如何使用TensorFlow的子分类方法预测二元分类的类？

我正在使用

Tensorflow

进行二元分类。我的代码是：

class ChurnClassifier(Model):
    def __init__(self):
        super(ChurnClassifier, self).__init__()
        self.layer1 = layers.Dense(20, input_dim = 20, activation = 'relu')
        self.layer2 = layers.Dense(41, activation = 'relu')
        self.layer3 = layers.Dense(83, activation = 'relu')
        self.layer4 = layers.Dense(2,  activation = 'sigmoid')
        
    def call(self, inputs):
        x = self.layer1(inputs)
        x = self.layer2(x)
        x = self.layer3(x)
        return self.layer4(x)
        
ChurnClassifier = ChurnClassifier()
ChurnClassifier.compile(optimizer = 'adam',
                        loss=tf.keras.losses.CategoricalCrossentropy(),
                       metrics = ['accuracy'])

现在我安装了模型：

history = ChurnClassifier.fit(X_train_nur, Y_train_nur, 
          epochs=20, 
          batch_size=512,
          validation_data=(X_val_nur, Y_val_nur),
          shuffle=True)

现在，我想预测类0或1，所以我使用了code-

prediction=crossClassifier.predict（X_val\u nur）

现在我想看看有多少是0和1来计算TN，FN，TP，FP。所以我用板条箱装了一个数据框用于预测。代码-

pred_y = pd.DataFrame(prediction , columns=['pred_y']) 

但我得到以下数据帧-

我的X_列车样本：

array([[2.02124594e+08, 3.63743942e+04, 2.12000000e+02, ...,
        4.30000000e+01, 0.00000000e+00, 1.00000000e+00],
       [4.93794595e+08, 6.66593354e+02, 4.22000000e+02, ...,
        2.60000000e+01, 0.00000000e+00, 1.00000000e+00],
       [7.28506124e+08, 1.17953696e+04, 1.14000000e+03, ...,
        2.50000000e+01, 0.00000000e+00, 1.00000000e+00],
       ...,
       [4.63797916e+08, 1.19273275e+03, 4.10000000e+02, ...,
        9.00000000e+00, 0.00000000e+00, 1.00000000e+00],
       [4.04285400e+08, 1.87350825e+04, 3.01000000e+02, ...,
        1.60000000e+01, 0.00000000e+00, 1.00000000e+00],
       [5.08433538e+08, 3.19289528e+03, 4.18000000e+02, ...,
        9.00000000e+00, 0.00000000e+00, 1.00000000e+00]])

我的示例y_序列-

数组（[0,0,0，…，0,0,0]，dtype=int64）

列车号仅包含0和1

有什么问题吗

提前谢谢