1294580  female  55.0        lower extremity                unknown    benign            0         1\n",
       "ISIC_3359829  IP_5922049  male    25.0        torso                          unknown    benign            0         1\n",
       "ISIC_3371722  IP_4157535  female  45.0        upper extremity                unknown    benign            0         1\n",
       "ISIC_3373004  IP_8513373  male    35.0        upper extremity                nevus      benign            0         1\n",
       "ISIC_9999806  IP_0046310  male    45.0        torso                          nevus      benign            0         1\n",
       "Length: 2441, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benign.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "326a93b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZE=224\n",
    "train_set = []\n",
    "for image_name in train.image_name:\n",
    "    train_set.append(tf.keras.preprocessing.image.img_to_array(tf.keras.preprocessing.image.load_img(path = (train_img_dir + image_name + \".jpg\"), color_mode = \"rgb\", target_size = (SIZE,SIZE)), dtype=\"float32\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2517685f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"img\"] = train_set\n",
    "test[\"path\"] = [(test_img_dir + image_name + \".jpg\") for image_name in test.image_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f89c57c0",
   "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>index</th>\n",
       "      <th>image_name</th>\n",
       "      <th>patient_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age_approx</th>\n",
       "      <th>anatom_site_general_challenge</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>benign_malignant</th>\n",
       "      <th>target</th>\n",
       "      <th>img</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31079</td>\n",
       "      <td>ISIC_9391954</td>\n",
       "      <td>IP_7887363</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>head/neck</td>\n",
       "      <td>unknown</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[138.0, 68.0, 66.0], [137.0, 67.0, 59.0], [1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31439</td>\n",
       "      <td>ISIC_9498714</td>\n",
       "      <td>IP_1994743</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>torso</td>\n",
       "      <td>nevus</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[198.0, 129.0, 147.0], [199.0, 130.0, 148.0]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20164</td>\n",
       "      <td>ISIC_6127495</td>\n",
       "      <td>IP_8296470</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>head/neck</td>\n",
       "      <td>unknown</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[191.0, 186.0, 206.0], [128.0, 95.0, 104.0],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4126</td>\n",
       "      <td>ISIC_1346562</td>\n",
       "      <td>IP_4978372</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>lower extremity</td>\n",
       "      <td>unknown</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[142.0, 96.0, 109.0], [148.0, 102.0, 113.0],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3890</td>\n",
       "      <td>ISIC_1270307</td>\n",
       "      <td>IP_7606510</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>torso</td>\n",
       "      <td>unknown</td>\n",
       "      <td>benign</td>\n",
       "      <td>0</td>\n",
       "      <td>[[[192.0, 139.0, 155.0], [190.0, 142.0, 156.0]...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    image_name  patient_id     sex  age_approx  \\\n",
       "0  31079  ISIC_9391954  IP_7887363    male        60.0   \n",
       "1  31439  ISIC_9498714  IP_1994743    male        50.0   \n",
       "2  20164  ISIC_6127495  IP_8296470  female        55.0   \n",
       "3   4126  ISIC_1346562  IP_4978372    male        60.0   \n",
       "4   3890  ISIC_1270307  IP_7606510  female        45.0   \n",
       "\n",
       "  anatom_site_general_challenge diagnosis benign_malignant  target  \\\n",
       "0                     head/neck   unknown           benign       0   \n",
       "1                         torso     nevus           benign       0   \n",
       "2                     head/neck   unknown           benign       0   \n",
       "3               lower extremity   unknown           benign       0   \n",
       "4                         torso   unknown           benign       0   \n",
       "\n",
       "                                                 img  \n",
       "0  [[[138.0, 68.0, 66.0], [137.0, 67.0, 59.0], [1...  \n",
       "1  [[[198.0, 129.0, 147.0], [199.0, 130.0, 148.0]...  \n",
       "2  [[[191.0, 186.0, 206.0], [128.0, 95.0, 104.0],...  \n",
       "3  [[[142.0, 96.0, 109.0], [148.0, 102.0, 113.0],...  \n",
