我正在尝试从此笔记本下载的已保存模型中预测分数值

https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis/

它包含4个保存的模型,即:


encoder.pkl
模型h5
模型
tokenizer.pkl


我正在使用model.h5我的代码是:

from keras.models import load_model
s_model = load_model('model.h5')

#predict the result
result = model.predict("HI my name is Mansi")


但这无法预测。

我认为该错误是因为我必须先对其进行标记化和编码,但是我不知道如何使用多个保存的模型来执行此操作。

谁能指导我如何使用上面笔记本中提到的保存的模型预测值和分数。

最佳答案

在输入模型之前,应先对文本进行预处理,然后是最小的工作脚本(改编自https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis/):

import time
import pickle
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model

model = load_model('model.h5')
tokenizer = pickle.load(open('tokenizer.pkl', "rb"))
SEQUENCE_LENGTH = 300
decode_map = {0: "NEGATIVE", 2: "NEUTRAL", 4: "POSITIVE"}

POSITIVE = "POSITIVE"
NEGATIVE = "NEGATIVE"
NEUTRAL = "NEUTRAL"
SENTIMENT_THRESHOLDS = (0.4, 0.7)

def decode_sentiment(score, include_neutral=True):
    if include_neutral:
        label = NEUTRAL
        if score <= SENTIMENT_THRESHOLDS[0]:
            label = NEGATIVE
        elif score >= SENTIMENT_THRESHOLDS[1]:
            label = POSITIVE

        return label
    else:
        return NEGATIVE if score < 0.5 else POSITIVE

def predict(text, include_neutral=True):
    start_at = time.time()
    # Tokenize text
    x_test = pad_sequences(tokenizer.texts_to_sequences([text]), maxlen=SEQUENCE_LENGTH)
    # Predict
    score = model.predict([x_test])[0]
    # Decode sentiment
    label = decode_sentiment(score, include_neutral=include_neutral)

    return {"label": label, "score": float(score),
       "elapsed_time": time.time()-start_at}

predict("hello")


测试:

predict("hello")


其输出:

{'elapsed_time': 0.6313169002532959,
 'label': 'POSITIVE',
 'score': 0.9836862683296204}

07-24 09:52
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