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PyTorch Essentials
PyTorch Essentials
Multi-Step Backpropagation
Like Tensorflow, PyTorch also allows you to build more complex computational graphs involving multiple intermediate tensors.
import torch # Create a 2D tensor with gradient tracking x = torch.tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]], requires_grad=True) # Define intermediate layers y = 6 * x + 3 z = 10 * y ** 2 # Compute the mean of the final output output_mean = z.mean() print(f"Output: {output_mean}") # Perform backpropagation output_mean.backward() # Print the gradient of x print("Gradient of x:\n", x.grad)
The gradient of output_mean with respect to x is computed using the chain rule. The result shows how much a small change in each element of x affects output_mean.
Disabling Gradient Tracking
In some cases, you may want to disable gradient tracking to save memory and computation. Since requires_grad=False is the default behavior, you can simply create the tensor without specifying this parameter:
python
Swipe to start coding
You are tasked with building a simple neural network in PyTorch. Your goal is to compute the gradient of the loss with respect to the weight matrix.
- Define a random weight matrix (tensor)
Wof shape1x3initialized with values from a uniform distribution over [0, 1], with gradient tracking enabled. - Create an input matrix (tensor)
Xbased on this list:[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]. - Perform matrix multiplication between
WandXto calculateY. - Compute mean squared error (MSE):
loss = mean((Y - Ytarget)
2 ). - Calculate the gradient of the loss (
loss) with respect toWusing backpropagation. - Print the computed gradient of
W.
Oplossing
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Multi-Step Backpropagation
Like Tensorflow, PyTorch also allows you to build more complex computational graphs involving multiple intermediate tensors.
import torch # Create a 2D tensor with gradient tracking x = torch.tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]], requires_grad=True) # Define intermediate layers y = 6 * x + 3 z = 10 * y ** 2 # Compute the mean of the final output output_mean = z.mean() print(f"Output: {output_mean}") # Perform backpropagation output_mean.backward() # Print the gradient of x print("Gradient of x:\n", x.grad)
The gradient of output_mean with respect to x is computed using the chain rule. The result shows how much a small change in each element of x affects output_mean.
Disabling Gradient Tracking
In some cases, you may want to disable gradient tracking to save memory and computation. Since requires_grad=False is the default behavior, you can simply create the tensor without specifying this parameter:
python
Swipe to start coding
You are tasked with building a simple neural network in PyTorch. Your goal is to compute the gradient of the loss with respect to the weight matrix.
- Define a random weight matrix (tensor)
Wof shape1x3initialized with values from a uniform distribution over [0, 1], with gradient tracking enabled. - Create an input matrix (tensor)
Xbased on this list:[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]. - Perform matrix multiplication between
WandXto calculateY. - Compute mean squared error (MSE):
loss = mean((Y - Ytarget)
2 ). - Calculate the gradient of the loss (
loss) with respect toWusing backpropagation. - Print the computed gradient of
W.
Oplossing
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties