登录
View source on GitHub
|
An optimizer that applies loss scaling.
Inherits From: Optimizer
tf.compat.v1.mixed_precision.MixedPrecisionLossScaleOptimizer(
opt, loss_scale
)
Loss scaling is a process that multiplies the loss by a multiplier called the loss scale, and divides each gradient by the same multiplier. The pseudocode for this process is:
loss = ...
loss *= loss_scale
grads = gradients(loss, vars)
grads /= loss_scale
Mathematically, loss scaling has no effect, but can help avoid numerical underflow in intermediate gradients when float16 tensors are used for mixed precision training. By multiplying the loss, each intermediate gradient will have the same multiplier applied.
The loss scale can either be a fixed constant, chosen by the user, or be dynamically determined. Dynamically determining the loss scale is convenient as a loss scale does not have to be explicitly chosen. However it reduces performance.
This optimizer wraps another optimizer and applies loss scaling to it via a
LossScale. Loss scaling is applied whenever gradients are
computed, such as through minimize().
Args | |
|---|---|
use_locking
|
Bool. If True apply use locks to prevent concurrent updates to variables. |
name
|
A non-empty string. The name to use for accumulators created for the optimizer. |
Raises | |
|---|---|
ValueError
|
If name is malformed. |
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
conditionally applies gradients if all gradient values are finite.
Otherwise no update is performed (nor is global_step incremented).
| 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 conditionally applies the specified gradients. If
global_step was not None, that operation also increments global_step.
|
| Raises | |
|---|---|
RuntimeError
|
If you should use _distributed_apply() instead.
|
compute_gradients
compute_gradients(
loss, var_list=None, gate_gradients=optimizer.Optimizer.GATE_OP,
aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None
)
Compute gradients of loss for the variables in var_list.
This adjusts the dynamic range of the gradient evaluation by scaling up
the loss value. The gradient values are then scaled back down by the
reciprocal of the loss scale. This is useful in reduced precision training
where small gradient values would otherwise underflow the representable
range.
| 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.
|
colocate_gradients_with_ops
|
If True, try colocating gradients with the corresponding op. |
grad_loss
|
Optional. A Tensor holding the gradient computed for loss.
|
| Returns | |
|---|---|
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be None.
|
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, colocate_gradients_with_ops=False, name=None,
grad_loss=None
)
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.
|
colocate_gradients_with_ops
|
If True, try colocating gradients with the corresponding op. |
name
|
Optional name for the returned operation. |
grad_loss
|
Optional. A Tensor holding the gradient computed for loss.
|
| 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 no arguments and computes the value to be minimized. 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,
colocate_gradients_with_ops and grad_loss are ignored when eager
execution is enabled.
variables
variables()
Returns the variables of the Optimizer.
Class Variables | |
|---|---|
| GATE_GRAPH |
2
|
| GATE_NONE |
0
|
| GATE_OP |
1
|