Question

我写了我自己的Keras / Tensorflow层。将图像传递给它可以很好地工作，但是将其与其他层结合使用会产生潜在的错误。自定义图层的输出形状某种程度上应该是错误的，或者某种"Nonetype"。

简而言之，自定义层将图像从色彩空间A转换为B，然后在某些通道之外制作直方图。它是GAN鉴别符的预处理层，因此必须是生成器反向传播模型的一部分。

from keras import backend as K
from keras.layers import Layer

class 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 end

def 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]]

这是将自定义层输出传递到另一个CNN层时的相应错误消息，例如：

---------------------------------------------------------------------------

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

Answer 1

简短的回答：添加批次尺寸以输出通过compute_output_shape方法返回的图层的形状。

长答案： Keras模型始终对一批输入样本起作用，因此Keras层中的所有输入形状和输出形状值都包含批次尺寸。因此，在计算图层的输出形状时，还需要考虑以下因素：

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]
    ]

由于批大小不会随层的变化而变化，因此您只需要向返回的形状中添加input_shape[0]即可。

Keras / TensorFlow的自定义层中的输出形状错误

1 个答案: