Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Tsinghua University

Xiang Lan

Dianhai Yu

Yanjun Ma

Haifeng Wang

Hongyu Liu

Liujie Zhang

Huihuang Zheng

Wei Zhou

Weihang Chen

Yamei Li

Di Zhang

Tian Wu

Tao Luo

Xiaoguang Hu

Yuezhi Zhou

Fei Guo

Jiabin Yang

A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.

Data processing method and apparatus based on recurrent neural network, and device

The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.

Method for processing data, and electronic device, storage medium and program product

Embodiments of the present disclosure provide a method and device for a personal commute time period. The method includes: obtaining data of at least one commute trajectory of a user within a preset time period; performing cluster analysis on the data of the at least one commute trajectory to obtain at least one commute trajectory cluster, and obtaining a combined trajectory according to the commute trajectories in each commute trajectory cluster; for each commute trajectory T of the at least one commute trajectory, determining a commute speed of the user according to a combined trajectory corresponding to a commute trajectory cluster to which the commute trajectory T belongs; and determining a commute time period corresponding to the commute trajectory T according to the commute speed.

Method and device for determining personal commute time period

The present disclosure provides an operator registration method and apparatus for a deep learning framework, a device and a storage medium, relates to the field of computer technologies, and specifically to the field of artificial intelligence such as deep learning. The operator registration method for a deep learning framework includes: receiving registration information provided by a user for registering operators with the deep learning framework, the registration information including: a custom calculation function, the custom calculation function being written in a manner irrelevant to the deep learning framework; building operator meta-information in the deep learning framework based on the registration information; and constructing a to-be-registered operator within the deep learning framework based on the operator meta-information, and registering the to-be-registered operator in a global operator table within the deep learning framework. The present disclosure can simplify an operator registration process.

Operator registration method and apparatus for deep learning framework, device and storage medium

A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.

Method and apparatus of converting schema in deep learning framework, and computer storage medium

The present application discloses a method and apparatus for generating error reporting content of a deep learning framework, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application to generate error reporting content of a deep learning framework is: acquiring an error code and error information corresponding to the error code; generating an error file according to the error code and the error information corresponding thereto, and packaging the error file into the deep learning framework; running the deep learning framework, and in response to the deep learning framework receiving an error code returned by a third-party library when an error occurs in calling of a third-party library application programming interface (API), extracting, from the error file, error information corresponding to the received error code; and generating error reporting content according to the error information. The present application can automatically generate error reporting content including richer information.

Method and apparatus for generating error reporting content of deep learning framework

The present disclosure discloses a method, an apparatus and an electronic device for deploying an operator in a deep learning framework and relates to the field of artificial intelligence technology such as deep learning. And the solution is: acquiring a source file of the operator; compiling the source file of the operator to form a dynamic link library of the operator; generating an interface file transferred from the dynamic link library of the operator; generating an installable library file according to the dynamic link library and the interface file; installing the installable library file to a target programming language library.

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Beijing, China

Xiang Lan