tf.contrib.optimizer_v2.GradientDescentOptimizer

Optimizer that implements the gradient descent algorithm.

Inherits From: OptimizerV2

tf.contrib.optimizer_v2.GradientDescentOptimizer(
    learning_rate, use_locking=False, name='GradientDescent'
)

Args
learning_rate	A float hyperparameter. The learning rate to use.
use_locking	If True use locks for update operations.
name	Optional name prefix for the operations created when applying gradients. Defaults to "GradientDescent".

Methods

apply_gradients

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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

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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

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get_name()

get_slot

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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

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get_slot_names()

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

See get_slot().

Returns
A list of strings.

minimize

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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

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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