# tf.contrib.linear_optimizer.SdcaModel

### class tf.contrib.linear_optimizer.SdcaModel

Stochastic dual coordinate ascent solver for linear models.

This class currently only supports a single machine (multi-threaded) implementation. We expect the weights and duals to fit in a single machine.

Loss functions supported:

• Binary logistic loss
• Squared loss
• Hinge loss
• Smooth hinge loss

This class defines an optimizer API to train a linear model.

### Usage

# Create a solver with the desired parameters.
lr = tf.contrib.linear_optimizer.SdcaModel(examples, variables, options)
min_op = lr.minimize()
opt_op = lr.update_weights(min_op)

predictions = lr.predictions(examples)
# Primal loss + L1 loss + L2 loss.
regularized_loss = lr.regularized_loss(examples)
# Primal loss only
unregularized_loss = lr.unregularized_loss(examples)

examples: {
sparse_features: list of SparseFeatureColumn.
dense_features: list of dense tensors of type float32.
example_labels: a tensor of type float32 and shape [Num examples]
example_weights: a tensor of type float32 and shape [Num examples]
example_ids: a tensor of type string and shape [Num examples]
}
variables: {
sparse_features_weights: list of tensors of shape [vocab size]
dense_features_weights: list of tensors of shape [dense_feature_dimension]
}
options: {
symmetric_l1_regularization: 0.0
symmetric_l2_regularization: 1.0
loss_type: "logistic_loss"
num_loss_partitions: 1 (Optional, with default value of 1. Number of
partitions of the global loss function, 1 means single machine solver,
and >1 when we have more than one optimizer working concurrently.)
num_table_shards: 1 (Optional, with default value of 1. Number of shards
of the internal state table, typically set to match the number of
parameter servers for large data sets.
}


In the training program you will just have to run the returned Op from minimize().

# Execute opt_op and train for num_steps.
for _ in range(num_steps):
opt_op.run()

# You can also check for convergence by calling
lr.approximate_duality_gap()


## Methods

### __init__(examples, variables, options)

Create a new sdca optimizer.

### approximate_duality_gap()

Add operations to compute the approximate duality gap.

#### Returns:

An Operation that computes the approximate duality gap over all examples.

### minimize(global_step=None, name=None)

Add operations to train a linear model by minimizing the loss function.

#### Args:

• global_step: Optional Variable to increment by one after the variables have been updated.
• name: Optional name for the returned operation.

#### Returns:

An Operation that updates the variables passed in the constructor.

### predictions(examples)

Add operations to compute predictions by the model.

If logistic_loss is being used, predicted probabilities are returned. Otherwise, (raw) linear predictions (w*x) are returned.

#### Args:

• examples: Examples to compute predictions on.

#### Returns:

An Operation that computes the predictions for examples.

#### Raises:

• ValueError: if examples are not well defined.

### regularized_loss(examples)

Add operations to compute the loss with regularization loss included.

#### Args:

• examples: Examples to compute loss on.

#### Returns:

An Operation that computes mean (regularized) loss for given set of examples. Raises: * ValueError: if examples are not well defined.

### unregularized_loss(examples)

Add operations to compute the loss (without the regularization loss).

#### Args:

• examples: Examples to compute unregularized loss on.

#### Returns:

An Operation that computes mean (unregularized) loss for given set of examples.

#### Raises:

• ValueError: if examples are not well defined.

### update_weights(train_op)

Updates the model weights.

This function must be called on at least one worker after minimize. In distributed training this call can be omitted on non-chief workers to speed up training.

#### Args:

• train_op: The operation returned by the minimize call.

#### Returns:

An Operation that updates the model weights.