heesos/debugging_nhl

Intake task for NHL

Debugging

Solution to the debugging tasks from NHL as a part of Computer Vision & Data Science intake.

link to the code: https://github.com/heesos/debugging_nhl/blob/main/debugging.ipynb

Task 1

The solution here was to change the fruits parameter to List type making all of the items in it ordered

def id_to_fruit(fruit_id: int, fruits: List[str]) -> str:

Task 2

First obvious bug was that there was a wrong column put in the swap line. I have changed the column 1 to 0

coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3] = coords[:, 1], coords[:, 0], coords[:,3],coords[:, 2]

This was not enough, because we kept assigning only Y value to the X. It did not work vice versa The solution to it was making the hard copy of the coordinates and fetching the data from the copy array.

copy=coords.copy()
coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3] = coords[:, 1], copy[:, 0], coords[:, 3], copy[:, 2]

Task 3

Values from csv were intially fetched as strings had to change them to floats

coords_array = np.array(results, dtype=float)

Task 4

The issue with 64 batches was because there are 60 000 MNIST pictures. 60 000/64 = 937.5 At the end we were left with 32 pictures rather than 64. real_samples were 32 while generated_samples were still 64 which gave 96 in total. The solution to this bug was to reassign the value of batch_size in for loop.

batch_size=real_samples.size()[0]

The visual bug: I believe it was the issue that epoch number was showing incorrectly in the plot.

 name = f"Generate images\n Epoch: {epoch + 1} Loss D.: {loss_discriminator:.2f} Loss G.: {loss_generator:.2f}"