# load model
learning_rate = criterion.CustomSchedule(par.embedding_dim)
opt = Adam(l_r, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
strategy = tf.distribute.MirroredStrategy()
# define model
with strategy.scope():
mt = MusicTransformer(embedding_dim=256,
vocab_size=par.vocab_size,
num_layer=6,
max_seq=max_seq,
dropout=0.2,
debug=False,
loader_path=load_path)
mt.compile(optimizer=opt, loss=criterion.transformer_dist_train_loss)
# Train Start
for e in range(epochs):
mt.reset_metrics()
for b in range(len(dataset.files) // batch_size):
try:
batch_x, batch_y = dataset.seq2seq_batch(batch_size, max_seq)
except:
continue
result_metrics = mt.train_on_batch(batch_x, batch_y)
if b % 100 == 0:
eval_x, eval_y = dataset.seq2seq_batch(batch_size, max_seq,
'eval')
eval_result_metrics = mt.evaluate(eval_x, eval_y)
mt.save(save_path)
# load model
learning_rate = callback.CustomSchedule(par.embedding_dim)
opt = Adam(l_r, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
strategy = tf.distribute.MirroredStrategy()
# define model
with strategy.scope():
mt = MusicTransformer(embedding_dim=256,
vocab_size=par.vocab_size,
num_layer=6,
max_seq=max_seq,
dropout=0.2,
debug=False,
loader_path=load_path)
mt.compile(optimizer=opt, loss=callback.transformer_dist_train_loss)
# Train Start
for e in range(epochs):
mt.reset_metrics()
for b in range(len(dataset.files) // batch_size):
try:
batch_x, batch_y = dataset.seq2seq_batch(batch_size, max_seq)
except:
continue
result_metrics = mt.train_on_batch(batch_x, batch_y)
if b % 100 == 0:
eval_x, eval_y = dataset.seq2seq_batch(batch_size, max_seq,
'eval')
eval_result_metrics = mt.evaluate(eval_x, eval_y)
mt.save(save_path)
import params as par
from tensorflow.python import enable_eager_execution
from tensorflow.python.keras.optimizer_v2.adam import Adam
from tensorflow.python.keras.optimizer_v2.gradient_descent import SGD
from data import Data
import utils
enable_eager_execution()
tf.executing_eagerly()
if __name__ == '__main__':
epoch = 100
batch = 1000
dataset = Data('dataset/processed/')
opt = Adam(0.0001)
# opt = SGD(lr=0.0001, momentum=0.0, decay=0.0, nesterov=False)
mt = MusicTransformer(embedding_dim=par.embedding_dim,
vocab_size=par.vocab_size,
num_layer=6,
max_seq=100,
debug=True)
mt.compile(optimizer=opt, loss=callback.TransformerLoss())
for e in range(epoch):
for b in range(batch):
batch_x, batch_y = dataset.seq2seq_batch(2, 100)
result_metrics = mt.train_on_batch(batch_x, batch_y)
print('Loss: {:6.6}, Accuracy: {:3.2}'.format(
result_metrics[0], result_metrics[1]))
