我在U-Net中遇到模块不可调用的错误
def unet():inputs = Input((1,512, 512))conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(inputs)conv1 = BatchNormalization(axis = 1)(conv1)conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv1)pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(pool1)conv2 = BatchNormalization(axis = 1)(conv2)conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv2)pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(pool2)conv3 = BatchNormalization(axis = 1)(conv3)conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv3)pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(pool3)conv4 = BatchNormalization(axis = 1)(conv4)conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv4)pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(pool4)conv5 = BatchNormalization(axis = 1)(conv5)conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(conv5)up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)conv6 = SpatialDropout2D(0.35)(up6)conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)conv7 = SpatialDropout2D(0.35)(up7)conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)conv8 = SpatialDropout2D(0.35)(up8)conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)conv9 = SpatialDropout2D(0.35)(up9)conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)conv10 = Conv2D(1, 1, 1, activation='sigmoid')(conv9)model = Model(input=inputs, output=conv10)model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])return model请查看代码并回复我解决方案
以下是错误信息
<ipython-input-12-dd57276e32d9> in unet() 38 conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(conv5) 39 ---> 40 up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1) 41 conv6 = SpatialDropout2D(0.35)(up6) 42 conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)TypeError: 'module' object is not 回答：
Keras中的合并层已经在之前进行了重构，因此你需要在你的用例中使用concatenate函数，像这样：
from keras.layers import concatenateup6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=1)你的其他合并调用也需要同样处理。
