大多数类似之前问题的答案建议将有问题的张量包装在Lambda
层中。我已经这样做了,然而(并尝试了许多修复方案),它仍然抛出相同的错误。
我的当前模型定义的伪代码如下:
# [previous layers of model definition not shown here for simplicity]
out_duration = Reshape((30, 3))(out_duration)
out_position = Reshape((30, 3))(out_position)
low = tf.constant([(30x3) numpy array of lower bounds)]) # can't use K.clip here because the lower bound is different for every element
high = tf.constant([(30x3) numpy array of upper bounds)])
clipped_out_position = Lambda(lambda x: tf.clip_by_value(*x), output_shape=out_position.get_shape().as_list())([out_position, low, high])
model = Model(inputs=x, outputs=[out_duration, clipped_out_position]
错误输出:
File "multitask_inverter.py", line 107, in <module>
main()
File "multitask_inverter.py", line 75, in main
model = Model(inputs=x, outputs=[out_duration, clipped_out_position])
File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.5/site-packages/keras/legacy/interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.5/site-packages/keras/engine/topology.py", line 1705, in __init__
build_map_of_graph(x, finished_nodes, nodes_in_progress)
File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.5/site-packages/keras/engine/topology.py", line 1695, in build_map_of_graph
layer, node_index, tensor_index)
File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.5/site-packages/keras/engine/topology.py", line 1665, in build_map_of_graph
layer, node_index, tensor_index = tensor._keras_history
AttributeError: 'Tensor' object has no attribute '_keras_history'
ValueError: No data provided for "lambda_1". Need data for each key in: ['out_duration', 'lambda_1']
-- 我不太确定发生了什么。 - Jessfit()
函数? - Yu-Yangfit()
引发的错误,而不是模型构建。 - Yu-Yangarguments
参数是这种情况的正确修复方法吗? - Jesslow
和high
不是Keras张量。 Keras张量在_keras_history
中记录一些拓扑信息。当模型构建期间找不到该信息时,您将看到像那样的错误。要将不是Keras层输出的内容传递给Lambda
层,可以使用arguments
参数。 - Yu-Yang