4장. 3절. CNN 알고리즘 - 3.5. ResNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac57578a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 28, 28, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 28, 28, 128)  3584        ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 28, 28, 64)   73792       ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 28, 28, 128)  73856       ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 28, 28, 128)  0           ['conv2d_2[0][0]',               \n",
      "                                                                  'conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 28, 28, 128)  0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 151,232\n",
      "Trainable params: 151,232\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import Model, layers\n",
    "\n",
    "input = layers.Input(shape=(28,28,3))\n",
    "\n",
    "x = layers.Conv2D(128, 3, padding='same', activation='relu')(input)\n",
    "\n",
    "conv = layers.Conv2D(64, 3, padding='same', activation='relu')(x)\n",
    "conv = layers.Conv2D(128, 3, padding='same')(conv)\n",
    "\n",
    "y = layers.add([conv, x])\n",
    "y = layers.Activation('relu')(y)\n",
    "\n",
    "model = Model(inputs=input, outputs=y)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e015573",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 32, 32, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 32, 32, 32)   896         ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 32, 32, 32)  128         ['conv2d_3[0][0]']               \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 32, 32, 32)   9248        ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 32, 32, 32)  128         ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 32, 32, 32)   9248        ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 32, 32, 32)  128         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 32, 32, 32)   0           ['batch_normalization[0][0]',    \n",
      "                                                                  'batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 32, 32, 32)   0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 32, 32, 32)   9248        ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 32, 32, 32)  128         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 32, 32, 32)   9248        ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 32, 32, 32)  128         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 32, 32, 32)   0           ['activation_1[0][0]',           \n",
      "                                                                  'batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 32, 32, 32)   0           ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 4, 4, 32)    0           ['activation_2[0][0]']           \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 512)          0           ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 512)          262656      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 10)           5130        ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 306,314\n",
      "Trainable params: 305,994\n",
      "Non-trainable params: 320\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Conv2D\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation\n",
    "from tensorflow.keras.layers import AveragePooling2D, Flatten\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
    "print('x_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "\n",
    "input_shape = X_train.shape[1:]\n",
    "\n",
    "X_train = X_train.astype('float32') / 255\n",
    "X_test = X_test.astype('float32') / 255\n",
    "\n",
    "inputs = Input(shape=input_shape)\n",
    "\n",
    "conv = Conv2D(filters=32, kernel_size=3, padding='same', activation='relu')\n",
    "x = conv(inputs)\n",
    "x = BatchNormalization()(x)\n",
    "\n",
    "conv = Conv2D(filters=32, kernel_size=3, padding='same', activation='relu',\n",
    "              kernel_regularizer=l2(1e-4))\n",
    "y = conv(x)\n",
    "y = BatchNormalization()(y)\n",
    "\n",
    "conv = Conv2D(filters=32, kernel_size=3, padding='same',\n",
    "              kernel_regularizer=l2(1e-4))\n",
    "y = conv(y)\n",
    "y = BatchNormalization()(y)\n",
    "\n",
    "x = keras.layers.add([x, y])\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "conv = Conv2D(filters=32, kernel_size=3, padding='same',\n",
    "              activation='relu',\n",
    "              kernel_regularizer=l2(1e-4))\n",
    "y = conv(x)\n",
    "y = BatchNormalization()(y)\n",
    "\n",
    "conv = Conv2D(filters=32, kernel_size=3, padding='same',\n",
    "              kernel_regularizer=l2(1e-4))\n",
    "y = conv(y)\n",
    "y = BatchNormalization()(y)\n",
    "\n",
    "x = keras.layers.add([x, y])\n",
    "x = Activation('relu')(x)\n",
    "\n",
    "x = AveragePooling2D(pool_size=8)(x)\n",
    "y = Flatten()(x)\n",
    "\n",
    "y = Dense(512, activation='relu')(y)\n",
    "outputs = Dense(NUM_CLASSES, activation='softmax')(y)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a11e8ba",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.001\n",
      "Epoch 1/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 1.3778 - accuracy: 0.5103\n",
      "Epoch 1: val_accuracy improved from -inf to 0.14490, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-001-0.1449.keras\n",
      "250/250 [==============================] - 7s 16ms/step - loss: 1.3772 - accuracy: 0.5106 - val_loss: 3.3387 - val_accuracy: 0.1449 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 2/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.9900 - accuracy: 0.6525\n",
      "Epoch 2: val_accuracy improved from 0.14490 to 0.50700, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-002-0.5070.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.9892 - accuracy: 0.6528 - val_loss: 1.4193 - val_accuracy: 0.5070 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 3/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.8400 - accuracy: 0.7095\n",
      "Epoch 3: val_accuracy improved from 0.50700 to 0.66410, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-003-0.6641.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.8400 - accuracy: 0.7095 - val_loss: 0.9977 - val_accuracy: 0.6641 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 4/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.7399 - accuracy: 0.7455\n",
