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329394e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | #pragma once
#include "../layers/layer.hpp"
#include "../layers/dense.hpp"
#include <vector>
#include <memory>
namespace newnet {
class Sequential {
public:
std::vector<std::unique_ptr<Layer>> layers;
// Add a layer to the network
void add(Layer* layer) {
layers.emplace_back(layer);
}
// Forward pass: input flows through each layer in order
// Layer 0 output → Layer 1 input → Layer 1 output → Layer 2 input → ...
Tensor forward(const Tensor& input) {
Tensor current = input;
for (auto& layer : layers) {
current = layer->forward(current);
}
return current;
}
// Backward pass: gradient flows through each layer in REVERSE order
// This is the chain rule: dL/dx1 = dL/dx3 * dx3/dx2 * dx2/dx1
void backward(const Tensor& grad_output) {
Tensor current_grad = grad_output;
for (int i = (int)layers.size() - 1; i >= 0; i--) {
current_grad = layers[i]->backward(current_grad);
}
}
// Collect all learnable parameters from all layers
std::vector<Tensor*> parameters() {
std::vector<Tensor*> params;
for (auto& layer : layers) {
auto layer_params = layer->parameters();
params.insert(params.end(), layer_params.begin(), layer_params.end());
}
return params;
}
};
} // namespace newnet
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