U-Net_Cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35787630-2223-464b-bbc9-bd1cf6f2ef25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.utils import plot_model\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.layers import Input, Add, Dense, Activation,\\\n",
    "     Flatten, Conv2D, MaxPooling2D, UpSampling2D, Dropout, concatenate\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.callbacks import *\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e591e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6af4df94",
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c4130d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images/255.0\n",
    "test_images = test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81857741",
   "metadata": {},
   "outputs": [],
   "source": [
    "w, h = 32, 32\n",
    "train_images = train_images.reshape(train_images.shape[0], w, h, 3)\n",
    "test_images = test_images.reshape(test_images.shape[0], w, h, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04bff7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74f22a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device:/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "if tf.test.gpu_device_name(): \n",
    "\n",
    "    print('Default GPU Device:{}'.format(tf.test.gpu_device_name()))\n",
    "\n",
    "else:\n",
    "    print(\"Please install GPU version of TF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8935258",
   "metadata": {},
   "outputs": [],
   "source": [
    "classess = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d7f70f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Structure(X):\n",
    "    conv1 = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = glorot_uniform(seed = 0))(X)\n",
    "    conv1 = Conv2D(32, 2, activation='relu', padding='same')(conv1)\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    pool1 = Dropout(0.2)(pool1)\n",
    "    conv2 = Conv2D(64, 2, activation='relu', padding='same')(pool1)\n",
    "    conv2 = Conv2D(64, 2, activation='relu', padding='same')(conv2)\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "    pool2 = Dropout(0.3)(pool2)\n",
    "    conv3 = Conv2D(128, 2, activation='relu', padding='same')(pool2)\n",
    "    conv3 = Conv2D(128, 2, activation='relu', padding='same')(conv3)\n",
    "    # conv3 = Conv2D(64, 2, activation='relu', padding='same')(conv3)\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)\n",
    "    # X = MaxPooling2D(pool_size=(2, 2))(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)\n",
    "    # X = MaxPooling2D(pool_size=(2, 2))(X)\n",
    "    # X = UpSampling2D(size=(2, 2))(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(128, 3, activation='relu', padding='same')(X)   \n",
    "    # X = UpSampling2D(size=(2, 2))(X)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)\n",
    "    # X = Conv2D(256, 3, activation='relu', padding='same')(X)   \n",
    "    up1 = UpSampling2D(size=(2, 2))(pool3)\n",
    "    up1 = concatenate([conv3, up1], axis=-1)\n",
    "    conv4 = Conv2D(128, 2, activation='relu', padding='same')(up1)\n",
    "    # conv4 = Conv2D(64, 3, activation='relu', padding='same')(conv4)\n",
    "    conv4 = Conv2D(128, 2, activation='relu', padding='same')(conv4)   \n",
    "    conv4 = Dropout(0.3)(conv4)\n",
    "    up2 = UpSampling2D(size=(2, 2))(conv4)\n",
    "    up2 = concatenate([conv2, up2], axis=-1)\n",
    "    conv5 = Conv2D(64, 2, activation='relu', padding='same')(up2)\n",
    "    conv5 = Conv2D(64, 2, activation='relu', padding='same')(conv5)\n",
    "    conv5 = Dropout(0.2)(conv5)\n",
    "    up3 = UpSampling2D(size=(2, 2))(conv5)\n",
    "    up3 = concatenate([conv1, up3], axis=-1)\n",
    "    conv6 = Conv2D(32, 2, activation='relu', padding='same')(up3)\n",
    "    conv6 = Conv2D(32, 2, activation='relu', padding='same')(conv6)\n",
    "    return conv6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4fccd0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def UNET(input_shape = (w, h, 3), classes = classess):\n",
    "    X_input = Input(shape = input_shape)\n",
    "    X = Structure(X_input)\n",
    "    X = Conv2D(len(classes), 1, activation = 'softmax')(X)\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(len(classess), activation='softmax')(X)\n",
    "    model = Model(inputs = X_input, outputs = X, name = 'U-net')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e99b66e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clear unnecessary memory\n",
    "from tensorflow.keras import backend as K\n",
