How can I create a custom model using PyTorch for image classification?

How can I create a custom model using PyTorch for image classification?

To create a custom model in PyTorch for image classification, you can start by defining a neural network architecture using torch.nn.Module. You will need to specify the layers and the forward pass.

What kind of layers should I include in my model?

Common layers for image classification include convolutional layers (nn.Conv2d), pooling layers (nn.MaxPool2d), and fully connected layers (nn.Linear). You can also add activation functions like ReLU.

How do I train the model once it's defined?

You can train the model by defining a loss function (like CrossEntropyLoss) and an optimizer (like Adam). Then, loop through your dataset, perform forward passes, compute the loss, and update the weights.

Can you show me a simple code example?

Sure! Here's a basic example:

import torch
import torch.nn as nn
import torch.optim as optim

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.fc1 = nn.Linear(32 * 14 * 14, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = x.view(-1, 32 * 14 * 14)
        x = self.fc1(x)
        return x

model = SimpleCNN()
optimizer = optim.Adam(model.parameters())
loss_function = nn.CrossEntropyLoss()