我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用tensorflow.segment_sum()。
def segment_indices(segment_ids, name=None): """Returns a `Tensor` of indices within each segment. segment_ids should be a sequence of non-decreasing non-negative integers that define a set of segments, e.g. [0, 0, 1, 2, 2, 2] defines 3 segments of length 2, 1 and 3. The return value is a `Tensor` containing the indices within each segment. Example input: [0, 0, 1, 2, 2, 2] Example output: [0, 1, 0, 0, 1, 2] Args: segment_ids: A 1-d `Tensor` containing an non-decreasing sequence of non-negative integers with type `tf.int32` or `tf.int64`. name: (Optional) A name for this operation. Returns: A `Tensor` containing the indices within each segment. """ with tf.name_scope(name, 'segment_indices'): segment_lengths = tf.segment_sum(tf.ones_like(segment_ids), segment_ids) segment_starts = tf.gather(tf.concat([[0], tf.cumsum(segment_lengths)], 0), segment_ids) return (tf.range(tf.size(segment_ids, out_type=segment_ids.dtype)) - segment_starts)
def output_embedding_layer(self, node_emb, scope): # Path to hyperparameters and configuration settings for the fingerprint output layers prefix = 'model/fingerprint_output_layers' with tf.variable_scope(scope, reuse=not self.is_training): # Compute node-level activation node_fp = tf.contrib.layers.fully_connected(inputs=node_emb, num_outputs=self.getitem('config', 'num_outputs', prefix), activation_fn=self.string_to_tf_act(self.getitem('config', 'activation_fn', prefix)), weights_initializer=self.weights_initializer_fp_out, weights_regularizer=self.weights_regularizer_fp_out, biases_initializer=tf.constant_initializer(0.0, tf.float32), trainable=self.getitem('config', 'trainable', prefix)) # Apply dropout (if necessary). Alternatively, could have also forced keep_prob to 1.0 when is_training is # False if self.is_training: node_fp = tf.nn.dropout(node_fp, self.getitem('config', 'keep_prob', prefix)) # Compute the graph-level activation as the sum of the node-level activations for all nodes in the graph graph_fp = tf.segment_sum(data=node_fp, segment_ids=self.input['node_graph_map']) return graph_fp, node_fp
def segment_logsumexp(xs, segments): """ Similar tf.segment_sum but compute logsumexp rather then sum """ # Stop gradients following the implementation of tf.reduce_logsumexp maxs = tf.stop_gradient(tf.reduce_max(xs, axis=1)) segment_maxes = tf.segment_max(maxs, segments) xs -= tf.expand_dims(tf.gather(segment_maxes, segments), 1) sums = tf.reduce_sum(tf.exp(xs), axis=1) return tf.log(tf.segment_sum(sums, segments)) + segment_maxes
def _compute_vert_context_soft(self, edge_factor, vert_factor, reuse=False): """ attention-based vertex(node) message pooling """ out_edge = utils.pad_and_gather(edge_factor, self.edge_pair_mask_inds[:,0]) in_edge = utils.pad_and_gather(edge_factor, self.edge_pair_mask_inds[:,1]) # gather correspounding vert factors vert_factor_gathered = tf.gather(vert_factor, self.edge_pair_segment_inds) # concat outgoing edges and ingoing edges with gathered vert_factors out_edge_w_input = tf.concat(concat_dim=1, values=[out_edge, vert_factor_gathered]) in_edge_w_input = tf.concat(concat_dim=1, values=[in_edge, vert_factor_gathered]) # compute compatibility scores (self.feed(out_edge_w_input) .fc(1, relu=False, reuse=reuse, name='out_edge_w_fc') .sigmoid(name='out_edge_score')) (self.feed(in_edge_w_input) .fc(1, relu=False, reuse=reuse, name='in_edge_w_fc') .sigmoid(name='in_edge_score')) out_edge_w = self.get_output('out_edge_score') in_edge_w = self.get_output('in_edge_score') # weight the edge factors with computed weigths out_edge_weighted = tf.mul(out_edge, out_edge_w) in_edge_weighted = tf.mul(in_edge, in_edge_w) edge_sum = out_edge_weighted + in_edge_weighted vert_ctx = tf.segment_sum(edge_sum, self.edge_pair_segment_inds) return vert_ctx
def test_SegmentSum(self): t = tf.segment_sum(self.random(4, 2, 3), np.array([0, 1, 1, 2])) self.check(t)
