Keras自定义层

需要实现三个方法：

• build(input_shape):定义你自己权重的地方，需要设置self.built=True.你可以通过调用super([Layer],self).build()来实现
• call(x):定义层逻辑的地方。除非你需要支持mask，否则你只需要关系传递给call的第一个参数
• compute_output_shape(input_shape):防止你更改你输入的shape，你需要定义shape改变的逻辑

from keras import backend as K
from keras.layers import Layer

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 at the end

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

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