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Logistic Regression - PyTorch Beginner 08

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.

Logistic Regression in PyTorch

import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# 0) Prepare data
bc = datasets.load_breast_cancer()
X, y =,

n_samples, n_features = X.shape

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)

# scale
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

X_train = torch.from_numpy(X_train.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.float32))

y_train = y_train.view(y_train.shape[0], 1)
y_test = y_test.view(y_test.shape[0], 1)

# 1) Model
# Linear model f = wx + b , sigmoid at the end
class Model(nn.Module):
    def __init__(self, n_input_features):
        super(Model, self).__init__()
        self.linear = nn.Linear(n_input_features, 1)

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

model = Model(n_features)

# 2) Loss and optimizer
num_epochs = 100
learning_rate = 0.01
criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 3) Training loop
for epoch in range(num_epochs):
    # Forward pass and loss
    y_pred = model(X_train)
    loss = criterion(y_pred, y_train)

    # Backward pass and update

    # zero grad before new step

    if (epoch+1) % 10 == 0:
        print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')

with torch.no_grad():
    y_predicted = model(X_test)
    y_predicted_cls = y_predicted.round()
    acc = y_predicted_cls.eq(y_test).sum() / float(y_test.shape[0])
    print(f'accuracy: {acc.item():.4f}')

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