hub.create_module_spec

hub.create_module_spec(
    module_fn,
    tags_and_args=None,
    drop_collections=None
)

Creates a ModuleSpec from a function that builds the module's graph.

The module_fn is called on a new graph (not the current one) to build the graph of the module and define its signatures via hub.add_signature(). Example:

# Define a text embedding module.
def my_text_module_fn():
  text_input = tf.placeholder(dtype=tf.string, shape=[None])
  embeddings = compute_embedding(text)
  hub.add_signature(inputs=text_input, outputs=embeddings)

See add_signature() for documentation on adding multiple input/output signatures.

NOTE: In anticipation of future TF-versions, module_fn is called on a graph that uses resource variables by default. If you want old-style variables then you can use with tf.variable_scope("", use_resource=False) in module_fn.

Multiple graph variants can be defined by using the tags_and_args argument. For example, the code:

hub.create_module_spec(
    module_fn,
    tags_and_args=[({"train"}, {"is_training":True}),
                   (set(), {"is_training":False})])

calls module_fn twice, once as module_fn(is_training=True) and once as module_fn(is_training=False) to define the respective graph variants: for training with tags {"train"} and for inference with the empty set of tags. Using the empty set aligns the inference case with the default in Module.init().

Args:

• module_fn: a function to build a graph for the Module.
• tags_and_args: Optional list of tuples (tags, kwargs) of tags and keyword args used to define graph variants. If omitted, it is interpreted as [set(), {}], meaning module_fn is called once with no args.
• drop_collections: list of collection to drop.

Returns:

A ModuleSpec.

Raises:

• ValueError: if it fails to construct the ModuleSpec due to bad or unsupported values in the arguments or in the graphs constructed by module_fn.