[PYTHON] How to create data to put in CNN (Chainer)

I tried to make my own model by learning with a hackathon, but it took time to find out how to make the data to be put in CNN, so memo

from PIL import Image
import numpy as np
import glob
import random

def load_image():
    filepaths = glob.glob('data/*.png')

    datasets = []
    for filepath in filepaths:
        img = Image.open(filepath).convert('L')  #Load with Pillow.'L'Means grayscale
        img = img.resize((32, 32)) #Resized to 32x32x
        label = int(filepath.split('/')[-1].split('_')[0]) #label(Integer greater than or equal to 0) (In my case, I often put a label name at the beginning of the file name.)

        x = np.array(img, dtype=np.float32)
        x = x.reshape(1,32,32) # (Channel, height, width)
        t = np.array(label, dtype=np.int32) 

        datasets.append((x,t)) #List x and t as tuples

    random.shuffle(datasets) #shuffle
    train = datasets[:1000] #The first thousand for learning
    test = datasets[1000:1100] #For testing from the 1000th to the 1100th
    return train, test


def main(): #Below, refer to cifer10 of chainer example

    class_labels = 10
    train, test = load_image()
    
    model = L.Classifier(models.VGG.VGG(class_labels))
    if args.gpu >= 0:
        # Make a specified GPU current
        chainer.cuda.get_device_from_id(args.gpu).use()
        model.to_gpu()  # Copy the model to the GPU

    optimizer = chainer.optimizers.MomentumSGD(args.learnrate)
    optimizer.setup(model)
    optimizer.add_hook(chainer.optimizer.WeightDecay(5e-4))

    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
                                                 repeat=False, shuffle=False)
    # Set up a trainer
    updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)

    # Evaluate the model with the test dataset for each epoch
    trainer.extend(TestModeEvaluator(test_iter, model, device=args.gpu))

    # Reduce the learning rate by half every 25 epochs.
    trainer.extend(extensions.ExponentialShift('lr', 0.5),
                   trigger=(25, 'epoch'))

    # Dump a computational graph from 'loss' variable at the first iteration
    # The "main" refers to the target link of the "main" optimizer.
    trainer.extend(extensions.dump_graph('main/loss'))

    # Take a snapshot at each epoch
    trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))

    # Write a log of evaluation statistics for each epoch
    trainer.extend(extensions.LogReport())

    # Print selected entries of the log to stdout
    # Here "main" refers to the target link of the "main" optimizer again, and
    # "validation" refers to the default name of the Evaluator extension.
    # Entries other than 'epoch' are reported by the Classifier link, called by
    # either the updater or the evaluator.
    trainer.extend(extensions.PrintReport(
        ['epoch', 'main/loss', 'validation/main/loss',
         'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))

    # Print a progress bar to stdout
    trainer.extend(extensions.ProgressBar())

    if args.resume:
        # Resume from a snapshot
        chainer.serializers.load_npz(args.resume, trainer)

    # Run the training
    trainer.run()