template <typename T>Tensor<T> forward(const Tensor<T>& x) { return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();net->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;template <typename T>Tensor<T> forward(const Tensor<T>& x) { return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();net->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;let mut grad = vec![0.0f32; n];data.par_iter().for_each(|s| { acc.fetch_add(s.w, Ordering::Relaxed);});impl Layer for Dense { fn step(&mut self, lr: f32) {}}let net = Net::builder().depth(12).build()?;let loss = cross_entropy(&logits, &y);println!("epoch {e} loss={loss:.4}");fn forward(&self, x: &Tensor) -> Tensor { relu(&self.w * x + &self.b)}// fold gradients in parallellet mut grad = vec![0.0f32; n];data.par_iter().for_each(|s| { acc.fetch_add(s.w, Ordering::Relaxed);});impl Layer for Dense { fn step(&mut self, lr: f32) {}}let net = Net::builder().depth(12).build()?;let loss = cross_entropy(&logits, &y);println!("epoch {e} loss={loss:.4}");fn forward(&self, x: &Tensor) -> Tensor { relu(&self.w * x + &self.b)}// fold gradients in parallelnet->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;template <typename T>Tensor<T> forward(const Tensor<T>& x) { return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();net->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;template <typename T>Tensor<T> forward(const Tensor<T>& x) { return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();let loss = cross_entropy(&logits, &y);println!("epoch {e} loss={loss:.4}");fn forward(&self, x: &Tensor) -> Tensor { relu(&self.w * x + &self.b)}// fold gradients in parallellet mut grad = vec![0.0f32; n];data.par_iter().for_each(|s| { acc.fetch_add(s.w, Ordering::Relaxed);});impl Layer for Dense { fn step(&mut self, lr: f32) {}}let net = Net::builder().depth(12).build()?;let loss = cross_entropy(&logits, &y);println!("epoch {e} loss={loss:.4}");fn forward(&self, x: &Tensor) -> Tensor { relu(&self.w * x + &self.b)}// fold gradients in parallellet mut grad = vec![0.0f32; n];data.par_iter().for_each(|s| { acc.fetch_add(s.w, Ordering::Relaxed);});impl Layer for Dense { fn step(&mut self, lr: f32) {}}let net = Net::builder().depth(12).build()?; return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();net->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;template <typename T>Tensor<T> forward(const Tensor<T>& x) { return relu(w_ * x + b_);}// backprop through the graphfor (int i = 0; i < n; ++i) grad[i] += lr * delta[i];auto net = std::make_unique<Net>();net->compile(Device::CUDA);double loss = cross_entropy(logits, y);loss.backward();optimizer.step();std::cout << "epoch " << e << "\n";constexpr float kEps = 1e-8f;template <typename T>Tensor<T> forward(const Tensor<T>& x) {Welcome, my name is Ali Hariri Movahed.Known as code by Mov.
I'm a computer scientist based between the USA and Germany.ML researcher, software developer, and builder.