Question

我试图连接模型中的各层，但出现错误，我在StackOverflow上检查了错误的解决方案，但无法在我的代码中实现。

我尝试实现['FmriPictures','FmriVernike','FmriWgWords','RestingState']和tf.concat()，但是对他们来说，我也遇到类似keras.layers.Concatenate对象没有属性NoneType的错误。

_inbound_nodes

对于Concatenate出现以下错误

# Define the model

def my_model(input_shape, output):

  if K.image_dim_ordering() == 'tf':
            input_shape= (input_shape[1], input_shape[2], input_shape[0])

  input = Input(input_shape)


  conv_1 = bn_conv(32, (3,3), 1, padding="same")(input)   #RF-3
 #conv_2 = bn_conv(48, (3,3), 1, padding="same")(conv_1)  #RF-5
 #conv_3 = bn_conv(64, (3,3), 1, border_mode="same")(conv_2)  #RF-7

  conv_4 = bn_conv(32, (1,3), 1, padding = "same")(conv_1)
  conv_5 = bn_conv(64,(3,1), 1, padding = "same")(conv_4) #RF-7  

  skip1 = space_to_depth_x2(conv_5)



  conv_6 = keras.layers.SeparableConv2D(filters = 128, kernel_size = (3,3), 
           strides= 1, padding = "same")(conv_5)
  conv_7 = bn_conv(128, (1,1), 1, padding = "same")(conv_6) # RF-9

  skip2 = space_to_depth_x2(conv_7)



  max_1 = MaxPooling2D((2,2),2)(conv_7) #RF-18, size - 16
  conv_8 = bn_conv(128, (1,1), 1)(max_1)

  #skip3 = space_to_depth_x3(conv_8)



  #E_merge = merge([skip1, skip2, conv_8], mode = "concat", concat_axis =-1)
   E_merge = concatenate([skip1, skip2, conv_8], axis=-1)



   conv_g1 = bn_conv(32, (1,1),1, padding="same")(E_merge)
   conv_g2 = bn_conv(64, (3,3),1, padding = "same")(conv_g1)# RF - 20

   conv_g3 = bn_conv(32, (1,1), 1, padding = "same")(E_merge)
   conv_g4 = bn_conv(64, (5,5), 1, padding = "same")(conv_g3)# RF - 22

   max_g1 = AveragePooling2D((2,2),strides=(1,1), padding="same")(E_merge) 

   conv_g5 = Conv2D(64, (1,1))(max_g1)

   conv_9 = Conv2D(64,(1,1))(E_merge) #RF - 18



   #merge_1 = merge([conv_g2, conv_g4, conv_g5, conv_9], mode = "concat", 
              concat_axis =-1)# RF - 18, 22, 20, 36
   merge_1 = Concatenate(axis=-1)([conv_g2, conv_g4, conv_g5, conv_9])

   conv_g6 = bn_conv(32, (1,1), 1, padding = "same")(merge_1)
   conv_g7 = bn_conv(64, (3,3), 1, padding = "same", dilation_rate = (1,1)) 
             (conv_g6)

   conv_g8 = bn_conv(32, (1,1), 1, padding = "same")(merge_1)
   conv_g9 = bn_conv(128, (3,3),1,  padding = "same", dilation_rate =(2,2)) 
             (conv_g8)

    #max_g2 = MaxpPooling2D(2,2, border_mode="same")(merge_1)
    conv_g10 = Conv2D(64, (1,1), padding ="same")(merge_1)  


    #merge_2 = merge([conv_g7, conv_g9, conv_g10], mode = "concat", 
                 concat_axis =-1) 
    merge_2 = Concatenate(axis=-1)([conv_g7, conv_g9, conv_g10])

    #conv_9 = bn_conv(64, (3,3), 1, border_mode = "valid")(merge_2)  

    #conv_10 = bn_conv(128, (3,3), 1, border_mode = "valid")(conv_9)

    conv_11 = Conv2D(32, (1,1))(merge_2)

    conv_12 = Conv2D(10,(16,16))(conv_11)

    flat = Flatten() (conv_12)

    act = Activation("softmax")(flat)

    model = Model(inputs=input, outputs=act)

    return model

model = my_model([3, 32, 32], 10)

Answer 1

merge模块在一段时间前已从keras中删除，您有两个选择：

连接功能API：

from keras.layers import concatenate

E_merge = concatenate([skip1, skip2, conv_8], axis=-1)

连接层：

from keras.layers import Concatenate

E_merge = Concatenate(axis=-1)([skip1, skip2, conv_8])

TypeError：“模块”对象不可调用，“ NoneType”对象没有属性“ _inbound_nodes”

1 个答案: