SENN.py 文件源码

def _double_conv_layer_wrapper(self, input1, input2, out_feature_maps,
                                   filter_length, is_train):
        '''Two parallele convolution layers for each channel
        using shared weights'''
        filter_width = input1.get_shape()[1].value
        in_feature_maps = input1.get_shape()[-1].value
        # shared weights
        W_conv = weight_variable(
            [filter_width, filter_length, in_feature_maps, out_feature_maps],
            regularizer=tf.contrib.layers.l2_regularizer(self.reg_fac))
        # shared bias
        b_conv = bias_variable([out_feature_maps])
        h_conv_t1 = tf.add(conv2d(input1, W_conv), b_conv)
        h_conv_b1 = self._batch_norm_wrapper(h_conv_t1, is_train)
        h_conv_t2 = tf.add(conv2d(input2, W_conv), b_conv)
        h_conv_b2 = self._batch_norm_wrapper(h_conv_t2, is_train)
        return tf.nn.relu(h_conv_b1), tf.nn.relu(h_conv_b2)