      "Epoch 4: val_accuracy improved from 0.66410 to 0.71310, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-004-0.7131.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.7401 - accuracy: 0.7455 - val_loss: 0.8353 - val_accuracy: 0.7131 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 5/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.6750 - accuracy: 0.7698\n",
      "Epoch 5: val_accuracy improved from 0.71310 to 0.71750, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-005-0.7175.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.6753 - accuracy: 0.7698 - val_loss: 0.8227 - val_accuracy: 0.7175 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 6/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.6065 - accuracy: 0.7935\n",
      "Epoch 6: val_accuracy improved from 0.71750 to 0.72730, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-006-0.7273.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.6060 - accuracy: 0.7937 - val_loss: 0.8204 - val_accuracy: 0.7273 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 7/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.5628 - accuracy: 0.8097\n",
      "Epoch 7: val_accuracy improved from 0.72730 to 0.76580, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-007-0.7658.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.5627 - accuracy: 0.8097 - val_loss: 0.6999 - val_accuracy: 0.7658 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 8/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.5191 - accuracy: 0.8260\n",
      "Epoch 8: val_accuracy did not improve from 0.76580\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.5196 - accuracy: 0.8258 - val_loss: 0.7085 - val_accuracy: 0.7597 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 9/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.4766 - accuracy: 0.8401\n",
      "Epoch 9: val_accuracy did not improve from 0.76580\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.4766 - accuracy: 0.8402 - val_loss: 0.7396 - val_accuracy: 0.7627 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 10/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.4446 - accuracy: 0.8508\n",
      "Epoch 10: val_accuracy did not improve from 0.76580\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.4443 - accuracy: 0.8509 - val_loss: 0.8071 - val_accuracy: 0.7400 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 11/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.4089 - accuracy: 0.8658\n",
      "Epoch 11: val_accuracy improved from 0.76580 to 0.77090, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-011-0.7709.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.4091 - accuracy: 0.8657 - val_loss: 0.7283 - val_accuracy: 0.7709 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 12/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.3738 - accuracy: 0.8756\n",
      "Epoch 12: val_accuracy did not improve from 0.77090\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.3738 - accuracy: 0.8757 - val_loss: 0.7238 - val_accuracy: 0.7671 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 13/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.3484 - accuracy: 0.8847\n",
      "Epoch 13: val_accuracy did not improve from 0.77090\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.3487 - accuracy: 0.8845 - val_loss: 0.7100 - val_accuracy: 0.7676 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 14/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.3162 - accuracy: 0.8967\n",
      "Epoch 14: val_accuracy improved from 0.77090 to 0.77750, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-014-0.7775.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.3165 - accuracy: 0.8966 - val_loss: 0.7206 - val_accuracy: 0.7775 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 15/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.2915 - accuracy: 0.9045\n",
      "Epoch 15: val_accuracy improved from 0.77750 to 0.79720, saving model to C:\\Users\\user\\ai-torch\\saved_models\\cifar10_model-015-0.7972.keras\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.2913 - accuracy: 0.9045 - val_loss: 0.6613 - val_accuracy: 0.7972 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 16/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.2647 - accuracy: 0.9153\n",
      "Epoch 16: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.2646 - accuracy: 0.9153 - val_loss: 0.7513 - val_accuracy: 0.7860 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 17/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.2394 - accuracy: 0.9267\n",
      "Epoch 17: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.2393 - accuracy: 0.9267 - val_loss: 0.7763 - val_accuracy: 0.7783 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 18/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.2200 - accuracy: 0.9312\n",
      "Epoch 18: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.2199 - accuracy: 0.9312 - val_loss: 0.7397 - val_accuracy: 0.7922 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 19/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.2016 - accuracy: 0.9378\n",
      "Epoch 19: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.2018 - accuracy: 0.9377 - val_loss: 0.7481 - val_accuracy: 0.7892 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 20/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1734 - accuracy: 0.9487\n",
      "Epoch 20: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1736 - accuracy: 0.9485 - val_loss: 0.7545 - val_accuracy: 0.7909 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 21/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1607 - accuracy: 0.9539\n",
      "Epoch 21: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1609 - accuracy: 0.9538 - val_loss: 0.8253 - val_accuracy: 0.7928 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 22/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1419 - accuracy: 0.9605\n",
      "Epoch 22: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1421 - accuracy: 0.9604 - val_loss: 0.8627 - val_accuracy: 0.7893 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 23/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1378 - accuracy: 0.9611\n",
      "Epoch 23: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1379 - accuracy: 0.9610 - val_loss: 0.9565 - val_accuracy: 0.7692 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 24/40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249/250 [============================>.] - ETA: 0s - loss: 0.1241 - accuracy: 0.9658\n",
