def test(params, infos):
from seq_to_seq import Seq2Seq
from data_utils import batch_flow
x_data, _ = pickle.load(open('chatbot.pkl', 'rb'))
ws = pickle.load(open('wx.pkl', 'rb'))
for x in x_data[:5]:
print(' '.join(x))
config = tf.ConfigProto(device_count={
'CPU': 1,
'GPU': 0
},
allow_soft_placement=True,
log_device_placement=False)
##使用抗语言模型进行训练，train_anti.py
save_path = './model3/s2ss_chatbot_anti.ckpt'
tf.reset_default_graph()
model_pred = Seq2Seq(input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=0,
**params)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
while True:
#user_text = input('请输入你的句子：')
#if user_text in ('exit', 'quit'):
# exit(0)
x_test = [list(infos.lower())]
bar = batch_flow([x_test], ws, 1)
x, xl = next(bar)
x = np.flip(x, axis=1)
#print(x, xl)
pred = model_pred.predict(sess, np.array(x), np.array(xl))
#print(pred)
#print(ws.inverse_transform(x[0]))
total_word = ""
for p in pred:
ans = ws.inverse_transform(p)
if ans[0] == '</S>':
#print(ans)
total_word += ""
else:
total_word += ans[0]
print(ans)
return total_word
def test(params):
from seq_to_seq import Seq2Seq
from data_utils import batch_flow_bucket as batch_flow
from thread_generator import ThreadedGenerator
x_data, y_data = pickle.load(open('chatbot.pkl', 'rb'))
ws = pickle.load(open('wx.pkl', 'rb'))
# 训练
# n_epoch是轮数(越大越容易过拟合)
n_epoch = 1
batch_size = 128
steps = int(len(x_data) / batch_size) + 1
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
save_path = './model/s2ss_chatbot.ckpt'
tf.reset_default_graph()
with tf.Graph().as_default():
random.seed(0)
np.random.seed(0)
tf.set_random_seed(0)
with tf.Session(config=config) as sess:
# 定义模型
model = Seq2Seq(input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=batch_size,
**params)
init = tf.global_variables_initializer()
sess.run(init)
flow = ThreadedGenerator(batch_flow([x_data, y_data],
ws,
batch_size,
add_end=[False, True]),
queue_maxsize=30)
for epoch in range(1, n_epoch + 1):
costs = []
bar = tqdm(range(steps),
total=steps,
desc='epoch {}, loss=0.000000'.format(epoch))
for _ in bar:
x, xl, y, yl = next(flow)
# [[1,2],[3,4]]
# [[3,4],[1,2]]
# 按axis=1反转
x = np.flip(x, axis=1)
cost, lr = model.train(sess, x, xl, y, yl, return_lr=True)
costs.append(cost)
bar.set_description(
'epoch {} loss={:.6f} lr={:.6f}'.format(
epoch, np.mean(costs), lr))
model.save(sess, save_path=save_path)
# Testing
tf.reset_default_graph()
model_pred = Seq2Seq(input_vocab_size=len(ws),
target_vocab_size=len(ws),
batch_size=1,
mode='decode',
beam_width=12,
parallel_iterations=1,
**params)
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
model_pred.load(sess, save_path)
bar = batch_flow([x_data, y_data], ws, 1, add_end=False)
t = 0
for x, xl, y, yl in bar:
x = np.flip(x, axis=1)
pred = model_pred.predict(sess, np.array(x), np.array(xl))
print(ws.inverse_transform(x[0]))
print(ws.inverse_transform(y[0]))
print(ws.inverse_transform(pred[0]))
t += 1
if t >= 3:
break