class LSTM(NoiseAwareModel):
"""Long Short-Term Memory."""
def __init__(self):
self.lstm = None
self.w = None
def train(self, training_candidates, training_marginals, **hyperparams):
self.lstm = LSTMModel(training_candidates, training_marginals)
self.lstm.train(**hyperparams)
def marginals(self, test_candidates):
return self.lstm.test(test_candidates)
def save(self, session, param_set_name):
raise NotImplementedError()
def main(_):
if FLAGS.seed != -1:
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
print("Creating model...")
model = LSTMModel(FLAGS.input_size,
FLAGS.output_size,
FLAGS.num_layers,
FLAGS.num_units,
FLAGS.direction,
FLAGS.learning_rate,
FLAGS.dropout,
FLAGS.seed,
is_training=FLAGS.action == 0,
model=FLAGS.model)
if FLAGS.action == 0: # TRAINING
assert FLAGS.model != 1
data_storage = data_processing.prepare_all_data(FLAGS.time_len)
model.train(data_storage, FLAGS.batch_size, FLAGS.num_epochs)
# inputs, labels, inputs_valid, labels_valid = data_processing.prepare_data(FLAGS.time_len)
# model.train(inputs, inputs_valid, labels, labels_valid,
# FLAGS.batch_size, FLAGS.num_epochs)
elif FLAGS.action == 1: # EVALUATING
assert FLAGS.model == 1 or FLAGS.model == 2, \
"main(): evaluated model must be LSTMBlockCell or LSTMCell"
set_idx, aug_idx = 7, 4
inputs, labels = data_processing.prepare_indexed_data(
None, set_idx, aug_idx)
model.predict(inputs, labels)
# pkl_index = 7
# world_data, mocap_data = data_processing.load_data(
# open("processed_data/data-" + str(pkl_index) + ".pkl", "rb"))
# inputs = data_processing.sequence_split_data(world_data, world_data.shape[0])
# labels = data_processing.sequence_split_data(mocap_data, mocap_data.shape[0])
# #inputs, labels = data_processing.augment_data(inputs, labels)
# model.predict(inputs, labels)
elif FLAGS.action == 2: # EXPORTING
assert FLAGS.model == 1 or FLAGS.model == 2, \
"main(): evaluated model must be LSTMBlockCell or LSTMCell"
if FLAGS.model == 1:
model.export_weights()
elif FLAGS.model == 2:
model.export()
elif FLAGS.action == 3: # PREDICTING
assert FLAGS.model == 1 or FLAGS.model == 2, \
"main(): predicting model must be LSTMBlockCell or LSTMCell"
set_idx, aug_idx = 0, 0
inputs, labels = data_processing.prepare_indexed_data(
None, set_idx, aug_idx)
loss, logits = model.predict(inputs, labels)
predicts = data_processing.convert_logits_to_predicts(logits)
org_labels = data_processing.convert_logits_to_predicts(labels)
print(predicts[:, :3], org_labels[:, :3])
mae = np.mean(np.abs(predicts - org_labels), axis=0)
line = ""
for i in range(22):
line += "{0:.5f}".format(np.mean(mae[3 * i:3 * i + 3])) + " "
print(line)
print(np.mean(mae[3:]))