通过输入形状为'conv2d_4/convolution'（op：'Conv2D'）从2减去5引起的负尺寸大小：[？，5,2,64]，[5,5,64,64]

2 回答

• 1

  问题是层 conv2d_4 的输出变为零或负 . 要解决此问题，必须设计网络，以便不会对输入数据进行高度下采样 .

  这是一些可能的解决方案：

  • 使用较少的图层 . 特别是删除一个最大池化层，它会大量下采样（在此设置下减少三分之一） .

  • 使用较小的最大池，例如 pool_size=(2, 2) ，导致下采样一半 .

  • 对 Conv2D 图层使用"same padding"，这会导致卷积步骤中没有下采样 .

  回复于 2024-05-19T07:49:57+08:00
• 3

  将步幅值更改为1,1，如下所示 . 这解决了我的问题

  model.add(MaxPooling2D(pool_size=(2, 2),strides=(1,1),padding='same',name = 'pool2'))

  我的代码的完整层图是

  def getModel():
  
      optim = Adam(lr= LR, decay=0)
  
      model =Sequential()
      model.add(Conv2D(32,(3,3),activation = 'relu',input_shape =[28,28,1],kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),data_format='channels_last',name='1st'))
      model.add(BatchNormalization())
      model.add(MaxPooling2D(pool_size=(2, 2),strides=(1,1),padding='same',name = 'pool1'))
      model.add(Dropout(0.2,name='2'))
  
  
      model.add(Conv2D(64,(3,3),activation = 'relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='3'))
      model.add(BatchNormalization())
      model.add(MaxPooling2D(pool_size=(2, 2),strides=(1,1),padding='same',name = 'pool2'))
      model.add(Dropout(0.2,name='4'))
  
      model.add(Conv2D(64,(5,5),activation = 'relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='5'))
  
      model.add(BatchNormalization())
      model.add(MaxPooling2D(pool_size=(2, 2),strides=(1,1),padding='same',name = 'pool3'))
  
      model.add(Dropout(0.2, name='6'))
  
  # =============================================================================
      model.add(Conv2D(128,(3,3),activation = 'relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='7'))
      model.add(BatchNormalization())
      model.add(MaxPooling2D(pool_size=(2, 2),strides=(2,2),padding='same',name = 'pool5'))
      model.add(Dropout(0.2,name='8'))
  #    
  # =============================================================================
      model.add(Conv2D(128,(3,3),activation = 'relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='9'))
      model.add(BatchNormalization())
      model.add(MaxPooling2D(pool_size=(2, 2),strides=(2,2),padding='same',name = 'pool6'))
  
      model.add(Dropout(0.2))
  
  
      model.add(Flatten(name='12'))
  
      model.add(Dense(512,activation='relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda), name='13'))
      model.add(Dropout(0.2,name='14'))
  
  
      model.add(Dense(512,activation='relu',kernel_initializer='he_uniform',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='15'))
      model.add(Dropout(0.2,name='16'))
  
      model.add(Dense(40,activation='softmax',kernel_initializer='glorot_uniform    ',
                                  kernel_regularizer=regularizers.l2(l2_labmda),name='17'))
  
      model.compile(optimizer=optim,loss='categorical_crossentropy',metrics=['accuracy'])
      print(model.summary())
  
      return model
  

  model = getModel（）

  回复于 2024-05-19T07:49:57+08:00