Artificial intelligence tutorial: Spring Boot+OAuth2, a note to fix single sign-on!
Neural networks are deep…The following code is best run on GPUs
Environmental parameters: Keras == 2.1.2
Tensorflow = 1.4.0
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Activation
from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D,GlobalMaxPooling2D
#Load Data Set
batch_size = 32
num_classes = 10
epochs = 1600
data_augmentation = True
(x_train,y_train),(x_test,y_test) = cifar10.load_data()
print('x_train shape:',x_train.shape)
print(x_train.shape[0],'train samples')
print(x_test.shape[0],'test samples')
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train =keras.utils.to_categorical(y_train,num_classes)
y_test =keras.utils.to_categorical(y_test,num_classes)
#Building a network
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(48, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(48, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(80, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(80, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(80, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(80, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(80, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same',input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(GlobalMaxPooling2D())
model.add(Dropout(0.25))
model.add(Dense(500))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.summary()
#Model Compilation Training
opt = keras.optimizers.Adam(lr = 0.0001)
model.compile(loss='categorical_crossentropy',optimizer = opt,metrics = ['accuracy'])
print("---------train---------")
model.fit(x_train,y_train,epochs = 600,batch_size = 128,)
print("---------test---------")
loss,acc = model.evaluate(x_test,y_test)
print("loss=",loss)
print("accuracy=",acc)#Training method based on data enhancement
if not data_augmentation:
print('Not using data augmentation. ')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True, callbacks=[tbCallBack])
else:
print('Using real-time data augmentation. ')
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
datagen.fit(x_train)
model.fit_generator(datagen.flow(x_train,y_train,batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=epochs,
validation_data=(x_test, y_test), callbacks=[tbCallBack])
