Contenido del Curso
Neural Networks with PyTorch
Neural Networks with PyTorch
Gradients in PyTorch
Gradients are fundamental in optimization tasks like training neural networks, where they help adjust weights and biases to minimize error. In PyTorch, they are calculated automatically using the autograd module, which tracks operations on tensors and computes derivatives efficiently.
Enabling Gradient Tracking
To enable gradient tracking for a tensor, the requires_grad=True argument is used when creating the tensor. This tells PyTorch to track all operations on the tensor for gradient computation.
import torch # Create a tensor with gradient tracking enabled tensor = torch.tensor(2.0, requires_grad=True) print(tensor)
Building a Computational Graph
PyTorch builds a dynamic computational graph as you perform operations on tensors with requires_grad=True. This graph stores the relationships between tensors and operations, enabling automatic differentiation.
We'll start by defining a rather simple polynomial function:
y = 5x
Our goal is to calculate the derivative with respect to x at x = 2.
import tensor # Define the tensor x = torch.tensor(2.0, requires_grad=True) # Define the function y = 5 * x ** 3 + 2 * x ** 2 + 4 * x + 8 print(f"Function output: {y}")
The visualization of this computational graph created using the PyTorchViz library may appear somewhat complex, but it effectively conveys the key idea behind it:
Calculating Gradients
To compute the gradient, the backward() method should be called on the output tensor. This computes the derivative of the function with respect to the input tensor.
The computed gradient itself can then be accessed with the .grad attribute.
import tensor x = torch.tensor(2.0, requires_grad=True) y = 5 * x ** 3 + 2 * x ** 2 + 4 * x + 8 # Perform backpropagation y.backward() # Print the gradient of x grad = x.grad print(f"Gradient of x: {grad}")
The computed gradient is the derivative of y with respect to x, evaluated at x = 2.
¡Gracias por tus comentarios!
