#!/usr/bin/env python
# coding: utf-8

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get_ipython().run_line_magic('matplotlib', 'inline')


# 
# What is PyTorch?
# ================
# 
# It’s a Python-based scientific computing package targeted at two sets of
# audiences:
# 
# -  A NumPy replacement that can leverage the power of GPUs
# -  a deep learning research platform 
# 
# 
# Tensors
# ---------------
# 
# Tensors are similar to NumPy’s ndarrays, with the added benefit of the use of a GPU to accelerate computation speed.
# 
# 

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import torch


# Construct a 5x3 matrix, uninitialized:
# 
# 

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


# Construct a randomly initialized matrix:
# 
# 

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


# Construct a tensor  from data:

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x = torch.tensor([5.5, 3])
print(x)


# or create a tensor based on an existing tensor. These methods
# will reuse properties of the input tensor:
# 

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# new_* methods create a tensor of the same type:
x = x.new_ones(5, 3)
print(x)

# create a tensor like x and override dtype:
x = torch.randn_like(x, dtype=torch.double) 
print(x)  # result has the same size


# Get its size:
# 
# 

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print(x.size())


# Note that ``torch.Size`` is in fact a tuple, so it supports all tuple operations.
# 
# ### Operators
# 
# **Addition:**
# 

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


# You can also use the torch add function:
# 

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print(torch.add(x, y))


# You also have the option of providing an output tensor as argument:
# 
# 

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result = torch.empty(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)


# <div class="alert alert-info"><h4>Note</h4><p>Any operation that mutates a tensor in-place has an added ``_`` in the name.
#     For example: x.copy_(y), x.t_(), will change x.</p></div>
# 
# You can use standard NumPy-like indexing as we have gotten used to:
# 
# 

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print(x[:, 1])


# Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:
# 
# 

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x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())


# ### Torch documentation
# 
# You can read all about pytorch tensors, including transposing, indexing, slicing, mathematical operations, linear algebra, random numbers, etc.,
# in the [pytorch documentation](https://pytorch.org/docs/stable/torch.html).
# 
# 
# ### NumPy Bridge
# 
# Converting a Torch Tensor to a NumPy array and vice versa is a easy.
# 
# The Torch Tensor and NumPy array will share their underlying memory
# locations, and changing one will change the other.
# 

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


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b = a.numpy()
print(b)


# See how the numpy array changes in value:
# 
# 

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


# All tensors on  CPU except a CharTensor support converting to NumPy and back.
# 
# ### CUDA Tensors
# 
# Tensors can be moved onto any device using the ``.to`` method.
# 
# 

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# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
    device = torch.device("cuda")          # a CUDA device object
    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU
    x = x.to(device)                       # or just use strings ``.to("cuda")``
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))       # ``.to`` can also change dtype together!


# 

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x = torch.tensor(1., requires_grad=True)
w = torch.tensor(2., requires_grad=True)
b = torch.tensor(3., requires_grad=True)


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# Build a computational graph.
y = w * x + b


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y.backward()


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print(x.grad)    # x.grad = 2 
print(w.grad)    # w.grad = 1 
print(b.grad)    # b.grad = 1 
print(y.grad)