from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
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, 10)
y_test = keras.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=x_train.shape[1:], activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D())
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.rmsprop(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=False)
datagen.fit(x_train)
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=32),
epochs=100,
validation_data=(x_test, y_test),
workers=4)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
conv2d_2 (Conv2D) (None, 30, 30, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 15, 15, 64) 18496
_________________________________________________________________
conv2d_4 (Conv2D) (None, 13, 13, 64) 36928
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 1180160
_________________________________________________________________
dropout_3 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 5130
=================================================================
Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0
这次的示例是采样cifar数据集进行的图像分类训练，用的是卷积神经网络，但是与之前的玩法不同，这次使用了ImageDataGenerator 这个工具来做图像增广。与之前最大的区别就是，我们一共fit了2次，第一次是对图像进行增广出来，第二次，也就是真正训练模型的时候，使用的是fit_generator 这个方式
ImageDataGenerator的参数描述及处理效果可以查看这篇文章，非常的好图片数据集太少？看我七十二变，Keras Image Data Augmentation 各参数详解
这个训练还有个特点，就是fit_generator里面，我们传入的是
datagen.flow(x_train, y_train,batch_size=32)
他会以32个作为一个批次，不断的产生新的图片，这样我们就可以在我们原有的少量数据集中，获得非常多的不同处理的图片作为训练了，训练集一下就多了起来。最后在fit_generator的时候，我们指定了用4个线程去跑，加快训练的速度
