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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 inf
s only.
inf_nan_callback(...)
: An execution callback that checks for inf
s and nan
s 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 nan
s 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.