Question

这是我的第二篇文章。如果我听起来很尴尬，我真的很抱歉。我是机器学习的新手。报告问题或提出负面意见将对我有帮助。再次抱歉，我无法清除我的问题。

现在问我一个问题，我正在研究下一个单词Prediction的作业。我正在尝试创建一个模型，该模型可用于基于像快速键盘这样的输入来生成下一个单词。我创建了一个能够生成下一个单词的模型。但是我想为一个输入单词生成多个单词。例如，我吃热___。对于空白处，我希望预测像狗，比萨饼，巧克力。但是，我当前的模型能够生成具有最大概率的下一个单词。但是我想要3个输出。我正在使用LSTM和Keras作为框架。我正在使用Keras的顺序模型。该代码包含三个主要部分：数据集准备，模型训练和生成预测。

def dataset_preparation():

    # basic cleanup
    corpus = text.split("\n\n")

    # tokenization  
    tokenizer.fit_on_texts(str(corpus).split("##"))
    total_words = len(tokenizer.word_index) + 1

    # create input sequences using list of tokens
    input_sequences = []
    for line in str(corpus).split("\n\n"):
        #print(line)
        token_list = tokenizer.texts_to_sequences([line])[0]
        #print("printing token_list",token_list)
        for i in range(1, len(token_list)):
            n_gram_sequence = token_list[:i+1]
            #print("printing n_gram_sequence",n_gram_sequence)
            #print("printing n_gram_sequence length",len(n_gram_sequence))
            input_sequences.append(n_gram_sequence)
    #print("Printing Input Sequence:",input_sequences)
    # pad sequences 
    max_sequence_len = 378
    input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

    # create predictors and label
    predictors, label = input_sequences[:,:-1],input_sequences[:,-1]
    label = ku.to_categorical(label, num_classes=total_words)

    return predictors, label, max_sequence_len, total_words

def create_model(predictors, label, max_sequence_len, total_words):

    model = Sequential()
    model.add(Embedding(total_words, 10, input_length=max_sequence_len-1))
    model.add(LSTM(150, return_sequences = True))
    # model.add(Dropout(0.2))
    model.add(LSTM(100))
    model.add(Dense(total_words, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=5, verbose=0, mode='auto')
    model.fit(predictors, label, epochs=1, verbose=1, callbacks=[earlystop])
    print (model.summary())
    return model

def generate_text(seed_text, next_words, max_sequence_len):
    for _ in range(next_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        print("Printing token list:",token_list)
        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        predicted = model.predict_classes(token_list, verbose=0)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
        seed_text += " " + output_word
    return seed_text

我正在关注medium中提到的文章预先谢谢你。

下一个词的预测RNN

0 个答案: