logistic_regression.py

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision

input_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = .001

train_dataset = torchvision.datasets.MNIST(root='../../data',
                                           train=True,
                                           transform=transforms.ToTensor(),
                                           download=True
                                           )

test_dataset = torchvision.datasets.MNIST(root='../../data',
                                          train=False,
                                          transform=transforms.ToTensor())

train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=False)

model = nn.Linear(input_size, num_classes)

criterion = nn.CrossEntropyLoss()

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

total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.reshape(-1, 28 * 28)

        outputs = model(images)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()

        optimizer.step()

        if (i + 1) % 100 == 0:
            print('Epoch: [{}/{}], Step: [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step,
                                                                       loss.item()))

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.reshape(-1, 28*28)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()

    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))