if '__main__' == __name__:
from utility.jaychou.load_jaychou_lyrics import get_jaychou_lyrics
from utility.gen_char_index import gen_char_index
from utility.train_rnn import train_rnn
num_epoch = 250
lr = 0.01
theta = 0.01
hiddens = 256
batch_size = 128
num_step = 35
num_epochs = 250
pred_len = 50
corpus_data = get_jaychou_lyrics()
index_to_char_list, char_to_index_dict = gen_char_index(corpus_data)
net = LSTM(len(index_to_char_list), hiddens, len(index_to_char_list))
params = list(net.parameters())
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_rnn(
corpus_data,
index_to_char_list,
char_to_index_dict,
True,
lr,
nn.CrossEntropyLoss(),
num_step,
batch_size,
num_epoch,
theta,
yy_device = yy.to(self.device)
loss_sum = loss(y_hat, yy_device).sum()
loss_sum.backward()
gradient_clipping(params=params,
theta=0.01,
device=self.device)
sgd(params, learn_rate, 1)
n += yy.shape[0]
l_sum += loss_sum.cpu().item() * yy.shape[0]
if (epoch + 1) % pred_period == 0:
print('epoch %d, perplexity %f, time %.2f sec' %
(epoch + 1, math.exp(l_sum / n), time.time() - start))
for prefix in prefixes:
print(
' -',
self.predict(prefix, pred_len, index_to_char_list,
char_to_index_dict))
if '__main__' == __name__:
from torch import nn
from utility.jaychou.load_jaychou_lyrics import get_jaychou_lyrics
lr = 100
corpus_char_list = get_jaychou_lyrics()
index_to_char_list, char_to_index_dict = gen_char_index(corpus_char_list)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
rnn = RNN(len(index_to_char_list), len(index_to_char_list), 256, device)
rnn.train(corpus_char_list, False, lr, nn.CrossEntropyLoss(), 35, 32, 1000,
['分开', '不分开'], 50)