train, test, field = dataset_reader(train=True, stop=900000)

evl, _ = dataset_reader(train=False, fields=field)

field.build_vocab(train, evl)

_, evl_iter = BucketIterator.splits(

(train, evl),

batch_sizes=(1024, 1024),

device=device,

sort_within_batch=False,

repeat=False,

sort=False

)

model = RNNCNN(num_embeddings=len(field.vocab), embedding_dim=256).to(device)

model.load_state_dict(torch.load('model/rnn_cnn_model.pkl'))

with open('data/rnn_cnn_result.txt', 'w+') as f:

f.write('')

model.eval()

with torch.no_grad():

for i, data in tqdm.tqdm(enumerate(evl_iter), total=evl_iter.__len__()):

inputs = torch.cat((data.plat_form, data.biz_type,

data.payed_day, data.payed_hour,

data.cate1_id, data.cate2_id, data.cate3_id,

data.preselling_shipped_day,

data.seller_uid_field, data.company_name,

data.rvcr_prov_name, data.rvcr_city_name,

), dim=1)

outputs = model(inputs, 'test', field)

day = outputs * 3 + 3

with open('data/rnn_cnn_result.txt', 'a+') as f:

for b in range(day.size(0)):

sign_day = str(float(day[b]))