View source on GitHub |
Optimizer that implements the RMSProp algorithm.
Inherits From: OptimizerV2
tf.contrib.optimizer_v2.RMSPropOptimizer(
learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False,
centered=False, name='RMSProp'
)
See the paper.
Args | |
---|---|
learning_rate
|
A float hyperparameter. The learning rate. |
decay
|
A float hyperparameter. Discounting factor for the history/coming gradient. |
momentum
|
A float hyperparameter. |
epsilon
|
A float hyperparameter. Small value to initialize the average square gradient variable and avoid zero denominator. |
use_locking
|
If True use locks for update operation. |
centered
|
If True, gradients are normalized by the estimated variance of the gradient; if False, by the uncentered second moment. Setting this to True may help with training, but is slightly more expensive in terms of computation and memory. Defaults to False. |
name
|
Optional name prefix for the operations created when applying gradients. Defaults to "RMSProp". |
Methods
apply_gradients
apply_gradients(
grads_and_vars, global_step=None, name=None
)
Apply gradients to variables.
This is the second part of minimize()
. It returns an Operation
that
applies gradients.
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs as returned by
compute_gradients() .
|
global_step
|
Optional Variable to increment by one after the variables
have been updated.
|
name
|
Optional name for the returned operation. Default to the name
passed to the Optimizer constructor.
|
Returns | |
---|---|
An Operation that applies the specified gradients. If global_step
was not None, that operation also increments global_step .
|
Raises | |
---|---|
TypeError
|
If grads_and_vars is malformed.
|
ValueError
|
If none of the variables have gradients. |
compute_gradients
compute_gradients(
loss, var_list=None, gate_gradients=GATE_OP, aggregation_method=None,
grad_loss=None, stop_gradients=None, scale_loss_by_num_replicas=False
)
Compute gradients of loss
for the variables in var_list
.
This is the first part of minimize()
. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a Tensor
, an
IndexedSlices
, or None
if there is no gradient for the
given variable.
Args | |
---|---|
loss
|
A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable. |
var_list
|
Optional list or tuple of tf.Variable to update to minimize
loss . Defaults to the list of variables collected in the graph under
the key GraphKeys.TRAINABLE_VARIABLES .
|
gate_gradients
|
How to gate the computation of gradients. Can be
GATE_NONE , GATE_OP , or GATE_GRAPH .
|
aggregation_method
|
Specifies the method used to combine gradient terms.
Valid values are defined in the class AggregationMethod .
|
grad_loss
|
Optional. A Tensor holding the gradient computed for loss .
|
stop_gradients
|
Optional. A Tensor or list of tensors not to differentiate through. |
scale_loss_by_num_replicas
|
Optional boolean. If true, scale the loss down by the number of replicas. DEPRECATED and generally no longer needed. |
Returns | |
---|---|
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be None .
|
Raises | |
---|---|
TypeError
|
If var_list contains anything else than Variable objects.
|
ValueError
|
If some arguments are invalid. |
RuntimeError
|
If called with eager execution enabled and loss is
not callable.
|
Eager Compatibility
When eager execution is enabled, gate_gradients
, and aggregation_method
are ignored.
get_name
get_name()
get_slot
get_slot(
var, name
)
Return a slot named name
created for var
by the Optimizer.
Some Optimizer
subclasses use additional variables. For example
Momentum
and Adagrad
use variables to accumulate updates. This method
gives access to these Variable
objects if for some reason you need them.
Use get_slot_names()
to get the list of slot names created by the
Optimizer
.
Args | |
---|---|
var
|
A variable passed to minimize() or apply_gradients() .
|
name
|
A string. |
Returns | |
---|---|
The Variable for the slot if it was created, None otherwise.
|
get_slot_names
get_slot_names()
Return a list of the names of slots created by the Optimizer
.
See get_slot()
.
Returns | |
---|---|
A list of strings. |
minimize
minimize(
loss, global_step=None, var_list=None, gate_gradients=GATE_OP,
aggregation_method=None, name=None, grad_loss=None, stop_gradients=None,
scale_loss_by_num_replicas=False
)
Add operations to minimize loss
by updating var_list
.
This method simply combines calls compute_gradients()
and
apply_gradients()
. If you want to process the gradient before applying
them call compute_gradients()
and apply_gradients()
explicitly instead
of using this function.
Args | |
---|---|
loss
|
A Tensor containing the value to minimize.
|
global_step
|
Optional Variable to increment by one after the variables
have been updated.
|
var_list
|
Optional list or tuple of Variable objects to update to
minimize loss . Defaults to the list of variables collected in the
graph under the key GraphKeys.TRAINABLE_VARIABLES .
|
gate_gradients
|
How to gate the computation of gradients. Can be
GATE_NONE , GATE_OP , or GATE_GRAPH .
|
aggregation_method
|
Specifies the method used to combine gradient terms.
Valid values are defined in the class AggregationMethod .
|
name
|
Optional name for the returned operation. |
grad_loss
|
Optional. A Tensor holding the gradient computed for loss .
|
stop_gradients
|
Optional. A Tensor or list of tensors not to differentiate through. |
scale_loss_by_num_replicas
|
Optional boolean. If true, scale the loss down by the number of replicas. DEPRECATED and generally no longer needed. |
Returns | |
---|---|
An Operation that updates the variables in var_list . If global_step
was not None , that operation also increments global_step .
|
Raises | |
---|---|
ValueError
|
If some of the variables are not Variable objects.
|
Eager Compatibility
When eager execution is enabled, loss
should be a Python function that
takes elements of var_list
as arguments and computes the value to be
minimized. If var_list
is None, loss
should take no arguments.
Minimization (and gradient computation) is done with respect to the
elements of var_list
if not None, else with respect to any trainable
variables created during the execution of the loss
function.
gate_gradients
, aggregation_method
, and grad_loss
are ignored when
eager execution is enabled.
variables
variables()
A list of variables which encode the current state of Optimizer
.
Includes slot variables and additional global variables created by the optimizer in the current default graph.
Returns | |
---|---|
A list of variables. |