WO2022141840A1

WO2022141840A1 - Network architecture search method and apparatus, electronic device, and medium

Info

Publication number: WO2022141840A1
Application number: PCT/CN2021/083181
Authority: WO
Inventors: 张楠; 王健宗; 瞿晓阳
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-12-29
Filing date: 2021-03-26
Publication date: 2022-07-07
Also published as: CN112700006A; CN112700006B

Abstract

The present application relates to the intelligent decision making technology. Disclosed is a network architecture search method, comprising: acquiring position information of a search space; according to the position information, performing network architecture search in the search space using a cuckoo algorithm, to obtain a multi-target network architecture set; performing evaluation processing on a plurality of target network architectures in the multi-target network architecture set according to a preset performance evaluation model, to obtain an evaluation result; determining whether the evaluation result satisfies a preset evaluation condition; and if the evaluation result satisfies the evaluation condition, determining that a target network architecture corresponding to the evaluation result is a final network architecture. The present application also relates to blockchain technology, and the evaluation result, etc. can be stored in a blockchain node. Also disclosed are a network architecture search apparatus, an electronic device and a storage medium. The present application can solve the problem that an existing search method cannot find a network architecture which satisfies a plurality of targets at the same time.

Description

Network architecture search method, device, electronic device and medium

This application claims the priority of the Chinese patent application with the application number CN202011603641.8 and the title of the invention "Network Architecture Search Method, Device, Electronic Device and Medium", which was filed with the China Patent Office on December 29, 2020. Reference is incorporated in this application.

technical field

The present application relates to the technical field of intelligent decision-making, and in particular, to a network architecture search method, apparatus, electronic device, and computer-readable storage medium.

Background technique

Deep neural networks have been widely used in image recognition, speech recognition, and language modeling, but it is difficult to deploy these networks on resource-constrained platforms such as mobile or embedded devices, so it is often necessary to search for Network architecture for resource-constrained mobile devices.

The inventors realized that the existing network architecture search methods use gradient-based methods to search in the search space, but gradient-based methods focus on a single objective to minimize the error metric of a task, that is, only applicable to a single-target search , it is impossible to search for a network architecture that satisfies multiple different goals at the same time.

SUMMARY OF THE INVENTION

A network architecture search method, comprising:

Obtain the location information of the search space;

According to the location information, a network architecture search is performed in the search space through the cuckoo algorithm to obtain a multi-objective network architecture set;

Perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, to obtain evaluation results;

Judging whether the evaluation result satisfies a preset evaluation condition;

If the evaluation result satisfies the evaluation condition, it is determined that the target network architecture corresponding to the evaluation result is the final network architecture.

A network architecture search device, the device includes:

The information acquisition module is used to acquire the location information of the search space;

an architecture search module, configured to perform a network architecture search in the search space through the cuckoo algorithm according to the location information to obtain a multi-target network architecture set;

A performance evaluation module, configured to perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain evaluation results;

The architecture acquisition module is configured to judge whether the evaluation result satisfies the preset evaluation condition, and if the evaluation result satisfies the evaluation condition, determine the target network architecture corresponding to the evaluation result as the final network architecture.

An electronic device comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores computer program instructions executable by the at least one processor, the computer program instructions being executed by the at least one processor to enable the at least one processor to perform the steps of:

Obtain the location information of the search space;

Judging whether the evaluation result satisfies a preset evaluation condition;

A computer-readable storage medium storing a computer program, the computer program implements the following steps when executed by a processor:

Obtain the location information of the search space;

Judging whether the evaluation result satisfies a preset evaluation condition;

The present application can solve the problem that the existing search methods cannot search for a network architecture that satisfies multiple targets at the same time.

Description of drawings

1 is a schematic flowchart of a network architecture search method provided by an embodiment of the present application;

2 is a schematic diagram of a module of a network architecture search apparatus provided by an embodiment of the present application;

FIG. 3 is a schematic diagram of an internal structure of an electronic device for implementing a network architecture search method according to an embodiment of the present application.

The realization, functional characteristics and advantages of the purpose of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

An embodiment of the present application provides a method for searching a network architecture, where an execution body of the method for searching a network architecture includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present application, such as a server and a terminal. kind. In other words, the network architecture search method can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Referring to FIG. 1 , it is a schematic flowchart of a network architecture search method provided by an embodiment of the present application. In this embodiment, the network architecture search method includes:

S1. Obtain location information of the search space.

In this embodiment of the present application, the search space may be pre-constructed, and specifically, the search space may be constructed through a neural architecture search (Neural Architecture Search, NAS) mechanism.

Specifically, in the embodiment of the present application, before acquiring the location information of the search space, the method further includes: constructing a search space.

The building search space includes:

Get the preset neural network unit;

The neural network units are arranged and combined in a preset connection manner to obtain a search space.

In detail, in the embodiment of the present application, the neural network unit includes but is not limited to 3x3 convolution, 5x5 convolution, 7x7 convolution, a max pooling layer and an average pooling layer.

In detail, the connection manner includes enabling incoming connections and disabling incoming connections.

Wherein, the search space consists of all candidate architectures that can be searched.

In an optional embodiment, the search space may be a chain structure neural network, or a multi-branch neural network including multiple branches and skip connections.

In this embodiment of the present application, the search space includes neural network units, and the structures of these repeated neural network units may be different.

Preferably, in order to reduce the size of the search space, the search space includes repeated neural network units, and the structures of these repeated neural network units are the same.

S2. According to the location information, a network architecture search is performed in the search space by using a cuckoo algorithm to obtain a multi-objective network architecture set.

In the embodiment of the present application, the network architecture search is performed in the search space through the cuckoo algorithm according to the location information, and a multi-target network architecture set is obtained, including:

Obtain a candidate architecture set in the search space and related parameters of the candidate architecture set according to the location information;

calculating the fitness value of each candidate architecture in the candidate architecture set;

Summarize the candidate architectures whose fitness value is greater than the preset adaptation threshold in the candidate architecture set to obtain an initial candidate architecture set;

calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set;

If there is no adaptive change rate less than a fixed threshold, determining the initial candidate architecture set as the multi-objective network architecture set;

If there is an adaptive change rate less than the fixed threshold, update the candidate architecture corresponding to the adaptive change rate less than the fixed threshold to obtain a standard network architecture set;

Obtain a preset random number, and compare the random number with the size of the target probability corresponding to any network architecture in the standard network architecture set;

The network architecture corresponding to the target probability smaller than the random number is deleted, the network architecture corresponding to the target probability greater than or equal to the random number is retained, and the multi-target network architecture set is obtained.

In detail, the candidate architecture set includes the permuted and combined neural network units randomly selected from the search space. Specifically, the candidate architectures in the candidate architecture set are the randomly selected permuted and combined neural network units in the search space, The relevant parameters include, but are not limited to, the scale parameter of the candidate architecture set and the target probability of the candidate architecture.

Wherein, the target probability corresponding to the network architecture is the probability value of the network architecture being searched, which is its own fixed parameter.

When calculating the fitness value, the fitness value of each candidate architecture in the initial candidate architecture set may be used. Specifically, a preset adaptation formula is used to calculate the fitness value of each candidate architecture in the candidate architecture set.

The preset adaptation formula includes:

in,

represents the fitness value of the candidate architecture,

represents the position of the γth candidate architecture at t iterations, β is the step size factor,

Represents point-to-point multiplication, levy(λ) represents the random search path of Levy flight, and λ is a preset parameter.

