我编写了自己的Keras/TensorFlow层。将图像传入其中可以正常工作，但与其他层结合使用时会出现底层错误。不知何故，我的自定义层的输出形状可能有误，或者是某种"Nonetype"类型的问题。
简而言之，自定义层将图像从颜色空间A转换到B，然后对某些通道进行直方图处理。这是一个用于GAN判别器的前处理层，因此必须是生成器反向传播模型的一部分。
from keras import backend as Kfrom keras.layers import Layerclass Identity_Loss(Layer):def __init__(self, output_dim, **kwargs): self.output_dim = output_dim super(Identity_Loss, self).__init__(**kwargs)def build(self, input_shape): super(Identity_Loss, self).build(input_shape) # Be sure to call this at the enddef call(self, x): assert isinstance(x, list) input_1,input_2=x # Transform BGR to RGB and than to HSV channels = tf.unstack (input_1, axis=-1) RGB = tf.stack ([channels[2], channels[1], channels[0]], axis=-1) RGB=tf.cast(tf.multiply(tf.truediv(tf.add(RGB,1.0),2.0),255.0),dtype=tf.int32) RGB=tf.cast(RGB,dtype=tf.float32) HSV=tf.image.rgb_to_hsv(RGB,name=None)########################################################### SV=HSV[:,:,:,1:]############################################################ #make mask binary and multiply with image y=tf.math.greater(input_2, 0) y=tf.cast(y, tf.float32, name=None) HSV_mask = tf.math.multiply(HSV, y) #Count color occurences ########################### shape=tf.shape(HSV_mask) length=shape[1]*shape[2] #transform Hue=HSV_mask[:,:,:,:1] Hue=tf.cast(tf.multiply(Hue,255.0),dtype=tf.int32) Hue2 = tf.reshape(Hue, [length]) #prevent that the shape changes filler=tf.range(0,length, 1,dtype=tf.int32) filler = tf.reshape(filler, [length]) Hue3 = tf.stack([Hue2,filler],axis=-1) Hue3 = tf.reshape(Hue3, [2*length]) #Count Hue y1, idx1, count1 = tf.unique_with_counts(Hue3) maximum=tf.cast(tf.math.reduce_max(count1[1:257]),dtype=tf.int32) diff=tf.reshape(count1[1:257],(16,16)) diff=tf.expand_dims(diff, axis=-1) diff=tf.expand_dims(diff, axis=0) diff=tf.truediv(diff,maximum) diff=tf.cast(diff,dtype=tf.float32) return [SV,HSV_mask,diff]def compute_output_shape(self, input_shape): assert isinstance(input_shape, list) return [[None,None,2],[None,None,3],[None,None,1]]当将自定义层的输出传入另一个CNN层时，相应的错误消息如下所示：
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-89-3caaa8c77e0c> in <module>() 5 c,d,e=mod([d,input_B]) 6 ----> 7 dd=model(d) 8 9 bb = Model([input_A,input_B],[c,d,dd])3 frames/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs) 487 # Actually call the layer, 488 # collecting output(s), mask(s), and shape(s).--> 489 output = self.call(inputs, **kwargs) 490 output_mask = self.compute_mask(inputs, previous_mask) 491 /usr/local/lib/python3.6/dist-packages/keras/engine/network.py in call(self, inputs, mask) 581 return self._output_tensor_cache[cache_key] 582 else:--> 583 output_tensors, _, _ = self.run_internal_graph(inputs, masks) 584 return output_tensors 585 /usr/local/lib/python3.6/dist-packages/keras/engine/network.py in run_internal_graph(self, inputs, masks) 796 input_shapes = unpack_singleton( 797 [x._keras_shape for x in computed_tensors])--> 798 shapes = to_list(layer.compute_output_shape(input_shapes)) 799 uses_learning_phase = any( 800 [x._uses_learning_phase for x in computed_tensors])/usr/local/lib/python3.6/dist-packages/keras/layers/convolutional.py in compute_output_shape(self, input_shape) 191 def compute_output_shape(self, input_shape): 192 if self.data_format == 'channels_last':--> 193 space = input_shape[1:-1] 194 elif self.data_format == 'channels_first': 195 space = input_shape[2:]TypeError: 'NoneType' object is not subscriptable回答：
简短回答：在compute_output_shape方法返回的层输出形状中添加批次维度。
详细回答： Keras模型总是处理一批输入样本，因此Keras层中的所有输入形状和输出形状值都包含批次维度。因此，在计算层输出形状时也需要考虑这一点：
def compute_output_shape(self, input_shape): return [ [input_shape[0], None, None, 2], [input_shape[0], None, None, 3], [input_shape[0], None, None, 1] ]由于批次大小在层与层之间不会改变，因此您只需如上所示在返回的形状中添加input_shape[0]即可。
