Linear Regression - PyTorch Beginner 07
In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far.
Learn all the basics you need to get started with this deep learning framework! In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far:
- Training Pipeline in PyTorch
- Model Design
- Loss and Optimizer
- Automatic Training steps with forward pass, backward pass, and weight updates
All code from this course can be found on GitHub.
Linear Regression in PyTorch¶
import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
# 0) Prepare data
X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)
# cast to float Tensor
X = torch.from_numpy(X_numpy.astype(np.float32))
y = torch.from_numpy(y_numpy.astype(np.float32))
y = y.view(y.shape[0], 1)
n_samples, n_features = X.shape
# 1) Model
# Linear model f = wx + b
input_size = n_features
output_size = 1
model = nn.Linear(input_size, output_size)
# 2) Loss and optimizer
learning_rate = 0.01
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# 3) Training loop
num_epochs = 100
for epoch in range(num_epochs):
# Forward pass and loss
y_predicted = model(X)
loss = criterion(y_predicted, y)
# Backward pass and update
loss.backward()
optimizer.step()
# zero grad before new step
optimizer.zero_grad()
if (epoch+1) % 10 == 0:
print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
# Plot
predicted = model(X).detach().numpy()
plt.plot(X_numpy, y_numpy, 'ro')
plt.plot(X_numpy, predicted, 'b')
plt.show()
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