Tensorflow 在使用深度学习的多标签分类中，准确度结果不符合逻辑

我有一个多标签分类问题，我使用了以下代码，但验证精度不符合逻辑，精度低于20%。在相同的数据条件下，采用机器学习的方法，其结果超过80%。这里可能有什么问题。 这是代码

data_path = "TestData.csv"
data_raw = pd.read_csv(data_path)
data_raw.shape #(156004, 9)
categories = list(data_raw.columns.values)
categories = categories[1:]#for the labels 
data = data_raw
data.shape #(156004, 9)
num_words = 20000 
max_features = 150000
max_len = 200 
embedding_dims = 128
num_epochs = 5 
X_train = data["text"].values
X_test  = data["text"].values


#Tokenization
tokenizer = tokenizer = Tokenizer(num_words)
tokenizer.fit_on_texts(list(X_train))
X_train = tokenizer.texts_to_sequences(X_train)
X_test = tokenizer.texts_to_sequences(X_test)
X_train = sequence.pad_sequences(X_train, max_len)
X_test  = sequence.pad_sequences(X_test,  max_len)
y_train = data[categories].values
y_test = data[categories].values
X_tra, X_val, y_tra, y_val = train_test_split(X_train, y_train)#, test_size =0.2, random_state=0)

CNN_model = Sequential([
    Embedding(input_dim=max_features, input_length=max_len, output_dim=embedding_dims),
    SpatialDropout1D(0.5),
    Conv1D(filters=20, kernel_size=8, padding='same', activation='relu'),
    BatchNormalization(),
    Dropout(0.5),
    GlobalMaxPool1D(),
    Dense(8, activation = 'sigmoid')
def mean_pred(y_true, y_pred):
    return K.mean(y_pred)
from tensorflow.python.keras.optimizer_v2.adam import Adam

adam = Adam()
CNN_model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])

for category in categories:
    print('**Processing {} ...**'.format(category))
    pred = CNN_model.fit(X_tra, y_tra, batch_size=128, epochs=5, validation_data=(X_val, y_val))

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标准的keras精度不支持多标签分类。@Dr.Snoopy，但我在另一个数据集中应用了此代码（有毒），并且它工作正常。@Dr.Snoopy在这种情况下如何获得正确的精度？