tf.contrib.optimizer_v2.OptimizerV2

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:

1. Compute the gradients with compute_gradients().
2. Process the gradients as you wish.
3. 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

View source

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

View source

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

View source

get_name()

get_slot

View source

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

View source

get_slot_names()

Return a list of the names of slots created by the Optimizer.

See get_slot().

Returns
A list of strings.

minimize

View source

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

View source

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.

Class Variables

• GATE_GRAPH = 2
• GATE_NONE = 0
• GATE_OP = 1