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tensorflow/tensorflow/core/ops/sdca_ops.cc
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/* Copyright 2016 The TensorFlow Authors. All Rights Reserved. | |
Licensed under the Apache License, Version 2.0 (the "License"); | |
you may not use this file except in compliance with the License. | |
You may obtain a copy of the License at | |
http://www.apache.org/licenses/LICENSE-2.0 | |
Unless required by applicable law or agreed to in writing, software | |
distributed under the License is distributed on an "AS IS" BASIS, | |
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
See the License for the specific language governing permissions and | |
limitations under the License. | |
==============================================================================*/ | |
#include "tensorflow/core/framework/common_shape_fns.h" | |
#include "tensorflow/core/framework/op.h" | |
#include "tensorflow/core/framework/shape_inference.h" | |
namespace tensorflow { | |
using shape_inference::InferenceContext; | |
using shape_inference::ShapeHandle; | |
// -------------------------------------------------------------------------- | |
static Status ApplySdcaOptimizerShapeFn(InferenceContext* c) { | |
std::vector<ShapeHandle> sparse_handles; | |
if (c->input("sparse_weights", &sparse_handles).ok()) { | |
TF_RETURN_IF_ERROR( | |
c->set_output("out_delta_sparse_weights", sparse_handles)); | |
} | |
std::vector<ShapeHandle> dense_handles; | |
if (c->input("dense_weights", &dense_handles).ok()) { | |
TF_RETURN_IF_ERROR(c->set_output("out_delta_dense_weights", dense_handles)); | |
} | |
return c->set_output( | |
"out_example_state_data", | |
{c->Matrix(InferenceContext::kUnknownDim, c->MakeDim(4))}); | |
} | |
REGISTER_OP("SdcaOptimizer") | |
.Attr( | |
"loss_type: {'logistic_loss', 'squared_loss', 'hinge_loss'," | |
"'smooth_hinge_loss', 'poisson_loss'}") | |
.Attr("adaptative : bool=false") | |
.Attr("num_sparse_features: int >= 0") | |
.Attr("num_sparse_features_with_values: int >= 0") | |
.Attr("num_dense_features: int >= 0") | |
.Attr("l1: float") | |
.Attr("l2: float") | |
.Attr("num_loss_partitions: int >= 1") | |
.Attr("num_inner_iterations: int >= 1") | |
.Input("sparse_example_indices: num_sparse_features * int64") | |
.Input("sparse_feature_indices: num_sparse_features * int64") | |
.Input("sparse_feature_values: num_sparse_features_with_values * float") | |
.Input("dense_features: num_dense_features * float") | |
.Input("example_weights: float") | |
.Input("example_labels: float") | |
.Input("sparse_indices: num_sparse_features * int64") | |
.Input("sparse_weights: num_sparse_features * float") | |
.Input("dense_weights: num_dense_features * float") | |
.Input("example_state_data: float") | |
.Output("out_example_state_data: float") | |
.Output("out_delta_sparse_weights: num_sparse_features * float") | |
.Output("out_delta_dense_weights: num_dense_features * float") | |
.SetShapeFn(ApplySdcaOptimizerShapeFn); | |
// The SdcaOptimizerV2 op fixes the "adaptative" typo in v1. | |
REGISTER_OP("SdcaOptimizerV2") | |
.Attr( | |
"loss_type: {'logistic_loss', 'squared_loss', 'hinge_loss'," | |
"'smooth_hinge_loss', 'poisson_loss'}") | |
.Attr("adaptive : bool=false") | |
.Attr("num_sparse_features: int >= 0") | |
.Attr("num_sparse_features_with_values: int >= 0") | |
.Attr("num_dense_features: int >= 0") | |
.Attr("l1: float") | |
.Attr("l2: float") | |
.Attr("num_loss_partitions: int >= 1") | |
.Attr("num_inner_iterations: int >= 1") | |
.Input("sparse_example_indices: num_sparse_features * int64") | |
.Input("sparse_feature_indices: num_sparse_features * int64") | |
.Input("sparse_feature_values: num_sparse_features_with_values * float") | |
.Input("dense_features: num_dense_features * float") | |
.Input("example_weights: float") | |
.Input("example_labels: float") | |
.Input("sparse_indices: num_sparse_features * int64") | |
.Input("sparse_weights: num_sparse_features * float") | |
.Input("dense_weights: num_dense_features * float") | |
.Input("example_state_data: float") | |
.Output("out_example_state_data: float") | |
.Output("out_delta_sparse_weights: num_sparse_features * float") | |
.Output("out_delta_dense_weights: num_dense_features * float") | |
.SetShapeFn(ApplySdcaOptimizerShapeFn); | |
REGISTER_OP("SdcaShrinkL1") | |
.Attr("num_features: int >= 0") | |
.Attr("l1: float") | |
.Attr("l2: float") | |
.Input("weights: Ref(num_features * float)") | |
.SetShapeFn(shape_inference::UnknownShape); | |
REGISTER_OP("SdcaFprint") | |
.Input("input: string") | |
.Output("output: int64") | |
.SetShapeFn([](InferenceContext* c) { | |
ShapeHandle handle; | |
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &handle)); | |
ShapeHandle output_shape; | |
TF_RETURN_IF_ERROR(c->Concatenate(handle, c->Vector(2), &output_shape)); | |
c->set_output(0, output_shape); | |
return OkStatus(); | |
}); | |
} // namespace tensorflow |