A C++ Tensor library that can be used to work with machine learning or deep learning project.

Build your own neural network models with this library.

Installing <tt>Tensor-Array</tt>

You need to clone repository by using Git

You need to install Tensor-Array with CMake

git clone https://github.com/Tensor-Array/Tensor-Array.git
cd Tensor-Array
mkdir build
cd build
cmake ..
cmake --build .
cmake --install .
cd ..

Why this repository named <tt>Tensor-Array</tt>

We created a template struct that named TensorArray. That struct is a multi-dimensional array wrapper.

 {C++}
#include <tensor-array/core/tensorbase.hh>
 
using namespace tensor_array::value;
 
int main()
{
  TensorArray<float, 4, 4> example_tensor_array =
  {{
    {{ 1, 2, 3, 4 }},
    {{ 5, 6, 7, 8 }},
    {{ 9, 10, 11, 12 }},
    {{ 13, 14, 15, 16 }},
  }};
  return 0;
}

That code is wrapping for:

 {C++}
int main()
{
  float example_tensor_array[4][4] =
  {
    { 1, 2, 3, 4 },
    { 5, 6, 7, 8 },
    { 9, 10, 11, 12 },
    { 13, 14, 15, 16 },
  };
  return 0;
}

The <tt>Tensor</tt> class.

The Tensor class is a storage that store value and calculate the tensor.

The Tensor::calc_grad() method can do automatic differentiation.

The Tensor::get_grad() method can get the gradient after call Tensor::calc_grad().

 {C++}
#include <iostream>
#include <tensor-array/core/tensor.hh>
 
using namespace std;
using namespace tensor_array::value;
 
int main()
{
  TensorArray<float, 4, 4> example_tensor_array =
  {{
    {{ 1, 2, 3, 4 }},
    {{ 5, 6, 7, 8 }},
    {{ 9, 10, 11, 12 }},
    {{ 13, 14, 15, 16 }},
  }};
  TensorArray<float> example_tensor_array_scalar = {100};
  Tensor example_tensor_1(example_tensor_array);
  Tensor example_tensor_2(example_tensor_array_scalar);
  Tensor example_tensor_sum = example_tensor_1 + example_tensor_2;
  cout << example_tensor_sum << endl;
  example_tensor_sum.calc_grad();
  cout << example_tensor_1.get_grad() << endl;
  cout << example_tensor_2.get_grad() << endl;
  return 0;
}