def evaluate(model, x, y, args, sess, ite=None, writer=None, name=''):
loss_meter = AverageMeter()
output_all = np.array([]).reshape((-1, 1))
for i in range(0, len(y), args.batch_size):
batch_x = x[i:i + args.batch_size]
batch_y = y[i:i + args.batch_size]
n_flows = len(batch_y)
if n_flows != args.batch_size:
batch_x = np.concatenate(
(batch_x,
np.zeros((args.batch_size - n_flows, batch_x.shape[1]))),
axis=0)
batch_y = np.concatenate(
(batch_y, np.zeros(args.batch_size - n_flows)), axis=0)
input_feed = {
model['feat']: batch_x,
model['labels']: batch_y,
model['learning_rate']: args.lr,
model['data_len']: n_flows,
model['keep_probs']: np.ones(len(args.keep_probs)),
}
loss_b, output_b = sess.run([model['loss'], model['output']],
feed_dict=input_feed)
loss_meter.update(loss_b)
output_all = np.concatenate((output_all, output_b[:n_flows].reshape(
(-1, 1))))
if writer is not None:
write_summary(writer, '%s loss' % name, loss_meter.avg, ite)
return loss_meter.avg, output_all
def train(model, x, y, args, sess, ite, writer, idx=None):
if idx is None:
idx = [i for i in range(len(y))]
random.shuffle(idx)
assert len(y) == len(idx), "idx (order) needs to have the same length as y"
loss_meter = AverageMeter()
# prepare the indices in advance
for i in range(0, len(y), args.batch_size):
batch_idx = idx[i:i+args.batch_size]
batch_x = x[batch_idx]
batch_y = y[batch_idx]
n_flows = len(batch_y)
if n_flows != args.batch_size:
batch_x = np.concatenate((batch_x, np.zeros((args.batch_size - n_flows, batch_x.shape[1]))), axis=0)
batch_y = np.concatenate((batch_y, np.zeros(args.batch_size - n_flows)), axis=0)
input_feed = {
model['feat']: batch_x,
model['labels']: batch_y,
model['learning_rate']: args.lr,
model['data_len']: n_flows,
model['keep_probs']: args.keep_probs,
}
_, loss_b = sess.run([model['update_op'], model['loss']], feed_dict=input_feed)
loss_meter.update(loss_b)
if ite % 100 == 0:
write_summary(writer, 'train loss', loss_b, ite)
ite = ite + 1
return loss_meter.avg, ite