EigenFlow

This project was inspired by Deep Learning From Scratch: Theory and Implementation blog post by Daniel Sabinasz. This project implements an API to create computational graphs and neural nets using Eigen libraray in C++ from scratch.

Currently, it only includes a gradient descent optimizer to minimize any user defined loss function. It currently uses dynamicly sized matrices everywhere. However, for large matrices the overhead might be reasonable and still efficient according to Eigen library documentation.

This project was created with educational purposes in mind and does not offer the most efficient way of implementing neural nets. It is best suited for people who are begineers in Machine Learning and Deep Learning. Re-implementation of different algorithms from scratch will help you increase your understanding.

Dependencies for Running

cmake >= 2.8
- All OSes: click here for installation instructions
make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
gcc/g++ >= 5.4
- Linux: Install build -essentials sudo apt-get install build-essential
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
Eigen3 >= 3.3
- All Oses: clone the [git repository] (https://gitlab.com/libeigen/eigen.git) download and extract Eigen's source code.
- build using cmake and make install
- The directory in which you placed Eigen's source code must be in the include path
- Or use the -I option with GCC to achieve: g++ -I /path/to/eigen/ my_program.cpp -o my_program

Basic Build Instructions

Clone this repo.
Make a build directory in the top level directory: mkdir build && cd build
Compile: cmake .. && make
Run it: ./nn.

File Structure

Classes are template, but they are separated into ".h" and ".tpp" files to increase code readability. Relevant .tpp files are included at the end of each class declaration.

.
├── CMakeLists.txt
├── LICENSE
├── README.md
├── data
│   ├── dev
│   └── test
├── image
│   ├── out1.gif
│   └── out2.gif
├── include
│   ├── NN.h
│   ├── gradientDescentOptimizer.h
│   ├── graph.h
│   ├── lockingPtr.h
│   ├── node.h
│   ├── operation.h
│   ├── optimization.h
│   └── session.h
└── src
    ├── NN.tpp
    ├── gradientDescentOptimizer.tpp
    ├── graph.tpp
    ├── lockingPtr.tpp
    ├── main.cpp
    ├── node.tpp
    ├── operation.tpp
    └── session.tpp

Class Structure

General information for class structure of the API. For more detailed information about the interface, see the header files.

node.h
- BaseNode:
  - Abstract base class for each node. Base class is implemented so that nodes of different types can be stored in a single container in computational graph
  - Stores pointers to inputs and consumer nodes and provides necessary accessors and mutators
  - Stores relevant informaiton about each node such as node's type and name
  - Provides pure virtual functions for computing inherited nodes values and gradients. Possible design patterns are CRTP, multi dispatching, and a map of pointers to functions. Since it's an abstract class upcasting to derived Node<T> class is safe as long as T is known and its the easiest to implement
- Node<T>:
  - Inherits from BaseNode.
  - Stores output values and gradients with flags and conditional variables to check for data availability
  - Is a template abstract class; does not implement compute() and gradient() methods of the BaseNode
  - Overrides clearGrads() of the base class
- Variable<T>:
  - Inherits from Node<T>
  - Template class for variable nodes with relevant constructor that take value
  - Does not hold any data
  - Implemets the virtual methods of the base clas
- Placeholder<T>:
  - Inherits from Node<T>
  - Template class for constant variable nodes in the graph.
  - Constructor takes the name of the placeholder.
  - Holds no value
operation.h
- Operation<T>:
  - Inherits from Node<T>
  - Abstract base clase for operation
- UnaryOperation<T>:
  - Inherits from Operation<T>
  - Factory constructor that accepts a single pointer to a node
- BinaryOperation<T>:
  - Inherits from Operation<T>
  - Factory constructor that accepts two pointers to nodes
- Add<T,T1,T2>: Performs addition and brodcasting when necessary
- MatMultiply<T,T1,T2>: Performs matrix multiply
- Dot<T,T1,T2>: Performs dot product
- Multiply<T,T1,T2>: Performs element-wise product
  - All inherit from BinaryOperation<T>
  - Have relevant constructors
  - Override Compute() and gradient()
- Negative<T>: Performs element-wise negation
- Log<T>: Performs element-wise log
- Sigmoid<T>: Element-wise sigmoid operation
- Sum<T>: Performs reduce sum on the given axis
  - All inherit from UnaryOperation<T>
  - Have relevant constructors
  - Override Compute() and gradient()
- Minimizer<T>:
  - Inherits from Operation<T>
  - Has relevant move/assignment constructors (copy/assignment c'tor is deleted)
  - Overrides Compute() that performs gradient update
graph.h
- Stores pointers to nodes of the computational graph and ensures their existence thrughout the computation
session.h
- Provides methods to get the nodes in post-ordet traversal and relevant accesor
- Provides a Run method that takes a pointer to the node in the computational graph and runs relevant operations by calling compute() for each node. Also feeds the data to placeholders.
gradientDescentOptimizer.h
- Takes the learning rate and provides methods to perform backward propogation concurrently.
NN.h
- Provides factory methods to create new unary and binary operations with dynamic memory allocation and store them using graph methods
- Provides methods to run the operations by using methods from session
- Provides helper methods to check gradient calculations numerically for all the nodes
lockingPtr.h
- a wrapper class for raw and smart pointers with locking
- locks the object that the pointer points to when deferencing and calling other methods on them

