def test(model, device, test_loader, epoch, writer=None):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(
output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(
dim=1,
keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print(
'\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
if writer:
writer.add_scalar('test loss', test_loss, epoch)
writer.add_scalar('test acc', correct / len(test_loader.dataset),
epoch)
openbayestool.log_metric('loss', test_loss)
openbayestool.log_metric('acc', correct / len(test_loader.dataset))
def on_batch_end(self, batch, logs=None):
"""Print Training Metrics"""
if batch % 5000 == 0:
# 如果在 tensorflow 2.0 必须使用 accuracy 而不是 acc
# openbayestool.log_metric('acc', float(logs.get('accuracy')))
openbayestool.log_metric('precision', float(logs.get('acc')))
openbayestool.log_metric('loss', float(logs.get('loss')))
def on_batch_end(self, batch, logs=None):
"""Print Training Metrics"""
if batch % 5000 == 0:
if tf.__version__.startswith('2'):
openbayestool.log_metric('acc', float(logs.get('accuracy')))
else:
openbayestool.log_metric('acc', float(logs.get('acc')))
openbayestool.log_metric('loss', float(logs.get('loss')))
def main(args):
# process input file
input_file = util.ensure_local_file(args['train_file'])
user_map, item_map, tr_sparse, test_sparse = model.create_test_and_train_sets(
args, input_file, args['data_type'])
# train model
output_row, output_col = model.train_model(args, tr_sparse)
# save trained model to job directory
model.save_model(args, user_map, item_map, output_row, output_col)
# log results
train_rmse = wals.get_rmse(output_row, output_col, tr_sparse)
test_rmse = wals.get_rmse(output_row, output_col, test_sparse)
if args['hypertune']:
# write test_rmse metric for hyperparam tuning
util.write_hptuning_metric(args, test_rmse)
tf.logging.info('train RMSE = %.2f' % train_rmse)
tf.logging.info('test RMSE = %.2f' % test_rmse)
openbayestool.log_metric('rmse', test_rmse)
optimizer.step()
print(
"Train Loss: {:.6f}, Acc: {:.6f}".format(
train_loss / (len(train_data)), train_acc / (len(train_data))
)
)
# evaluation--------------------------------
model.eval()
eval_loss = 0.0
eval_acc = 0.0
for batch_x, batch_y in test_loader:
batch_x, batch_y = Variable(batch_x, volatile=True), Variable(
batch_y, volatile=True
)
batch_x = batch_x.to(device)
batch_y = batch_y.to(device)
out = model(batch_x)
loss = loss_func(out, batch_y)
eval_loss += loss.item()
pred = torch.max(out, 1)[1]
num_correct = (pred == batch_y).sum()
eval_acc += num_correct.item()
print(
"Test Loss: {:.6f}, Acc: {:.6f}".format(
eval_loss / (len(test_data)), eval_acc / (len(test_data))
)
)
openbayestool.log_metric("loss", eval_loss / (len(test_data)))
openbayestool.log_metric("acc", eval_acc / (len(test_data)))
def on_batch_end(self, batch, logs=None):
"""Print Training Metrics"""
if batch % 5000 == 0:
openbayestool.log_metric('acc', float(logs.get('acc')))
openbayestool.log_metric('loss', float(logs.get('loss')))