    "import tensorflow as tf\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a78b2c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"U-net\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 32, 32, 32)   416         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 32, 32)   4128        conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 16, 16, 32)   0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 16, 16, 32)   0           max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 16, 16, 64)   8256        dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 16, 16, 64)   16448       conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 8, 8, 64)     0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 8, 8, 64)     0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 8, 8, 128)    32896       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 8, 8, 128)    65664       conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 128)    0           conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 8, 8, 128)    0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 8, 8, 256)    0           conv2d_5[0][0]                   \n",
      "                                                                 up_sampling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 8, 8, 128)    131200      concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 8, 8, 128)    65664       conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 8, 8, 128)    0           conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 16, 16, 128)  0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 16, 16, 192)  0           conv2d_3[0][0]                   \n",
      "                                                                 up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 64)   49216       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 64)   16448       conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 16, 16, 64)   0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 32, 32, 64)   0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 32, 32, 96)   0           conv2d_1[0][0]                   \n",
      "                                                                 up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 32, 32, 32)   12320       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 32)   4128        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 32, 10)   330         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 10240)        0           conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 10)           102410      flatten[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 509,524\n",
      "Trainable params: 509,524\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = UNET(input_shape = (w, h, 3), classes = classess)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a436d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    EarlyStopping(patience=10, verbose=1),\n",
    "    ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),\n",
    "    ModelCheckpoint('model-Unet-cifar10.h5', verbose=1, save_best_only=True, save_weights_only=True),\n",
    "    CSVLogger(\"dataUnetcifar10.csv\"),\n",
    "    TensorBoard(log_dir='./logs-Unet-Cifar', histogram_freq=1)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eaf489db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer = 'adam',\n",
    "              loss= 'sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45ebc787",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "167/167 [==============================] - 14s 52ms/step - loss: 1.9844 - accuracy: 0.2719 - val_loss: 2.0960 - val_accuracy: 0.2629\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 2.09604, saving model to model-Unet-cifar10.h5\n",
      "Epoch 2/100\n",
      "167/167 [==============================] - 8s 47ms/step - loss: 1.5979 - accuracy: 0.4201 - val_loss: 2.1137 - val_accuracy: 0.3265\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 2.09604\n",
      "Epoch 3/100\n",
      "167/167 [==============================] - 8s 47ms/step - loss: 1.4362 - accuracy: 0.4863 - val_loss: 1.3661 - val_accuracy: 0.5024\n",
      "\n",
      "Epoch 00003: val_loss improved from 2.09604 to 1.36613, saving model to model-Unet-cifar10.h5\n",
      "Epoch 4/100\n",
      "167/167 [==============================] - 8s 47ms/step - loss: 1.2900 - accuracy: 0.5376 - val_loss: 1.3493 - val_accuracy: 0.5268\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.36613 to 1.34929, saving model to model-Unet-cifar10.h5\n",
      "Epoch 5/100\n",
      "167/167 [==============================] - 8s 48ms/step - loss: 1.1863 - accuracy: 0.5766 - val_loss: 1.5630 - val_accuracy: 0.4791\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 1.34929\n",
      "Epoch 6/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 1.1363 - accuracy: 0.5964 - val_loss: 1.5557 - val_accuracy: 0.4389\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 1.34929\n",
      "Epoch 7/100\n",
      "167/167 [==============================] - 8s 51ms/step - loss: 1.1035 - accuracy: 0.6096 - val_loss: 1.1873 - val_accuracy: 0.5749\n",
      "\n",
      "Epoch 00007: val_loss improved from 1.34929 to 1.18731, saving model to model-Unet-cifar10.h5\n",
      "Epoch 8/100\n",
