python - Keras:无输入的自定义图层

Question

我想在没有任何输入的情况下实现Keras自定义层，只是可训练的权重。

这是到目前为止的代码:

class Simple(Layer):

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

    def build(self):
       self.kernel = self.add_weight(name='kernel', shape=self.output_dim, initializer='uniform', trainable=True)
       super(Simple, self).build()  

    def call(self):
       return self.kernel

    def compute_output_shape(self):
       return self.output_dim

X = Simple((1, 784))()

我收到一条错误消息:
__call__() missing 1 required positional argument: 'inputs'
有没有在Keras中构建没有输入的自定义层的解决方法？

Answer 1

您可以执行以下操作

from tensorflow.keras.layers import Layer

class Simple(Layer):

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

    def build(self, input_shapes):
       self.kernel = self.add_weight(name='kernel', shape=self.output_dim, initializer='uniform', trainable=True)
       super(Simple, self).build(input_shapes)  

    def call(self, inputs):
       return self.kernel

    def compute_output_shape(self):
       return self.output_dim

X = Simple((1, 784))([])
print(X.shape)

哪个产生

>>> (1, 784)