python-pytorchHow do I use Python and PyTorch to create a model for the MNIST dataset?

To create a model for the MNIST dataset using Python and PyTorch, you will need to:

1. Import the necessary PyTorch modules and other Python libraries:

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

2. Load the MNIST dataset:

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=64, shuffle=True)

3. Define the model architecture:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

4. Instantiate the model and define the loss function and optimizer:

model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

5. Train the model:

for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()

6. Evaluate the model:

test_loss = 0
correct = 0
for data, target in test_loader:
    output = model(data)
    test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
    pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
    correct += pred.eq(target.data.view_as(pred)).cpu().sum()

test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(test_loader.dataset),
    100. * correct / len(test_loader.dataset)))

7. Output:

Test set: Average loss: 0.0445, Accuracy: 9876/10000 (99%)

Helpful links

• PyTorch Tutorials
• MNIST Dataset

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