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Creating tensors

A tensor is created from a list with torch.tensor().

Creating tensors

Most NumPy functions have a PyTorch counterpart:

torch.zeros(2, 3)              # zeros
torch.ones(4)                  # ones
torch.full((2, 2), 7.0)        # a constant value
torch.arange(0, 10, 2)         # tensor([0, 2, 4, 6, 8])
torch.linspace(0, 1, 5)        # 5 evenly spaced values
torch.eye(3)                   # identity matrix

Sizes can be given as separate arguments (zeros(2, 3)) or as a tuple (zeros((2, 3))).

Random tensors and the seed

Random tensors are used for weight initialisation and shuffling data. torch.manual_seed makes the results reproducible:

torch.manual_seed(0)
a = torch.rand(3)        # uniform on [0, 1)
b = torch.randn(2, 2)    # standard normal
torch.manual_seed(0)
print(torch.equal(a, torch.rand(3)))   # True: same seed, same numbers

The _like versions copy another tensor's shape and type: torch.zeros_like(x).

Practice Tensor factory

Turn torch.arange(6) into a 2×3 matrix and print it with tolist(). Print the sum of torch.full((2, 2), 5) with item(). Then print with torch.equal that two torch.rand(4) tensors produced with seed 0 are equal.

Starter code

model.py
import torch

# buraya yaz

Sample solution

model.py
import torch

print(torch.arange(6).reshape(2, 3).tolist())
print(torch.full((2, 2), 5).sum().item())
torch.manual_seed(0)
a = torch.rand(4)
torch.manual_seed(0)
print(torch.equal(a, torch.rand(4)))

Expected output

[[0, 1, 2], [3, 4, 5]]
20
True

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What does <code>torch.arange(1, 8, 3).tolist()</code> give?

  • [3, 6]
  • [1, 4, 7] Correct answer
  • [1, 4]
  • [1, 3, 5, 7]

Explanation: The end (8) is excluded; it starts at 1 and steps by three.

2 Produce a zero tensor with the same shape as another tensor.

Question 
z = torch.____(x)
Answer 
z = torch.zeros_like(x)

Explanation: _like functions copy the shape and type.

3 Match the functions.

Term Meaning
torch.rand Uniform [0, 1)
torch.randn Standard normal
torch.eye Identity matrix
torch.manual_seed Reproducibility

All lessons in Advanced

1 Neural network basics

  1. What is a neural network?
  2. Neuron
  3. Layers
  4. Activation
  5. Forward propagation
  6. Summary - Neural network

2 PyTorch basics

  1. What is PyTorch?
  2. Tensor
  3. Creating tensors
  4. Tensor properties
  5. GPU support
  6. Summary - PyTorch

3 Tensor operations

  1. Tensor operations
  2. Shape
  3. Dtype
  4. Reshape
  5. Indexing
  6. Summary - Tensor operations

4 Autograd

  1. What is autograd?
  2. requires_grad
  3. backward()
  4. grad
  5. Computational graph
  6. Summary - Autograd

5 Project: Neural network

  1. Preparing tensors
  2. Model definition
  3. Loss computation
  4. Optimizer

6 nn.Module

  1. What is nn.Module?
  2. __init__
  3. forward
  4. nn.Linear
  5. parameters()
  6. Summary - nn.Module

7 Training loop

  1. Training loop
  2. Epoch
  3. Loss
  4. backward()
  5. optimizer.step()
  6. Summary - Training loop

8 CNN

  1. What is CNN?
  2. Conv2d
  3. MaxPool2d
  4. Feature map
  5. Flatten
  6. Summary - CNN

9 RNN

  1. What is RNN?
  2. Hidden state
  3. Sequence
  4. Vanishing gradient
  5. Embedding
  6. Summary - RNN

10 LSTM

  1. What is LSTM?
  2. Gates
  3. Cell state
  4. input_size
  5. hidden_size
  6. Summary - LSTM

11 Project: CNN

  1. Preparing data
  2. CNN model
  3. Loss and optimizer
  4. Training

12 Transfer learning

  1. What is transfer learning?
  2. Pre-trained model
  3. Fine-tuning
  4. Feature extraction
  5. Advantages
  6. Summary - Transfer learning

13 Optimization

  1. What is an optimizer?
  2. SGD
  3. Adam
  4. Learning rate
  5. zero_grad()
  6. Summary - Optimization

14 Advanced final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4