m04a.py 文件源码

def define_model():
    img_input = Input(shape=(32, 32, 2))

    x = Convolution2D(32, 3, 3, subsample=(1, 1), border_mode='same')(img_input)

    x = res_block(x, nb_filters=32, block=0, subsample_factor=1)
    x = res_block(x, nb_filters=32, block=0, subsample_factor=1)
    x = res_block(x, nb_filters=32, block=0, subsample_factor=1)

    x = res_block(x, nb_filters=64, block=1, subsample_factor=2)
    x = res_block(x, nb_filters=64, block=1, subsample_factor=1)
    x = res_block(x, nb_filters=64, block=1, subsample_factor=1)

    x = res_block(x, nb_filters=128, block=2, subsample_factor=2)
    x = res_block(x, nb_filters=128, block=2, subsample_factor=1)
    x = res_block(x, nb_filters=128, block=2, subsample_factor=1)
    x = res_block(x, nb_filters=128, block=2, subsample_factor=1)

    x = res_block(x, nb_filters=256, block=3, subsample_factor=2)
    x = res_block(x, nb_filters=256, block=3, subsample_factor=1)
    x = res_block(x, nb_filters=256, block=3, subsample_factor=1)
    x = res_block(x, nb_filters=256, block=3, subsample_factor=1)

    x = BatchNormalization(axis=3)(x)
    x = Activation('relu')(x)

    x = AveragePooling2D(pool_size=(3, 3), strides=(2, 2), border_mode='valid')(x)
    x = Flatten()(x)

    bbox = Dense(4, activation='linear', name='bbox')(x)
    model_bbox = Model(img_input, bbox)
    model_bbox.compile(optimizer='adam', loss='mae')

    return model_bbox