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Summary - Tensor operations

In this group you learned tensor shape, dtype, and reshape operations.

Summary: tensor operations

In this group:

  • Element-wise operations, broadcasting, @; choosing axes with dim; in-place operations with _.
  • Tracking shape, unsqueeze/squeeze.
  • Types: float32 (default), float16/bfloat16 (half), int64 (labels), bool (masks); memory = element size × count.
  • view/reshape, permute, flatten(1).
  • Indexing, argmax, gather, computing accuracy.

Practice Batch processing

Apply a per-row softmax to the (5, 3) logits (torch.softmax(logit, dim=1)) and print the row sums as a list with round(..., 3). Then get each row's most likely class and its probability with max(dim=1); print the classes with tolist().

Starter code

model.py
import torch

logit = torch.tensor([[1., 2., 0.], [3., 0., 0.], [0., 0., 2.], [1., 1., 3.], [0., 4., 1.]])

# buraya yaz

Sample solution

model.py
import torch

logit = torch.tensor([[1., 2., 0.], [3., 0., 0.], [0., 0., 2.], [1., 1., 3.], [0., 4., 1.]])

p = torch.softmax(logit, dim=1)
print([round(v, 3) for v in p.sum(dim=1).tolist()])
olasilik, sinif = p.max(dim=1)
print(sinif.tolist())

Expected output

[1.0, 1.0, 1.0, 1.0, 1.0]
[1, 0, 2, 2, 1]

Questions and answers

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

1 Which operation is needed before giving a CNN output (32, 64, 7, 7) to a linear layer?

  • unsqueeze(0)
  • None
  • Flattening to (32, 3136) with flatten(1) Correct answer
  • permute

Explanation: A linear layer wants (example, feature); 64 × 7 × 7 = 3136.

2 Flatten all axes except the batch dimension.

Question 
d = x.____(1)
Answer 
d = x.flatten(1)

Explanation: flatten(1) keeps the first axis and merges the rest.

3 Match the functions.

Term Meaning
unsqueeze Adds an axis
permute Reorders axes
flatten Flattens
gather Picks by index

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