为什么我的自定义层在keras中失败了？

import tensorflow as tf
from tensorflow.keras.layers import Layer

class CustomLayer(Layer):
    def __init__(self, units=32, **kwargs):
        super(CustomLayer, self).__init__(**kwargs)
        self.units = units

    def build(self, input_shape):
        # 创建权重
        self.w = self.add_weight(shape=(input_shape[-1], self.units),
                                 initializer='random_normal',
                                 trainable=True)
        self.b = self.add_weight(shape=(self.units,),
                                 initializer='zeros',
                                 trainable=True)
        super(CustomLayer, self).build(input_shape)

    def call(self, inputs):
        # 定义前向传播逻辑
        return tf.matmul(inputs, self.w) + self.b

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

# 使用自定义层
inputs = tf.keras.Input(shape=(784,))
x = CustomLayer(units=64)(inputs)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)

model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.summary()