Example usage:
```python
- 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)
+ 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