提交 f3df1054 编写于 作者: Z zchen0211

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

......@@ -184,7 +184,7 @@ public:
}
void backward(const UpdateCallback& callback) override {
if (biases_) {
if (biases_ && biases_->getWGrad()) {
backwardActivation();
biases_->getWGrad()->collectBias(*getOutputGrad(), 1);
biases_->getParameterPtr()->incUpdate(callback);
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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
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
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. */
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. */
#pragma once
#include "paddle/framework/eigen.h"
......@@ -63,7 +63,7 @@ class RowwiseAddGradKernel : public framework::OpKernel {
// https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
// colwise add
Eigen::array<int, 1> dims{{1}}; /* dimension to reduce */
Eigen::array<int, 1> dims{{0}}; /* dimension to reduce */
EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
}
};
......
......@@ -20,7 +20,7 @@ class RowwiseAddGradOpTest(GradientChecker):
def test_rowwise_add(self):
op = create_op("rowwise_add")
inputs = {
"X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"),
"X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
"b": np.random.uniform(0.1, 1, [10]).astype("float32")
}
self.check_grad(op, inputs, set(["X", "b"]), "Out")
......
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