Further, the calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set includes:

calculating the first fitness value of the network architecture in the initial network architecture set at the preset first iteration number and the second fitness value at the preset second iteration number;

Calculate the adaptive change rate between the first fitness value and the second fitness value according to a preset rate of change formula;

The adaptive change rate is determined as an adaptive change rate of the candidate architecture.

specifically,

The preset rate of change formula includes:

where Δf is the adaptive change rate,

is the first fitness value,

is the second fitness value, t is the first iteration number, σ is the second iteration number, and L is the position of the candidate architecture in the initial candidate architecture set in the initial candidate architecture set.

Further, updating the candidate architecture corresponding to the adaptive change rate less than the fixed threshold includes:

Calculate the step size of the candidate architecture corresponding to the adaptive change rate smaller than the fixed threshold by using the preset step size formula;

The candidate architecture is replaced according to the step size.

In detail, the preset step size formula includes:

U～N(0, τ ² ), V～N(0, 1

Among them, s is the step size, U and V both refer to variables that obey a Gaussian distribution, ε is the number of neural network units in the initial network architecture, and N represents a Gaussian distribution.

Optionally, before the embodiment of the present application performs a network architecture search in the search space through the cuckoo algorithm according to the location information, and obtains a multi-target network architecture set, the method further includes:

Combining all candidate architectures in the search space to obtain a large super network;

using the constructed training set to train the large hyper-network;

The parameters of the candidate architectures in the search space are updated according to the large super-network that has been trained.

In the embodiment of the present application, the parameters of the candidate architectures in the search space are updated by using the large-scale super network that has been trained, so that the candidate architectures are more adaptable, thereby improving the accuracy of the search.

S3. Perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain an evaluation result.

In the embodiment of the present application, the preset performance evaluation model includes, but is not limited to, a proxy model, weight sharing, and a hypernetwork.

Preferably, in this embodiment of the present application, a super network is used to perform evaluation processing on multiple target network architectures in the multi-target network architecture set to obtain an evaluation result.

Specifically, performing evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain evaluation results, including:

constructing super-networks corresponding to the multiple target network architectures, and performing preset rounds of training on the super-networks;

Perform a spatial performance evaluation process on the trained super-network to obtain an evaluation result.

In detail, the back-propagation method and the gradient descent method can be selected for training the super-network for preset rounds, and the operator parameters and structural parameters on the super-network are alternately trained.

Specifically, the space performance evaluation process is performed on the trained super network, and the evaluation result obtained is to test and evaluate the performance of the super network on the demand task and demand data set, and use the relevant performance indicators of the super network as the multiple target network architectures performance indicators for subsequent evaluation and comparison.

Optionally, in this embodiment of the present application, the performance indicators of the multiple target network architectures include classification accuracy, inference delay, FLOPs (floating-point operations per second, the number of floating-point operations performed per second), and the number of parameters. .

S4. Determine whether the evaluation result satisfies a preset evaluation condition.

In the embodiment of the present application, the evaluation result includes four performance indicators of the multiple target network architectures, and the preset evaluation condition means that the corresponding values of the performance indicators in the evaluation result are all greater than or equal to the preset value performance threshold.

S5. If the evaluation result satisfies the evaluation condition, determine the target network architecture corresponding to the evaluation result as the final network architecture.

In this embodiment of the present application, if only one of the multiple target network architectures satisfies the evaluation condition, the target network architecture that satisfies the evaluation condition is used as the final network architecture.

In another optional embodiment of the present application, if all evaluation results do not satisfy the evaluation condition, the operation returns to the operation of re-searching in the search space by using the cuckoo algorithm, and the network architecture search is performed again.

In the embodiment of the present application, a network architecture search is first performed in the search space through the cuckoo algorithm, and a multi-objective network architecture set is obtained. an optimal network architecture, perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, obtain an evaluation result, determine whether the searched network architecture meets the requirements according to the evaluation result, and improve The accuracy of the final target network architecture that meets multiple objectives. Therefore, the network architecture search method proposed in the present application can solve the problem that the existing search methods cannot search for a network architecture that satisfies multiple objectives at the same time.

