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Summary - nn.Module

In this group you learned nn.Module, __init__, forward, and nn.Linear.

Summary: nn.Module

In this group:

  • Every model is an nn.Module; layers in __init__, computation in forward; called as m(x).
  • super().__init__() is required; nn.ModuleList for lists of layers (a plain list hides the parameters).
  • nn.Sequential for simple chains; torch.nn.functional for parameter-free operations.
  • nn.Linear: weight (out, in), y = xWᵀ + b; initialisation with nn.init.
  • parameters(), named_parameters(), freezing and state_dict.

Practice Your own classifier

Write the class Siniflandirici(girdi, gizli, sinif): two nn.Linear in __init__, a ReLU between them in forward. For Siniflandirici(4, 16, 3), print the output shape for the input torch.zeros(10, 4) and the parameter count on one line.

Starter code

model.py
import torch
from torch import nn

# buraya yaz

Sample solution

model.py
import torch
from torch import nn

class Siniflandirici(nn.Module):
    def __init__(self, girdi, gizli, sinif):
        super().__init__()
        self.l1 = nn.Linear(girdi, gizli)
        self.l2 = nn.Linear(gizli, sinif)

    def forward(self, x):
        return self.l2(torch.relu(self.l1(x)))

m = Siniflandirici(4, 16, 3)
print(m(torch.zeros(10, 4)).shape, sum(p.numel() for p in m.parameters()))

Expected output

torch.Size([10, 3]) 131

Questions and answers

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

1 What is the recommended way to save a model?

  • print(model)
  • Printing the model.parameters() list
  • The whole Python session with pickle
  • torch.save(model.state_dict(), path) Correct answer

Explanation: A state_dict contains only tensors; the model class is rebuilt in code and loaded.

2 Load the saved weights into the model.

Question 
model.____(torch.load("model.pt"))
Answer 
model.load_state_dict(torch.load("model.pt"))

Explanation: load_state_dict copies tensors with matching names.

3 Match the tools.

Term Meaning
nn.Module Base class
nn.Sequential Classless chain
nn.ModuleList Registered list
state_dict Weight dict

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