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Learn Challenge: Initializing Model Weights and Biases | PyTorch Basics
PyTorch Essentials
course content

Course Content

PyTorch Essentials

PyTorch Essentials

1. PyTorch Basics
2. Preparing for Neural Networks
3. Neural Networks

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Challenge: Initializing Model Weights and Biases

Task
test

Swipe to begin your solution

You are tasked with creating random tensors to initialize weights and biases for a simple neural network.

  1. Ensure reproducibility by setting a manual seed to an arbitrary number before generating the tensors.
  2. Create a 3x4 tensor filled with random values from a uniform distribution between 0 and 1 (weights for the first layer).
  3. Create a 1x4 tensor filled with zeros (biases for the first layer).
  4. Create a 4x2 tensor with random integers between -5 and 5 (weights for the second layer).

Note

Initializing weights with random integers is not recommended in practice; this approach is used here solely for the purpose of this task.

Solution

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Section 1. Chapter 6
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book
Challenge: Initializing Model Weights and Biases

Task
test

Swipe to begin your solution

You are tasked with creating random tensors to initialize weights and biases for a simple neural network.

  1. Ensure reproducibility by setting a manual seed to an arbitrary number before generating the tensors.
  2. Create a 3x4 tensor filled with random values from a uniform distribution between 0 and 1 (weights for the first layer).
  3. Create a 1x4 tensor filled with zeros (biases for the first layer).
  4. Create a 4x2 tensor with random integers between -5 and 5 (weights for the second layer).

Note

Initializing weights with random integers is not recommended in practice; this approach is used here solely for the purpose of this task.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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