我正在实现我的第一个神经网络，它是用于二进制情感分析分类的LSTM。我已经通过降低字母，标记和删除大多数标点符号（仅保留。，'）对数据进行了预处理。 我还为此使用了GloVe的100d预训练嵌入。

问题是：无论我做什么，准确性都是很糟糕的，并且不会随着epoc的改变而改变（在更改LSTM架构时也不会改变）

我试图更改优化器及其学习率，向LSTM添加更多神经元，更改时期数和批量大小。

似乎没有任何作用

def setLSTM(data,stopRem,stemm,lemma,negHand):
    #pre-processing data
    data = pre_processing(data,negHand)
    print(data[1])


    #splitting data
    X_train,X_test,y_train,y_test = datasplit(data)

    #Setting the words as unique indexes (max 10k unique indexes)
    tokenizer = Tokenizer(num_words=5000)
    tokenizer.fit_on_texts(X_train)
    X_train = tokenizer.texts_to_sequences(X_train)
    X_test = tokenizer.texts_to_sequences(X_test)

    #getting vocabulary
    vocab = tokenizer.word_index.items()
    print(vocab)
    vocab_size = vocab_size = len(tokenizer.word_index) + 1

    #maxlen = Maxlen is correspondes to the maximum tweet length (so that we can add padding to shorter ones)
    maxlen = len(max((X_train + X_test)))
    print("Maxlen is: ",maxlen)

    #Padding the sequences to guarantee that all tweets have the same length
    X_train = pad_sequences(X_train,padding='post',maxlen=maxlen)
    X_test = pad_sequences(X_test,maxlen=maxlen)
    #Create embedding matrix with zeros (because some of the vocabulary might not exist in the embeddings)
    #and adding the embeddings we have
    embedding_matrix = zeros((vocab_size,100))
    for idx,word in vocab:
        embedding_vector = embeddings.get(word)
        if embedding_vector is not None:
            embedding_matrix[idx] = embedding_vector

    #creating the model with its layers (embedding layer,lstm layer,dense layer)
    model = Sequential()
    #The embedding layer has "trainable=False" because we're using pre-trained embeddings
    embedding_layer = Embedding(vocab_size,100,weights=[embedding_matrix],input_length=maxlen,trainable=False)
    model.add(embedding_layer)
    model.add(Dropout(0.2))
    #Adding an LSTM layer with 128 neurons
    model.add(LSTM(units=100))
    model.add(Dropout(0.2))
    #Adding dense layer with sigmoid activation
    model.add(Dense(1,activation='sigmoid'))

    #opt = Adam(learning_rate=0.0001,beta_1=0.9,beta_2=0.999,amsgrad=False)
    #Compiling model ("loss='binary_crossentropy'" because we're dealing with a binary classification problem)
    model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['acc'])
    print(model.summary())

    history = model.fit(X_train,batch_size=64,epochs=5,verbose=1,validation_split=0.2)
    score = model.evaluate(X_test,y_test,verbose=1)

    print("Test Score:",score[0])
    print("Test accuracy:",score[1])



setLSTM(tweets,False,False)

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_9 (Embedding)      (None,13,100)           1916600   
_________________________________________________________________
dropout_1 (Dropout)          (None,100)           0         
_________________________________________________________________
lstm_9 (LSTM)                (None,100)               80400     
_________________________________________________________________
dropout_2 (Dropout)          (None,100)               0         
_________________________________________________________________
dense_9 (Dense)              (None,1)                 101       
=================================================================
Total params: 1,997,101
Trainable params: 80,501
Non-trainable params: 1,916,600
_________________________________________________________________
None
Train on 10852 samples,validate on 2713 samples

Epoch 1/5
10852/10852 [==============================] - 5s 448us/step - loss: 0.6920 - acc: 0.5275 - val_loss: 0.6916 - val_acc: 0.5404

Epoch 2/5
10852/10852 [==============================] - 4s 360us/step - loss: 0.6917 - acc: 0.5286 - val_loss: 0.6908 - val_acc: 0.5404

Epoch 3/5
10852/10852 [==============================] - 4s 365us/step - loss: 0.6920 - acc: 0.5286 - val_loss: 0.6907 - val_acc: 0.5404

Epoch 4/5
10852/10852 [==============================] - 4s 382us/step - loss: 0.6916 - acc: 0.5286 - val_loss: 0.6903 - val_acc: 0.5404

Epoch 5/5
10852/10852 [==============================] - 4s 383us/step - loss: 0.6916 - acc: 0.5264 - val_loss: 0.6906 - val_acc: 0.5404

4522/4522 [==============================] - 1s 150us/step
Test Score: 0.6925433831950933
Test accuracy: 0.5176913142204285