Hey, I’m new to ML. I have a small dataset and I want to make the most of my RTX 3080. Therefore, I’m trying to use a large batch size of 64. When I used a batch size of 8, it took 97 minutes, but with a batch size of 64, it only took 65 minutes. However, I’m concerned about potential data loss.
The following code prints the number of required images (for complete batch usage) and the actual number of available images:
print(len(train_loader) * 64, train_data_len) print(len(validation_loader) * 64, val_data_len) print(len(test_loader) * 64, test_data_len)
7616 7563 896 841 960 924
With a batch size of 64, I have 118 iterations to process the entire dataset, which means I need 7616 images. However, I only have 7563 images available for training. What happens to the last batch with just 7616 - 7563 = 53 images?
Will it be ignored or filled with random data?