我正在尝试使用keras创建一个卷积网络，其中包括以下代码:

from keras.layers import Input, LSTM, concatenatefrom keras.models import Modelfrom keras.utils.vis_utils import model_to_dotfrom IPython.display import display, SVGinputs = Input(shape=(None, 4))filter_unit = LSTM(1)conv = concatenate([filter_unit(inputs[..., 0:2]),                    filter_unit(inputs[..., 2:4])])model = Model(inputs=inputs, outputs=conv)SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))

我尝试沿特征维度切片输入张量，以便将（人为缩小的）输入分成两部分，分别用于两个过滤单元。在这个例子中，过滤器是一个单一的LSTM单元。我希望能够用任意的模型替换LSTM单元。

然而，在model = ...这一行上出现了错误:

---------------------------------------------------------------------------AttributeError                            Traceback (most recent call last)<ipython-input-6-a9f7f2ffbe17> in <module>()      9 conv = concatenate([filter_unit(inputs[..., 0:2]),     10                     filter_unit(inputs[..., 2:4])])---> 11 model = Model(inputs=inputs, outputs=conv)     12 SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))~/.local/opt/anaconda3/envs/trafficprediction/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)     86                 warnings.warn('Update your `' + object_name +     87                               '` call to the Keras 2 API: ' + signature, stacklevel=2)---> 88             return func(*args, **kwargs)     89         wrapper._legacy_support_signature = inspect.getargspec(func)     90         return wrapper~/.local/opt/anaconda3/envs/trafficprediction/lib/python3.6/site-packages/keras/engine/topology.py in __init__(self, inputs, outputs, name)   1703         nodes_in_progress = set()   1704         for x in self.outputs:-> 1705             build_map_of_graph(x, finished_nodes, nodes_in_progress)   1706    1707         for node in reversed(nodes_in_decreasing_depth):~/.local/opt/anaconda3/envs/trafficprediction/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)   1693                 tensor_index = node.tensor_indices[i]   1694                 build_map_of_graph(x, finished_nodes, nodes_in_progress,-> 1695                                    layer, node_index, tensor_index)   1696    1697             finished_nodes.add(node)~/.local/opt/anaconda3/envs/trafficprediction/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)   1693                 tensor_index = node.tensor_indices[i]   1694                 build_map_of_graph(x, finished_nodes, nodes_in_progress,-> 1695                                    layer, node_index, tensor_index)   1696    1697             finished_nodes.add(node)~/.local/opt/anaconda3/envs/trafficprediction/lib/python3.6/site-packages/keras/engine/topology.py in build_map_of_graph(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index)   1663             """   1664             if not layer or node_index is None or tensor_index is None:-> 1665                 layer, node_index, tensor_index = tensor._keras_history   1666             node = layer.inbound_nodes[node_index]   1667 AttributeError: 'Tensor' object has no attribute '_keras_history'

如果将LSTM替换为Dense，也会出现相同的问题。我不太明白这个错误信息的含义。我做错了什么？

关于相同错误有一个问题（链接如下），但我不清楚如何使用Lambda层，或者这是否是正确的解决方案。

AttributeError: ‘Tensor’ object has no attribute ‘_keras_history’

回答：

问题出在输入的切片方式上。LSTM层期望接收一个Layer对象作为输入，而您提供的是一个Tensor对象。您可以尝试添加一个lambda层（在这个例子中可能需要两个）来切片输入，以便为LSTM层提供输入。类似于:

y = Lambda(lambda x: x[:,0,:,:], output_shape=(1,) + input_shape[2:])(x)

然后这个y层将作为后续层的（切片后的）输入。