Demystifying the “Issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)” Error

Are you tired of wrestling with the frustrating “Issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)” error? You’re not alone! This pesky issue has plagued many a developer, but fear not, dear reader, for we’re about to embark on a journey to conquer this beast once and for all.

What’s the cause of this error?

The error message itself is quite cryptic, isn’t it? It seems to be telling us that there’s an issue with using TensorFlow, but what exactly does “contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt'” mean? To understand this, let’s take a step back and examine the context in which this error typically occurs.

When you’re working with TensorFlow, you often need to load pre-trained models or save your own models for later use. This is where the `saved_model` format comes in. TensorFlow provides two ways to save a model: as a `saved_model.pb` file (a binary format) or as a `saved_model.pbtxt` file (a text format). The error message is telling us that it can’t find either of these files.

The Anatomy of a Saved Model

Before we dive into the solution, it’s essential to understand the anatomy of a saved model. A saved model consists of three components:

• Assets: These are the files that your model relies on, such as images, datasets, or other external dependencies.
• Variables: These are the model’s parameters, which are stored in a binary format (`.pb` file) or a human-readable format (`.pbtxt` file).
• SIGNATURES: These define the inputs and outputs of your model, allowing TensorFlow to understand how to use the model.

Solving the “Issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)” Error

Now that we’ve explored the cause of the error, let’s get to the good stuff – solving it! Here are the step-by-step instructions to overcome this hurdle:

Step 1: Check Your Model Directory

Make sure you’re in the correct directory where your saved model files exist. Verify that the `saved_model.pb` or `saved_model.pbtxt` file is present in the directory.

$ cd /path/to/model/directory
$ ls -l

Step 2: Verify the Model Files

Check the contents of your `saved_model.pb` or `saved_model.pbtxt` file to ensure it’s not empty:

$ cat saved_model.pb

If the file is empty or doesn’t contain the expected data, it might be a sign of a deeper issue during the model-saving process.

Step 3: Check the Model’s MetaGraphDef

In some cases, the error occurs because the MetaGraphDef is not properly defined. You can check the MetaGraphDef using the following command:

$ SavedModelBundle Loader = SavedModelBundle.Loader("path/to/model")
meta_graph_def = loader.get_meta_graph_def/tags=["serve"])
print(meta_graph_def)

This will display the MetaGraphDef, which should contain information about the model’s inputs, outputs, and signatures.

Step 4: Re-Save the Model

If the above steps don’t reveal any issues, try re-saving the model using the following code:

import tensorflow as tf

# Create a new session
sess = tf.Session()

# Load the model
loader = tf.train.import_meta_graph("path/to/model.ckpt.meta")
loader.restore(sess, "path/to/model.ckpt")

# Save the model in the SavedModel format
builder = tf.saved_model.builder.SavedModelBuilder("path/to/export/directory")
builder.add_meta_graph([tf.saved_model.tag_constants.SERVING])
builder.save()

This will re-save the model in the `saved_model.pb` format. If you’re using a `.pbtxt` file, modify the code accordingly.

Step 5: Load the Model Again

After re-saving the model, try loading it again:

import tensorflow as tf

# Load the saved model
loader = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], "path/to/export/directory")

If everything goes smoothly, you should now be able to use your TensorFlow model without encountering the “Issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)” error.

Troubleshooting Tips and Tricks

Still stuck? Don’t worry, we’ve got some additional troubleshooting tips to help you overcome this error:

• Check the model’s version compatibility: Ensure that the TensorFlow version used to save the model is compatible with the version you’re using to load it.
• Verify the model’s architecture: Double-check that the model’s architecture matches the expected inputs and outputs.
• Use the TensorBoard visualization tool: Visualize your model using TensorBoard to identify any issues with the model’s structure or data flow.
• Check for corrupted files: Verify that the model files haven’t been corrupted during saving or loading.

Conclusion

And that’s it! By following these steps and troubleshooting tips, you should now be able to overcome the frustrating “Issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)” error. Remember to stay calm, be patient, and methodically work through each step to ensure a successful resolution.

Happy coding, and may the tensors be ever in your favor!

Error Message	Solution
-issue on using tensor flow (contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt’)	Check model directory, verify model files, check MetaGraphDef, re-save model, and load model again

Don’t forget to bookmark this article for future reference, and share it with your fellow developers who might be struggling with the same issue. Happy coding!

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Frequently Asked Question

If you’re having trouble with TensorFlow and encountering the infamous “contains neither ‘saved_model.pb’ nor ‘saved_model.pbtxt'” error, you’re not alone! Check out these frequently asked questions to get back on track: