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WIP Refactor Layer and Activation classes

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This commit is contained in:
Matúš Námešný
2025-11-18 19:10:18 +01:00
parent 6340b27055
commit 24606491a3
5 changed files with 74 additions and 41 deletions
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8
src/backends/tensor.cpp
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@@ -1,10 +1,10 @@
#include "backend/tensor.hpp"
#include <stdexcept>
using namespace CUDANet::Backend;
#include "tensor.hpp"
Tensor::Tensor(Shape shape, DType dtype, IBackend* backend)
using namespace CUDANet;
Tensor::Tensor(Shape shape, DType dtype, Backend* backend)
: shape(shape), dtype(dtype), backend(backend), d_ptr(nullptr) {
// Count total elements
size_t count = 1;

46
src/layers/activation.cpp
View File

@@ -1,22 +1,28 @@
#include <format>
#include <stdexcept>
#include <vector>
#include "activation.hpp"
#include "backend/tensor.hpp"
#include "tensor.hpp"
using namespace CUDANet::Layers;
Activation::Activation(CUDANet::Backend::IBackend* backend, ActivationType activation, const int length)
: backend(backend), activationType(activation), length(length) {
Activation::Activation(CUDANet::Backend* backend, ActivationType activation, const CUDANet::Shape &shape)
: backend(backend), activationType(activation), shape(shape) {
if (shape.size() != 1) {
throw std::runtime_error(std::format("Invalid shape. Expected [1], got {}", shape));
}
auto length = shape[0];
if (activationType == SOFTMAX) {
softmax_sum = CUDANet::Backend::Tensor({static_cast<size_t>(length)}, CUDANet::Backend::DType::FLOAT32, backend);
tensor_max = CUDANet::Backend::Tensor({static_cast<size_t>(length)}, CUDANet::Backend::DType::FLOAT32, backend);
softmax_sum = CUDANet::Tensor({static_cast<size_t>(length)}, CUDANet::DType::FLOAT32, backend);
tensor_max = CUDANet::Tensor({static_cast<size_t>(length)}, CUDANet::DType::FLOAT32, backend);
}
}
void Activation::activate(CUDANet::Backend::Tensor input) {
CUDANet::Tensor& Activation::forward(CUDANet::Tensor &input) {
switch (activationType)
{
case ActivationType::SIGMOID:
@@ -31,4 +37,30 @@ void Activation::activate(CUDANet::Backend::Tensor input) {
default:
break;
}
}
return input;
}
CUDANet::Shape Activation::input_shape() {
return shape;
}
CUDANet::Shape Activation::output_shape() {
return shape;
}
size_t Activation::input_size() {
return shape[0];
}
size_t Activation::output_size() {
return shape[0];
}
void Activation::set_weights(CUDANet::Tensor &input) {}
CUDANet::Tensor& Activation::get_weights() {}
void Activation::set_biases(CUDANet::Tensor &input) {}
CUDANet::Tensor& Activation::get_biases() {}