tfr.keras.saved_model.Signatures

Defines signatures to support regress and predict serving.

tfr.keras.saved_model.Signatures(
    model: tf.keras.Model,
    context_feature_spec: Dict[str, Union[tf.io.FixedLenFeature, tf.io.RaggedFeature]],
    example_feature_spec: Dict[str, Union[tf.io.FixedLenFeature, tf.io.RaggedFeature]],
    mask_feature_name: str
)

This wraps the trained Keras model in two serving functions that can be saved with tf.saved_model.save or model.save, and loaded with corresponding signature names. The regress serving signature takes a batch of serialized tf.Examples as input, whereas the predict serving signature takes a batch of serialized ExampleListWithContext as input.

Example usage:

A ranking model can be saved with signatures as follows:

tf.saved_model.save(model, path, signatures=Signatures(model, ...)())

For regress serving, scores can be generated using REGRESS signature as follows:

loaded_model = tf.saved_model.load(path)
predictor = loaded_model.signatures[tf.saved_model.REGRESS_METHOD_NAME]
scores = predictor(serialized_examples)[tf.saved_model.REGRESS_OUTPUTS]

For predict serving, scores can be generated using PREDICT signature as follows:

loaded_model = tf.saved_model.load(path)
predictor = loaded_model.signatures[tf.saved_model.PREDICT_METHOD_NAME]
scores = predictor(serialized_elwcs)[tf.saved_model.PREDICT_OUTPUTS]

Args
model	A Keras ranking model.
context_feature_spec	(dict) A mapping from feature keys to FixedLenFeature or RaggedFeature values for context in tensorflow.serving.ExampleListWithContext proto.
example_feature_spec	(dict) A mapping from feature keys to FixedLenFeature or Ragged values for examples in tensorflow.serving.ExampleListWithContext proto.
mask_feature_name	(str) Name of feature for example list masks.

Attributes
name	Returns the name of this module as passed or determined in the ctor. Note: This is not the same as the self.name_scope.name which includes parent module names.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
submodules	Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). a = tf.Module() b = tf.Module() c = tf.Module() a.b = b b.c = c list(a.submodules) == [b, c] True list(b.submodules) == [c] True list(c.submodules) == [] True
trainable_variables	Sequence of trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
variables	Sequence of variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.

Methods

normalize_outputs

View source

normalize_outputs(
    default_key: str, outputs: Union[tf.Tensor, Dict[str, tf.Tensor]]
) -> Dict[str, tf.Tensor]

Returns a dict of Tensors for outputs.

Args
default_key	If outputs is a Tensor, use the default_key to make a dict.
outputs	outputs to be normalized.

Returns
A dict maps from str-like key(s) to Tensor(s).

Raises
TypeError if outputs is not a Tensor nor a dict.

predict_tf_function

View source

predict_tf_function() -> Callable[[tf.Tensor], Dict[str, tf.Tensor]]

Makes a tensorflow function for predict.

regress_tf_function

View source

regress_tf_function() -> Callable[[tf.Tensor], Dict[str, tf.Tensor]]

Makes a tensorflow function for regress.

with_name_scope

@classmethod
with_name_scope(
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
method	The method to wrap.

Returns
The original method wrapped such that it enters the module's name scope.

__call__

View source

__call__(
    serving_default: str = 'regress'
) -> Dict[str, Callable[[tf.Tensor], Dict[str, tf.Tensor]]]

Returns a dict of signatures.

Args
serving_default	Specifies "regress" or "predict" as the serving_default signature.

Returns
A dict of signatures.