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Creating a New Module

General approach

To define a new module, a publisher calls hub.create_module_spec() with a function module_fn. This function constructs a graph representing the module's internal structure, using tf.placeholder() for inputs to be supplied by the caller. Then it defines signatures by calling hub.add_signature(name, inputs, outputs) one or more times.

For example:

def module_fn():
  inputs = tf.placeholder(dtype=tf.float32, shape=[None, 50])
  layer1 = tf.layers.dense(inputs, 200)
  layer2 = tf.layers.dense(layer1, 100)
  outputs = dict(default=layer2, hidden_activations=layer1)
  # Add default signature.
  hub.add_signature(inputs=inputs, outputs=outputs)

...
spec = hub.create_module_spec(module_fn)

The result of hub.create_module_spec() can be used, instead of a path, to instantiate a module object within a particular TensorFlow graph. In such case, there is no checkpoint, and the module instance will use the variable initializers instead.

Any module instance can be serialized to disk via its export(path, session) method. Exporting a module serializes its definition together with the current state of its variables in session into the passed path. This can be used when exporting a module for the first time, as well as when exporting a fine tuned module.

For compatibility with TensorFlow Estimators, hub.LatestModuleExporter exports modules from the latest checkpoint, just like tf.estimator.LatestExporter exports the entire model from the latest checkpoint.

Module publishers should implement a common signature when possible, so that consumers can easily exchange modules and find the best one for their problem.

Real example

Take a look at our text embedding module exporter for a real-world example of how to create a module from a common text embedding format.