project_1.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e64668",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow opencv-python matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f46eef44",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sujit\\AppData\\Local\\Temp\\ipykernel_19244\\2199825133.py:4: DeprecationWarning: 'imghdr' is deprecated and slated for removal in Python 3.13\n",
      "  import imghdr\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import cv2\n",
    "import imghdr\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c47ca70",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f17c777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2d09e288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image not in ext list data\\AiArtData\\107357836.jpg\n",
      "Image not in ext list data\\AiArtData\\52624466992_efee602089_b.jpg\n",
      "Image not in ext list data\\AiArtData\\99997140.jpg\n",
      "Image not in ext list data\\AiArtData\\ai-image-generator-nature.png\n",
      "Image not in ext list data\\AiArtData\\image-variations-for-nature.png\n"
     ]
    }
   ],
   "source": [
    "data_dir = 'data' \n",
    "image_exts = ['jpeg', 'jpg', 'bmp', 'png']\n",
    "for image_class in os.listdir(data_dir): \n",
    "    for image in os.listdir(os.path.join(data_dir, image_class)):\n",
    "        image_path = os.path.join(data_dir, image_class, image)\n",
    "        try: \n",
    "            img = cv2.imread(image_path)\n",
    "            tip = imghdr.what(image_path)\n",
    "            if tip not in image_exts: \n",
    "                print('Image not in ext list {}'.format(image_path))\n",
    "                os.remove(image_path)\n",
    "        except Exception as e: \n",
    "            print('Issue with image {}'.format(image_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e34fa19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10065 files belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "data = tf.keras.utils.image_dataset_from_directory('data')\n",
    "data = data.map(lambda x, y: (x / 255.0, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d79f434f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(data) * 0.7)\n",
    "val_size = int(len(data) * 0.2)\n",
    "test_size = int(len(data) * 0.1)\n",
    "train = data.take(train_size)\n",
    "val = data.skip(train_size).take(val_size)\n",
    "test = data.skip(train_size + val_size).take(test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "976d24ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),\n",
    "    MaxPooling2D(),\n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(),\n",
    "    Conv2D(128, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(),\n",
    "    Flatten(),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5affa1a1",
   "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\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\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",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">254</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">127</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">127</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<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\">115200</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<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\">256</span>)                 │      <span style=\"color: #00af00; text-decoration-color: #00af00\">29,491,456</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<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\">257</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",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m254\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m896\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m127\u001b[0m, \u001b[38;5;34m127\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m18,496\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m115200\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │      \u001b[38;5;34m29,491,456\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m257\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\">29,584,961</span> (112.86 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m29,584,961\u001b[0m (112.86 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\">29,584,961</span> (112.86 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m29,584,961\u001b[0m (112.86 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\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss=tf.losses.BinaryCrossentropy(), metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf4803a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m161s\u001b[0m 718ms/step - accuracy: 0.5917 - loss: 0.9151 - val_accuracy: 0.7262 - val_loss: 0.5308\n",
