Upload Skin Cancer Classification.ipynb

Skin Cancer Classification.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c6c2112d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "677fd6f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels=['Cancer', 'Non Cancer']\n",
+    "img_path='Skin Data/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1ae9f20f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_list=[]\n",
+    "label_list=[]\n",
+    "\n",
+    "for label in labels:\n",
+    "    for img_file in os.listdir(img_path+label):\n",
+    "        img_list.append(img_path+label+'/'+img_file)\n",
+    "        label_list.append(label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6736fc5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'img': img_list, 'label': label_list})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bb9a0009",
+   "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>img</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>Skin Data/Non Cancer/614.JPG</td>\n",
+       "      <td>Non Cancer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Skin Data/Cancer/2301-1.JPG</td>\n",
+       "      <td>Cancer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Skin Data/Non Cancer/1111.JPG</td>\n",
+       "      <td>Non Cancer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>Skin Data/Non Cancer/1248-1.JPG</td>\n",
+       "      <td>Non Cancer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Skin Data/Non Cancer/1065.jpg</td>\n",
+       "      <td>Non Cancer</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 img       label\n",
+       "130     Skin Data/Non Cancer/614.JPG  Non Cancer\n",
+       "73       Skin Data/Cancer/2301-1.JPG      Cancer\n",
+       "202    Skin Data/Non Cancer/1111.JPG  Non Cancer\n",
+       "211  Skin Data/Non Cancer/1248-1.JPG  Non Cancer\n",
+       "199    Skin Data/Non Cancer/1065.jpg  Non Cancer"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "54440c37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d={'Non Cancer': 0, 'Cancer': 1}\n",
+    "df['encode_label']=df['label'].map(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "53cd4b47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = []\n",
+    "\n",
+    "for img in df['img']:\n",
+    "    img = cv2.imread(str(img))\n",
+    "    img = cv2.resize(img, (170, 170))\n",
+    "    img = img / 255.0 #normalize\n",
+    "    x.append(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d81879b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6578ffef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y=df['encode_label']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0f9856e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-05-15 10:21:21.775290: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv2D, Dense, Flatten, Input, MaxPooling2D, Dropout, BatchNormalization, Reshape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6eefab7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x,y, test_size=.20, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "281f7cae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CNN = Convolutional Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4e283b50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model=Sequential()\n",
+    "model.add(Input(shape=(170,170,3)))\n",
+    "model.add(Conv2D(32,kernel_size=(3,3),activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
+    "model.add(Conv2D(64,kernel_size=(3,3),activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128))\n",
+    "model.add(Dense(2, activation='softmax')) # 10 fakli cevap classification 0-9 a kadar olan rakamlar\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fcc1a740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 660ms/step - accuracy: 0.5291 - loss: 9.7854 - val_accuracy: 0.7414 - val_loss: 2.5156\n",
+      "Epoch 2/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 610ms/step - accuracy: 0.6474 - loss: 2.9485 - val_accuracy: 0.2586 - val_loss: 0.7912\n",
+      "Epoch 3/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 623ms/step - accuracy: 0.6237 - loss: 0.6547 - val_accuracy: 0.7586 - val_loss: 0.5047\n",
+      "Epoch 4/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 669ms/step - accuracy: 0.7573 - loss: 0.5762 - val_accuracy: 0.7931 - val_loss: 0.4346\n",
+      "Epoch 5/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 650ms/step - accuracy: 0.7664 - loss: 0.4830 - val_accuracy: 0.7414 - val_loss: 0.6113\n",
+      "Epoch 6/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 619ms/step - accuracy: 0.7919 - loss: 0.4656 - val_accuracy: 0.8448 - val_loss: 0.3715\n",
+      "Epoch 7/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 643ms/step - accuracy: 0.8623 - loss: 0.3305 - val_accuracy: 0.8276 - val_loss: 0.4111\n",
+      "Epoch 8/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 617ms/step - accuracy: 0.8871 - loss: 0.3118 - val_accuracy: 0.8103 - val_loss: 0.3918\n",
+      "Epoch 9/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 612ms/step - accuracy: 0.8852 - loss: 0.2627 - val_accuracy: 0.7241 - val_loss: 0.7321\n",
+      "Epoch 10/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 624ms/step - accuracy: 0.8766 - loss: 0.2683 - val_accuracy: 0.7931 - val_loss: 0.4346\n",
+      "Epoch 11/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 632ms/step - accuracy: 0.9435 - loss: 0.1946 - val_accuracy: 0.8103 - val_loss: 0.3652\n",
+      "Epoch 12/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 622ms/step - accuracy: 0.9718 - loss: 0.1293 - val_accuracy: 0.8621 - val_loss: 0.4700\n",
+      "Epoch 13/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 613ms/step - accuracy: 0.9279 - loss: 0.1620 - val_accuracy: 0.8276 - val_loss: 0.4200\n",
