Base class for optimizers.
Inherits From: CheckpointableBase
tf.train.Optimizer( 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
.
# 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()
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_gradients()
.apply_gradients()
.# 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)
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.
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.
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. |
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. |
RuntimeError | If you should use _distributed_apply() instead. |
compute_gradients
compute_gradients( loss, var_list=None, gate_gradients=GATE_OP, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None )
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 . |
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 . |
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. |
When eager execution is enabled, gate_gradients
, aggregation_method
, and colocate_gradients_with_ops
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, 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. |
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()
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. |
GATE_GRAPH = 2
GATE_NONE = 0
GATE_OP = 1
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/Optimizer