Stochastic Gradient Descent and zero_grad in PyTorch

Stochastic Gradient Descent (SGD) is a popular optimization algorithm used in machine learning for training neural networks.

In the context of PyTorch, a popular deep learning framework, the zero_grad() function is used in conjunction with SGD to clear the gradients of the parameters before the next iteration of the optimization process. This is an important step to ensure that the gradients are not accumulated from previous iterations, which could lead to incorrect updates of the network weights.

What is Stochastic Gradient Descent?

Stochastic Gradient Descent is a variant of gradient descent, which is an optimization algorithm used to minimize the loss function of a neural network. In SGD, rather than computing the gradient of the entire dataset at each iteration, a random subset of data points is used to compute the gradient. This approach can speed up the training process, especially for large datasets.

Why use zero_grad in PyTorch?

In PyTorch, when using the SGD optimizer to update the parameters of a neural network, the zero_grad() function is called on the optimizer before the backward() function is called on the loss. This clears the gradients of the network parameters, ensuring that the gradients are not accumulated from previous iterations.

Without calling zero_grad(), the gradients from the previous iteration would be added to the gradients of the current iteration, leading to incorrect updates of the network weights. This could result in slower convergence or even divergence of the optimization process.

Example of using zero_grad in PyTorch

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

# Define a neural network
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc = nn.Linear(10, 1)

model = MyModel()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Forward pass
inputs = torch.randn(1, 10)
output = model(inputs)

# Compute the loss
loss = criterion(output, torch.tensor([[1.0]]))

# Zero the gradients
optimizer.zero_grad()

# Backward pass
loss.backward()

# Update the parameters
optimizer.step()
    

In the above example, the zero_grad() function is called on the optimizer before the backward() function is called on the loss. This ensures that the gradients are cleared before the backward pass, preventing accumulation of gradients from previous iterations.

In conclusion, zero_grad() is an important function in PyTorch when using SGD for training neural networks. It helps to ensure that the gradients are not accumulated and allows for correct updates of the network parameters, leading to better convergence of the optimization process.