python-3.x - keras连接多个层导致AttributeError : 'NoneType' object has no attribute '_inbound_nodes'

Question

我正在尝试添加一些固定的 kernels在我的 CNN 中，请参阅下面的代码。

这就是我创建内核的方式:

# Kernels
def create_kernel(x):
    t = pipe(
        x, 
        lambda x: tf.constant(x, dtype=tf.float32), 
        lambda x: tf.reshape(x, [3, 3, 1, 1]))
    return t

k_edge1 = create_kernel([1, 0, -1, 0, 0, 0, -1, 0, 1])
k_edge2 = create_kernel([0, 1, 0, 1, -4, 1, 0, 1, 0])
k_edge3 = create_kernel([-1, -1, -1, -1, 8, -1, -1, -1, -1])

我的卷积网络是这样的:

# Convolution network
# Input layer
l_input = Input(shape=(28**2, ))
# Reshape layer
l_reshape = Reshape(target_shape=(28, 28, 1))(l_input)
# Convolution layers
l_conv1 = Conv2D(filters=20, kernel_size=(3, 3), padding='valid')(l_reshape)
l_edge1 = tf.nn.conv2d(l_reshape, k_edge1, strides=[1, 1, 1, 1], padding='VALID')
l_edge2 = tf.nn.conv2d(l_reshape, k_edge2, strides=[1, 1, 1, 1], padding='VALID')
l_edge3 = tf.nn.conv2d(l_reshape, k_edge3, strides=[1, 1, 1, 1], padding='VALID')
l_conv1a = Concatenate(axis=3)([l_conv1, l_edge1, l_edge2, l_edge3])  # <- The error should be caused by this line.
l_conv2 = Conv2D(filters=20, kernel_size=(3, 3), padding='valid')(l_conv1a)
l_pool1 = MaxPooling2D(pool_size=(2, 2), border_mode='valid')(l_conv2)
# Flatten layer
l_flat = Flatten()(l_pool1)
# Fully connected layers
l_fc1 = Dense(50, kernel_initializer='he_normal')(l_flat)
l_act1 = PReLU()(l_fc1)
l_fc3 = Dense(10, kernel_initializer='he_normal')(l_act1)
l_output = Activation('softmax')(l_fc1)

# Model
cnn_model = Model(l_input, l_output)

但是，我收到以下错误:

Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 1353, in _map_graph_network
    tensor_index=tensor_index)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)
[Previous line repeated 2 more times]
File "C:\Users\Perry Cheng\AppData\Local\conda\conda\envs\ml_py_3_6\lib\site-packages\keras\engine\network.py", line 1312, in build_map
    node = layer._inbound_nodes[node_index]
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

经过一些测试，我认为错误来自:

l_conv1a = Concatenate(axis=3)([l_conv1, l_edge1, l_edge2, l_edge3])

有什么办法可以解决吗？

Answer 1

Keras 层接受 Keras 张量而不是张量作为输入。因此，如果您想在 Keras 中使用 tf.nn.conv2d 而不是 Conv2D 层，则需要将它们包装在 Lambda 层中:

l_edge1 = Lambda(lambda x: tf.nn.conv2d(x, k_edge1, strides=[1, 1, 1, 1], padding='VALID'))(l_reshape)
l_edge2 = Lambda(lambda x: tf.nn.conv2d(x, k_edge2, strides=[1, 1, 1, 1], padding='VALID'))(l_reshape)
l_edge3 = Lambda(lambda x: tf.nn.conv2d(x, k_edge3, strides=[1, 1, 1, 1], padding='VALID'))(l_reshape)