adgm.py 文件源码

def gaussian_stochastic(self, input_tensor, num_maps, scope):
        """
        :param inputs_list: list of Tensors to be added and input into the block
        :return: random variable single draw, mean, standard deviation, and intermediate representation
        """
        with tf.variable_scope(scope):
            input_tensor = tf.expand_dims(tf.expand_dims(input_tensor, 1), 1) if len(input_tensor.get_shape()) != 4 \
                else input_tensor
            intermediate = slim.conv2d(input_tensor, self._hidden_size, [1, 1], weights_initializer=self._initializer,
                                       scope='conv1')
            mean = slim.conv2d(intermediate, num_maps, [1, 1], weights_initializer=self._initializer,
                               activation_fn=None, scope='mean')
            sigma2 = tf.nn.softplus(
                slim.conv2d(intermediate, num_maps, [1, 1], weights_initializer=self._initializer,
                            activation_fn=None, scope='sigma2'))
            rv_single_draw = mean + tf.sqrt(sigma2) * tf.random_normal(tf.shape(mean))

        self.split_labeled_unlabeled(mean, '{}_mu'.format(scope))
        self.split_labeled_unlabeled(sigma2, '{}_sigma2'.format(scope))
        self.split_labeled_unlabeled(rv_single_draw, '{}_sample'.format(scope))
        return rv_single_draw