Renaming Custom Layer breaks TFMA Evaluator about model-analysis HOT 6 OPEN

Hi @abbyDC
Can you take a look at the workaround proposed in this link and see if it helps in resolving your issue? Also you can refer to TFMA Evaluator, Hope this helps. Thanks!

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Hi @abbyDC Can you take a look at the workaround proposed in this link and see if it helps in resolving your issue? Also you can refer to TFMA Evaluator, Hope this helps. Thanks!

Hi @pindinagesh! The link you attached doesn't show anything on my end when i click on it. May I ask for the working link for this so I can take a look at it? Thanks! :)

ks!

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Sorry for the inconvenience,
I have updated it again, Could you please check it?

from model-analysis.

Sorry for the inconvenience, I have updated it again, Could you please check it?

Hi yup the link works now. Will take a look at the post first to check which of the workarounds I have already tried

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Hi @abbyDC

Could you please tell us the status of this issue?

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Hi @abbyDC

Could you please tell us the status of this issue?

Hello! Upon further investigation and experimentations, the problem still looks the same for me. Several things I've tried similar to the issue above:

1. Adding "serving_raw" in output signature - it has already been implemented in my code as "serving_evaluator" with these lines but I still get the same error

def _get_tf_examples_serving_signature(model, tf_transform_output):
    """Returns a serving signature that accepts `tensorflow.Example`."""

    @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")])
    def serve_tf_examples_fn(serialized_tf_example):
        """Returns the output to be used in the serving signature."""

        transformed_specs = tf_transform_output.transformed_feature_spec()
        transformed_features = tf.io.parse_example(serialized_tf_example, transformed_specs)
        transformed_features["audio"] = tf.sparse.to_dense(transformed_features["audio"])
        transformed_features["target_phones"] = tf.sparse.to_dense(transformed_features["target_phones"])

        audio = transformed_features["audio"]
        labels = transformed_features["target_phones"]
        outputs = model((audio, labels))
        return outputs

    return serve_tf_examples_fn

  signatures = {
      "serving_default": default_signature,
      "serving_evaluator": _get_tf_examples_serving_signature(model, tf_transform_output),
  }

2. I have tried using both "examples=example_gen.outputs['examples']" and "examples=transform.outputs['transformed_examples']" as input to the evaluator but is no difference when I run the pipeline

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