Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
tf.train.sdca_optimizer(
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, adaptative=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
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
| Args | |
|---|---|
sparse_example_indices | A list of Tensor objects with type int64. a list of vectors which contain example indices. |
sparse_feature_indices | A 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_values | A list of Tensor objects with type float32. a list of vectors which contains feature value associated with each feature group. |
dense_features | A list of Tensor objects with type float32. a list of matrices which contains the dense feature values. |
example_weights | A Tensor of type float32. a vector which contains the weight associated with each example. |
example_labels | A Tensor of type float32. a vector which contains the label/target associated with each example. |
sparse_indices | A 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_weights | A 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_weights | A 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_data | A Tensor of type float32. a list of vectors containing the example state data. |
loss_type | A 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. |
l1 | A float. Symmetric l1 regularization strength. |
l2 | A float. Symmetric l2 regularization strength. |
num_loss_partitions | An int that is >= 1. Number of partitions of the global loss function. |
num_inner_iterations | An int that is >= 1. Number of iterations per mini-batch. |
adaptative | An optional bool. Defaults to True. Whether to use Adaptive SDCA for the inner loop. |
name | A name for the operation (optional). |
| Returns | |
|---|---|
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights). | |
out_example_state_data | A Tensor of type float32. |
out_delta_sparse_weights | A list with the same length as sparse_example_indices of Tensor objects with type float32. |
out_delta_dense_weights | A list with the same length as dense_features of Tensor objects with type float32. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/sdca_optimizer