keras_training.py 文件源码

def __init__(self, **kwargs):
        super(KerasCifar10CNN, self).__init__(**kwargs)
        norm_shape = self.norm_shape
        model = Sequential()
        model.add(Conv2D(32, (3, 3), padding='same',
                         input_shape=(norm_shape[0], norm_shape[1], 1)))
        model.add(Activation('relu'))
        model.add(Conv2D(32, (3, 3), ))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2),))
        model.add(Dropout(0.25))

        model.add(Conv2D(64, (3, 3), padding='same', ))
        model.add(Activation('relu'))
        model.add(Conv2D(64, (3, 3), ))
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2),))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(512))
        model.add(Activation('relu'))
        model.add(Dropout(0.5))
        model.add(Dense(self.max_n_label))
        model.add(Activation('softmax'))
        # initiate RMSprop optimizer
        opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

        # Let's train the model using RMSprop
        model.compile(loss='categorical_crossentropy',
                      optimizer=opt,
              metrics=['accuracy'])
        self.model = model