How to Use

Explanation of the example in main.cpp

Include the NN.h in your file:

 #include "../include/NN.h"

Create an alias for the dynamic eigen matrix type

typedef Eigen::Matrix<long double, Eigen::Dynamic, Eigen::Dynamic> matxxf;

Then initialize a neural network NN class:

NN nn = NN();

Define the number of steps for optimization

int const STEPS = 10000;

Use nn.spaceholders for constants and nn.variables for learnable variables, see the main.cpp for example:

(Use long double if you want to check the gradient calculations numerically.)

// matrix of scalar 1
Eigen::Matrix<long double, 1, 1> One;
One << 1; 

// cast to dynamic matrix
matxxf n = One;
BaseNode *one = nn.placeholder<long double>("one");

// Bias (m*1)
Eigen::Matrix<long double, 1, 1> B;
B << 0.1;
BaseNode *b = nn.variable<long double>(std::move(B));
    
// Wieghts (nh*nx)
Eigen::Matrix<long double, 1, 2> W;
W << 0.1, 0.2;
BaseNode *w = nn.variable<long double>(std::move(W));

// Training Data (nx*m)
Eigen::Matrix<long double, 2, 1> X;
X << 3, 2;
// cast to dynamic matrix
matxxf x = X;

// Labels (1*m)
Eigen::Matrix<long double, 1, 1> Y;
Y << 1;
// cast to dynamic matrix
matxxf yy = Y;
BaseNode *y = nn.placeholder<long double>("Y");

Create the activation function:

// activation unit sigmoid(w^T*x+b) (nh*m)
BaseNode *a = nn.sigmoid<long double>(nn.add<long double>(nn.matmultiply<long double>(w, nn.placeholder<long double>("X")), b));

Create the loss function:

// intermidiate loss function
// create loss function -(y*log(a)+(1-y)*log(1-a))
BaseNode *L = nn.negative<long double>(nn.add<long double>(nn.matmultiply<long double>(y, nn.log<long double>(a)), nn.matmultiply<long double>(nn.add<long double>(one, nn.negative<long double>(y)), nn.log<long double>(nn.add<long double>(one, nn.negative<long double>(a))))));

Create optimization operation:

 // Create gradient descent optimization
    auto opt = GradientDescentOptimizer(0.01).minimize<matxxf>(L);

Create an unordered_map to feed the data for placeholders:

// Create a map to feed data to the placeholders (i.e. "X" = X)
    std::unordered_map<std::string, matxxf *>
        feed = {};
    feed["X"] = &x;
    feed["Y"] = &yy;
    feed["one"] = &n;

Use nn.run to run the operations:

// Run  operation
nn.run<long double>(L, feed);

Create a loop for optimizaiton and run optimization oprations

for (int i = 1; i < STEPS; i++)
    {
        nn.run<long double>(&opt, feed);
        nn.run<long double>(L, feed);
        if (i % 1000 == 0)
        {
            std::cout << "Step " << i << std::endl;
            std::cout << "Activation: " << *(a->getValue<matxxf>()) << std::endl;
            std::cout << "loss: " << *(L->getValue<matxxf>()) << std::endl;
            std::cout << "Weights: " << *(w->getValue<matxxf>()) << std::endl;
            std::cout << "Bias: " << *(b->getValue<matxxf>()) << std::endl;
        }
    }

Use nn.checkAllGradient() to see if the gradient calculations are correct. It compares the gradients with numerically obtained values. For best results make sure the learning rate is set to zero. See the implementaiton for further information:

 /* Check gradients -- Make sure to set learning rate to zero befor checking!! -- */
    nn.checkAllGradient(L, feed);

Expected Output

References and Useful Resources

1 - To learn about the matrix calculus required for Neural Nets see explaned.ai by Terence Parr and Jeremy Howard

2 - To learn more about Deep Learning read the book "Deep Learning" by Ian Goodfellow and Yoshua Bengio and Aaron Courville

3 - Deep Learning From Scratch: Theory and Implementation blog post by Daniel Sabinasz

4 - To learn more about Eigen library see the documentation

5 - To learn the basics of C++ and get started see Udacity Cpp ND

License

farzingkh/EigenFlow

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