tf.compat.v1.train.AdagradDAOptimizer

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Adagrad Dual Averaging algorithm for sparse linear models.

Inherits From: Optimizer

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.

References:

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization :Duchi et al., 2011 (pdf)

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".

ValueError If the initial_gradient_squared_accumulator_value is invalid.

Methods

apply_gradients

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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

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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.

Eager Compatibility

When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored.

get_name

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get_slot

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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 m