As shown in FIG. 2 , it is a schematic block diagram of a network architecture search apparatus provided by an embodiment of the present application.

The network architecture search apparatus 100 described in this application may be installed in an electronic device. According to the implemented functions, the network architecture search apparatus 100 may include an information acquisition module 101 , an architecture search module 102 , a performance evaluation module 103 and an architecture acquisition module 104 . The modules described in this application may also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can perform fixed functions, and are stored in the memory of the electronic device.

In this embodiment, the functions of each module/unit are as follows:

The information acquisition module 101 is used to acquire the location information of the search space;

The architecture search module 102 is configured to perform a network architecture search in the search space through the cuckoo algorithm according to the location information to obtain a multi-target network architecture set;

The performance evaluation module 103 is configured to perform evaluation processing on a plurality of target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain evaluation results;

The architecture obtaining module 104 is configured to determine whether the evaluation result satisfies a preset evaluation condition, and if the evaluation result satisfies the evaluation condition, determine the target network architecture corresponding to the evaluation result as the final network architecture.

The information acquisition module 101 is configured to acquire location information of the search space.

Specifically, in the embodiment of the present application, the apparatus further includes a search space building module, and the search space building module is used for:

Before obtaining the location information of the search space, obtain a preset neural network unit;

The architecture search module 102 is configured to perform a network architecture search in the search space by using the cuckoo algorithm according to the location information to obtain a multi-target network architecture set.

In this embodiment of the present application, the architecture search module 102 is specifically configured to:

The preset adaptation formula includes:

in,

represents the fitness value of the candidate architecture,

Specifically, the preset rate of change formula includes:

where Δf is the adaptive change rate,

is the first fitness value,

The candidate architecture is replaced according to the step size.

In detail, the preset step size formula includes:

U～N(0, τ ² ), V～N(0, 1

Optionally, the apparatus described in this embodiment of the present application further includes a parameter update module, where the parameter update module is configured to:

Before obtaining the multi-objective network architecture set, combine all candidate architectures in the search space to obtain a large super network;

using the constructed training set to train the large hyper-network;

The performance evaluation module 103 is configured to perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain evaluation results.

Specifically, the performance evaluation module 103 is specifically used for:

Specifically, performing spatial performance evaluation processing on the trained super-network, and obtaining the evaluation result is to test and evaluate the performance of the super-network on the demand task and demand data set, and use the relevant performance indicators of the super-network as the multiple target network architectures performance indicators for subsequent evaluation and comparison.

The architecture obtaining module 104 is configured to judge whether the evaluation result satisfies a preset evaluation condition.

The architecture obtaining module 104 is configured to determine the target network architecture corresponding to the evaluation result as the final network architecture if the evaluation result satisfies the evaluation condition.

If the evaluation result does not satisfy the evaluation condition, return to using the cuckoo algorithm to re-search in the search space; if the evaluation result satisfies the evaluation condition, use the multi-objective network architecture corresponding to the evaluation result as the final network architecture.

In the embodiment of the present application, a network architecture search is first performed in the search space through the cuckoo algorithm, and a multi-objective network architecture set is obtained. an optimal network architecture, perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, obtain an evaluation result, determine whether the searched network architecture meets the requirements according to the evaluation result, and improve The accuracy of the final target network architecture that meets multiple objectives. Therefore, the network architecture search device proposed in the present application can solve the problem that a network architecture that satisfies multiple objectives at the same time cannot be searched.

As shown in FIG. 3 , it is a schematic structural diagram of an electronic device implementing the network architecture search method of the present application.

The electronic device 1 may include a processor 10, a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a network architecture search program 12.

Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, magnetic disk, CD etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1 . In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a pluggable mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital) equipped on the electronic device 1. , SD) card, flash memory card (Flash Card), etc. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various data, such as the code of the network architecture search program 12, etc., but also can be used to temporarily store data that has been output or will be output.

