.. _sphx_glr_beginner_blitz_tensor_tutorial.py:


What is PyTorch?
================

It’s a Python based scientific computing package targeted at two sets of
audiences:

-  A replacement for numpy to use the power of GPUs
-  a deep learning research platform that provides maximum flexibility
   and speed

Getting Started
---------------

Tensors
^^^^^^^

Tensors are similar to numpy’s ndarrays, with the addition being that
Tensors can also be used on a GPU to accelerate computing.



.. code-block:: python


    from __future__ import print_function
    import torch


Construct a 5x3 matrix, uninitialized:



.. code-block:: python


    x = torch.Tensor(5, 3)
    print(x)


Construct a randomly initialized matrix



.. code-block:: python


    x = torch.rand(5, 3)
    print(x)


Get its size



.. code-block:: python


    print(x.size())


.. note::
    ``torch.Size`` is in fact a tuple, so it supports the same operations

Operations
^^^^^^^^^^
There are multiple syntaxes for operations. Let's see addition as an example

Addition: syntax 1



.. code-block:: python

    y = torch.rand(5, 3)
    print(x + y)


Addition: syntax 2



.. code-block:: python


    print(torch.add(x, y))


Addition: giving an output tensor



.. code-block:: python

    result = torch.Tensor(5, 3)
    torch.add(x, y, out=result)
    print(result)


Addition: in-place



.. code-block:: python


    # adds x to y
    y.add_(x)
    print(y)


.. note::
    Any operation that mutates a tensor in-place is post-fixed with an ``_``
    For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.

You can use standard numpy-like indexing with all bells and whistles!



.. code-block:: python


    print(x[:, 1])


**Read later:**


  100+ Tensor operations, including transposing, indexing, slicing,
  mathematical operations, linear algebra, random numbers, etc are described
  `here <http://pytorch.org/docs/torch>`_

Numpy Bridge
------------

Converting a torch Tensor to a numpy array and vice versa is a breeze.

The torch Tensor and numpy array will share their underlying memory
locations, and changing one will change the other.

Converting torch Tensor to numpy Array
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



.. code-block:: python


    a = torch.ones(5)
    print(a)



.. code-block:: python



    b = a.numpy()
    print(b)


See how the numpy array changed in value.



.. code-block:: python


    a.add_(1)
    print(a)
    print(b)


Converting numpy Array to torch Tensor
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
See how changing the np array changed the torch Tensor automatically



.. code-block:: python


    import numpy as np
    a = np.ones(5)
    b = torch.from_numpy(a)
    np.add(a, 1, out=a)
    print(a)
    print(b)


All the Tensors on the CPU except a CharTensor support converting to
NumPy and back.

CUDA Tensors
------------

Tensors can be moved onto GPU using the ``.cuda`` function.



.. code-block:: python


    # let us run this cell only if CUDA is available
    if torch.cuda.is_available():
        x = x.cuda()
        y = y.cuda()
        x + y

**Total running time of the script:** ( 0 minutes  0.000 seconds)



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