tensorflow type_error ai_generated true

ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, 64), dtype=tf.float32, name='input_2')) at layer 'dense_2'

ID: tensorflow/keras-functional-api-merging-bug

Also available as: JSON · Markdown · 中文
93%Fix Rate
89%Confidence
1Evidence
2023-02-14First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.7 active — — —
tensorflow 2.8 active — — —
tensorflow 2.9 active — — —

Root Cause

A Keras Functional API model has a disconnected graph because an intermediate tensor was not passed as input to a downstream layer, often due to incorrect merging of two sub-models or missing skip connection.

generic

中文

Keras 函数式 API 模型的图断开连接，因为中间张量未作为输入传递给下游层，通常是由于两个子模型合并不正确或缺少跳跃连接。

Official Documentation

https://www.tensorflow.org/guide/keras/functional_api#graph_of_layers

Workarounds

  1. 95% success Trace the model definition and ensure every intermediate tensor used in a layer call is passed as an argument. For example, if merging two branches: merged = layers.concatenate([branch1_output, branch2_output]) then pass merged to the next layer. Check that all KerasTensors are connected in the functional graph. 
    Trace the model definition and ensure every intermediate tensor used in a layer call is passed as an argument. For example, if merging two branches: merged = layers.concatenate([branch1_output, branch2_output]) then pass merged to the next layer. Check that all KerasTensors are connected in the functional graph.
  2. 90% success Use model.summary() and plot_model(model, show_shapes=True) to visualize the graph and identify the disconnected tensor, then correct the layer call that should use that tensor. 
    Use model.summary() and plot_model(model, show_shapes=True) to visualize the graph and identify the disconnected tensor, then correct the layer call that should use that tensor.

中文步骤

  1. 跟踪模型定义，确保在层调用中使用的每个中间张量都作为参数传递。例如，合并两个分支：merged = layers.concatenate([branch1_output, branch2_output])，然后将 merged 传递给下一层。检查所有 KerasTensor 在函数图中是否已连接。
  2. 使用 model.summary() 和 plot_model(model, show_shapes=True) 可视化图并识别断开的张量，然后修正应使用该张量的层调用。

Dead Ends

Common approaches that don't work:

  1. 95% fail

    The error is topological; adding layers does not automatically connect the missing tensor.

  2. 80% fail

    Sequential API does not support branching or merging, which is often the intended architecture.