def train(self, epoch=10, batch_size=32, gpu=False):
if gpu:
cuda.check_cuda_available()
xp = cuda.cupy if gpu else np
self.batch_size = batch_size
label_types = ['none', 'tap', 'up', 'down', 'right', 'left']
self.model = Alex(len(label_types))
optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9)
optimizer.setup(self.model)
if gpu:
self.model.to_gpu()
training_data = TrainingData(IMAGE_ROOT, NOTE_ROOT, VIDEO_ROOT, SONG_LIST_PATH)
self.x_train, self.x_test, self.y_train, self.y_test = training_data.get_train_data(label_types)
data_size = self.x_train.shape[0]
for ep in range(epoch):
print('epoch {0}/{1}: (learning rate={2})'.format(ep + 1, epoch, optimizer.lr))
indexes = np.random.permutation(data_size)
for i in range(0, data_size, self.batch_size):
x_batch = self.x_train[indexes[i:i + self.batch_size]]
y_batch = self.y_train[indexes[i:i + self.batch_size]]
x = chainer.Variable(x_batch)
t = chainer.Variable(y_batch)
optimizer.update(self.model, x, t)
print("loss: {0}".format(self.model.loss.data))
serializers.save_npz(MODEL_PATH, self.model)
optimizer.lr *= 0.97
def main():
training_data = TrainingData(IMAGE_ROOT, NOTE_ROOT, VIDEO_ROOT, SONG_LIST_PATH)
x_train, x_test, y_train, y_test = training_data.get_train_data(['none', 'tap', 'up', 'down', 'right', 'left'])
