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TensorFlow Eager execution prototype.
EXPERIMENTAL: APIs here are unstable and likely to change without notice.
To use, at program startup, call tf.compat.v1.enable_eager_execution().
Modules
metrics module: Metrics namespace.
Classes
class Checkpoint: Groups trackable objects, saving and restoring them.
class Checkpointable: Manages dependencies on other objects.
class EagerVariableStore: Wrapper allowing functional layers to be used with eager execution.
class ExecutionCallback: Valid callback actions.
class GradientTape: Record operations for automatic differentiation.
class Iterator: An iterator producing tf.Tensor objects from a tf.data.Dataset.
class Network: Represents the composition of a set of Layers.
class Saver: A tf.compat.v1.train.Saver adapter for use when eager execution is enabled.
class Sequential: Represents a linear sequence of Layers or functions.
class TensorSpec: Describes a tf.Tensor.
class Variable: Variable based on resource handles.
Functions
add_execution_callback(...): Add an execution callback to the default eager context.
async_clear_error(...): Clears errors raised during ASYNC execution mode.
async_wait(...): Waits for ops dispatched in ASYNC mode to finish.
clear_execution_callbacks(...): Clear all execution callbacks from the default eager context.
connect_to_remote_host(...): Connects to a single machine to enable remote execution on it.
custom_gradient(...): Decorator to define a function with a custom gradient.
defun(...): Compiles a Python function into a callable TensorFlow graph.
enable_eager_execution(...): Enables eager execution for the lifetime of this program.
enable_remote_eager_execution(...): Enables eager execution for the lifetime of this program.
errstate(...): Context manager setting error state.
executing_eagerly(...): Returns True if the current thread has eager execution enabled.
execution_mode(...): Context manager for setting execution mode for current thread.
function(...): Creates a callable TensorFlow graph from a Python function.
get_optimizer_variables(...): Returns a list of variables for the given tf.compat.v1.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 the current thread has eager execution enabled.
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 that executes it eagerly.
restore_network_checkpoint(...): Restore the Network from a checkpoint. (deprecated)
restore_variables_on_create(...): ContextManager that restores variables on creation.
run(...): Runs the program with an optional main function and argv list.
run_all_tests_in_graph_and_eager_modes(...): Execute all test methods in the given class with and without eager.
run_test_in_graph_and_eager_modes(...): Execute the decorated test with and without enabling eager execution.
save_network_checkpoint(...): Save variables from the Network to a checkpoint. (deprecated)
set_execution_mode(...): Sets execution mode for the current thread.
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.