Simple Convolutional Net

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')