LostTech.TensorFlow : API Documentation

Type AdagradDAOptimizer

Namespace tensorflow.train

Parent Optimizer

Interfaces IAdagradDAOptimizer

Adagrad Dual Averaging algorithm for sparse linear models.

See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).

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.



Public static methods

AdagradDAOptimizer NewDyn(object learning_rate, object global_step, ImplicitContainer<T> initial_gradient_squared_accumulator_value, ImplicitContainer<T> l1_regularization_strength, ImplicitContainer<T> l2_regularization_strength, ImplicitContainer<T> use_locking, ImplicitContainer<T> name)

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

Public properties

object PythonObject get;