Added Logistic Regression. This closes #2

Author: prabhuomkar

README.md

@@ -2,8 +2,7 @@
 
 --------------------------------------------------------------------------------
 
-This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch.  
-**Python Tutorial**: [https://github.com/yunjey/pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial)
+This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch.
 
 ## Getting Started
 - Fork/Clone and Install

tutorials/basics/linear_regression/main.cpp

@@ -24,6 +24,7 @@ int main() {
   auto criterion = torch::nn::L1Loss();
   auto optimizer = torch::optim::SGD(model->parameters(), torch::optim::SGDOptions(learning_rate));
 
+  // Train the model
   for (int epoch = 0; epoch < num_epochs; epoch++) {
     // Array to tensors
     auto inputs = x_train;

tutorials/basics/logistic_regression/main.cpp

@@ -4,4 +4,79 @@
 
 int main() {
   std::cout << "Logistic Regression" << std::endl;
+
+  // Device
+  torch::DeviceType device_type;
+  if (torch::cuda::is_available()) {
+    std::cout << "CUDA available. Training on GPU." << std::endl;
+    device_type = torch::kCUDA;
+  } else {
+    std::cout << "Training on CPU." << std::endl;
+    device_type = torch::kCPU;
+  }
+  torch::Device device(device_type);
+
+  // Hyper parameters
+  int input_size = 784;
+  int num_classes = 10;
+  int num_epochs = 5;
+  int batch_size = 100;
+  double learning_rate = 0.001;
+
+  // MNIST Dataset (images and labels)
+  auto train_dataset = torch::data::datasets::MNIST("../data")
+                           .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
+                           .map(torch::data::transforms::Stack<>());
+  auto test_dataset = torch::data::datasets::MNIST("../data", torch::data::datasets::MNIST::Mode::kTest)
+                           .map(torch::data::transforms::Normalize<>(0.1307, 0.3081))
+                           .map(torch::data::transforms::Stack<>());
+
+  // Data loader (input pipeline)
+  auto train_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(
+          std::move(train_dataset), batch_size);
+  auto test_loader = torch::data::make_data_loader<torch::data::samplers::SequentialSampler>(
+          std::move(test_dataset), batch_size);
+
+  // Logistic regression model
+  auto model = torch::nn::Linear(input_size, num_classes);
+
+  // Loss and optimizer
+  auto optimizer = torch::optim::SGD(model->parameters(), torch::optim::SGDOptions(learning_rate));
+
+  // Train the model
+  for (int epoch = 0; epoch < num_epochs; epoch++) {
+    int i = 0;
+    for (auto& batch : *train_loader) {
+      auto data = batch.data.to(device), labels = batch.target.to(device);
+
+      // Forward pass
+      auto outputs = model->forward(data);
+      auto loss = torch::nll_loss(outputs, labels);
+
+      // Backward and optimize
+      optimizer.zero_grad();
+      loss.backward();
+      optimizer.step();
+
+      if ((i+1) % 5 == 0) {
+        std::cout << "Epoch [" << (epoch+1) << "/" << num_epochs << "], Batch: "
+          << (i+1) << ", Loss: " << loss.item().toFloat() << std::endl;
+      }
+    }
+  }
+
+  // Test the model
+  torch::NoGradGuard no_grad;
+  int correct = 0;
+  int total = 0;
+  for (const auto& batch : *test_loader) {
+    auto data = batch.data.to(device), labels = batch.target.to(device);
+    auto outputs = model->forward(data);
+    auto predicted = outputs.argmax(1);
+    total += labels.size(0);
+    correct += predicted.eq(labels).sum().template item<int>();
+  }
+
+  std::cout << "Accuracy of the model on the 10000 test images: " <<
+    static_cast<double>(100 * correct / total) << std::endl;
 }