1. What is pytorch and why would you use it?

• pytorch is a python package which makes learning deep neaural networks relatively easy and fast

• it’s main “rival” is tensorflow, as pytorch was released by Facebook, but tensorflow by Google

More on pytorch you will find in this post.

2. “Hello world” example

inspired by this article

using pytorch version x.xx

Let’s define a simple quadratic function.

import numpy as np
import pandas as pd

theta = 2
x = np.random.rand(10) * 10
y = x ** theta
data = pd.DataFrame(dict(x=x, y=y))

theta is a parameter we’ll be estimating. We will see how close to 2 our optimization algorithm gets us. Let’s begin the estimation!

import torch
from torch.autograd import Variable

def rmse(y, y_hat):
    """Compute root mean squared error"""
    return torch.sqrt(torch.mean((y - y_hat).pow(2).sum()))

def forward(x, e):
    """Forward pass for our fuction"""
    return x.pow(e.repeat(x.size(0)))

# initial settings
learning_rate = 0.00005

x = Variable(torch.FloatTensor(data['x']), requires_grad=False)
y = Variable(torch.FloatTensor(data['y']), requires_grad=False)

theta_hat = Variable(torch.FloatTensor([1]), requires_grad=True)

loss_history = []
theta_history = []

for i in range(0, 600):
    y_hat = forward(x, theta_hat)
    loss = rmse(y, y_hat)
    loss_history.append(loss.data.item())
    loss.backward()

    theta_hat.data -= learning_rate * theta_hat.grad.data
    theta_hat.grad.data.zero_()
    theta_history.append(theta_hat.data.item())

To get the final estimation we use the following command:

print(theta_hat.data)