binary_layers.py 文件源码

def build(self, input_shape):
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1 
        if input_shape[channel_axis] is None:
                raise ValueError('The channel dimension of the inputs '
                                 'should be defined. Found `None`.')

        input_dim = input_shape[channel_axis]
        kernel_shape = self.kernel_size + (input_dim, self.filters)

        base = self.kernel_size[0] * self.kernel_size[1]
        if self.H == 'Glorot':
            nb_input = int(input_dim * base)
            nb_output = int(self.filters * base)
            self.H = np.float32(np.sqrt(1.5 / (nb_input + nb_output)))
            #print('Glorot H: {}'.format(self.H))

        if self.kernel_lr_multiplier == 'Glorot':
            nb_input = int(input_dim * base)
            nb_output = int(self.filters * base)
            self.kernel_lr_multiplier = np.float32(1. / np.sqrt(1.5/ (nb_input + nb_output)))
            #print('Glorot learning rate multiplier: {}'.format(self.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=kernel_shape,
                                 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((self.output_dim,),
                                     initializer=self.bias_initializers,
                                     name='bias',
                                     regularizer=self.bias_regularizer,
                                     constraint=self.bias_constraint)

        else:
            self.lr_multipliers = [self.kernel_lr_multiplier]
            self.bias = None

        # Set input spec.
        self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
        self.built = True