LinearRegression.py

import os
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable

img_size = 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# MNIST dataset (images and labels)
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())

# Data loader (input pipeline)
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)
# Linear regression model
model = nn.Linear(img_size, 1)

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)



# Training
total_step = len(train_loader)


for epoch in range(num_epochs):
    for  images, labels in (train_loader):
        # Reshape images to (batch_size, input_size)
        images = images.reshape(-1, 28 * 28)

        # Forward pass
        outputs = model(images)

        loss = criterion(torch.squeeze(outputs), labels.float())


        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()