这个keras神经网络的准确率是0%

我试图对MNIST数据集的子集进行二元分类。目标是预测样本是6还是8。因此,每个样本有784个像素特征,数据集中有8201个样本。我构建了一个由一个输入层、两个隐藏层和一个输出层组成的网络。我使用sigmoid作为激活函数来输出layer和relu,用于隐藏层。我不知道为什么我最终得到了0%的准确率。

#import libraries
from keras.models import Sequential
from keras.layers import Dense
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import os
np.random.seed(7)

os.chdir('C:/Users/olivi/Documents/Python workspace')

#data loading
data = pd.read_csv('MNIST_CV.csv')

#Y target label
Y = data.iloc[:,0]

#X: features
X = data.iloc[:,1:]

X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size=0.25,random_state=42)

# create model
model = Sequential()
model.add(Dense(392,kernel_initializer='normal',input_dim=784, 
activation='relu'))
model.add(Dense(196,kernel_initializer='normal', activation='relu'))
model.add(Dense(98,kernel_initializer='normal', activation='relu'))
model.add(Dense(1, activation='sigmoid'))


model.compile(loss = 'binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()


# Training the model
model.fit(X_train, y_train, epochs=100, batch_size=50)

print(model.predict(X_test,batch_size= 50))

score = model.evaluate(X_test, y_test)
print("\n Testing Accuracy:", score[1])