def predict():
from utils import FileIO, Utils
from word_process import WordProcess
# Path to the data txt file on disk.
path_base = '../data/'
path_file = path_base + 'bytecup.corpus.train.0.50k.txt'
fio = FileIO()
word = WordProcess(path_base, is_model_load=False, is_dict_load=True)
contents, titles = fio.load_from_json(path_file)
total_size = len(titles)
num_samples = int(total_size * 0.8)
num_test = total_size - num_samples
print('num samples:', num_samples, 'num tests:', num_test)
max_encoder_seq_length = int(max([len(txt) for txt in contents])) + 2
max_decoder_seq_length = max([len(txt) for txt in titles]) + 2
print('max_lengths:', max_encoder_seq_length, ' ', max_decoder_seq_length)
train_data = {
'contents': contents[0:num_samples],
'titles': titles[0:num_samples]
}
test_data = {
'contents': contents[num_samples:total_size],
'titles': titles[num_samples:total_size]
}
datasets = {
'train':
TextData2(train_data,
word.dic,
train_len=max_encoder_seq_length,
label_len=max_decoder_seq_length),
'val':
TextData2(test_data,
word.dic,
train_len=max_encoder_seq_length,
label_len=max_decoder_seq_length)
}
data_loads = {
x: DataLoader(datasets[x],
batch_size=batch_size,
shuffle=True,
num_workers=15)
for x in ['train', 'val']
}
encoder = Encoder3(voca_size=84031, embedd_size=128, hidden_size=256)
decoder = AttnDecoder3(hidden_size=256, vocab_size=84031)
if use_cuda:
encoder.cuda()
decoder.cuda()
best_model = torch.load(path_base + './50k.1.best_model_wts')
best_model = Utils().gpu_model_to_cpu_model(best_model)
encoder.load_state_dict(best_model[0])
decoder.load_state_dict(best_model[1])
out = evaluate(encoder, decoder, datasets)
file1 = open(path_base + '50k.1.predict', 'a')
for i, o in enumerate(out):
file1.write(str([word.dic[int(i)] for i in o.data[0]]))
file1.write(str(test_data['titles'][i]) + '\n')
file1.close()
print('predict done!')
def main():
from utils import FileIO
from word_process import WordProcess
# Path to the data txt file on disk.
path_base = '../data/'
path_file = path_base + 'bytecup.corpus.train.0.50k.txt'
fio = FileIO()
word = WordProcess(path_base, is_model_load=False, is_dict_load=True)
dic = word.dic
contents, titles = fio.load_from_json(path_file)
total_size = len(titles)
num_samples = int(total_size * 0.8)
num_test = total_size - num_samples
print('num samples:', num_samples, 'num tests:', num_test)
max_encoder_seq_length = int(max([len(txt) for txt in contents])) + 2
max_decoder_seq_length = max([len(txt) for txt in titles]) + 2
print('max_lengths:', max_encoder_seq_length, ' ', max_decoder_seq_length)
train_data = {
'contents': contents[0:num_samples],
'titles': titles[0:num_samples]
}
test_data = {
'contents': contents[num_samples:total_size],
'titles': titles[num_samples:total_size]
}
datasets = {
'train':
TextData2(train_data,
dic,
train_len=max_encoder_seq_length,
label_len=max_decoder_seq_length),
'val':
TextData2(test_data,
dic,
train_len=max_encoder_seq_length,
label_len=max_decoder_seq_length)
}
data_loads = {
x: DataLoader(datasets[x],
batch_size=batch_size,
shuffle=True,
num_workers=15)
for x in ['train', 'val']
}
encoder = Encoder3(voca_size=84031, embedd_size=128, hidden_size=256)
decoder = AttnDecoder3(hidden_size=256, vocab_size=84031)
optimizer = optim.SGD([{
'params': encoder.parameters(),
'lr': 0.01
}, {
'params': decoder.parameters(),
'lr': 0.01
}],
lr=0.01,
momentum=0.9)
lambda1 = lambda epoch: epoch // 30
lambda2 = lambda epoch: 0.95**epoch
scheduler = optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=[lambda2, lambda2])
criterion = {'loss': nn.CosineSimilarity(dim=2), 'acc': nn.MSELoss()}
loss_history = HistoryLoss()
train_model(encoder, decoder, data_loads, criterion, scheduler,
loss_history)
