Точность LSTM не меняется независимо от того, что я делаю

Question

Я реализую свою первую нейронную сеть, которая является LSTM для классификации бинарного анализа настроений. Я предварительно обработал данные с помощью опускания букв, разбивки на токены и удаления большинства знаков препинания (сохраняя только., '). Для этого я также использую 100d предварительно обученные вложения GloVe.

Проблема в следующем: что бы я ни делал, точность ужасна и не меняется с epocs (также не меняется при изменении архитектуры LSTM)

Я пытался изменить оптимизатор и скорость его обучения, добавить больше нейронов в LSTM, изменить количество эпох и размер пакета.

Кажется, ничего не работает

def setLSTM(data, stopRem, stemm, lemma, negHand):
    #pre-processing data
    data = pre_processing(data, stopRem, stemm, lemma, 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, padding='post', 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, y_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,False,False)

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_9 (Embedding)      (None, 13, 100)           1916600   
_________________________________________________________________
dropout_1 (Dropout)          (None, 13, 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