def run_neural_nets(dataset, url_weight="sp", encoder_length=24, encoder_size=15, decoder_length=8, decoder_size=9, is_test=False,
restore=False, model="NN", pre_train=False, forecast_factor=0):
tf.reset_default_graph()
print("training %s with decoder_length = %i" % (model, decoder_length))
if model == "NN":
model = NeuralNetwork(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length, decoder_vector_size=decoder_size)
elif model == "SAE":
model = StackAutoEncoder(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length, pre_train=pre_train, forecast_factor=forecast_factor)
else:
model = Adain(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length, forecast_factor=forecast_factor)
print('==> initializing models')
with tf.device('/%s' % p.device):
model.init_model()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
tconfig = get_gpu_options()
with tf.Session(config=tconfig) as session:
if not restore:
session.run(init)
else:
print("==> Reload pre-trained weights")
saver.restore(session, "weights/%s_%ih.weights" % (url_weight, decoder_length))
print("==> Loading dataset")
train, valid = utils.process_data_grid(len(dataset), p.batch_size, encoder_length, decoder_length, is_test)
model.set_data(dataset, train, valid, None, session)
if not is_test:
best_val_epoch = 0
best_val_loss = float('inf')
print('==> starting training')
for epoch in xrange(p.total_iteration):
print('Epoch {}'.format(epoch))
start = time.time()
train_loss, _ = model.run_epoch(session, train, epoch, None, train_op=model.train_op, train=True)
print('Training loss: {}'.format(train_loss))
valid_loss, _ = model.run_epoch(session, valid, epoch, None)
print('Validation loss: {}'.format(valid_loss))
if valid_loss < best_val_loss:
best_val_loss = valid_loss
best_val_epoch = epoch
print('Saving weights')
saver.save(session, 'weights/%s_%ih.weights' % (url_weight, decoder_length))
if (epoch - best_val_epoch) > p.early_stopping:
break
print('Total time: {}'.format(time.time() - start))
else:
# saver.restore(session, url_weight)
print('==> running model')
_, preds = model.run_epoch(session, model.train, shuffle=False, stride=2)
return preds
return None
def run_neural_nets(url_feature="", attention_url="", url_weight="sp", encoder_length=24, encoder_size=15, decoder_length=8, decoder_size=9, is_test=False, restore=False, model="NN", pre_train=False):
if model == "NN":
model = NeuralNetwork(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length, decoder_vector_size=decoder_size)
elif model == "SAE":
model = StackAutoEncoder(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length, pre_train=pre_train)
else:
model = Adain(encoder_length=encoder_length, encoder_vector_size=encoder_size, decoder_length=decoder_length)
print('==> initializing models')
with tf.device('/%s' % p.device):
model.init_model()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
utils.assert_url(url_feature)
tconfig = get_gpu_options()
sum_dir = 'summaries'
if not utils.check_file(sum_dir):
os.makedirs(sum_dir)
train_writer = None
with tf.Session(config=tconfig) as session:
if not restore:
session.run(init)
else:
print("==> Reload pre-trained weights")
saver.restore(session, url_weight)
url_weight = url_weight.split("/")[-1]
url_weight = url_weight.rstrip(".weights")
if not is_test:
suf = time.strftime("%Y.%m.%d_%H.%M")
train_writer = tf.summary.FileWriter(sum_dir + "/" + url_weight + "_train", session.graph, filename_suffix=suf)
valid_writer = tf.summary.FileWriter(sum_dir + "/" + url_weight + "_valid", session.graph, filename_suffix=suf)
print("==> Loading dataset")
dataset = utils.load_file(url_feature)
if dataset:
dataset = np.asarray(dataset, dtype=np.float32)
lt = len(dataset)
st = int(lt/2)
lt = lt - st
dataset = dataset[st:,:,:]
train, valid = utils.process_data_grid(lt, p.batch_size, encoder_length, decoder_length, is_test)
if attention_url:
attention_data = utils.load_file(attention_url)
else:
attention_data = None
model.set_data(dataset, train, valid, attention_data, session)
if not is_test:
best_val_epoch = 0
best_val_loss = float('inf')
# best_overall_val_loss = float('inf')
print('==> starting training')
for epoch in xrange(p.total_iteration):
print('Epoch {}'.format(epoch))
start = time.time()
train_loss, _ = model.run_epoch(session, train, epoch, train_writer, train_op=model.train_op, train=True)
print('Training loss: {}'.format(train_loss))
valid_loss, _ = model.run_epoch(session, valid, epoch, valid_writer)
print('Validation loss: {}'.format(valid_loss))
if valid_loss < best_val_loss:
best_val_loss = valid_loss
best_val_epoch = epoch
print('Saving weights')
saver.save(session, 'weights/%s.weights' % url_weight)
if (epoch - best_val_epoch) > p.early_stopping:
break
print('Total time: {}'.format(time.time() - start))
else:
# saver.restore(session, url_weight)
print('==> running model')
_, preds = model.run_epoch(session, model.train, shuffle=False)
pt = re.compile("weights/([A-Za-z0-9_.]*).weights")
name = pt.match(url_weight)
if name:
name_s = name.group(1)
else:
name_s = url_weight
utils.save_file("test_sp/%s" % name_s, preds)
