averagemethod.py 文件源码

def model_generate():
    img_rows, img_cols = 48, 48

    model = Sequential()
    model.add(Convolution2D(64, 5, 5, border_mode='valid',
                            input_shape=(1, img_rows, img_cols)))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(2, 2), dim_ordering='th'))
    model.add(MaxPooling2D(pool_size=(5, 5),strides=(2, 2)))

    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th')) 
    model.add(Convolution2D(64, 3, 3))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th')) 
    model.add(Convolution2D(64, 3, 3))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(keras.layers.convolutional.AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))

    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th'))
    model.add(Convolution2D(128, 3, 3))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th'))
    model.add(Convolution2D(128, 3, 3))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))

    model.add(keras.layers.convolutional.ZeroPadding2D(padding=(1, 1), dim_ordering='th'))
    model.add(keras.layers.convolutional.AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))

    model.add(Flatten())
    model.add(Dense(1024))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(Dropout(0.2))
    model.add(Dense(1024))
    model.add(keras.layers.advanced_activations.PReLU(init='zero', weights=None))
    model.add(Dropout(0.2))

    model.add(Dense(7))
    model.add(Activation('softmax'))

    ada = Adadelta(lr=0.1, rho=0.95, epsilon=1e-08)
    model.compile(loss='categorical_crossentropy',
                  optimizer=ada,
                  metrics=['accuracy'])
    model.summary()
    return model