您好，提前感谢您的任何回答。我正在为多类分类问题构建 NN。我已经通过 Word2vec 对我的数据进行了预训练，并生成了一个 300 维的带有值的向量。我有 6 个班级..我的数据包含 15000 行（x300 维度）。我的第一个问题是：

单位数是多少？它是我们可以从理论中提取的东西吗？我也设法获得了 0.85-0.90 的训练集准确度，但验证集的准确度总是很低：0.22-0.25。我不知道该怎么做，因为我是这类东西的新手。我的代码是：

import numpy as np
import pandas as pd
import keras
from sklearn.preprocessing import Imputer
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense,Dropout
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from keras import regularizers



dataset = pd.read_csv('word2vec.csv')


X = dataset.iloc[:, 0:300].values
y = dataset.iloc[:, 300].values

imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 0:300])
X[:, 0:300] = imputer.transform(X[:, 0:300])

counter = 0
for iterations in range(1):

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    classifier = Sequential()

    classifier.add(Dense(units=70, kernel_initializer='uniform',kernel_regularizer=regularizers.l2(0.0001),
                activity_regularizer=regularizers.l1(0.001), activation='relu', input_dim=300))

    classifier.add(Dropout(0.3))

    # classifier.add(Dense(units=70, kernel_initializer='uniform', activation='relu'))


    classifier.add(Dense(units=6, kernel_initializer='uniform', activation='softmax'))

    classifier.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

    history = classifier.fit(X_train, y_train,validation_split=0.1, batch_size=10, epochs=100,shuffle=True)

    y_pred = classifier.predict(X_test)
    # y_pred = (y_pred > 0.1666)

    classifier.summary()
    counter+=1
    print(classifier.layers)
    print("Running RNN with Dropout Layer")
    print("Number of layers used: "+str(len(classifier.layers)))
    # list all data in history
    print(history.history.keys())
    # summarize history for accuracy
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()
    # summarize history for loss
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()