PyTorch is a library that enables deep learning. This library is used by many big tech companies and research centers all over the globe. Amazon, NVIDIA, and Salesforce are a few of them. However, the training loop is where all the magics starts. The training loop provides the structure for extracting the patterns from the data by optimizing the loss function. In this Answer, we’ll implement a simple training loop and see the magic happen.
If we break down the training process of deep learning models, we can see how the following blocks build the entire training loop in PyTorch.
The data is loaded in batches.
The batches are passed to the model.
The model gives its predictions.
The loss function calculates the error.
The weights of the model are updated.
The process is repeated for the defined epochs.
Now, we’ll implement a training loop for a neural network to predict garments. The code snippet below shows a simple training loop in PyTorch:
# epochs refers to number of times to loop for on training datafor i in range(epochs):running_loss = 0.# Loading data in batchesfor data in training_loader:# Initialize the optimizeroptimizer.zero_grad()# Initialize the data for modelinputs, labels = data# Getting the output of the model on dataoutputs = model(inputs)# Computing the lossloss = loss_fn(outputs, labels)# Updating the weightsloss.backward()optimizer.step()
Line 7: Before each iteration, the calculated gradients for the model are reset. This ensures that weights are updated according to the error calculated for each iteration.
Line 9: The batch data is split into inputs and labels.
Line 11: The inputs are passed into the model for predictions.
Line 13: The loss function calculates the error by comparing model predictions with actual labels.
Line 15: The new gradients are calculated based on the model’s parameters to reduce the loss.
Line 16: The weights of the model are updated based on the calculated gradients.
Done! It’s that simple. But we haven’t seen the magic happen yet. In the following widget, click “Run” to see what happens.
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To sum up, PyTorch is a library used for building deep learning models. It provides the user with a looping mechanism to enable the training of models in a step-by-step manner.