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92 lines
4.3 KiB
92 lines
4.3 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/*!
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* \file sdca_ops.h
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* \brief
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*/
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#ifndef OPS_BUILT_IN_OP_PROTO_INC_SDCA_OPS_H_
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#define OPS_BUILT_IN_OP_PROTO_INC_SDCA_OPS_H_
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#include "graph/operator.h"
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
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*linear models with L1 + L2 regularization. As global optimization objective is
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*strongly-convex, the optimizer optimizes the dual objective at each step. The
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*optimizer applies each update one example at a time. Examples are sampled
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*uniformly, and the optimizer is learning rate free and enjoys linear convergence
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*rate . \n
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*@par Inputs:
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*@li sparse_example_indices: a list of vectors which contain example indices.It's a dynamic input.
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*@li sparse_feature_indices: a list of vectors which contain feature indices.It's a dynamic input.
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*@li sparse_feature_values: a list of vectors which contains feature value associated with each feature group.It's a dynamic input.
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*@li dense_features: a list of matrices which contains the dense feature values.It's a dynamic input.
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*@li example_weights: a vector which contains the weight associated with each example.
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*@li example_labels: a vector which contains the label/target associated with each example.
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*@li sparse_indices: a list of vectors where each value is the indices which has
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*corresponding weights in sparse_weights. This field maybe omitted for the dense approach.It's a dynamic input.
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*@li sparse_weights: a list of vectors where each value is the weight associated with a sparse feature group.
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*@li dense_weights: a list of vectors where the values are the weights associated with a dense feature group.It's a dynamic input.
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*@li example_state_data: a list of vectors containing the example state data.
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*@li loss_type: Type of the primal loss. Currently SdcaSolver supports logistic, squared and hinge losses.
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*@li l1: Symmetric l1 regularization strength.
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*@li l2: Symmetric l2 regularization strength.
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*@li num_loss_partitions: Number of partitions of the global loss function.
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*@li num_inner_iterations: Number of iterations per mini-batch . \n
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*@par Outputs:
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*y: A Returns a list of vectors containing the updated example state
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*data.a list of vectors where each value is the delta
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*weights associated with a sparse feature group.a list of vectors where the values are the delta
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*weights associated with a dense feature group . \n
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*@par Third-party framework compatibility
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* Compatible with tensorflow SdcaOptimizerV2 operator.
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*/
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REG_OP(SdcaOptimizerV2)
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.DYNAMIC_INPUT(sparse_example_indices, TensorType({DT_INT64}))
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.DYNAMIC_INPUT(sparse_feature_indices, TensorType({DT_INT64}))
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.DYNAMIC_INPUT(sparse_feature_values, TensorType({DT_FLOAT}))
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.DYNAMIC_INPUT(dense_features, TensorType({DT_FLOAT}))
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.INPUT(example_weights, TensorType({DT_FLOAT}))
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.INPUT(example_labels, TensorType({DT_FLOAT}))
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.DYNAMIC_INPUT(sparse_indices, TensorType({DT_INT64}))
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.DYNAMIC_INPUT(sparse_weights, TensorType({DT_FLOAT}))
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.DYNAMIC_INPUT(dense_weights, TensorType({DT_FLOAT}))
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.INPUT(example_state_data, TensorType({DT_FLOAT}))
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.OUTPUT(out_example_state_data, TensorType({DT_FLOAT}))
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.DYNAMIC_OUTPUT(out_delta_sparse_weights, TensorType({DT_FLOAT}))
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.DYNAMIC_OUTPUT(out_delta_dense_weights, TensorType({DT_FLOAT}))
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.ATTR(adaptive, Bool, false)
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.ATTR(num_sparse_features, Int, 0)
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.ATTR(num_sparse_features_with_values, Int, 0)
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.ATTR(num_dense_features, Int, 0)
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.ATTR(num_loss_partitions, Int, 1)
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.ATTR(num_inner_iterations, Int, 1)
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.ATTR(loss_type, String, "logistic_loss")
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.ATTR(l1, Float, 0.5)
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.ATTR(l2, Float, 0.5)
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.OP_END_FACTORY_REG(SdcaOptimizerV2)
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} // namespace ge
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#endif // OPS_BUILT_IN_OP_PROTO_INC_SDCA_OPS_H_
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