sd01a.py 文件源码

def define_model(image_shape):
    img_input = Input(shape=image_shape)

    x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input)

    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)
    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)

    x = res_block(x, nb_filters=32, block=1, subsample_factor=2)
    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)
    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)

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

    x = res_block(x, nb_filters=128, block=3, subsample_factor=2)
    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)
    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)

    x = BatchNormalization(axis=4)(x)
    x = Activation('relu')(x)

    x = AveragePooling3D(pool_size=(4, 4, 8))(x)
    x = Flatten()(x)
    x = Dense(1, activation='sigmoid', name='predictions')(x)

    model = Model(img_input, x)
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure'])
    model.summary()
    return model