      "Epoch 24: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1240 - accuracy: 0.9658 - val_loss: 0.8748 - val_accuracy: 0.7869 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 25/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1145 - accuracy: 0.9696\n",
      "Epoch 25: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1145 - accuracy: 0.9695 - val_loss: 0.9970 - val_accuracy: 0.7750 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 26/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1117 - accuracy: 0.9705\n",
      "Epoch 26: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1116 - accuracy: 0.9705 - val_loss: 0.9310 - val_accuracy: 0.7903 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 27/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.1009 - accuracy: 0.9745\n",
      "Epoch 27: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.1010 - accuracy: 0.9744 - val_loss: 1.0774 - val_accuracy: 0.7623 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 28/40\n",
      "248/250 [============================>.] - ETA: 0s - loss: 0.0936 - accuracy: 0.9770\n",
      "Epoch 28: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0936 - accuracy: 0.9770 - val_loss: 1.0016 - val_accuracy: 0.7846 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 29/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0842 - accuracy: 0.9806\n",
      "Epoch 29: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0845 - accuracy: 0.9804 - val_loss: 1.0500 - val_accuracy: 0.7899 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 30/40\n",
      "248/250 [============================>.] - ETA: 0s - loss: 0.0886 - accuracy: 0.9793\n",
      "Epoch 30: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 16ms/step - loss: 0.0885 - accuracy: 0.9793 - val_loss: 1.0471 - val_accuracy: 0.7865 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 31/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0705 - accuracy: 0.9852\n",
      "Epoch 31: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0708 - accuracy: 0.9851 - val_loss: 1.1877 - val_accuracy: 0.7751 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 32/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0905 - accuracy: 0.9761\n",
      "Epoch 32: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0906 - accuracy: 0.9760 - val_loss: 1.0633 - val_accuracy: 0.7854 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 33/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0773 - accuracy: 0.9825\n",
      "Epoch 33: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0773 - accuracy: 0.9825 - val_loss: 1.0894 - val_accuracy: 0.7850 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 34/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0728 - accuracy: 0.9839\n",
      "Epoch 34: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0728 - accuracy: 0.9839 - val_loss: 1.1425 - val_accuracy: 0.7833 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 35/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0813 - accuracy: 0.9807\n",
      "Epoch 35: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0812 - accuracy: 0.9808 - val_loss: 1.4012 - val_accuracy: 0.7421 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 36/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0838 - accuracy: 0.9793\n",
      "Epoch 36: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0838 - accuracy: 0.9793 - val_loss: 1.3410 - val_accuracy: 0.7570 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 37/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0652 - accuracy: 0.9864\n",
      "Epoch 37: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0654 - accuracy: 0.9864 - val_loss: 1.1341 - val_accuracy: 0.7868 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 38/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0615 - accuracy: 0.9878\n",
      "Epoch 38: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0617 - accuracy: 0.9878 - val_loss: 1.1728 - val_accuracy: 0.7942 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 39/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0637 - accuracy: 0.9869\n",
      "Epoch 39: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0639 - accuracy: 0.9869 - val_loss: 1.4348 - val_accuracy: 0.7637 - lr: 0.0010\n",
      "Learning rate:  0.001\n",
      "Epoch 40/40\n",
      "249/250 [============================>.] - ETA: 0s - loss: 0.0763 - accuracy: 0.9825\n",
      "Epoch 40: val_accuracy did not improve from 0.79720\n",
      "250/250 [==============================] - 4s 15ms/step - loss: 0.0764 - accuracy: 0.9825 - val_loss: 1.2475 - val_accuracy: 0.7731 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "EPOCHS = 40\n",
    "BATCH_SIZE = 200\n",
    "\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "model_name = 'cifar10_model-{epoch:03d}-{val_accuracy:.4f}.keras'\n",
    "\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "\n",
    "filepath = os.path.join(save_dir, model_name)\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath=filepath,\n",
    "                             monitor='val_accuracy',\n",
    "                             verbose=1,\n",
    "                             save_best_only=True)\n",
    "\n",
    "def lr_schedule(epoch):\n",
    "    lr = 1e-3\n",
    "    if epoch > 90:\n",
    "        lr *= 0.5e-3\n",
    "    elif epoch > 80:\n",
    "        lr *= 1e-3\n",
    "    elif epoch > 60:\n",
    "        lr *= 1e-2\n",
    "    elif epoch > 40:\n",
    "        lr *= 1e-1\n",
    "    print('Learning rate: ', lr)\n",
    "    return lr\n",
    "\n",
    "lr_scheduler = LearningRateScheduler(lr_schedule)\n",
    "\n",
    "my_callbacks = [checkpoint, lr_scheduler]\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer=Adam(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test),\n",
    "                    epochs=EPOCHS, batch_size=BATCH_SIZE,\n",
    "                    callbacks=my_callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "277cf670",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 1.2475 - accuracy: 0.7731\n",
      "Test loss: 1.2475316524505615\n",
      "Test accuracy: 0.7731000185012817\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=1)\n",
    "print(f'Test loss: {loss}')\n",
    "print(f'Test accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c6dbb55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(history.history.keys())\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e01e6b4",
   "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": "3.10.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}