Functions that produce variable initializer functions with signature:

foo(shape, dtype) : Tensor

These are typically consumed by functions in layers.py.

Functions that produce layer operations and associated weight & bias variables. Signatures will vary for different functions, but they will often take many of these arguments.

foo(x, num_outputs, …, weight_init=<DEFAULT>, bias_init=<DEFAULT>, weight_collections=(tf.GraphKeys.WEIGHTS,), bias_collections=(tf.GraphKeys.BIASES,), output_collections=(tf.GraphKeys.ACTIVATIONS,), weight_regularizer=None, bias_regularizer=None, name=None) : Tensor

x is the input tensor.

Weights, biases, and activations (i.e., outputs) are, by default, added to the specified collections. Weights and biases are also added to tf.GraphKeys.GLOBAL_VARIABLES and tf.GraphKeys.TRAINABLE_VARIABLES.

Functions that add optimization ops given loss and global_step tensors.

Functions that produce weight regularization functions with signature

foo(weight_vars, name=None) : Operation

These are typically consumed by functions in layers.py.

Functions that add summary ops to the standard tf.GraphKeys.SUMMARIES collection. They also avoid name conflicts in the summary key.