Convolution.

def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
        """Convolution."""
        with tf.variable_scope(name):
            n = filter_size * filter_size * out_filters
            kernel = tf.get_variable(
                "DW", [filter_size, filter_size, in_filters, out_filters],
                tf.float32,
                initializer=tf.random_normal_initializer(
                    stddev=np.sqrt(2.0 / n)))
            return tf.nn.conv2d(x, kernel, strides, padding="SAME")