Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

tfr.keras.saved_model.Signatures

Defines signatures to support regress and predict serving.

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]

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.

name Returns the name of this module as passed or determined in the ctor.

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.
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.

variables Sequence of variables owned by this module and its submodules.

Methods

normalize_outputs

View source

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

Makes a tensorflow function for predict.

regress_tf_function

View source

Makes a tensorflow function for regress.

with_name_scope

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

Returns a dict of signatures.

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

Returns
A dict of signatures.