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.

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

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

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.

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

Check that TensorFlow session has been initialized.

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

Find variable by local or global name.

Reset internal state of the underlying optimizer.

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.

r"""Upsample a batch of 2D images with the given filter. Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsam

Sum gradients across devices using TensorFlow collective ops (slow fallback path).

Internal helper for creating autosummary accumulators.

Get internal state for the given TensorFlow device.

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

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

r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`. Padding is performed only once at the beginning, not between the operations. The fu

r"""Downsample a batch of 2D images with the given filter. Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and dow

Evaluate if new feature vectors are in the estimated manifold.

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

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

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.