def _infer_metadata_entries_from_model(self, signature_name):
"""Infers metadata inputs and outputs."""
loaded_sig = self._loaded_model.signatures[signature_name]
_, input_sig = loaded_sig.structured_input_signature
output_sig = loaded_sig.structured_outputs
input_mds = {}
for name, tensor_spec in input_sig.items():
if tensor_spec.dtype.is_floating:
input_mds[name] = explain_metadata.InputMetadata(name, name)
else:
input_mds[name] = explain_metadata.InputMetadata(
name, name, modality=explain_metadata.Modality.CATEGORICAL)
if not self._explain_output and len(output_sig) > 1:
raise ValueError(
"Signature has multiple outputs. You must specify which"
" output to explain via 'outputs_to_explain' parameter.")
for name in output_sig:
if not self._explain_output or self._explain_output[0] == name:
output_mds = {
name: explain_metadata.OutputMetadata(name, name)
}
break
else:
raise ValueError("Specified output name cannot be found in given"
" signature outputs.")
return input_mds, output_mds
def _create_output_metadata_from_signature(
signature_outputs: Dict[str, tf.Tensor],
output_to_explain: Optional[str] = None
) -> Dict[str, explain_metadata.OutputMetadata]:
"""Creates OutputMetadata from signature outputs."""
return {key: explain_metadata.OutputMetadata(key, tensor.name)
for key, tensor in signature_outputs.items()
if not output_to_explain or output_to_explain == key}
def _create_output_metadata(self, output_dict: Dict[Text, tf.Tensor]):
"""Creates and returns a list of OutputMetadata.
Args:
output_dict: Dictionary from tf.feature_columns to list of dense tensors.
Returns:
A list of OutputMetadata.
"""
return [explain_metadata.OutputMetadata(name, tensor.name)
for name, tensor in output_dict.items()]
def add_output_metadata(self, output_tensor, name=None):
"""Adds output tensor as output metadata.
Only one output metadata can be added.
Args:
output_tensor: Output tensors to get the explanations for. Needs to be a
tensor of float type, such as probabilities, logits.
name: Unique friendly name for the output.
"""
if self._outputs:
raise ValueError('Only one output can be added.')
output_name = name if name else output_tensor.op.name
self._outputs[output_tensor.name] = explain_metadata.OutputMetadata(
name=output_name, output_tensor_name=output_tensor.name)
def set_output_metadata(self, output_name, new_name):
"""Updates an existing output metadata identified by output_name.
Args:
output_name: Name of the output that needs to be updated.
new_name: New (unique) friendly name for the output.
Raises:
ValueError: If output with the given name doesn't exist.
"""
if output_name not in self._outputs:
raise ValueError("Output with name '%s' does not exist." %
output_name)
if output_name == new_name:
return
old_output = self._outputs.pop(output_name)
self._outputs[new_name] = explain_metadata.OutputMetadata(
new_name, old_output.output_tensor_name)
