keras“NoneType”对象没有属性“_inbound_nodes”

Question

我正在尝试编写一个鉴别器来评估图像的补丁。因此，我从输入生成32x32非重叠的面片，然后将它们连接到一个新的轴上。

我使用时间分布层的原因是，最终，鉴别器应该评估整个图像是真是假。因此，我尝试分别在每个补丁上执行前向传递，然后通过lambda层对补丁上的鉴别器输出进行平均：

def my_average(x):
    x = K.mean(x, axis=1)
    return x

def my_average_shape(input_shape):
    shape = list(input_shape)
    del shape[1]
    return tuple(shape)


def defineD(input_shape):
    a = Input(shape=(256, 256, 1))

    cropping_list = []

    n_patches = 256/32
    for x in range(256/32):
        for y in  range(256/32):

            cropping_list += [
             K.expand_dims(
                Cropping2D((( x * 32,  256 - (x+1) * 32), ( y * 32,  256 - (y+1) * 32)))(a)
                , axis=1)
            ]

    x = Concatenate(1)(cropping_list)

    x = TimeDistributed(Conv2D(4 * 8, 3, padding='same'))(x) # 
    x = TimeDistributed(MaxPooling2D())(x)
    x = TimeDistributed(LeakyReLU())(x)                  # 16

    x = TimeDistributed(Conv2D(4 * 16, 3, padding='same'))(x)
    x = TimeDistributed(MaxPooling2D())(x)
    x = TimeDistributed(LeakyReLU())(x)                  # 8

    x = TimeDistributed(Conv2D(4 * 32, 3, padding='same'))(x)
    x = TimeDistributed(MaxPooling2D())(x)
    x = TimeDistributed(LeakyReLU())(x)                  # 4


    x = TimeDistributed(Flatten())(x)
    x = TimeDistributed(Dense(2, activation='sigmoid'))(x)
    x = Lambda(my_average, my_average_shape)(x)

    return keras.models.Model(inputs=a, outputs=x)

由于某种原因，我得到以下错误：

File "testing.py", line 41, in <module>
    defineD((256,256,1) )
  File "testing.py", line 38, in defineD
    return keras.models.Model(inputs=a, outputs=x)
  File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1353, in _map_graph_network
    tensor_index=tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1340, in build_map
    node_index, tensor_index)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/network.py", line 1312, in build_map
    node = layer._inbound_nodes[node_index]
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

Answer 1

您需要将裁剪操作放在一个函数中，然后在Lambda层中使用该函数：

def my_cropping(a):
    cropping_list = []
    n_patches = 256/32
    for x in range(256//32):
        for y in  range(256//32):

            cropping_list += [
             K.expand_dims(
                Cropping2D((( x * 32,  256 - (x+1) * 32), ( y * 32,  256 - (y+1) * 32)))(a)
                , axis=1)
            ]
    return cropping_list

要使用它：

cropping_list = Lambda(my_cropping)(a)

Answer 2

我遇到了同样的问题，它确实通过在张量周围包裹一个Lambda层来解决，就像@today提议的那样。

谢谢你的提示，它给我指明了正确的方向。我想把一个向量变成一个对角矩阵

我想要将一个向量与一个正方形图像连接起来，并将该向量转换为一个诊断矩阵。它与以下代码片段一起工作：

def diagonalize(vector):
  diagonalized = tf.matrix_diag(vector) # make diagonal matrix from vector
  out_singlechan = tf.expand_dims(diagonalized, -1) # append 1 channel to get compatible to the multichannel image dim
  return out_singlechan

lstm_out = Lambda(diagonalize, output_shape=(self.img_shape[0],self.img_shape[1],1))(lstm_out)