convolutional neural networks transformations object that will sequentially resize all images into crop them from center to convert them to tensorsand normalize their pixel values listing - transforming the data and creating the training and validation sets #compose sequence of transformations for image transformations transforms compose(transforms resize( )transforms centercrop( )transforms totensor()transforms normalize(mean=[ ]std=[ ]]load in each dataset and apply transformations using the torchvision datasets as datasets library train_set datasets imagefolder(os path join(new_path,"train"transform transformationsval_set datasets imagefolder(os path join(new_path,"test"transform transformationsput into dataloader using torch library train_loader torch utils data dataloader(train_set batch_size= shuffle=trueval_loader torch utils data dataloader(val_setbatch_size = shuffle=truenote that train_loader and val_loader are objects that take care of shuffling and creating mini-batches of images with labels for our training loop before creating mini-batchesthe transformations object ensures the augmentations are appropriately applied on all images nextlisting - defines our cnn