      "167/167 [==============================] - 8s 48ms/step - loss: 1.0472 - accuracy: 0.6301 - val_loss: 1.2366 - val_accuracy: 0.5607\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 1.18731\n",
      "Epoch 9/100\n",
      "167/167 [==============================] - 8s 49ms/step - loss: 1.0031 - accuracy: 0.6436 - val_loss: 1.1452 - val_accuracy: 0.5987\n",
      "\n",
      "Epoch 00009: val_loss improved from 1.18731 to 1.14524, saving model to model-Unet-cifar10.h5\n",
      "Epoch 10/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.9746 - accuracy: 0.6544 - val_loss: 1.1686 - val_accuracy: 0.5985\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 1.14524\n",
      "Epoch 11/100\n",
      "167/167 [==============================] - 9s 52ms/step - loss: 0.9205 - accuracy: 0.6751 - val_loss: 1.0604 - val_accuracy: 0.6328\n",
      "\n",
      "Epoch 00011: val_loss improved from 1.14524 to 1.06036, saving model to model-Unet-cifar10.h5\n",
      "Epoch 12/100\n",
      "167/167 [==============================] - 9s 55ms/step - loss: 0.8981 - accuracy: 0.6829 - val_loss: 1.0022 - val_accuracy: 0.6533\n",
      "\n",
      "Epoch 00012: val_loss improved from 1.06036 to 1.00216, saving model to model-Unet-cifar10.h5\n",
      "Epoch 13/100\n",
      "167/167 [==============================] - 8s 49ms/step - loss: 0.8465 - accuracy: 0.7021 - val_loss: 1.1022 - val_accuracy: 0.6247\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 1.00216\n",
      "Epoch 14/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.8273 - accuracy: 0.7092 - val_loss: 0.9541 - val_accuracy: 0.6663\n",
      "\n",
      "Epoch 00014: val_loss improved from 1.00216 to 0.95414, saving model to model-Unet-cifar10.h5\n",
      "Epoch 15/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.8014 - accuracy: 0.7167 - val_loss: 1.0704 - val_accuracy: 0.6372\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.95414\n",
      "Epoch 16/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.7976 - accuracy: 0.7195 - val_loss: 1.0230 - val_accuracy: 0.6387\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.95414\n",
      "Epoch 17/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.7764 - accuracy: 0.7270 - val_loss: 0.9891 - val_accuracy: 0.6603\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.95414\n",
      "Epoch 18/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.7398 - accuracy: 0.7403 - val_loss: 0.9803 - val_accuracy: 0.6684\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.95414\n",
      "Epoch 19/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.7161 - accuracy: 0.7460 - val_loss: 0.9496 - val_accuracy: 0.6795\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.95414 to 0.94963, saving model to model-Unet-cifar10.h5\n",
      "Epoch 20/100\n",
      "167/167 [==============================] - 9s 52ms/step - loss: 0.6731 - accuracy: 0.7644 - val_loss: 0.9557 - val_accuracy: 0.6779\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.94963\n",
      "Epoch 21/100\n",
      "167/167 [==============================] - 9s 53ms/step - loss: 0.6687 - accuracy: 0.7640 - val_loss: 1.1107 - val_accuracy: 0.6344\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.94963\n",
      "Epoch 22/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.6654 - accuracy: 0.7655 - val_loss: 1.0143 - val_accuracy: 0.6659\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.94963\n",
      "Epoch 23/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.6181 - accuracy: 0.7816 - val_loss: 1.1181 - val_accuracy: 0.6467\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.94963\n",
      "Epoch 24/100\n",
      "167/167 [==============================] - 9s 51ms/step - loss: 0.6179 - accuracy: 0.7807 - val_loss: 0.9761 - val_accuracy: 0.6867\n",
      "\n",
      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.94963\n",
      "Epoch 25/100\n",
      "167/167 [==============================] - 8s 48ms/step - loss: 0.4944 - accuracy: 0.8309 - val_loss: 0.8825 - val_accuracy: 0.7068\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.94963 to 0.88253, saving model to model-Unet-cifar10.h5\n",
      "Epoch 26/100\n",
      "167/167 [==============================] - 8s 49ms/step - loss: 0.4784 - accuracy: 0.8353 - val_loss: 0.8807 - val_accuracy: 0.7051\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.88253 to 0.88069, saving model to model-Unet-cifar10.h5\n",
      "Epoch 27/100\n",
      "167/167 [==============================] - 8s 48ms/step - loss: 0.4782 - accuracy: 0.8353 - val_loss: 0.8900 - val_accuracy: 0.7080\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.88069\n",
      "Epoch 28/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.4657 - accuracy: 0.8384 - val_loss: 0.8973 - val_accuracy: 0.7079\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.88069\n",
      "Epoch 29/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.4584 - accuracy: 0.8427 - val_loss: 0.8996 - val_accuracy: 0.7081\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.88069\n",
      "Epoch 30/100\n",
