Question

我在U-Net中遇到模块不可调用错误

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，并提供解决方案

下面是错误

<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

Answer 1

Keras中的merge层在不久前已经进行了重构，因此您必须在使用案例中使用concatenate函数，如下所示：

from keras.layers import concatenate

up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=1)

其他合并调用也是如此。

模块不可调用

1 个答案: