| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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 SRC_MODEL_OPTIMIZER_ADAGRAD_H_ |
| #define SRC_MODEL_OPTIMIZER_ADAGRAD_H_ |
| #include "singa/model/optimizer.h" |
| #include <functional> |
| namespace singa { |
| |
| void AdaGrad::Setup(const OptimizerConf& conf) { delta_ = conf.delta(); } |
| |
| // history += grad*grad; |
| // value = value - lr*grad/sqrt(history+delta) |
| void AdaGrad::Apply(int step, float lr, const string& name, const Tensor& grad, |
| Tensor& value) { |
| if (history_gradient_.find(name) == history_gradient_.end()) { |
| history_gradient_[name].ResetLike(value); |
| history_gradient_[name].SetValue(0.0f); |
| } |
| Tensor& history = history_gradient_[name]; |
| Tensor tmp = Square(grad); |
| history += tmp; |
| Add(history, delta_, &tmp); |
| Sqrt(tmp, &tmp); |
| Div(grad, tmp, &tmp); |
| Axpy(-lr, tmp, &value); |
| } |
| } // namespace singa |
| #endif // SRC_MODEL_OPTIMIZER_ADAGRAD_H_ |