def main():
data_path = './data/chatbot.txt'
voc, pairs = loadPrepareData(data_path)
# 把含有低频词的句子扔掉
MIN_COUNT = Config.MIN_COUNT
pairs = trimRareWords(voc, pairs, MIN_COUNT)
training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(Config.batch_size)])
for _ in range(Config.total_step)]
# 词嵌入部分
embedding = nn.Embedding(voc.num_words, Config.hidden_size)
# 定义编码解码器
encoder = EncoderRNN(Config.hidden_size, embedding, Config.encoder_n_layers, Config.dropout)
decoder = LuongAttnDecoderRNN(Config.attn_model, embedding, Config.hidden_size, voc.num_words, Config.decoder_n_layers, Config.dropout)
# 定义优化器
encoder_optimizer = optim.Adam(encoder.parameters(), lr=Config.learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=Config.learning_rate * Config.decoder_learning_ratio)
start_iteration = 1
save_every = 4000 # 多少步保存一次模型
for iteration in range(start_iteration, Config.total_step + 1):
training_batch = training_batches[iteration - 1]
input_variable, lengths, target_variable, mask, max_target_len = training_batch
start_time = time.time()
# Run a training iteration with batch
loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
decoder, embedding, encoder_optimizer, decoder_optimizer, Config.batch_size, Config.clip)
time_str = datetime.datetime.now().isoformat()
log_str = "time: {}, Iteration: {}; Percent complete: {:.1f}%; loss: {:.4f}, spend_time: {:6f}".format(time_str, iteration, iteration / Config.total_step * 100, loss, time.time() - start_time)
rainbow(log_str)
# Save checkpoint
if iteration % save_every == 0:
save_path = './save_model/'
if not os.path.exists(save_path):
os.makedirs(save_path)
torch.save({
'iteration': iteration,
'encoder': encoder.state_dict(),
'decoder': decoder.state_dict(),
'en_opt': encoder_optimizer.state_dict(),
'de_opt': decoder_optimizer.state_dict(),
'loss': loss,
'voc_dict': voc.__dict__,
'embedding': embedding.state_dict()
}, os.path.join(save_path, '{}_{}_model.tar'.format(iteration, 'checkpoint')))
def train(args):
input_lang, output_lang, pairs = prepareData(args)
print(random.choice(pairs))
model = {}
model['hidden_size'] = 1000
model['dropout'] = 0.1
model['input_lang'] = input_lang
model['output_lang'] = output_lang
model['max_length'] = max(input_lang.max_length,
output_lang.max_length) + 2
print('Max length: {}'.format(model['max_length']))
encoder1 = EncoderRNN(input_lang.n_words,
model['hidden_size']).to(getDevice())
encoder1.train()
attn_decoder1 = AttnDecoderRNN(model['hidden_size'],
output_lang.n_words,
dropout_p=model['dropout'],
max_length=model['max_length']).to(
getDevice())
attn_decoder1.train()
n_iters = 30000
training_pairs = [
tensorsFromPair(input_lang, output_lang, random.choice(pairs))
for _ in range(n_iters)
]
trainIters(training_pairs,
encoder1,
attn_decoder1,
n_iters,
print_every=1000,
optim=args.optim,
learning_rate=args.learning_rate,
max_length=model['max_length'])
print('saving models...')
model['encoder_state'] = encoder1.state_dict()
model['decoder_state'] = attn_decoder1.state_dict()
torch.save(
model,
"data/{}_model_checkpoint.pth".format(args.phase.split('_')[-1]))
init_learning_rate = 0.01
decay_rate = 0.5
rate = init_learning_rate
loss_list = []
for n in range(6):
loss_all = 0.0
epoch_num = n + 1
for i in range(360):
batch_no = i * 5
trainIters(encoder1,
attn_decoder1,
batch_no,
print_every=5,
learning_rate=0.005)
if (i % 30 == 0):
print('recorded!')
evaluateRandomly(encoder1, attn_decoder1)
torch.save(encoder1.state_dict(), 'encoder.pt')
torch.save(attn_decoder1.state_dict(), 'decoder.pt')
torch.save(encoder1.state_dict(), 'encoder.pt')
torch.save(attn_decoder1.state_dict(), 'decoder.pt')
loss_epoch = loss_all / 360
print('Avg. epoch loss: %f' % loss_epoch)
loss_list.append(loss_epoch)
print('Finished epoch %d' % epoch_num)
print(loss_list)
print('Done')
