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Abstract activation and implement softmax

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This commit is contained in:
Matúš Námešný
2024-03-17 18:37:15 +01:00
parent b1621819ca
commit 42d646750b
19 changed files with 370 additions and 205 deletions
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16
test/layers/test_dense.cu
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@@ -3,7 +3,7 @@
#include <iostream>
#include "activations.cuh"
#include "activation.cuh"
#include "dense.cuh"
class DenseLayerTest : public ::testing::Test {
@@ -15,10 +15,10 @@ class DenseLayerTest : public ::testing::Test {
float* weights,
float* biases,
float*& d_input,
CUDANet::Layers::Activation activation
CUDANet::Layers::ActivationType activationType
) {
// Create Dense layer
CUDANet::Layers::Dense denseLayer(inputSize, outputSize, activation);
CUDANet::Layers::Dense denseLayer(inputSize, outputSize, activationType);
// Set weights and biases
denseLayer.setWeights(weights);
@@ -53,7 +53,7 @@ TEST_F(DenseLayerTest, Init) {
int outputSize = j;
CUDANet::Layers::Dense denseLayer(
inputSize, outputSize, CUDANet::Layers::Activation::SIGMOID
inputSize, outputSize, CUDANet::Layers::ActivationType::SIGMOID
);
}
}
@@ -74,7 +74,7 @@ TEST_F(DenseLayerTest, setWeights) {
// clang-format on
CUDANet::Layers::Dense denseLayer(
inputSize, outputSize, CUDANet::Layers::Activation::SIGMOID
inputSize, outputSize, CUDANet::Layers::ActivationType::SIGMOID
);
denseLayer.setWeights(weights.data());
@@ -101,7 +101,7 @@ TEST_F(DenseLayerTest, ForwardUnitWeightMatrixLinear) {
CUDANet::Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights.data(), biases.data(), d_input,
CUDANet::Layers::Activation::NONE
CUDANet::Layers::ActivationType::NONE
);
d_output = denseLayer.forward(d_input);
@@ -142,7 +142,7 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixRelu) {
CUDANet::Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights.data(), biases.data(), d_input,
CUDANet::Layers::Activation::RELU
CUDANet::Layers::ActivationType::RELU
);
d_output = denseLayer.forward(d_input);
@@ -187,7 +187,7 @@ TEST_F(DenseLayerTest, ForwardRandomWeightMatrixSigmoid) {
CUDANet::Layers::Dense denseLayer = commonTestSetup(
inputSize, outputSize, input, weights.data(), biases.data(), d_input,
CUDANet::Layers::Activation::SIGMOID
CUDANet::Layers::ActivationType::SIGMOID
);
d_output = denseLayer.forward(d_input);