In some embodiments, the processor 10 may be composed of integrated circuits, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits packaged with the same function or different functions, including one or more integrated circuits. Central Processing Unit (CPU), microprocessor, digital processing chip, graphics processor and combination of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, by running or executing the program or module (for example, executing the program) stored in the memory 11. Network architecture search program, etc.), and call data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

The bus may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (Extended industry standard architecture, EISA for short) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. The bus is configured to implement connection communication between the memory 11 and at least one processor 10 and the like.

FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than those shown in the figure. components, or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) for powering the various components, preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that the power management The device implements functions such as charge management, discharge management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components. The electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

Further, the electronic device 1 may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.

Optionally, the electronic device 1 may further include a user interface, and the user interface may be a display (Display), an input unit (eg, a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. The display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

The network architecture search program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 10, can realize:

Obtain the location information of the search space;

Judging whether the evaluation result satisfies a preset evaluation condition;

Further, if the modules/units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile, for example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U Disk, removable hard disk, magnetic disk, CD-ROM, computer memory, read-only memory (ROM, Read-Only Memory).

The present application also provides a computer-readable storage medium. The readable storage medium may be volatile or non-volatile, and the readable storage medium stores a computer program. When executed by the processor of the electronic device, it can achieve:

Obtain the location information of the search space;

Judging whether the evaluation result satisfies a preset evaluation condition;

Further, the computer-usable storage medium may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required by at least one function, and the like; using the created data, etc.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division manners in actual implementation.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the application is to be defined by the appended claims rather than the foregoing description, which is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any accompanying reference signs in the claims should not be construed as limiting the involved claims.

Furthermore, it is clear that the word "comprising" does not exclude other units or steps and the singular does not exclude the plural. Several units or means recited in the system claims can also be realized by one unit or means by means of software or hardware. Second-class terms are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limitations. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present application.

Claims

A network architecture search method, wherein the method comprises:

Obtain the location information of the search space;

According to the location information, a network architecture search is performed in the search space through the cuckoo algorithm to obtain a multi-objective network architecture set;

Perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, to obtain evaluation results;

Judging whether the evaluation result satisfies a preset evaluation condition;

If the evaluation result satisfies the evaluation condition, it is determined that the target network architecture corresponding to the evaluation result is the final network architecture.
The network architecture search method according to claim 1, wherein, before acquiring the location information of the search space, the method further comprises:

Get the preset neural network unit;

The neural network units are arranged and combined in a preset connection manner to obtain a search space.
The network architecture search method according to claim 1, wherein the performing a network architecture search in the search space through the cuckoo algorithm according to the location information, to obtain a multi-target network architecture set, comprising:

Obtain a candidate architecture set in the search space and related parameters of the candidate architecture set according to the location information;

calculating the fitness value of each candidate architecture in the candidate architecture set;

Summarize the candidate architectures whose fitness value is greater than the preset adaptation threshold in the candidate architecture set to obtain an initial candidate architecture set;

calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set;

If there is no adaptive change rate less than a fixed threshold, determining the initial candidate architecture set as the multi-objective network architecture set;

If there is an adaptive change rate less than the fixed threshold, update the candidate architecture corresponding to the adaptive change rate less than the fixed threshold to obtain a standard network architecture set;

Obtain a preset random number, and compare the random number with the size of the target probability corresponding to any network architecture in the standard network architecture set;

The network architecture corresponding to the target probability smaller than the random number is deleted, the network architecture corresponding to the target probability greater than or equal to the random number is retained, and the multi-target network architecture set is obtained.
The network architecture search method according to claim 3, wherein the calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set comprises:

calculating the first fitness value of the network architecture in the initial network architecture set at the preset first iteration number and the second fitness value at the preset second iteration number;

Calculate the adaptive change rate between the first fitness value and the second fitness value according to a preset rate of change formula;

