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Using a Module

Instantiating a Module

A TensorFlow Hub module is imported into a TensorFlow program by creating a Module object from a string with its URL or filesystem path, such as:

m = hub.Module("path/to/a/module_dir")

This adds the module's variables to the current TensorFlow graph. Running their initializers will read their pre-trained values from disk. Likewise, tables and other state is added to the graph.

Caching Modules

When creating a module from a URL, the module content is downloaded and cached in the local system temporary directory. The location where modules are cached can be overridden using TFHUB_CACHE_DIR environment variable.

For example, setting TFHUB_CACHE_DIR to /my_module_cache:

$ export TFHUB_CACHE_DIR=/my_module_cache

and then creating a module from a URL:

m = hub.Module("https://hub.tensorflow.google.cn/google/progan-128/1")

results in downloading and unpacking the module into /my_module_cache.

Applying a Module

Once instantiated, a module m can be called zero or more times like a Python function from tensor inputs to tensor outputs:

y = m(x)

Each such call adds operations to the current TensorFlow graph to compute y from x. If this involves variables with trained weights, these are shared between all applications.

Modules can define multiple named signatures in order to allow being applied in more than one way (similar to how Python objects have methods). A module's documentation should describe the available signatures. The call above applies the signature named "default". Any signature can be selected by passing its name to the optional signature= argument.

If a signature has multiple inputs, they must be passed as a dict, with the keys defined by the signature. Likewise, if a signature has multiple outputs, these can be retrieved as a dict by passing as_dict=True, under the keys defined by the signature. (The key "default" is for the single output returned if as_dict=False.) So the most general form of applying a Module looks like:

outputs = m(dict(apples=x1, oranges=x2), signature="fruit_to_pet", as_dict=True)
y1 = outputs["cats"]
y2 = outputs["dogs"]

A caller must supply all inputs defined by a signature, but there is no requirement to use all of a module's outputs. TensorFlow will run only those parts of the module that end up as dependencies of a target in tf.Session.run(). Indeed, module publishers may choose to provide various outputs for advanced uses (like activations of intermediate layers) along with the main outputs. Module consumers should handle additional outputs gracefully.