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%matplotlib inline

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.



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from __future__ import print_function
import torch

Construct a 5x3 matrix, uninitialized:



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

Construct a randomly initialized matrix



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

Get its size



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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



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

Addition: syntax 2



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print(torch.add(x, y))

Addition: giving an output tensor



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

Addition: in-place



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# 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!



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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 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



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



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b = a.numpy()
print(b)

See how the numpy array changed in value.



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

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



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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.



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