Keras-tutorial-on-CNNs/simple_cnn.py at master · AlexGidiotis/Keras-tutorial-on-CNNs · GitHub

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import time

import numpy as np

from keras.datasets import cifar10
from keras.models import Model
from keras.layers import Dense, Dropout, Flatten, Convolution2D, MaxPooling2D, Input
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint, EarlyStopping

batch_size = 32
num_classes = 10
epochs = 100
STAMP = 'simple_cnn'

(x_train, y_train), (x_val, y_val) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_val.shape[0], 'test samples')

y_train = np_utils.to_categorical(y_train, num_classes)
y_val = np_utils.to_categorical(y_val, num_classes)

x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
x_train /= 255
x_val /= 255

input_layer = Input(shape=(x_train.shape[1:]))

#Block 1
conv1 = Convolution2D(32,(3,3),
	padding='same',
	activation='relu')(input_layer)
conv2 = Convolution2D(32,(3,3),
	padding='same',
	activation='relu')(conv1)
pool1 = MaxPooling2D(pool_size=(2,2))(conv2)
drop1 = Dropout(0.25)(pool1)

#Block 2
conv3 = Convolution2D(64,(3,3),
	padding='same',
	activation='relu')(drop1)
conv4 = Convolution2D(64,(3,3),
	padding='same',
	activation='relu')(conv3)
pool2 = MaxPooling2D(pool_size=(2,2))(conv4)
drop2 = Dropout(0.25)(pool2)

#Block 3
conv5 = Convolution2D(128,(1,1),
	padding='same',
	activation='relu')(drop2)
conv6 = Convolution2D(128,(1,1),
	padding='same',
	activation='relu')(conv5)
pool3 = MaxPooling2D(pool_size=(2,2))(conv6)
drop3 = Dropout(0.25)(pool3)

#Dense
flat = Flatten()(drop3)
dense1 = Dense(512,
	activation='relu')(flat)
drop4 = Dropout(0.5)(dense1)
output = Dense(num_classes,
	activation='softmax')(drop4)

model = Model(inputs=input_layer, outputs=output)


model.summary()

adam = Adam(lr=0.0001,
	decay=1e-6)

model.compile(loss='categorical_crossentropy',
	optimizer=adam,
	metrics=['accuracy'])

model_json = model.to_json()
with open('model/' + STAMP + ".json", "w") as json_file:
    json_file.write(model_json)

early_stopping = EarlyStopping(monitor='val_loss',
	patience=10,
	verbose=1)

bst_model_path = 'model/' + STAMP + '.h5'
model_checkpoint = ModelCheckpoint(bst_model_path,
    monitor='val_acc',
    verbose=1,
    save_best_only=True,
    save_weights_only=True)

#================================================== Train the Model =============================================================
print('Start training.')
start_time = time.time()

model.fit(x_train, y_train,
	batch_size=batch_size,
	epochs=epochs,
	validation_data=(x_val, y_val),
	shuffle=True,
	callbacks=[early_stopping, model_checkpoint])

score = model.evaluate(x_val,y_val,verbose=0)
end_time = time.time()
print("--- Training time: %s seconds ---" % (end_time - start_time))
print('Test score:', score[0])
print('Test accuracy:', score[1])