Esempio n. 1
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def predict_image(model, file_path):
    _, pred = forward(model, load_image(file_path), empty_label(), train=False)
    print '多分これかな?: %s' % (pred.data)
Esempio n. 2
0
    # init optimizer
    optimizer = optimizers.Adam()
    optimizer.setup(model)

    # load data
    data = []
    walk_dir(args.data_dir, lambda i, f: data.extend([(num_to_label(i), load_image(f))]))

    # learn
    for i in range(args.epoch):
        random.shuffle(data)

        t = 0
        pbar = ProgressBar(len(data))
        for (label, img) in data:

            optimizer.zero_grads()
            loss, acc = forward(model, img, label, train=True)
            loss.backward()
            optimizer.update()

            t += 1
            pbar.update(t)

        print '%s 回繰り返し学習を行った' % (i + 1)

    print 'ヨッシャ! 学習おわったでw'

    # dump model
    pickle.dump(model, open('AlexNet_epoch_%s.pickle' % (args.epoch), 'wb'), -1)