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## Linear Regression - PyTorch Beginner 07

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, 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

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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