python - 如何在keras中使用自定义层的偏差

Question

我创建了一个自定义层，但不确定如何使用和训练该层的偏置权重。

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

class MyLayer(Layer):

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

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel', 
                                      shape=(input_shape[1], self.output_dim),
                                      initializer='he_normal',
                                      trainable=True)
        super(MyLayer, self).build(input_shape)

    def call(self, x):
        ep = K.constant(value= 10e-5, shape=(1,513))
        v = K.dot(x, self.kernel)
        out = K.switch((v - ep)>0,  v,  - (ep)/(v-1-ep))
        return out


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

Answer 1

与您使用常规重量的方式完全相同......

def build(self, input_shape):
    self.kernel = self.add_weight(name='kernel', 
                                  shape=(input_shape[1], self.output_dim),
                                  initializer='he_normal',
                                  trainable=True)
    self.bias = self.add_weight(name='bias',
                                shape=(self.output_dim),
                                initializer='zeros',
                                trainable=True)
    super(MyLayer, self).build(input_shape)

def call(self, x):
    ep = K.constant(value= 10e-5, shape=(1,513))
    v = K.dot(x, self.kernel)   #maybe add bias here?
    out = K.switch((v - ep)>0,  v,  - (ep)/(v-1-ep))
    return out + self.bias