公开(公告)号：US20170132513A1

公开(公告)日：2017-05-11

申请号：US15347618

申请日：2016-11-09

Applicant: Google Inc.

Inventor： Yuan Yu ,  Manjunath Kudlur Venkatakrishna

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06F9/5038 ,  G06F9/5044 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/084

Abstract: Systems and Methods for training a neural network represented as a computational graph are disclosed. An example method begins with obtaining data representing a computational graph. The computational graph is then augmented to generate a training computational graph for training the neural network using a machine learning training algorithm that includes computing a gradient of an objective function with respect to each of the parameters of the neural network. Augmenting the computational graph includes inserting a plurality of gradient nodes and training edges into the computational graph to generate a backward path through the computational graph that represents operations for computing the gradients of the objective function with respect to the parameters of the neural network. The neural network is trained using the machine learning training algorithm by executing the training computational graph.

公开(公告)号：US20170124452A1

公开(公告)日：2017-05-04

申请号：US15337744

申请日：2016-10-28

Applicant: Google Inc.

Inventor： Paul A. Tucker ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat ,  Yuan Yu

IPC: G06N3/08 ,  G06N5/04

CPC classification number: G06N3/08 ,  G06F9/5038 ,  G06F9/5066 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/084 ,  G06N5/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.