ternary_layers.py 文件源码

def build(self, input_shape):
        assert len(input_shape) >= 2
        input_dim = input_shape[1]

        if self.H == 'Glorot':
            self.H = np.float32(np.sqrt(1.5 / (input_dim + self.units)))
            #print('Glorot H: {}'.format(self.H))
        if self.kernel_lr_multiplier == 'Glorot':
            self.kernel_lr_multiplier = np.float32(1. / np.sqrt(1.5 / (input_dim + self.units)))
            #print('Glorot learning rate multiplier: {}'.format(self.kernel_lr_multiplier))

        self.kernel_constraint = Clip(-self.H, self.H)
        self.kernel_initializer = initializers.RandomUniform(-self.H, self.H)
        self.kernel = self.add_weight(shape=(input_dim, self.units),
                                     initializer=self.kernel_initializer,
                                     name='kernel',
                                     regularizer=self.kernel_regularizer,
                                     constraint=self.kernel_constraint)

        if self.use_bias:
            self.lr_multipliers = [self.kernel_lr_multiplier, self.bias_lr_multiplier]
            self.bias = self.add_weight(shape=(self.output_dim,),
                                     initializer=self.bias_initializer,
                                     name='bias',
                                     regularizer=self.bias_regularizer,
                                     constraint=self.bias_constraint)
        else:
            self.lr_multipliers = [self.kernel_lr_multiplier]
            self.bias = None

        self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
        self.built = True