Try to implement linear regression using Pytorch with Google Colaboratory

For beginners! It is assumed that you have a basic knowledge of deep learning. Let's implement linear regression using Pytorch, a machine learning library. It's easy, so it's a great introduction to machine learning.

What is Pytorch?

An open source machine learning library for Python, initially developed by Facebook's Artificial Intelligence Research Group. The reading is pie torch.

It has become popular in recent years among deep learning libraries. It is recommended because it is very easy to describe.

What is Google Colaboratory?

It is a python execution environment that can be used for free provided by Google.

GPU can also be used, and the libraries required for machine learning are pre-installed. It is difficult to build a machine learning environment locally, so I will use Google Colaboratory this time.

See below for how to use it. You will be able to use it in about 1 minute. https://qiita.com/shoji9x9/items/0ff0f6f603df18d631ab

Try to implement linear regression

Install the required libraries.

import torch
from torch import nn
import matplotlib.pyplot as plt
import numpy as np

Let's create a random distribution model and plot it. Linear regression on this figure.

x = torch.randn(100, 1) * 10
y = x + torch.randn(100, 1) * 3
plt.plot(x.numpy(), y.numpy(), "o")
plt.ylabel("y")
plt.xlabel("x")

When I run it, I think the following figure is output.

Defines a model for linear regression. It inherits from nn.Module and defines a model in init that specifies the size of inputs and outputs.

class LR(nn.Module):
  def __init__(self, input_size, output_size):
    super().__init__()
    self.linear = nn.Linear(input_size, output_size)
  def forward(self, x):
    pred = self.linear(x)
    return pred

Fix the seed of the random number. Create an instance of the linear regression model.

torch.manual_seed(1)
model = LR(1, 1)

Define a function to retrieve model parameters.

[w, b] = model.parameters()
def get_params():
  return (w[0][0].item(), b[0].item())

Define the function to plot. It takes a parameter from the model and defines y1.

def plot_fit(title):
  plt_title = title
  w1, b1 = get_params()
  x1 = np.array([-30, 30])
  y1 = w1*x1 + b1
  plt.plot(x1, y1, "r")
  plt.scatter(x, y)
  plt.show()

Let's plot the figure before learning. You can see that the red line is off because it is before learning.

plot_fit("initial Model")

Now it's time to prepare for learning. The loss function is defined as the root mean square error, and the learning method is defined as the stochastic gradient descent method. The learning rate is 0.01.

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

Let's learn! The epoch is set to 100. Record the loss.

epochs = 100
losses = []
for i in range(epochs):
  y_pred = model.forward(x)
  loss = criterion(y_pred, y)
  print("epoch:", i, "loss:", loss.item())
  
  losses.append(loss)
  optimizer.zero_grad()
  loss.backward()
  optimizer.step()

Let's take a look at the learning process.

plt.plot(range(epochs), losses)
plt.ylabel("Loss")
plt.xlabel("epoch")

You can see that you are learning. Let's plot the figure after learning.

plot_fit("Trained Model")

You can see that you are learning properly.

that's all. Thank you for your hard work!

I tried to learn linear regression using pytorch. With Pytorch and Google Colaboratory, it's pretty easy to experience machine learning, so give it a try! !!