The Hitchhikers's Guide to PyTorch for Data Scientists

The Hitchhikers's Guide to PyTorch for Data Scientists

I will try to ease some of the pain with PyTorch for starters and go through some of the most important classes and modules that you will require while creating any Neural Network with Pytorch. I will also talk about the high customizability PyTorch provides and will talk about custom Layers, Datasets, Dataloaders, and Loss functions.

PyTorch has sort of became one of the de facto standard for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.

I remember picking PyTorch up only after some extensive experimentation a couple of years back. To tell you the truth, it took me a lot of time to pick it up but am I glad that I moved from Keras to PyTorch . With its high customizability and pythonic syntax, PyTorch is just a joy to work with, and I would recommend it to anyone who wants to do some heavy lifting with Deep Learning.

So, in this PyTorch guide, I will try to ease some of the pain with PyTorch for starters and go through some of the most important classes and modules that you will require while creating any Neural Network with Pytorch.

But, that is not to say that this is aimed at beginners only as I will also talk about the high customizability PyTorch provides and will talk about custom Layers, Datasets, Dataloaders, and Loss functions.

So let’s get some coffee ☕ ️and start it up.

Tensors

Tensors are the basic building blocks in PyTorch and put very simply, they are NumPy arrays but on GPU. In this part, I will list down some of the most used operations we can use while working with Tensors. This is by no means an exhaustive list of operations you can do with Tensors, but it is helpful to understand what tensors are before going towards the more exciting parts.

1. Create a Tensor

We can create a PyTorch tensor in multiple ways. This includes converting to tensor from a NumPy array. Below is just a small gist with some examples to start with, but you can do a whole lot of more things with tensors just like you can do with NumPy arrays.

## Using torch.Tensor
t = torch.Tensor([[1,2,3],[3,4,5]])
print(f"Created Tensor Using torch.Tensor:\n{t}")

## Using torch.randn
t = torch.randn(3, 5)
print(f"Created Tensor Using torch.randn:\n{t}")

## using torch.[ones|zeros](*size)
t = torch.ones(3, 5)
print(f"Created Tensor Using torch.ones:\n{t}")
t = torch.zeros(3, 5)
print(f"Created Tensor Using torch.zeros:\n{t}")

## using torch.randint - a tensor of size 4,5 with entries between 0 and 10(excluded)
t = torch.randint(low = 0,high = 10,size = (4,5))
print(f"Created Tensor Using torch.randint:\n{t}")

## Using from_numpy to convert from Numpy Array to Tensor
a = np.array([[1,2,3],[3,4,5]])
t = torch.from_numpy(a)
print(f"Convert to Tensor From Numpy Array:\n{t}")

## Using .numpy() to convert from Tensor to Numpy array
t = t.numpy()
print(f"Convert to Numpy Array From Tensor:\n{t}")

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