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Summary - PyTorch

In this group you learned tensor creation, properties, and GPU support.

Summary: PyTorch

In this group:

  • PyTorch: tensors, autograd and nn; code runs line by line.
  • Tensor: an n-dimensional array; back to Python with item() and tolist().
  • from_numpy shares memory, torch.tensor copies.
  • Creation: zeros, ones, arange, rand, randn; manual_seed.
  • shape, dtype (input float32, labels int64), device; .to(device) and .cpu().

Practice Prepare data as tensors

Convert the NumPy arrays X_np (float64) and y_np into tensors: X should be float32 and y int64. Print both dtype values on one line, then X.shape.

Starter code

model.py
import numpy as np
import torch

X_np = np.random.default_rng(0).normal(size=(8, 3))
y_np = np.array([0, 1, 1, 0, 2, 2, 1, 0])

# buraya yaz

Sample solution

model.py
import numpy as np
import torch

X_np = np.random.default_rng(0).normal(size=(8, 3))
y_np = np.array([0, 1, 1, 0, 2, 2, 1, 0])

X = torch.from_numpy(X_np).float()
y = torch.from_numpy(y_np).long()
print(X.dtype, y.dtype)
print(X.shape)

Expected output

torch.float32 torch.int64
torch.Size([8, 3])

Questions and answers

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

1 What should be done to float64 features from NumPy before giving them to the model?

  • Nothing
  • Convert to a list
  • Convert to float32 with .float() Correct answer
  • Convert to integers with .long()

Explanation: Model weights are float32; mismatched types raise an error.

2 Make the labels int64.

Question 
y = torch.from_numpy(y_np).____()
Answer 
y = torch.from_numpy(y_np).long()

Explanation: The cross-entropy loss needs class labels as long (int64).

3 Match the tools.

Term Meaning
torch.from_numpy Shared conversion
torch.manual_seed Seed
.float() float32
.cpu().numpy() Back to NumPy

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