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training_ops.h
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training_ops.h
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/* Copyright 2015 Google Inc. 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.
==============================================================================*/
#ifndef TENSORFLOW_KERNELS_TRAINING_OPS_H_
#define TENSORFLOW_KERNELS_TRAINING_OPS_H_
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor_types.h"
namespace tensorflow {
namespace functor {
// Each training algorithm has a ApplyXYZ functor struct declared in
// this header file. They are specialized for different devices
// (CPUDevice in training_ops.cc or GPUDevice in training_ops_gpu.cc).
template <typename Device, typename T>
struct ApplyGradientDescent {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::ConstScalar alpha,
typename TTypes<T>::ConstFlat delta);
};
template <typename Device, typename T>
struct ApplyAdadelta {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat accum,
typename TTypes<T>::Flat update_accum,
typename TTypes<T>::ConstScalar decay_rate,
typename TTypes<T>::ConstScalar epsilon,
typename TTypes<T>::ConstFlat grad);
};
template <typename Device, typename T>
struct ApplyAdagrad {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat accum,
typename TTypes<T>::ConstScalar lr,
typename TTypes<T>::ConstFlat grad);
};
template <typename Device, typename T>
struct ApplyMomentum {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat accum,
typename TTypes<T>::ConstScalar lr,
typename TTypes<T>::ConstFlat grad,
typename TTypes<T>::ConstScalar momentum);
};
template <typename Device, typename T>
struct ApplyAdam {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat m, typename TTypes<T>::Flat v,
typename TTypes<T>::ConstScalar beta1_power,
typename TTypes<T>::ConstScalar beta2_power,
typename TTypes<T>::ConstScalar lr,
typename TTypes<T>::ConstScalar beta1,
typename TTypes<T>::ConstScalar beta2,
typename TTypes<T>::ConstScalar epsilon,
typename TTypes<T>::ConstFlat grad);
};
template <typename Device, typename T>
struct ApplyRMSProp {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat ms, typename TTypes<T>::Flat mom,
typename TTypes<T>::ConstScalar lr,
typename TTypes<T>::ConstScalar rho,
typename TTypes<T>::ConstScalar momentum,
typename TTypes<T>::ConstScalar epsilon,
typename TTypes<T>::ConstFlat grad);
};
} // end namespace functor
} // end namespace tensorflow
#endif // TENSORFLOW_KERNELS_TRAINING_OPS_H_