tf.contrib.linear_optimizer.SdcaModel

Stochastic dual coordinate ascent solver for linear models.

tf.contrib.linear_optimizer.SdcaModel(
    examples, variables, options
)

Loss functions supported:

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

• Poisson log 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

approximate_duality_gap

View source

approximate_duality_gap()

Add operations to compute the approximate duality gap.

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

minimize

View source

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

View source

predictions(
    examples
)

Add operations to compute predictions by the model.

If logistic_loss is being used, predicted probabilities are returned. If poisson_loss is being used, predictions are exponentiated. 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

View source

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

View source

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

View source

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.