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Common utilities for TF-Agents.
Example of usage:
from tf_agents.utils import eager_utils
@eager_utils.run_in_graph_and_eager_modes
def loss_fn(x, y):
v = tf.get_variable('v', initializer=tf.ones_initializer(), shape=())
return v + x - y
with tfe.graph_mode():
# loss and train_step are Tensors/Ops in the graph
loss_op = loss_fn(inputs, labels)
train_step_op = eager_utils.create_train_step(loss_op, optimizer)
# Compute the loss and apply gradients to the variables using the optimizer.
with tf.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
for _ in range(num_train_steps):
loss_value = sess.run(train_step_op)
with tfe.eager_mode():
# loss and train_step are lambda functions that can be called.
loss = loss_fn(inputs, labels)
train_step = eager_utils.create_train_step(loss, optimizer)
# Compute the loss and apply gradients to the variables using the optimizer.
for _ in range(num_train_steps):
loss_value = train_step()
Classes
class Future
: Converts a function or class method call into a future callable.
Functions
add_gradients_summaries(...)
: Add summaries to gradients.
add_variables_summaries(...)
: Add summaries for variables.
clip_gradient_norms(...)
: Clips the gradients by the given value.
clip_gradient_norms_fn(...)
: Returns a transform_grads_fn
function for gradient clipping.
create_train_op(...)
: Creates an Operation
that evaluates the gradients and returns the loss.
create_train_step(...)
: Creates a train_step that evaluates the gradients and returns the loss.
dataset_iterator(...)
: Constructs a Dataset
iterator.
future_in_eager_mode(...)
: Decorator that allow a function/method to run in graph and in eager modes.
get_next(...)
: Returns the next element in a Dataset
iterator.
has_self_cls_arg(...)
: Checks if it is method which takes self/cls as the first argument.
is_unbound(...)
: Checks if it is an unbounded method.
np_function(...)
: Decorator that allow a numpy function to be used in Eager and Graph modes.
Other Members | |
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absolute_import |
Instance of __future__._Feature
|
division |
Instance of __future__._Feature
|
print_function |
Instance of __future__._Feature
|