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.

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

Check that TensorFlow session has been initialized.

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

Get the value of a given variable as NumPy array. Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever poss

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

Find variable by local or global name.

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.

Run the specified ops in the default session.

Internal helper for creating autosummary accumulators.

Get internal state for the given TensorFlow device.

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

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

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

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

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.

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

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

Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops.

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

Copy all necessary files into the run dir. Assumes that the dir exists, is local, and is writable.

Apply dynamic loss scaling for the given expression.

Convert a normal path to template and the convert it back to a normal path with given path type.

Create tf.Session based on config dict.

Create the necessary ops to include autosummaries in TensorBoard report. Note: This should be done only once per graph.

Finds the python object with the given name.

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

Convert a normal path back to its template representation.