# tf.contrib.linear_optimizer.SDCAOptimizer

## Class SDCAOptimizer

Wrapper class for SDCA optimizer.

The wrapper is currently meant for use as an optimizer within a tf.learn Estimator.

Example usage:

  real_feature_column = real_valued_column(...)
sparse_feature_column = sparse_column_with_hash_bucket(...)
sdca_optimizer = linear.SDCAOptimizer(example_id_column='example_id',
num_loss_partitions=1,
num_table_shards=1,
symmetric_l2_regularization=2.0)
classifier = tf.contrib.learn.LinearClassifier(
feature_columns=[real_feature_column, sparse_feature_column],
weight_column_name=...,
optimizer=sdca_optimizer)
classifier.fit(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 the number of partitions of the global loss function and should be set to (#concurrent train ops/per worker) x (#workers). Convergence of (global) loss is guaranteed if num_loss_partitions is larger or equal to the above product. Larger values for num_loss_partitions lead to slower convergence. The recommended value for num_loss_partitions in tf.learn (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.

## Methods

### __init__

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


### get_name

get_name()


### get_train_step

get_train_step(
columns_to_variables,
weight_column_name,
loss_type,
features,
targets,
global_step
)


Returns the training operation of an SdcaModel optimizer.