linear models with L1 + L2 regularization. As global optimization objective is
strongly-convex, the optimizer optimizes the dual objective at each step. The
optimizer applies each update one example at a time. Examples are sampled
uniformly, and the optimizer is learning rate free and enjoys linear convergence
rate.
A list of Tensor objects with type int64.
a list of vectors which contain example indices.
sparse_feature_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64.
a list of vectors which contain feature indices.
sparse_feature_values
A list of Tensor objects with type float32.
a list of vectors which contains feature value
associated with each feature group.
dense_features
A list of Tensor objects with type float32.
a list of matrices which contains the dense feature values.
example_weights
A Tensor of type float32.
a vector which contains the weight associated with each
example.
example_labels
A Tensor of type float32.
a vector which contains the label/target associated with each
example.
sparse_indices
A list with the same length as sparse_example_indices of Tensor objects with type int64.
a list of vectors where each value is the indices which has
corresponding weights in sparse_weights. This field maybe omitted for the
dense approach.
sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32.
a list of vectors where each value is the weight associated with
a sparse feature group.
dense_weights
A list with the same length as dense_features of Tensor objects with type float32.
a list of vectors where the values are the weights associated
with a dense feature group.
example_state_data
A Tensor of type float32.
a list of vectors containing the example state data.
loss_type
A string from: "logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss".
Type of the primal loss. Currently SdcaSolver supports logistic,
squared and hinge losses.
l1
A float. Symmetric l1 regularization strength.
l2
A float. Symmetric l2 regularization strength.
num_loss_partitions
An int that is >= 1.
Number of partitions of the global loss function.
num_inner_iterations
An int that is >= 1.
Number of iterations per mini-batch.
adaptive
An optional bool. Defaults to True.
Whether to use Adaptive SDCA for the inner loop.
name
A name for the operation (optional).
Returns
A tuple of Tensor objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).
out_example_state_data
A Tensor of type float32.
out_delta_sparse_weights
A list with the same length as sparse_example_indices of Tensor objects with type float32.
out_delta_dense_weights
A list with the same length as dense_features of Tensor objects with type float32.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]