       "4  [[[192.0, 139.0, 155.0], [190.0, 142.0, 156.0]...  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f24a7155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3084, 10)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fb053e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_cross_validation, y_train, y_cross_validation = train_test_split(train.img, train.target, test_size=0.2, shuffle = True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "87682775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2467,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ae4c815",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the pixels between 0 and 1\n",
    "x_train /= 255\n",
    "x_cross_validation /= 255\n",
    "# redefine dtypes\n",
    "y_train = np.array(y_train, dtype = \"float32\")\n",
    "y_cross_validation = np.array(y_cross_validation, dtype = \"float32\")\n",
    "x_train = np.array([np.array(val) for val in x_train])\n",
    "x_cross_validation = np.array([np.array(val) for val in x_cross_validation])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6164e673",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "num_classes = 2\n",
    "epochs = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7db7b609",
   "metadata": {},
   "outputs": [],
   "source": [
    "# focal loss\n",
    "import keras.backend as K\n",
    "def focal_loss(alpha=0.25,gamma=2.0):\n",
    "    def focal_crossentropy(y_true, y_pred):\n",
    "        bce = K.binary_crossentropy(y_true, y_pred)\n",
    "        \n",
    "        y_pred = K.clip(y_pred, K.epsilon(), 1.- K.epsilon())\n",
    "        p_t = (y_true*y_pred) + ((1-y_true)*(1-y_pred))\n",
    "        \n",
    "        alpha_factor = 1\n",
    "        modulating_factor = 1\n",
    "\n",
    "        alpha_factor = y_true*alpha + ((1-alpha)*(1-y_true))\n",
    "        modulating_factor = K.pow((1-p_t), gamma)\n",
    "\n",
    "        # compute the final loss and return\n",
    "        return K.mean(alpha_factor*modulating_factor*bce, axis=-1)\n",
    "    return focal_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "13ae4915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training and validation steps: 48 and 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vamsh\\anaconda3\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# we will use Adam optimizer\n",
    "opt = Adam(lr=1e-3)\n",
    "\n",
    "#total number of iterations is always equal to the total number of training samples divided by the batch_size.\n",
    "nb_train_steps = train.shape[0]//batch_size\n",
    "nb_val_steps=x_cross_validation.shape[0]//batch_size\n",
    "\n",
    "print(\"Number of training and validation steps: {} and {}\".format(nb_train_steps,nb_val_steps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "59b4d3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pixel Normalization and Image Augmentation\n",
    "train_datagen = ImageDataGenerator(rescale=1./255, zoom_range=0.3, rotation_range=50,\n",
    "                                   width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "\n",
    "# no need to create augmentation images for validation data, only rescaling the pixels\n",
    "val_datagen = ImageDataGenerator(rescale=1./255,zoom_range=0.3, rotation_range=50,\n",
    "                                   width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, \n",
    "                                   horizontal_flip=True, fill_mode='nearest')\n",
    "\n",
    "train_generator = train_datagen.flow(x_train, y_train, batch_size=32)\n",
    "val_generator = val_datagen.flow(x_cross_validation, y_cross_validation, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6b0abbf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "base_model = keras.applications.resnet50.ResNet50(input_shape=(SIZE,SIZE,3),include_top=False,weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8957d8af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "23fb72b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_3[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 0\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.trainable = False\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ce16d995",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers\n",
    "\n",
    "inputs = keras.Input(shape=(SIZE,SIZE,3))\n",
    "x = keras.applications.resnet50.preprocess_input(inputs)\n",
    "x = base_model(x, training=False)\n",
    "x = layers.GlobalAveragePooling2D()(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(128, activation='relu',kernel_regularizer=regularizers.l2(0.003))(x)\n",
    "x = layers.Dropout(0.35)(x)\n",
    "x = layers.Dense(64, activation='relu',kernel_regularizer=regularizers.l2(0.003))(x)\n",
    "x = layers.Dropout(0.35)(x)\n",
    "outputs = layers.Dense(1, activation='sigmoid')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "08007936",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " tf.__operators__.getitem_1   (None, 224, 224, 3)      0         \n",
