Added Basic Autograd Example and Linter

Author: prabhuomkar

CMakeLists.txt

@@ -9,11 +9,11 @@ target_link_libraries(pytorch-cpp "${TORCH_LIBRARIES}")
 
 add_executable(pytorch_basics tutorials/basics/pytorch_basics/main.cpp)
 target_link_libraries(pytorch_basics "${TORCH_LIBRARIES}")
-add_executable(linear_regression tutorials/basics/linear_regression/main.cpp)
-target_link_libraries(linear_regression "${TORCH_LIBRARIES}")
-add_executable(logistic_regression tutorials/basics/logistic_regression/main.cpp)
-target_link_libraries(logistic_regression "${TORCH_LIBRARIES}")
-add_executable(feedforward_neural_network tutorials/basics/feedforward_neural_network/main.cpp)
-target_link_libraries(feedforward_neural_network "${TORCH_LIBRARIES}")
+# add_executable(linear_regression tutorials/basics/linear_regression/main.cpp)
+# target_link_libraries(linear_regression "${TORCH_LIBRARIES}")
+# add_executable(logistic_regression tutorials/basics/logistic_regression/main.cpp)
+# target_link_libraries(logistic_regression "${TORCH_LIBRARIES}")
+# add_executable(feedforward_neural_network tutorials/basics/feedforward_neural_network/main.cpp)
+# target_link_libraries(feedforward_neural_network "${TORCH_LIBRARIES}")
 
 set_property(TARGET pytorch-cpp PROPERTY CXX_STANDARD 11)

main.cpp

@@ -1,6 +1,7 @@
+// Copyright 2019 Omkar Prabhu
 #include <torch/torch.h>
 #include <iostream>
 
 int main() {
   std::cout << "Welcome to PyTorch Tutorial in C++ for Deep Learning Researchers" << std::endl;
-}
+}

scripts.sh

@@ -14,9 +14,17 @@ function build() {
 	make
 }
 
+function lint() {
+	cpplint --linelength=120 main.cpp tutorials/*/*/**
+}
+
 if [ $1 = "install" ]
 then
 	install
-else
+elif [ $1 = "build" ]
+then
 	build
+elif [ $1 = "lint" ]
+then
+	lint
 fi

tutorials/basics/feedforward_neural_network/main.cpp

@@ -1,6 +1,7 @@
+// Copyright 2019 Omkar Prabhu
 #include <torch/torch.h>
 #include <iostream>
 
 int main() {
   std::cout << "FeedForward Neural Network" << std::endl;
-}
+}

tutorials/basics/linear_regression/main.cpp

@@ -1,6 +1,7 @@
+// Copyright 2019 Omkar Prabhu
 #include <torch/torch.h>
 #include <iostream>
 
 int main() {
   std::cout << "Linear Regression" << std::endl;
-}
+}

tutorials/basics/logistic_regression/main.cpp

@@ -1,6 +1,7 @@
+// Copyright 2019 Omkar Prabhu
 #include <torch/torch.h>
 #include <iostream>
 
 int main() {
   std::cout << "Logistic Regression" << std::endl;
-}
+}

tutorials/basics/pytorch_basics/main.cpp

@@ -1,6 +1,29 @@
+// Copyright 2019 Omkar Prabhu
 #include <torch/torch.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/autograd/function.h>
 #include <iostream>
 
 int main() {
   std::cout << "PyTorch Basics" << std::endl;
-}
+
+  ///////////////////////////////////////////////////////////
+  //                BASIC AUTOGRAD EXAMPLE 1               //
+  ///////////////////////////////////////////////////////////
+
+  // Create Tensors
+  torch::Tensor x = torch::tensor(1.0, torch::requires_grad());
+  torch::Tensor w = torch::tensor(2.0, torch::requires_grad());
+  torch::Tensor b = torch::tensor(3.0, torch::requires_grad());
+
+  // Build a computational graph
+  auto y = w * x + b;  // y = 2 * x + 3
+
+  // Compute the gradients
+  y.backward();
+
+  // Print out the gradients
+  std::cout << x.grad() << std::endl;  // x.grad() = 2
+  std::cout << w.grad() << std::endl;  // w.grad() = 1
+  std::cout << b.grad() << std::endl;  // b.grad() = 1
+}