| /** |
| * 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. |
| */ |
| |
| #include <stack> |
| #include "singa/model/loss.h" |
| |
| namespace singa { |
| |
| Tensor SoftmaxCrossEntropy::Forward(int flag, const Tensor& prediction, |
| const Tensor& target) { |
| CHECK(buf_.empty()) << "Do not call Forward successively for more than twice." |
| << " The calling pattern is [Forward|Evaluate] Backward"; |
| size_t batchsize = 1; |
| if (prediction.nDim() == 2) |
| batchsize = prediction.shape(0); |
| size_t dim = prediction.Size() / batchsize; |
| const Tensor& input = Reshape(prediction, Shape{batchsize, dim}); |
| Tensor prob = SoftMax(input); |
| // LOG(INFO) << "prob: " << prob.L2(); |
| |
| // buffer intermediate data |
| if (flag & kTrain) { |
| buf_.push(prob); |
| buf_.push(target); |
| } |
| Tensor loss(Shape{batchsize}, prob.device(), prob.data_type()); |
| |
| ComputeCrossEntropy(prob, target, &loss); |
| |
| return loss; |
| } |
| |
| Tensor SoftmaxCrossEntropy::Backward() { |
| const Tensor target = buf_.top(); |
| buf_.pop(); |
| Tensor prob = buf_.top(); |
| buf_.pop(); |
| SoftmaxCrossEntropyBwd(target, &prob); |
| return prob; |
| } |
| } // namespace singa |
| |