tf.train.AdagradDAOptimizer

class tf.train.AdagradDAOptimizer

See the guide: Training > Optimizers

Adagrad Dual Averaging algorithm for sparse linear models.

See this paper.

This optimizer takes care of regularization of unseen features in a mini batch by updating them when they are seen with a closed form update rule that is equivalent to having updated them on every mini-batch.

AdagradDA is typically used when there is a need for large sparsity in the trained model. This optimizer only guarantees sparsity for linear models. Be careful when using AdagradDA for deep networks as it will require careful initialization of the gradient accumulators for it to train.

Methods

__init__(learning_rate, global_step, initial_gradient_squared_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='AdagradDA')

Construct a new AdagradDA optimizer.

Args:

• learning_rate: A Tensor or a floating point value. The learning rate.
• global_step: A Tensor containing the current training step number.
• initial_gradient_squared_accumulator_value: A floating point value. Starting value for the accumulators, must be positive.
• l1_regularization_strength: A float value, must be greater than or equal to zero.
• l2_regularization_strength: A float value, must be greater than or equal to zero.
• use_locking: If True use locks for update operations.
• name: Optional name prefix for the operations created when applying gradients. Defaults to "AdagradDA".

Raises:

• ValueError: If the initial_gradient_squared_accumulator_value is invalid.

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(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.
• var_list: Optional list of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKey.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.

get_name()

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()

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

See get_slot().

Returns:

A list of strings.

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 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.

Class Members

GATE_GRAPH

GATE_NONE

GATE_OP

Defined in tensorflow/python/training/adagrad_da.py.