+      "Epoch 14/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 639ms/step - accuracy: 0.9648 - loss: 0.0937 - val_accuracy: 0.7586 - val_loss: 0.6257\n",
+      "Epoch 15/15\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 635ms/step - accuracy: 0.9669 - loss: 0.1067 - val_accuracy: 0.8448 - val_loss: 0.3362\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "history=model.fit( x_train, y_train,validation_data=(x_test,y_test), epochs=15, verbose=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8199ab93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save('cnn_model.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca51b883",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b26379a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VGGNET, ResNet50, Inceptionv3, Xception, MobileNetv2 Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d3f206da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv2D, Dense, Flatten, Input, MaxPooling2D, Dropout, BatchNormalization, Reshape\n",
+    "\n",
+    "from tensorflow.keras.applications import VGG16, ResNet50\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fccd2086",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 232 images belonging to 2 classes.\n",
+      "Found 56 images belonging to 2 classes.\n",
+      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+      "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+      "  self._warn_if_super_not_called()\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 5s/step - accuracy: 0.5209 - loss: 5.4695 - val_accuracy: 0.7143 - val_loss: 1.6115\n",
+      "Epoch 2/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 5s/step - accuracy: 0.6928 - loss: 2.1946 - val_accuracy: 0.3036 - val_loss: 1.9652\n",
+      "Epoch 3/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 5s/step - accuracy: 0.5871 - loss: 1.1498 - val_accuracy: 0.7679 - val_loss: 0.5415\n",
+      "Epoch 4/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 5s/step - accuracy: 0.8169 - loss: 0.4627 - val_accuracy: 0.7679 - val_loss: 0.5914\n",
+      "Epoch 5/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 5s/step - accuracy: 0.8383 - loss: 0.3790 - val_accuracy: 0.7857 - val_loss: 0.4250\n",
+      "Epoch 6/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 5s/step - accuracy: 0.9351 - loss: 0.1650 - val_accuracy: 0.8393 - val_loss: 0.3612\n",
+      "Epoch 7/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 4s/step - accuracy: 0.9531 - loss: 0.1619 - val_accuracy: 0.8393 - val_loss: 0.3391\n",
+      "Epoch 8/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━��━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 4s/step - accuracy: 0.9621 - loss: 0.1155 - val_accuracy: 0.8393 - val_loss: 0.3643\n",
+      "Epoch 9/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 5s/step - accuracy: 0.9667 - loss: 0.1090 - val_accuracy: 0.8214 - val_loss: 0.3249\n",
+      "Epoch 10/10\n",
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 4s/step - accuracy: 0.9823 - loss: 0.0831 - val_accuracy: 0.8214 - val_loss: 0.4653\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x169e63650>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dir='Skin Data'\n",
+    "img_width,img_heigth=224,224\n",
+    "\n",
+    "train_datagen=ImageDataGenerator(rescale=1/255, validation_split=.20)\n",
+    "train_datagenerator=train_datagen.flow_from_directory(directory=data_dir,target_size=(img_width,img_heigth),\n",
+    "                                                      class_mode='binary', subset='training')\n",
+    "\n",
+    " \n",
+    "test_datagen=ImageDataGenerator(rescale=1/255)\n",
+    "test_datagenerator=train_datagen.flow_from_directory(directory=data_dir,target_size=(img_width,img_heigth),\n",
+    "                                                     class_mode='binary', subset='validation')\n",
+    "\n",
+    " \n",
+    "base_model=VGG16(weights='imagenet', input_shape=(img_width,img_heigth,3),include_top=False)\n",
+    "\n",
+    "model=Sequential()\n",
+    "\n",
+    "model.add(base_model)\n",
+    "for layer in base_model.layers:\n",
+    "    layer.trainable=False\n",
+    "\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(1024,activation='relu'))\n",
+    "model.add(Dense(1,activation='sigmoid'))\n",
+    "\n",
+    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
+    "\n",
+    "model.fit(train_datagenerator,epochs=10,validation_data=test_datagenerator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ffef776f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ vgg16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │    <span style=\"color: #00af00; text-decoration-color: #00af00\">25,691,136</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,025</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ vgg16 (\u001b[38;5;33mFunctional\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │    \u001b[38;5;34m14,714,688\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │    \u001b[38;5;34m25,691,136\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │         \u001b[38;5;34m1,025\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,791,173</span> (350.16 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,791,173\u001b[0m (350.16 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,692,161</span> (98.01 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m25,692,161\u001b[0m (98.01 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> (56.13 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">51,384,324</span> (196.02 MB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m51,384,324\u001b[0m (196.02 MB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b483c57",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eee6be78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}