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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 What is PyTorch?
It’s a Python based scientific computing package targeted at two sets of audiences
Step1:  | Data type                | dtype                         |CPU tensor          |GPU tensor               |
|
Step2:  However, they point to the same place in memory...
Step3:  Numpy -> Tensor
Step4:  Speed and Efficiency
Torch is faster than Numpy
Step5:  What about optimizing with some class methods?
Step6:  Tensors have 100+ built-in class methods
including all your favorite numpy convenience methods and a a growing linear algebra library
What about the GPU?
There is a class method call 'to()', that sends a tensor to a specific device. This is new in 0.4.0.
Step7:  a deep learning research platform that provides maximum flexibility and speed
Central to all neural networks in PyTorch is the autograd package. Let’s first briefly visit this, and we will then go to training our first neural network.
The autograd package provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.
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Python Code:
import torch as t
# Tensors
a = t.tensor([1,2,3])
# Can specify type during construction
a = t.tensor([1,2,3], dtype=t.half)
# Can cast to different types once constructed
a
a.double()
a.float()
a.short()
a.long()
Explanation: 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
A replacement for NumPy to use the power of GPUs...
End of explanation
import numpy as np
x = t.Tensor([1,2,3])
x
y = x.numpy()
type(y)
Explanation: | Data type                | dtype                         |CPU tensor          |GPU tensor               |
|:------------------------:|:-----------------------------:|:------------------:|:-----------------------:|
| 32-bit floating point    | torch.float32 or torch.float  | torch.FloatTensor  | torch.cuda.FloatTensor  |
| 64-bit floating point    | torch.float64 or torch.double | torch.DoubleTensor | torch.cuda.DoubleTensor |
| 16-bit floating point    | torch.float16 or torch.half   | torch.HalfTensor   | torch.cuda.HalfTensor   |
| 8-bit integer (unsigned) | torch.uint8                   | torch.ByteTensor   | torch.cuda.ByteTensor   |
| 8-bit integer (signed)   | torch.int8                    | torch.CharTensor   | torch.cuda.CharTensor   |
| 16-bit integer (signed)  | torch.int16 or torch.short    | torch.ShortTensor  | torch.cuda.ShortTensor  |
| 32-bit integer (signed)  | torch.int32 or torch.int      | torch.IntTensor    | torch.cuda.IntTensor    |
| 64-bit integer (signed)  | torch.int64 or torch.long     | torch.LongTensor   | torch.cuda.LongTensor   |
Converting between Tensors and Numpy Arrays...
Tensor -> Numpy
End of explanation
x
y
x += 10
x
y
Explanation: However, they point to the same place in memory...
End of explanation
y = np.array([5,4,3])
type(y)
x = t.from_numpy(y)
type(x)
y
x
y += 10
y
x
Explanation: Numpy -> Tensor
End of explanation
import time
def timer(f, trials=5):
    avg_time = 0
    for i in range(trials):
        start = time.time()
        f()
        stop = time.time()
        avg_time += (stop - start)/trials
    return avg_time
np_arr = np.random.rand(10000,10000)
t_arr = t.rand((10000,10000))
print('type(t_arr) = ', type(t_arr))
print('t_arr.shape = ', t_arr.shape)
print('type(np_arr) = ', type(np_arr))
print('np_arr.shape = ', np_arr.shape)
timer(lambda: t_arr*t_arr)
timer(lambda: np_arr*np_arr)
Explanation: Speed and Efficiency
Torch is faster than Numpy
End of explanation
def exp1():
    y = t_arr * t_arr
y = t.empty(t_arr.shape)
def exp2():
    t.mul(t_arr, t_arr, out=y)
def exp3():
    t_arr.mul_(t_arr)
timer(exp1)
timer(exp2)
timer(exp3)
Explanation: What about optimizing with some class methods?
End of explanation
t_arr.shape
t_arr.device # Should currently be on the cpu
device = t.device('cuda')
t_arr.to(device)
t.cuda.is_available()
Explanation: Tensors have 100+ built-in class methods
including all your favorite numpy convenience methods and a a growing linear algebra library
What about the GPU?
There is a class method call 'to()', that sends a tensor to a specific device. This is new in 0.4.0.
End of explanation
b = t.tensor([2])
b.requires_grad
y=b*b
y.backward()
b.requires_grad = True # or b.requires_grad_()
y=b*b
y.backward()
b.grad
c = b.detach()
y=c*c
y.backward()
b.grad
Explanation: a deep learning research platform that provides maximum flexibility and speed
Central to all neural networks in PyTorch is the autograd package. Let’s first briefly visit this, and we will then go to training our first neural network.
The autograd package provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.
End of explanation
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