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Last Updated: Mar 27, 2024
Difficulty: Easy

Regression With PyTorch

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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

One of the most fundamental algorithms in machine learning is linear regression. Linear regression creates a straight line between input features (X) and output labels (y).

Each output label is described as a linear function of input features using weights and biases in linear regression. These weights and biases are model parameters that are started at random and then adjusted with each training/learning cycle through the dataset. One epoch is defined as the process of training the model and changing the parameters after a single iteration of training data. We must prepare the model for numerous epochs for the weights and biases to understand the linear relationship between the input characteristics and output labels. Each target label in linear regression is expressed as a weighted sum of input variables plus a bias. The weights and biases are randomly initialized and then modified as needed during the training process.

So now, let's get started with implementation using Pytorch.

Implementation

Importing Libraries

The flame nn modules assist us in creating and training neural networks. So that's something we require.

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

We need them cause we have to do some preprocessing on the dataset we will be using.

Reading Data

df=pd.read_csv("TSLA.csv")

The dataset I am going to use is Celsius to Fahrenheit data which can be found here.

Data preprocessing

X_train = df.iloc[:,0].values
y_train = df.iloc[:,-1].values

Standardizing the data

sc = MinMaxScaler()
sct = MinMaxScaler()
X_train=sc.fit_transform(X_train.reshape(-1,1))
y_train =sct.fit_transform(y_train.reshape(-1,1))

Because the values are so vast and variable, standardize the data.

So, suppose you don't do it for this particular example. In that case, you'll most likely get inf or nan for loss values later on when training the model, indicating that it can't do backpropagation effectively and will result in a flawed model.

Converting Numpy arrays to Tensors

X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1,1)
y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1,1)

We must ensure that X train and y train are both two-dimensional. In tensor, the view, like reshape in numpy, takes care of the 2d item.

Model Building

We can build the model using two ways. The first way is given below:

input_size = 1
output_size = 1
class LinearRegressionModel(torch.nn.Module):

    def __init__(self):
        super(LinearRegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # One in and one out

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

The second way is:

model = nn.Linear(input_size , output_size)

In both cases, we're using nn. Linear generates our initial linear layer, which essentially performs a linear transformation on the data, such as y = w*x for a straight line, where y is the label and x is the feature. W stands for weight. We are pleased with one layer in our data because Celsius and Fahrenheit have a linear relationship. Still, in some circumstances where the relationship is non-linear, we add additional steps to account for the non-linearity, such as adding a sigmoid function.

Loss Function and Optimizer

The loss function in this situation is "mse" or "mean squared error," as we can see. Our goal will be to reduce the loss, which can be accomplished by utilizing an optimizer, such as stochastic gradient descent in this example. SGD requires initial model parameters or weights and a learning rate.

learning_rate = 0.0001

l = nn.MSELoss()

optimizer = torch.optim.SGD(model.parameters(), lr =learning_rate )

Training

num_epochs = 100
for epoch in range(num_epochs):
    #forward feed
    y_pred = model(X_train.requires_grad_())

    #calculate the loss
    loss= l(y_pred, y_train)

    #backward propagation: calculate gradients
    loss.backward()

    #update the weights
    optimizer.step()

    #clear out the gradients from the last step loss.backward()
    optimizer.zero_grad()
    
    print('epoch {}, loss {}'.format(epoch, loss.item()))

forward feed: we're just computing the y pred using some initial weights and feature values in this step.

Loss phase: After the y pred, we need to figure out how much prediction error there was. To do so, we're going to use mse.

backpropagation: gradients are determined in this step.

Steps: The weights have been updated as a result of this step.

zero grad: last but not least, remove the gradients from the previous stage to make place for the new ones.

Visualization

We don't need to keep gradients any longer, so detach them from the tensor with detach(). Let's look at the first 100 data points to see how good the model is.

predicted = model(X_train).detach().numpy()
plt.scatter(X_train.detach().numpy()[:100] , y_train.detach().numpy()[:100])
plt.plot(X_train.detach().numpy()[:100] , predicted[:100] , "red")
plt.xlabel("Celcius")
plt.ylabel("Farenhite")
plt.show()

Notice how the accuracy of the forecasts improves as the number of epochs grows. Other methods for optimizing the network include adjusting the learning rate, weight initialization techniques, etc.

Finally, test the model with a known celsius value to determine if it can correctly predict the Fahrenheit value. Because the values have been transformed, use sc.inverse transform() and sct.inverse transform() to recover the original values.

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FAQs

1. How do you train a regression model in Pytorch?
Six steps are involved in developing a PyTorch neural network for regression:
1. Prepare the data for training and testing.
2. To serve the data in batches, create a Dataset object.
3. Create a neural network and put it into action.
4. The model is evaluated after the network has been trained (the trained network)
5. Save the model and use it to create predictions based on new, previously unknown data.

2. How does gradient descent work in Linear Regression?
The Gradient Descent Algorithm determines the linear regression equation's best m and c values. We'll have the best-fit line equation with these values of m and c, and we'll be ready to make predictions.

3. Is PyTorch capable of auto-initializing weights?
Weight initialization is built into PyTorch and works pretty well, so you won't worry about it. The default initialization of the Conv layer and Linear layer may be viewed. Uniform, normal, constant, kaiming and Xavier are some of the different initialization strategies.

Key Takeaways

Let us brief the article.

Firstly, we saw the definition of linear regression and its use cases. Later, we implemented the linear regression with the help of PyTorch and visualized the training part.

That's all from the article. I hope you all like it.

Do not worry if we do not get this article at first; we have a perfect tutor to help us out.

Happy Learning, Ninjas!

 

Topics covered
1.
Introduction
2.
Implementation
2.1.
Data preprocessing
2.1.1.
Standardizing the data
2.1.2.
Converting Numpy arrays to Tensors
2.2.
Model Building
2.3.
Loss Function and Optimizer
2.4.
Training
2.5.
Visualization
3.
FAQs
4.
Key Takeaways