Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).

Create a new autosummary. Args: name: Name to use in TensorBoard value: TensorFlow expression or python value to track

Do general housekeeping and keep the state of the context up-to-date. Should be called often enough but not in a tight loop.

Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s). Args: input_transform: A dict speci

Create a clone of this network with its own copy of the variables.

Close the context and clean up. Should only be called once.

Find variable by local or global name.

Apply dynamic loss scaling for the given expression.

Undo the effect of dynamic loss scaling for the given expression.

Check that TensorFlow session has been initialized.

Flush, close possible files, and remove stdout/stderr mirroring.

Get or create variable representing log2 of the current dynamic loss scaling factor.

Traverses the object name and returns the last (rightmost) python object.

Replace tags in the given path template and return either Windows or Linux formatted path.

Print a summary table of the network structure.

Reset internal state of the underlying optimizer.

Run the specified ops in the default session.

Internal helper for creating autosummary accumulators.

Forcefully enter the specified name scope, ignoring any surrounding scopes.

Construct training op to update the registered variables based on their gradients.

Copy the values of all variables from the given network, including sub-networks.

Flush written text to both stdout and a file, if open.

How much time passed between the previous two calls to update.

Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part r

Get the current user name.

Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation.

Register the gradients of the given loss function with respect to the given variables. Intended to be called once per GPU.

Re-initialize all variables of this network, excluding sub-networks.

Construct summary ops to include histograms of all trainable parameters in TensorBoard.

Write text to stdout (and a file) and optionally flush.

Create a new run dir with increasing ID number at the start.

Reads all directory names in a given directory (non-recursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the d