      " (SlicingOpLambda)                                               \n",
      "                                                                 \n",
      " tf.nn.bias_add_1 (TFOpLambd  (None, 224, 224, 3)      0         \n",
      " a)                                                              \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 7, 7, 2048)        23587712  \n",
      "                                                                 \n",
      " global_average_pooling2d_1   (None, 2048)             0         \n",
      " (GlobalAveragePooling2D)                                        \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               262272    \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,858,305\n",
      "Trainable params: 270,593\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Model(inputs, outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d06cb583",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=focal_loss(), metrics=[\"accuracy\"],optimizer=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "91108e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = train[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6e12a4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "# from sklearn.utils import compute_class_weight\n",
    "# classWeight = compute_class_weight('balanced', np.unique(out), out) \n",
    "# classWeight = dict(enumerate(classWeight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2d3c2fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping\n",
    "es = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=1, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8a3dd2",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "\n",
    "history = model.fit_generator(train_generator, steps_per_epoch=nb_train_steps, epochs=epochs,callbacks=[es],\n",
    "                              validation_data=val_generator, validation_steps=nb_val_steps, \n",
    "                              verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "039cf48b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "78/78 [==============================] - 194s 2s/step - loss: 0.3800 - accuracy: 0.7981 - val_loss: 0.1556 - val_accuracy: 0.8266\n",
      "Epoch 2/5\n",
      "78/78 [==============================] - 189s 2s/step - loss: 0.1278 - accuracy: 0.8066 - val_loss: 0.0950 - val_accuracy: 0.8266\n",
      "Epoch 3/5\n",
      "78/78 [==============================] - 174s 2s/step - loss: 0.0892 - accuracy: 0.8066 - val_loss: 0.0732 - val_accuracy: 0.8266\n",
      "Epoch 4/5\n",
      "78/78 [==============================] - 184s 2s/step - loss: 0.0725 - accuracy: 0.8066 - val_loss: 0.0658 - val_accuracy: 0.8266\n",
      "Epoch 5/5\n",
      "78/78 [==============================] - 185s 2s/step - loss: 0.0643 - accuracy: 0.8066 - val_loss: 0.0559 - val_accuracy: 0.8266\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_generator, epochs=epochs,callbacks=[es], validation_data=val_generator,  verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "76f31f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.gcf()\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.axis(ymin=0.4,ymax=1)\n",
    "plt.grid()\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5398ec75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.grid()\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "afbe39e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "852d3f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(\n",
    "      x=x_cross_validation\n",
    "    , batch_size=10\n",
    "    , verbose=0\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f65d9f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_truth = y_cross_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a9dc87f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.27849025]\n",
      "[0.27849984]\n",
      "[0.27848256]\n",
      "[0.28070652]\n",
      "[0.2782615]\n",
      "[0.27838635]\n",
      "[0.279369]\n",
      "[0.2811876]\n",
      "[0.27950293]\n",
      "[0.27849472]\n",
      "[0.2784335]\n",
      "[0.27856737]\n",
      "[0.27912268]\n",
      "[0.27895615]\n",
      "[0.27834645]\n",
      "[0.27898544]\n",
      "[0.27933103]\n",
      "[0.27964512]\n",
      "[0.27884895]\n",
      "[0.278345]\n",
      "[0.27862945]\n",
      "[0.27831826]\n",
      "[0.2784289]\n",
      "[0.2784176]\n",
      "[0.27839595]\n",
      "[0.27886415]\n",
      "[0.27830896]\n",
      "[0.2786615]\n",
      "[0.27895942]\n",
      "[0.27862304]\n",
      "[0.2785017]\n",
      "[0.27862495]\n",
      "[0.27850494]\n",
      "[0.27841318]\n",
      "[0.28015417]\n",
      "[0.2785123]\n",
      "[0.27841926]\n",
      "[0.2789434]\n",
      "[0.278446]\n",
      "[0.2792595]\n",
      "[0.2783784]\n",
      "[0.27865204]\n",
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      "[0.27859762]\n",
