def test(train_file=train_file,
test_file=test_file,
uid_voc=uid_voc,
mid_voc=mid_voc,
cat_voc=cat_voc,
batch_size=128,
maxlen=100):
# sample_io
sample_io = SampleIO(train_file,
test_file,
uid_voc,
mid_voc,
cat_voc,
batch_size,
maxlen,
embedding_dim=EMBEDDING_DIM,
light_embedding_dim=LIGHT_EMBEDDING_DIM)
model = Model_DIEN(EMBEDDING_DIM,
HIDDEN_SIZE,
ATTENTION_SIZE,
LIGHT_EMBEDDING_DIM,
LIGHT_HIDDEN_SIZE,
LIGHT_ATTENTION_SIZE,
use_rocket_training=use_rocket_training())
# test
datas = sample_io.next_test()
test_ops = tf_test_model(*model.xdl_embedding(
datas, EMBEDDING_DIM, LIGHT_EMBEDDING_DIM, *sample_io.get_n()))
eval_sess = xdl.TrainSession()
print(
'test_auc: %.4f ----test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f'
% eval_model(eval_sess, test_ops))
def test(train_file=train_file,
test_file=test_file,
uid_voc=uid_voc,
mid_voc=mid_voc,
cat_voc=cat_voc,
batch_size=128,
maxlen=100):
# sample_io
sample_io = SampleIO(train_file, test_file, uid_voc, mid_voc,
cat_voc, batch_size, maxlen, EMBEDDING_DIM)
if xdl.get_config('model') == 'din':
model = Model_DIN(
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE)
elif xdl.get_config('model') == 'dien':
model = Model_DIEN(
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE)
else:
raise Exception('only support din and dien model')
# test
datas = sample_io.next_test()
test_ops = tf_test_model(
*model.xdl_embedding(datas, EMBEDDING_DIM, *sample_io.get_n()))
eval_sess = xdl.TrainSession()
print('test_auc: %.4f ----test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f' %
eval_model(eval_sess, test_ops))
def test(train_file=train_file,
test_file=test_file,
uid_voc=uid_voc,
mid_voc=mid_voc,
cat_voc=cat_voc,
batch_size=128,
maxlen=100):
# sample_io
sample_io = SampleIO(train_file, test_file, uid_voc, mid_voc,
cat_voc, batch_size, maxlen,
embedding_dim=EMBEDDING_DIM,
light_embedding_dim=LIGHT_EMBEDDING_DIM)
model = Model_DIEN(
EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE, LIGHT_EMBEDDING_DIM,
LIGHT_HIDDEN_SIZE, LIGHT_ATTENTION_SIZE, use_rocket_training=use_rocket_training())
# test
datas = sample_io.next_test()
test_ops = tf_test_model(
*model.xdl_embedding(datas, EMBEDDING_DIM, LIGHT_EMBEDDING_DIM, *sample_io.get_n()))
eval_sess = xdl.TrainSession()
print('test_auc: %.4f ----test_loss: %.4f ---- test_accuracy: %.4f ---- test_aux_loss: %.4f' %
eval_model(eval_sess, test_ops))