cnn.py

## cnn.py
from tensorflow import keras
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, BatchNormalization
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np

# Hyperparameters
NUM_CLASSES = 10
ACTIVATION = "relu"
LEARNING_RATE = 1e-3
BATCH_SIZE = 64
EPOCHS = 64
LOSS_FUNCTION = "categorical_crossentropy"
LAMBDA = 1e-4
REGULARIZER = keras.regularizers.l2(LAMBDA)

# Loading CIFAR-10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Normalizing training samples
mean_train = np.mean(x_train)
std_train = np.std(x_train)
x_train = (x_train - mean_train) / std_train

# Normalizing test samples
mean_test = np.mean(x_test)
std_test = np.std(x_test)
x_test = (x_test - mean_test) / std_test

# One-hot encoding training and testing labels
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)
y_test  = keras.utils.to_categorical(y_test, NUM_CLASSES)

# Model architecture
model = Sequential()

model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=REGULARIZER, input_shape=x_train.shape[1:]))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=REGULARIZER))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=REGULARIZER))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=REGULARIZER))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=REGULARIZER))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=REGULARIZER))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Activation(ACTIVATION))
model.add(BatchNormalization())

model.add(Flatten())

model.add(Dense(32, activation=ACTIVATION))
model.add(Dense(NUM_CLASSES, activation='softmax'))

model.summary() # Print out a summary

# Data augmentation
datagen = ImageDataGenerator(rotation_range=45, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)
datagen.fit(x_train)
augmented_data = datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)

# Training
adam = keras.optimizers.Adam(lr=LEARNING_RATE)
model.compile(loss=LOSS_FUNCTION, optimizer=adam, metrics=['accuracy'])
model.fit_generator(augmented_data, epochs=EPOCHS, verbose=1, validation_data=(x_test, y_test))

# Saving the model to disk
model_json = model.to_json()
with open('model.json', 'w') as json_file:
    json_file.write(model_json)
model.save_weights('model.h5')