SdcaModel
Defined in tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
.
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:
This class defines an optimizer API to train a linear model.
# 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()
__init__
__init__( examples, variables, options )
Create a new sdca optimizer.
approximate_duality_gap
approximate_duality_gap()
Add operations to compute the approximate duality gap.
An Operation that computes the approximate duality gap over all examples.
minimize
minimize( global_step=None, name=None )
Add operations to train a linear model by minimizing the loss function.
global_step
: Optional Variable
to increment by one after the variables have been updated.name
: Optional name for the returned operation.An Operation that updates the variables passed in the constructor.
predictions
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.
examples
: Examples to compute predictions on.An Operation that computes the predictions for examples.
ValueError
: if examples are not well defined.regularized_loss
regularized_loss(examples)
Add operations to compute the loss with regularization loss included.
examples
: Examples to compute loss on.An Operation that computes mean (regularized) loss for given set of examples.
ValueError
: if examples are not well defined.unregularized_loss
unregularized_loss(examples)
Add operations to compute the loss (without the regularization loss).
examples
: Examples to compute unregularized loss on.An Operation that computes mean (unregularized) loss for given set of examples.
ValueError
: if examples are not well defined.update_weights
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.
train_op
: The operation returned by the minimize
call.An Operation that updates the model weights.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/linear_optimizer/SdcaModel