CIFAR10-DeepNet.py

import numpy as np
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

np.random.seed(10)

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

num_train, img_rows, img_cols, img_channel = X_train.shape
num_test, img_rows, img_cols, img_channel = X_test.shape
X_train = X_train.astype('float32')/255
X_test = X_test.astype('float32')/255

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1]

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

sgd = SGD(lr=0.01, momentum=0.9, decay=0.01/25, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=25, batch_size=64)
score = model.evaluate(X_test, y_test, verbose=0)
print 'Accuracy: %.2f%%'%(score[1]*100)

fig1 = plt.figure()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.grid()
plt.show()
fig1.savefig('accuracy2.png')

fig2 = plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.grid()
plt.show()
fig2.savefig('loss2.png')