tf.estimator.experimental.LinearSDCA

Class LinearSDCA

Stochastic Dual Coordinate Ascent helper for linear estimators.

Aliases:

  • Class tf.compat.v1.estimator.experimental.LinearSDCA
  • Class tf.compat.v2.estimator.experimental.LinearSDCA
  • Class tf.estimator.experimental.LinearSDCA

Defined in python/estimator/canned/linear.py.

Objects of this class are intended to be provided as the optimizer argument (though LinearSDCA objects do not implement the tf.train.Optimizer interface) when creating tf.estimator.LinearClassifier or tf.estimator.LinearRegressor.

SDCA can only be used with LinearClassifier and LinearRegressor under the following conditions:

  • Feature columns are of type V2.
  • Multivalent categorical columns are not normalized. In other words the sparse_combiner argument in the estimator constructor should be "sum".
  • For classification: binary label.
  • For regression: one-dimensional label.

Example usage:

real_feature_column = numeric_column(...)
sparse_feature_column = categorical_column_with_hash_bucket(...)
linear_sdca = tf.estimator.experimental.LinearSDCA(
    example_id_column='example_id',
    num_loss_partitions=1,
    num_table_shards=1,
    symmetric_l2_regularization=2.0)
classifier = tf.estimator.LinearClassifier(
    feature_columns=[real_feature_column, sparse_feature_column],
    weight_column=...,
    optimizer=linear_sdca)
classifier.train(input_fn_train, steps=50)
classifier.evaluate(input_fn=input_fn_eval)

Here the expectation is that the input_fn_* functions passed to train and evaluate return a pair (dict, label_tensor) where dict has example_id_column as key whose value is a Tensor of shape [batch_size] and dtype string. num_loss_partitions defines sigma' in eq (11) of [3]. Convergence of (global) loss is guaranteed if num_loss_partitions is larger or equal to the product (#concurrent train ops/per worker) x (#workers). Larger values for num_loss_partitions lead to slower convergence. The recommended value for num_loss_partitions in tf.estimator (where currently there is one process per worker) is the number of workers running the train steps. It defaults to 1 (single machine). num_table_shards defines the number of shards for the internal state table, typically set to match the number of parameter servers for large data sets.

The SDCA algorithm was originally introduced in [1] and it was followed by the L1 proximal step [2], a distributed version [3] and adaptive sampling [4]. [1] www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf [2] https://arxiv.org/pdf/1309.2375.pdf [3] https://arxiv.org/pdf/1502.03508.pdf [4] https://arxiv.org/pdf/1502.08053.pdf Details specific to this implementation are provided in: https://github.com/tensorflow/estimator/tree/master/tensorflow_estimator/python/estimator/canned/linear_optimizer/doc/sdca.ipynb

__init__

__init__(
    example_id_column,
    num_loss_partitions=1,
    num_table_shards=None,
    symmetric_l1_regularization=0.0,
    symmetric_l2_regularization=1.0,
    adaptive=False
)

Construct a new SDCA optimizer for linear estimators.

Args:

  • example_id_column: The column name containing the example ids.
  • num_loss_partitions: Number of workers.
  • num_table_shards: Number of shards of the internal state table, typically set to match the number of parameter servers.
  • symmetric_l1_regularization: A float value, must be greater than or equal to zero.
  • symmetric_l2_regularization: A float value, must be greater than zero and should typically be greater than 1.
  • adaptive: A boolean indicating whether to use adaptive sampling.

Methods

get_train_step

get_train_step(
    state_manager,
    weight_column_name,
    loss_type,
    feature_columns,
    features,
    targets,
    bias_var,
    global_step
)

Returns the training operation of an SdcaModel optimizer.