      "Epoch 2/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 722ms/step - accuracy: 0.7634 - loss: 0.4987 - val_accuracy: 0.7946 - val_loss: 0.4431\n",
      "Epoch 3/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 723ms/step - accuracy: 0.8079 - loss: 0.4246 - val_accuracy: 0.8090 - val_loss: 0.4062\n",
      "Epoch 4/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 720ms/step - accuracy: 0.8574 - loss: 0.3339 - val_accuracy: 0.7922 - val_loss: 0.4720\n",
      "Epoch 5/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 721ms/step - accuracy: 0.8912 - loss: 0.2624 - val_accuracy: 0.8006 - val_loss: 0.4661\n",
      "Epoch 6/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 725ms/step - accuracy: 0.9253 - loss: 0.2005 - val_accuracy: 0.8070 - val_loss: 0.4983\n",
      "Epoch 7/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 722ms/step - accuracy: 0.9533 - loss: 0.1234 - val_accuracy: 0.8006 - val_loss: 0.6946\n",
      "Epoch 8/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 720ms/step - accuracy: 0.9620 - loss: 0.1079 - val_accuracy: 0.8209 - val_loss: 0.5870\n",
      "Epoch 9/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 720ms/step - accuracy: 0.9797 - loss: 0.0681 - val_accuracy: 0.8338 - val_loss: 0.6914\n",
      "Epoch 10/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 718ms/step - accuracy: 0.9872 - loss: 0.0429 - val_accuracy: 0.8041 - val_loss: 0.8132\n",
      "Epoch 11/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 719ms/step - accuracy: 0.9800 - loss: 0.0586 - val_accuracy: 0.8259 - val_loss: 0.7614\n",
      "Epoch 12/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 725ms/step - accuracy: 0.9884 - loss: 0.0404 - val_accuracy: 0.8110 - val_loss: 0.8657\n",
      "Epoch 13/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 725ms/step - accuracy: 0.9894 - loss: 0.0356 - val_accuracy: 0.8323 - val_loss: 0.8099\n",
      "Epoch 14/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 721ms/step - accuracy: 0.9892 - loss: 0.0474 - val_accuracy: 0.8180 - val_loss: 0.8270\n",
      "Epoch 15/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 721ms/step - accuracy: 0.9905 - loss: 0.0310 - val_accuracy: 0.8398 - val_loss: 0.8612\n",
      "Epoch 16/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 776ms/step - accuracy: 0.9907 - loss: 0.0296 - val_accuracy: 0.8026 - val_loss: 1.3005\n",
      "Epoch 17/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 787ms/step - accuracy: 0.9867 - loss: 0.0470 - val_accuracy: 0.8318 - val_loss: 0.9972\n",
      "Epoch 18/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m161s\u001b[0m 729ms/step - accuracy: 0.9891 - loss: 0.0326 - val_accuracy: 0.8378 - val_loss: 1.0324\n",
      "Epoch 19/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 724ms/step - accuracy: 0.9946 - loss: 0.0190 - val_accuracy: 0.8219 - val_loss: 0.9955\n",
      "Epoch 20/20\n",
      "\u001b[1m220/220\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m160s\u001b[0m 724ms/step - accuracy: 0.9962 - loss: 0.0133 - val_accuracy: 0.8224 - val_loss: 1.3606\n"
     ]
    }
   ],
   "source": [
    "logdir = 'logs'\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\n",
    "hist = model.fit(train, epochs=20, validation_data=val, callbacks=[tensorboard_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a8c044b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "ax[0].plot(hist.history['loss'], label='loss', color='teal')\n",
    "ax[0].plot(hist.history['val_loss'], label='val_loss', color='orange')\n",
    "ax[0].set_title('Loss')\n",
    "ax[0].legend()\n",
    "ax[1].plot(hist.history['accuracy'], label='accuracy', color='teal')\n",
    "ax[1].plot(hist.history['val_accuracy'], label='val_accuracy', color='orange')\n",
    "ax[1].set_title('Accuracy')\n",
    "ax[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7ee0b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 377ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step\n",
      "Precision: 0.7921146750450134, Recall: 0.8752475380897522, Accuracy: 0.819556474685669\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy\n",
    "pre = Precision()\n",
    "re = Recall()\n",
    "acc = BinaryAccuracy()\n",
    "\n",
    "for batch in test:\n",
    "    X, y = batch\n",
    "    yhat = model.predict(X)\n",
    "    pre.update_state(y, yhat)\n",
    "    re.update_state(y, yhat)\n",
    "    acc.update_state(y, yhat)\n",
    "\n",
    "print(f'Precision: {pre.result().numpy()}, Recall: {re.result().numpy()}, Accuracy: {acc.result().numpy()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24db060d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('project_01_02.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e3818e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fb40fb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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