"lr": 0.01,
"hidden_dim": 10,
# "dropout": 0.5,
}
model = AttRCNN(param)
model.fit(train_x, train_y, test_x, test_y, 2, 32, save_best=False)
model.param["lr"] = 0.003
model.fit(train_x, train_y, test_x, test_y, 4, 64, save_best=False)
# model.save("test")
# x = FastText.load(str(MODEL_PATH / "intent" / "test.FastText"))
s = [
"你真是可爱阿", "你很喜欢学习哦", "我再也不想理你了", "吃饭没", "明天会下雨马", "你哥哥是谁", "你有哥哥么",
"弟弟是谁", "我想买手机", "我是你主人", "我可以给你打分吗，评价"
]
from chatbot.preprocessing.text import cut
for i in s:
print(
i,
label.reverse_one(
model.infer(
np.array(vocab.transform_one(cut(i), max_length=10)))[0]))
from chatbot.evaluate.plot import plot_attention_1d
idx = 1200
att = model.get_attention(
torch.tensor(
np.array(vocab.transform_one(train_x[idx],
max_length=10)).reshape(-1, 10)))
print(label.reverse_one(model.infer(train_x[idx])[0]))
plot_attention_1d([vocab.reverse_one(train_x[idx]).split(" ")],
att.detach().numpy())
train_x = np.array(vocab.transform(train_x, max_length=10))
test_x = np.array(vocab.transform(test_x, max_length=10))
train_y = np.array(label.transform(train_y))
test_y = np.array(label.transform(test_y))
textCNN_param = {
"vocab_size": len(vocab),
"embed_dim": 60,
"class_num": len(label),
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
"lr": 0.01
}
model = TextCNN(textCNN_param)
# new_model = TextCNN(textCNN_param)
# new_model.load_state_dict(torch.load("/home/zhouzr/project/Task-Oriented-Chatbot/chatbot/results/intent/TextCNN_epoch_4_step_100_acc_0.9881.params.pkl"))
model.fit(train_x, train_y, test_x, test_y, 1, 64, save_best=False)
s = [
"你真是可爱阿", "你很喜欢学习哦", "我再也不想理你了", "吃饭没", "明天会下雨马", "你哥哥是谁", "你有哥哥么",
"弟弟是谁", "我想买手机", "我是你主人"
]
from chatbot.preprocessing.text import cut
for i in s:
print(
i,
label.reverse_one(
model.infer(
np.array(vocab.transform_one(cut(i), max_length=10)))[0]))