discuss.md

05/08/2024 1.

class MyDataset(Dataset):

def __init__(self, X, y):
    super().__init__() - THERE IS NO CONSTRUCTOR IN DATASET
    self.features = torch.tensor(X, dtype=torch.float32)
    self.labels = torch.tensor(y, dtype=torch.float32)

2. plt.legend(loc="upper left") instead of plt.legend(loc=2)

3. you can avoid model.train() because there are no layers that perform diffently at training and evaluation stages (like dropout and BatchNorm)

4. you do not need model = model.eval()

5. plot_boundary

26/08/24

1. Best coding practice - do not specify variable you don't use

all_x = [] for x, _ in train_loader: all_x.append(x)

train_std = torch.concat(all_x).std(dim=0) train_mean = torch.concat(all_x).mean(dim=0)

2. explain why the formula has this concrete form

$H(p, q) = -\sum_i p_i \log q_i = -y \log \hat{y} - (1 - y) \log(1 - \hat{y}).$

https://en.wikipedia.org/wiki/Cross-entropy

https://en.wikipedia.org/wiki/Entropy_(information_theory)

3. Do not remember the functions and parameters - read the documentation

4. plt.imshow(np.transpose(torchvision.utils.make_grid(images[:64], padding=4, pad_value=0.5, normalize=True), (1, 2, 0)))

16/09/24

1. name == "main" in python

2. Implications with multiprocessing

   Let’s break down the behavior of Python's multiprocessing on macOS and Windows more clearly, focusing on why not using the if __name__ == "__main__": block can lead to problems.

   Understanding Multiprocessing and Script Execution

   When using multiprocessing in Python (like when you set num_workers > 0 in a DataLoader), the operating system needs to create new processes to handle these additional tasks.

   1. Linux (Fork Method):
   • On Linux, the new processes are created using the fork method.
   • This method duplicates the parent process, including its current state (variables, code execution point, etc.).
   • No re-importing of the script occurs, so the new process doesn't execute the script from the start.
   2. macOS and Windows (Spawn Method):
   • On macOS and Windows, the default method for creating new processes is spawn.
   • This method starts a brand new, fresh Python interpreter process.
   • The new process needs to know what code to run, so it re-imports the script that created it.

   Why the if __name__ == "__main__": Block is Crucial

   When the new interpreter spawns (on macOS/Windows), it starts by re-importing your script to understand what it should execute. Here’s what happens step-by-step if your script doesn't use if __name__ == "__main__"::

   1. Script Import: When the new process starts, it imports your script, running from the top.

   2. Uncontrolled Execution: If the DataLoader and dataset creation code is outside of if __name__ == "__main__":, it runs as soon as the script is imported, not just when intended.

   3. Recursive Spawning: Because the DataLoader with num_workers > 0 creates new processes, those processes will also re-import and re-execute the script from the top, trying to create their own DataLoader instances.

   4. Infinite Loop: This leads to a chain reaction where every new process spawns more processes, which again import and execute the script, trying to spawn even more processes. This results in infinite recursion of process creation.

   How the if __name__ == "__main__": Block Prevents This

   • The if __name__ == "__main__": block ensures that certain code only runs when the script is executed directly, not when it is imported.
   • When a new process spawns and imports the script, it does not execute the code inside if __name__ == "__main__":, preventing the recursive spawning issue.

   For example:

   if __name__ == "__main__":
       # This code will only run when the script is executed directly.
       train_loader = DataLoader(...)

   In a new process, this block is ignored because __name__ is not "__main__" (it’s the module name). This prevents unintended and repeated execution of the DataLoader creation code, keeping the multiprocessing controlled and functional.

   Key Point

   The if __name__ == "__main__": block ensures that multiprocessing doesn’t lead to runaway process creation on platforms that use the spawn method, making your script work correctly and efficiently on all operating systems.

The transforms.Normalize((0.5,), (0.5,)) step is included to normalize your image data so that its pixel values fall within the ([-1, 1]) range instead of the default ([0, 1]) range produced by transforms.ToTensor(). Here's why this is beneficial:

• Normalization Process: The transforms.Normalize(mean, std) function adjusts the pixel values using the formula:

  [ \text{output} = \frac{\text{input} - \text{mean}}{\text{std}} ]

  By setting mean to ((0.5,)) and std to ((0.5,)), the transformation becomes:

  [ \text{output} = \frac{\text{input} - 0.5}{0.5} = 2 \times \text{input} - 1 ]

  This maps the input pixel values from the ([0, 1]) range to ([-1, 1]).

• Benefits of ([-1, 1]) Range:

  • Neural Network Performance: Many neural network architectures, especially those using activation functions like tanh, perform better when inputs are centered around zero. This can lead to faster convergence during training.
  • Stability: Normalizing data can improve numerical stability and make the training process less sensitive to the scale of input features.
  • Consistency: If pre-trained models (e.g., those trained on ImageNet) expect inputs in a specific range, normalizing your data accordingly ensures compatibility.

• Channel-wise Normalization: The (0.5,) tuple indicates that this normalization is applied to each channel individually. For grayscale images (single-channel), this is straightforward. For RGB images, you would provide a mean and standard deviation for each channel.

In summary, the transforms.Normalize((0.5,), (0.5,)) step scales your image data to a ([-1, 1]) range, which is often preferred for training neural networks due to improved performance and stability.

Answer:

Because it scales image pixels from [0, 1] to [–1, 1]; using Normalize((0.5,), (0.5,)) centers and scales the data so neural networks train better with inputs in the [–1, 1] range