Optimizer that implements the Proximal Adagrad algorithm.

Inherits From: Optimizer


Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: Duchi et al., 2011 (pdf) Efficient Learning using Forward-Backward Splitting: Duchi et al., 2009 (pdf)

learning_rate A Tensor or a floating point value. The learning rate.
initial_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 "Adagrad".

ValueError If the initial_accumulator_value is invalid.



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Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

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.

An Operation that applies the specified gradients. If