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Updated base class for optimizers.
Inherits From: Optimizer
tf.contrib.optimizer_v2.OptimizerV2(
use_locking, name
)
This class defines the API to add Ops to train a model. You never use this
class directly, but instead instantiate one of its subclasses such as
GradientDescentOptimizer, AdagradOptimizer, or MomentumOptimizer.
Usage
# Create an optimizer with the desired parameters.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Add Ops to the graph to minimize a cost by updating a list of variables.
# "cost" is a Tensor, and the list of variables contains tf.Variable
# objects.
opt_op = opt.minimize(cost, var_list=<list of variables>)
In the training program you will just have to run the returned Op.
# Execute opt_op to do one step of training:
opt_op.run()
Processing gradients before applying them.
Calling minimize() takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps:
- Compute the gradients with
compute_gradients(). - Process the gradients as you wish.
- Apply the processed gradients with
apply_gradients().
Example:
# Create an optimizer.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Compute the gradients for a list of variables.
grads_and_vars = opt.compute_gradients(loss, <list of variables>)
# grads_and_vars is a list of tuples (gradient, variable). Do whatever you
# need to the 'gradient' part, for example cap them, etc.
capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]
# Ask the optimizer to apply the capped gradients.
opt.apply_gradients(capped_grads_and_vars)
Gating Gradients
Both minimize() and compute_gradients() accept a gate_gradients
argument that controls the degree of parallelism during the application of
the gradients.
The possible values are: GATE_NONE, GATE_OP, and GATE_GRAPH.
GATE_NONE: Compute and apply gradients in parallel. This provides
the maximum parallelism in execution, at the cost of some non-reproducibility
in the results. For example the two gradients of matmul depend on the input
values: With GATE_NONE one of the gradients could be applied to one of the
inputs before the other gradient is computed resulting in non-reproducible
results.
GATE_OP: For each Op, make sure all gradients are computed before
they are used. This prevents race conditions for Ops that generate gradients
for multiple inputs where the gradients depend on the inputs.
GATE_GRAPH: Make sure all gradients for all variables are computed
before any one of them is used. This provides the least parallelism but can
be useful if you want to process all gradients before applying any of them.
Slots
Some optimizer subclasses, such as MomentumOptimizer and AdagradOptimizer
allocate and manage additional variables associated with the variables to
train. These are called Slots. Slots have names and you can ask the
optimizer for the names of the slots that it uses. Once you have a slot name
you can ask the optimizer for the variable it created to hold the slot value.
This can be useful if you want to log debug a training algorithm, report stats about the slots, etc.
Non-slot variables
Some optimizer subclasses, such as AdamOptimizer have variables that
are not associated with the variables to train, just the step itself.
Hyper parameters
These are arguments passed to the optimizer subclass constructor
(the __init__ method), and then passed to self._set_hyper().
They can be either regular Python values (like 1.0), tensors, or
callables. If they are callable, the callable will be called during
apply_gradients() to get the value for the hyper parameter.
State
Internal methods are passed a state argument with the correct
values to use for the slot and non-slot variables, and the hyper
parameters.
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. |
RuntimeError
|
If _create_slots has been overridden instead of _create_vars. |
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. |