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| * |
| * 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 |
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| * http://www.apache.org/licenses/LICENSE-2.0 |
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| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
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| * KIND, either express or implied. See the License for the |
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| *************************************************************/ |
| |
| #include "../src/model/layer/softmax.h" |
| #include "gtest/gtest.h" |
| #include <math.h> // exp |
| |
| using singa::Softmax; |
| using singa::Shape; |
| TEST(Softmax, Setup) { |
| Softmax sft; |
| // EXPECT_EQ("Softmax", sft.layer_type()); |
| |
| singa::LayerConf conf; |
| sft.Setup(Shape{3}, conf); |
| } |
| |
| #ifdef USE_CBLAS |
| TEST(Softmax, Forward) { |
| const float x[] = {1.0f, 2.0f, 0.0f, -2.0f, -3.0f, -1.0}; |
| size_t row = 2; |
| size_t col = 3; |
| size_t n = row * col; |
| singa::Tensor in(singa::Shape{row, col}); |
| in.CopyDataFromHostPtr<float>(x, row * col); |
| |
| Softmax sft; |
| singa::LayerConf conf; |
| sft.Setup(Shape{col}, conf); |
| |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| const float* yptr = out.data<float>(); |
| EXPECT_EQ(n, out.Size()); |
| |
| float* sigma = new float[row]; |
| for (size_t i = 0; i < row; i++) |
| sigma[i] = 0.f; |
| for (size_t i = 0; i < n; i++) |
| sigma[i / col] += exp(x[i]); |
| //EXPECT_EQ(0, sigma[1]); |
| for (size_t i = 0; i < row; i++) |
| for (size_t j = 0; j < col; j++) { |
| EXPECT_FLOAT_EQ(yptr[i * col + j], exp(x[i * col + j]) / sigma[i]); |
| } |
| delete[] sigma; |
| } |
| |
| TEST(Softmax, Backward) { |
| const float x[] = {1.0f, 2.0f, 0.0f, -2.0f, -3.0f, -1.0}; |
| size_t n = sizeof(x) / sizeof(float); |
| size_t row = 2; |
| size_t col = 3; |
| singa::Tensor in(singa::Shape{row, col}); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| Softmax sft; |
| singa::LayerConf conf; |
| sft.Setup(Shape{col}, conf); |
| singa::Tensor out = sft.Forward(singa::kTrain, in); |
| const float* yptr = out.data<float>(); |
| |
| const float grad[] = {2.0f, -3.0f, 1.0f, 3.0f, -1.0f, -2.0}; |
| singa::Tensor out_diff(singa::Shape{row, col}); |
| out_diff.CopyDataFromHostPtr<float>(grad, n); |
| const auto in_diff = sft.Backward(singa::kTrain, out_diff); |
| const float* xptr = in_diff.first.data<float>(); |
| |
| float* dx = new float[n]; |
| float* sigma = new float[row]; |
| for (size_t i = 0; i < row; i++) |
| sigma[i] = 0.f; |
| for (size_t i = 0; i < n; i++) |
| sigma[i / col] += grad[i] * yptr[i]; |
| // EXPECT_EQ(0, sigma[0]); |
| // EXPECT_EQ(0, sigma[1]); |
| for (size_t i = 0; i < row; i++) |
| for (size_t j = 0; j < col; j++) |
| dx[i * col + j] = (grad[i * col + j] - sigma[i]) * yptr[i * col +j]; |
| EXPECT_FLOAT_EQ(dx[0], xptr[0]); |
| EXPECT_FLOAT_EQ(dx[4], xptr[4]); |
| EXPECT_FLOAT_EQ(dx[5], xptr[5]); |
| delete[] dx; |
| delete[] sigma; |
| } |
| #endif |