CS 372 / CS 477: PyTorch Data Loaders / Training Loop for Mini-Batch Gradient Descent

PyTorch is a popular open-source machine learning library that provides a flexible and powerful platform for building deep learning models. In this course, we will focus on using PyTorch to train neural networks using mini-batch gradient descent. This approach allows us to efficiently train models on large datasets by processing data in small batches rather than in a single pass.

Data Loaders in PyTorch

One of the key components of training deep learning models in PyTorch is the data loader. Data loaders are responsible for loading and batching the training data, making it easier to feed the data into the model during training. PyTorch provides a convenient DataLoader class that allows you to customize how data is loaded and preprocessed.

Training Loop for Mini-Batch Gradient Descent

Once we have our data loader set up, we can start training our model using mini-batch gradient descent. The training loop consists of iterating over the batches of data, computing the loss, backpropagating the gradients, and updating the model parameters. This process is repeated until the model converges to the optimal solution.

Here is a basic outline of a training loop using mini-batch gradient descent in PyTorch:

“`python
for epoch in range(num_epochs):
for inputs, labels in data_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
“`

In this code snippet, we iterate over each epoch and then loop over each batch of data in the data loader. We zero out the gradients, compute the output of the model, calculate the loss, backpropagate the gradients, and update the model parameters using an optimizer such as stochastic gradient descent (SGD) or Adam.

By using mini-batch gradient descent and data loaders in PyTorch, we can efficiently train deep learning models on large datasets and achieve better performance. This course will provide you with the knowledge and skills to leverage these tools effectively in your machine learning projects.