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PyTorch Essentials

bookMulti-Step Backpropagation

Like Tensorflow, PyTorch also allows you to build more complex computational graphs involving multiple intermediate tensors.


              
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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)
copy

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:

x = torch.tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]])
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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.

  1. Define a random weight matrix (tensor) W of shape 1x3 initialized with values from a uniform distribution over [0, 1], with gradient tracking enabled.
  2. Create an input matrix (tensor) X based on this list: [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]].
  3. Perform matrix multiplication between W and X to calculate Y.
  4. Compute mean squared error (MSE): loss = mean((Y - Ytarget)2).
  5. Calculate the gradient of the loss (loss) with respect to W using backpropagation.
  6. Print the computed gradient of W.

Lösning

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Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 2
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bookMulti-Step Backpropagation

Like Tensorflow, PyTorch also allows you to build more complex computational graphs involving multiple intermediate tensors.


              
12345678910111213

            
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)
copy

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:

x = torch.tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]])
Uppgift

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.

  1. Define a random weight matrix (tensor) W of shape 1x3 initialized with values from a uniform distribution over [0, 1], with gradient tracking enabled.
  2. Create an input matrix (tensor) X based on this list: [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]].
  3. Perform matrix multiplication between W and X to calculate Y.
  4. Compute mean squared error (MSE): loss = mean((Y - Ytarget)2).
  5. Calculate the gradient of the loss (loss) with respect to W using backpropagation.
  6. Print the computed gradient of W.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 2
single

single

close

Awesome!

Completion rate improved to 5

bookMulti-Step Backpropagation

Svep för att visa menyn

Like Tensorflow, PyTorch also allows you to build more complex computational graphs involving multiple intermediate tensors.


              
12345678910111213

            
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)
copy

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:

x = torch.tensor([[1.0, 2.0, 3.0], [3.0, 2.0, 1.0]])
Uppgift

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.

  1. Define a random weight matrix (tensor) W of shape 1x3 initialized with values from a uniform distribution over [0, 1], with gradient tracking enabled.
  2. Create an input matrix (tensor) X based on this list: [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]].
  3. Perform matrix multiplication between W and X to calculate Y.
  4. Compute mean squared error (MSE): loss = mean((Y - Ytarget)2).
  5. Calculate the gradient of the loss (loss) with respect to W using backpropagation.
  6. Print the computed gradient of W.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

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