tf.contrib.eager

 

upper level

 

Modules

 

metrics:

Metrics namespace.

 

Classes

 

EagerVariableStore:

Wrapper allowing functional layers to be used with eager execution.

GradientTape:

Records operations to use to compute gradients.

IsolateTest:

A context manager which isolates resources in its block.

Iterator:

An iterator producing tf.Tensor objects from a tf.data.Dataset.

Network:

Represents the composition of a set of Layers.

Saver:

A tf.train.Saver adapter for use when eager execution is enabled.

Sequential:

Represents a linear sequence of Layers or functions.

Variable:

Variable based on resource handles.

 

Functions

 

add_execution_callback(…):

Add an execution callback to the default eager context.

clear_execution_callbacks(…):

Clear all execution callbacks from the default eager context.

custom_gradient(…):

Decorator to define a function with a custom gradient.

defun(…):

Decorator to compile func into graph_mode.

enable_eager_execution(…):

Enables, for the rest of the lifetime of this program, eager execution.

get_optimizer_variables(…):

Returns a list of variables for the given tf.train.Optimizer.

gradients_function(…):

Returns a function which differentiates f with respect to params.

implicit_gradients(…):

Returns a function which differentiates f with respect to variables.

implicit_value_and_gradients(…):

Returns a function which differentiates f with respect to variables.

in_eager_mode(…):

Returns True if current thread is in EAGER mode for default context.

in_graph_mode(…):

Returns True if current thread is in GRAPH mode for default context.

inf_callback(…):

A specialization of inf_nan_callback that checks for infs only.

inf_nan_callback(…):

An execution callback that checks for infs and nans in output tensors.

list_devices(…):

List the names of the available devices.

make_template(…):

Make a template, optionally compiling func_ into a graph function.

nan_callback(…):

A specialization of inf_nan_callback that checks for nans only.

num_gpus(…):

Get the number of available GPU devices.

py_func(…):

Wraps a python function into a TensorFlow op.

restore_network_checkpoint(…):

Restore the Network from a checkpoint.

restore_variables_on_create(…):

ContextManager that restores variables on creation.

run(…):

Runs the program with an optional main function and argv list.

run_test_in_graph_and_eager_modes(…):

Runs the test in both graph and eager modes.

save_network_checkpoint(…):

Save variables from the Network to a checkpoint.

seterr(…):

Set how abnormal conditions are handled by the default eager context.

value_and_gradients_function(…):

Returns a function that computes f and its derivative w.r.t. params.