      "167/167 [==============================] - 8s 49ms/step - loss: 0.4613 - accuracy: 0.8422 - val_loss: 0.8972 - val_accuracy: 0.7067\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.88069\n",
      "Epoch 31/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.4486 - accuracy: 0.8472 - val_loss: 0.8890 - val_accuracy: 0.7100\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.88069\n",
      "Epoch 32/100\n",
      "167/167 [==============================] - 8s 50ms/step - loss: 0.4380 - accuracy: 0.8506 - val_loss: 0.8942 - val_accuracy: 0.7075\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.88069\n",
      "Epoch 33/100\n",
      "167/167 [==============================] - 9s 51ms/step - loss: 0.4381 - accuracy: 0.8506 - val_loss: 0.8919 - val_accuracy: 0.7080\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.88069\n",
      "Epoch 34/100\n",
      "167/167 [==============================] - 9s 51ms/step - loss: 0.4371 - accuracy: 0.8508 - val_loss: 0.8920 - val_accuracy: 0.7088\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.88069\n",
      "Epoch 35/100\n",
      "167/167 [==============================] - 8s 48ms/step - loss: 0.4371 - accuracy: 0.8513 - val_loss: 0.8938 - val_accuracy: 0.7091\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.88069\n",
      "Epoch 36/100\n",
      "167/167 [==============================] - 8s 49ms/step - loss: 0.4367 - accuracy: 0.8506 - val_loss: 0.8945 - val_accuracy: 0.7093\n",
      "\n",
      "Epoch 00036: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.88069\n",
      "Epoch 00036: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_images, \n",
    "                    train_labels,\n",
    "                    batch_size = 256,\n",
    "                    validation_split=0.15,\n",
    "                    epochs = 100,\n",
    "                    shuffle = True,\n",
    "                    callbacks= callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80ed47b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('U-Net model loss', fontsize = 15)\n",
    "plt.ylabel('loss', fontsize = 15)\n",
    "plt.xlabel('epoch', fontsize = 15)\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1619fbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accuracy\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('U-Net model train accuracy', fontsize = 15)\n",
    "plt.xlabel('epoch', fontsize = 15)\n",
    "plt.ylabel('accuracy', fontsize = 15)\n",
    "plt.legend(['train accuracy', 'test accuracy'], loc = 'upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7b11c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the model using the test set\n",
    "# loss, accuracy, sparse = model.evaluate(test_images, test_labels, verbose = 1)\n",
    "# print('\\nTest accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f1dc2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9867d5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "classess_text = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',\\\n",
    "         'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7484ef53",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape(train_images.shape[0], w, h, 3)\n",
    "test_images = test_images.reshape(test_images.shape[0], w, h, 3)\n",
    "\n",
    "\n",
    "# Cifar's label need to reshape to scalar array\n",
    "test_labels = test_labels.reshape(10000, )\n",
    "\n",
    "\n",
    "\n",
    "# Helper functions to plot images \n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(classess_text[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                classess_text[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88265776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32d45153",
   "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>classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>automobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>deer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>frog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>horse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      classes\n",
       "0    airplane\n",
       "1  automobile\n",
       "2        bird\n",
       "3         cat\n",
       "4        deer\n",
       "5         dog\n",
       "6        frog\n",
       "7       horse\n",
       "8        ship\n",
       "9       truck"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "classss = pd.DataFrame({\n",
    "    'classes' : ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog',\\\n",
    "         'frog', 'horse', 'ship', 'truck'],\n",
    "})\n",
    "classss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47cf0a23",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7a2a5b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow-gpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "vscode": {
   "interpreter": {
    "hash": "7789ff83f194acf566cc5f743b97f52059d095d5da8c0625de7b48fea8fb60ab"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}