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      "[0.27865523]\n",
      "[0.27847835]\n",
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      "[0.27952987]\n",
      "[0.27867895]\n",
      "[0.27848408]\n",
      "[0.27857918]\n",
      "[0.27837834]\n",
      "[0.27897263]\n",
      "[0.27821308]\n",
      "[0.2782146]\n",
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      "[0.27868807]\n",
      "[0.27841756]\n",
      "[0.27823216]\n",
      "[0.27830166]\n",
      "[0.27882764]\n",
      "[0.27837119]\n",
      "[0.27901942]\n",
      "[0.27978674]\n",
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      "[0.27818066]\n",
      "[0.27792752]\n",
      "[0.27864742]\n",
      "[0.2774164]\n",
      "[0.2783073]\n",
      "[0.27854523]\n",
      "[0.27842253]\n",
      "[0.27835342]\n",
      "[0.2782628]\n",
      "[0.27867448]\n",
      "[0.2781948]\n",
      "[0.2791579]\n",
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      "[0.27843785]\n",
      "[0.28124386]\n",
      "[0.27851284]\n",
      "[0.27891055]\n",
      "[0.27859384]\n",
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      "[0.27818418]\n",
      "[0.2784686]\n",
      "[0.2797762]\n",
      "[0.2788017]\n",
      "[0.27846304]\n"
     ]
    }
   ],
   "source": [
    "for i in predictions :\n",
    "   print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ec09b22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "rounded_predictions = np.argmax(predictions, axis=-1)\n",
    "\n",
    "for i in rounded_predictions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0d9232d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_true=y_truth, y_pred=rounded_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "93560a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                        normalize=False,\n",
    "                        title='Confusion matrix',\n",
    "                        cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "            horizontalalignment=\"center\",\n",
    "            color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "42aaff19",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_plot_labels = ['Benign','melanoma']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0e13066a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[510   0]\n",
      " [107   0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fbaa9aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "import cv2\n",
    "\n",
    "model.save('2020_resnet.h5')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "02d160eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "\n",
    "\n",
    "load_status = keras.models.load_model(\"2020_resnet.h5\",custom_objects ={'focal_crossentropy':focal_loss()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fbcccc24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[139 118 186]\n",
      "  [142 121 189]\n",
      "  [147 126 194]\n",
      "  ...\n",
      "  [130 113 164]\n",
      "  [131 114 165]\n",
      "  [131 114 165]]\n",
      "\n",
      " [[145 124 192]\n",
      "  [148 127 195]\n",
      "  [151 130 198]\n",
      "  ...\n",
      "  [131 114 165]\n",
      "  [132 115 166]\n",
      "  [132 115 166]]\n",
      "\n",
      " [[146 125 193]\n",
      "  [148 127 195]\n",
      "  [149 128 196]\n",
      "  ...\n",
      "  [133 116 167]\n",
      "  [133 116 167]\n",
      "  [133 116 167]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[113 104 161]\n",
      "  [113 104 161]\n",
      "  [112 103 160]\n",
      "  ...\n",
      "  [ 85  72 116]\n",
      "  [ 85  72 116]\n",
      "  [ 85  72 116]]\n",
      "\n",
      " [[114 105 162]\n",
      "  [114 105 162]\n",
      "  [113 104 161]\n",
      "  ...\n",
      "  [ 85  72 116]\n",
      "  [ 84  72 114]\n",
      "  [ 84  72 114]]\n",
      "\n",
      " [[118 106 164]\n",
      "  [117 105 163]\n",
      "  [117 105 163]\n",
      "  ...\n",
      "  [ 84  72 114]\n",
      "  [ 84  72 114]\n",
      "  [ 84  72 114]]]\n",
      "(1, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "inp = r\"./ISIC_2020_Training_JPEG/train/ISIC_0052212.jpg\"\n",
    "import cv2\n",
    "\n",
    "img=cv2.imread(inp)\n",
    "\n",
    "print(img)\n",
    "image=cv2.imread(inp)\n",
    "image_resized= cv2.resize(image, (224,224))\n",
    "image=np.expand_dims(image_resized,axis=0)\n",
    "print(image.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9cb7e6ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 732ms/step\n",
      "[[0.37613428]]\n"
     ]
    }
   ],
   "source": [
    "nfkej = load_status.predict(image)\n",
    "print(nfkej)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9ed27f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d38f635",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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