Python 将keras.engine.Layer转换为tensorflow

大家好，我想把用keras编写的自定义图层转换成tensorflow

我知道如何编写自定义图层keras，这是keras站点的示例：

from keras import backend as K
from keras.engine.topology import Layer
import numpy as np

class MyLayer(Layer):

    def __init__(self, output_dim, **kwargs):
       self.output_dim = output_dim
       super(MyLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='kernel', 
                                  shape=(input_shape[1],   
                                  self.output_dim),
                                  initializer='uniform',
                                  trainable=True)
        super(MyLayer, self).build(input_shape)  # Be sure to call this somewhere!

    def call(self, x):
       return K.dot(x, self.kernel)

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

我想知道我是否应该用类构建tensorflow自定义层？

---

如果可能的话，举个例子

如果有人有同样的问题，我会使用tf.layers.Layer来解决