tf.raw_ops.SdcaOptimizerV2

Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for

tf.raw_ops.SdcaOptimizerV2(
    sparse_example_indices,
    sparse_feature_indices,
    sparse_feature_values,
    dense_features,
    example_weights,
    example_labels,
    sparse_indices,
    sparse_weights,
    dense_weights,
    example_state_data,
    loss_type,
    l1,
    l2,
    num_loss_partitions,
    num_inner_iterations,
    adaptive=True,
    name=None
)

linear models with L1 + L2 regularization. As global optimization objective is strongly-convex, the optimizer optimizes the dual objective at each step. The optimizer applies each update one example at a time. Examples are sampled uniformly, and the optimizer is learning rate free and enjoys linear convergence rate.

Proximal Stochastic Dual Coordinate Ascent.
Shai Shalev-Shwartz, Tong Zhang. 2012

\[Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|\]

Adding vs. Averaging in Distributed Primal-Dual Optimization.
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik, Martin Takac. 2015

Stochastic Dual Coordinate Ascent with Adaptive Probabilities.
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015

sparse_example_indicesA list of Tensor objects with type int64. a list of vectors which contain example indices.
sparse_feature_indicesA list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors which contain feature indices.
sparse_feature_valuesA list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group.
dense_featuresA list of Tensor objects with type float32. a list of matrices which contains the dense feature values.
example_weightsA Tensor of type float32. a vector which contains the weight associated with each example.
example_labelsA Tensor of type float32. a vector which contains the label/target associated with each example.
sparse_indicesA list with the same length as sparse_example_indices of Tensor objects with type int64. a list of vectors where each value is the indices which has corresponding weights in sparse_weights. This field maybe omitted for the dense approach.
sparse_weightsA list with the same length as sparse_example_indices of Tensor objects with type float32. a list of vectors where each value is the weight associated with a sparse feature group.
dense_weightsA list with the same length as dense_features of Tensor objects with type float32. a list of vectors where the values are the weights associated with a dense feature group.
example_state_dataA Tensor of type float32. a list of vectors containing the example state data.
loss_typeA string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss". Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
l1A float. Symmetric l1 regularization strength.
l2A float. Symmetric l2 regularization strength.
num_loss_partitionsAn int that is >= 1. Number of partitions of the global loss function.
num_inner_iterationsAn int that is >= 1. Number of iterations per mini-batch.
adaptiveAn optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop.
nameA name for the operation (optional).

A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).
out_example_state_dataA Tensor of type float32.
out_delta_sparse_weightsA list with the same length as sparse_example_indices of Tensor objects with type float32.
out_delta_dense_weightsA list with the same length as dense_features of Tensor objects with type float32.