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)
# 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)