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))