TensorFlow 1.1.0模型权重无法恢复：文本摘要复现异常求助

Hey there, let's break down why your Amazon food review summarization model is underperforming after you commented out the training code and restarted inference. Here are targeted checks to identify and fix the weight restoration issue:

1. Verify Weight Loading Logic #

TensorFlow 1.x relies heavily on tf.train.Saver() for checkpoint management, so let's start here:

• Ensure Saver is initialized correctly: The Saver must be created after all model variables are defined (not before). If you define it too early, it won't capture all variables, leading to partial weight restoration.
• Check for uninitialized variables: After calling saver.restore(sess, save_path), run this command to see if any variables weren't loaded:

  uninitialized_vars = sess.run(tf.report_uninitialized_variables())
  print("Uninitialized variables:", uninitialized_vars)
  

  If there are any uninitialized variables, they're being set to random default values—this will destroy your model's performance.
• Confirm the checkpoint path: Double-check that you're pointing to the correct .ckpt file (not an old, incomplete, or default checkpoint).

2. Align Preprocessing Between Training and Inference #

Even if weights are loaded correctly, mismatched input processing will ruin results:

• Mirror training preprocessing: Make sure you're using the exact same tokenization, sequence truncation/padding lengths, and vocabulary mapping as during training. For example, if you truncated reviews to 512 tokens during training, don't use 256 tokens in inference.
• Check for normalization layers: If your model uses batch normalization or any input scaling, ensure inference mode uses the correct settings (e.g., is_training=False for BN layers—TF1.x treats training and inference BN behavior differently).

3. Ensure Graph Consistency #

Changing the graph structure between training and inference can break weight restoration:

• Avoid modifying the inference graph: When you commented out training code, did you accidentally alter any part of the inference graph? For example, removing a layer, changing dropout rates, or modifying output logit calculations? The graph used for inference must be identical to the one used to save the checkpoints.
• Check for training-only ops: Make sure you're not leaving training-specific operations (like dropout with high rates, or optimizer-related variables) enabled during inference. These ops can introduce noise or use uninitialized variables.

4. Validate Checkpoint Integrity #

Corrupted or incomplete checkpoints will lead to bad results:

• List checkpoint variables: Use this code to inspect what's stored in your checkpoint and compare it to the variables in your inference graph:

  from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
  print_tensors_in_checkpoint_file(file_name=save_path, tensor_name='', all_tensors=True)
  

  Ensure all model weights (e.g., encoder/decoder layers, embedding matrices) are present and have the correct shapes.

5. Test with a Known Sample #

Isolate the issue by testing a sample you know worked during training:

• Grab a review that produced a good summary when you ran inference right after training. Run it through your restored model—if the summary is drastically worse, the weight restoration is definitely the problem. If it's the same, the issue might lie in your test dataset's preprocessing or inference loop.

内容的提问来源于stack exchange，提问作者Abhisek