The adaptive change rate is determined as an adaptive change rate of the candidate architecture.
The network architecture search method according to claim 4, wherein the preset rate of change formula comprises:

where Δf is the adaptive change rate,
is the first fitness value,
is the second fitness value, t is the first iteration number, σ is the second iteration number, and L is the position of the candidate architecture in the initial candidate architecture set in the initial candidate architecture set.
The network architecture search method according to claim 3, wherein the updating the candidate architecture corresponding to the adaptive change rate less than a fixed threshold value comprises:

Calculate the step size of the candidate architecture corresponding to the adaptive change rate smaller than the fixed threshold by using the preset step size formula;

The candidate architecture is replaced according to the step size.
The network architecture search method according to any one of claims 1 to 6, wherein, before the network architecture search is performed in the search space through the cuckoo algorithm according to the location information, and a multi-target network architecture set is obtained, The method also includes:

Combining all candidate architectures in the search space to obtain a large super network;

using the constructed training set to train the large hyper-network;

The parameters of the candidate architectures in the search space are updated according to the large super-network that has been trained.
A network architecture search device, wherein the device includes:

The information acquisition module is used to acquire the location information of the search space;

an architecture search module, configured to perform a network architecture search in the search space through the cuckoo algorithm according to the location information to obtain a multi-target network architecture set;

A performance evaluation module, configured to perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model to obtain evaluation results;

The architecture acquisition module is configured to judge whether the evaluation result satisfies the preset evaluation condition, and if the evaluation result satisfies the evaluation condition, determine the target network architecture corresponding to the evaluation result as the final network architecture.
An electronic device, wherein the electronic device comprises:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores computer program instructions executable by the at least one processor, the computer program instructions being executed by the at least one processor to enable the at least one processor to perform the steps of:

Obtain the location information of the search space;

According to the location information, a network architecture search is performed in the search space through the cuckoo algorithm to obtain a multi-objective network architecture set;

Perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, to obtain evaluation results;

Judging whether the evaluation result satisfies a preset evaluation condition;

If the evaluation result satisfies the evaluation condition, it is determined that the target network architecture corresponding to the evaluation result is the final network architecture.
The electronic device according to claim 9, wherein, before acquiring the location information of the search space, the at least one processor further performs the following steps:

Get the preset neural network unit;

The neural network units are arranged and combined in a preset connection manner to obtain a search space.
The electronic device according to claim 9, wherein, according to the location information, performing a network architecture search in the search space through a cuckoo algorithm to obtain a multi-target network architecture set, comprising:

Obtain a candidate architecture set in the search space and related parameters of the candidate architecture set according to the location information;

calculating the fitness value of each candidate architecture in the candidate architecture set;

Summarize the candidate architectures whose fitness value is greater than the preset adaptation threshold in the candidate architecture set to obtain an initial candidate architecture set;

calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set;

If there is no adaptive change rate less than a fixed threshold, determining the initial candidate architecture set as the multi-objective network architecture set;

If there is an adaptive change rate less than the fixed threshold, update the candidate architecture corresponding to the adaptive change rate less than the fixed threshold to obtain a standard network architecture set;

Obtain a preset random number, compare the random number and the size of the target probability corresponding to any network architecture in the standard network architecture set;

The network architecture corresponding to the target probability smaller than the random number is deleted, the network architecture corresponding to the target probability greater than or equal to the random number is retained, and the multi-target network architecture set is obtained.
The electronic device of claim 11, wherein the calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set comprises:

calculating the first fitness value of the network architecture in the initial network architecture set at the preset first iteration number and the second fitness value at the preset second iteration number;

Calculate the adaptive change rate between the first fitness value and the second fitness value according to a preset rate of change formula;

The adaptive change rate is determined as an adaptive change rate of the candidate architecture.
The electronic device of claim 12, wherein the preset rate of change formula comprises:

where Δf is the adaptive change rate,
is the first fitness value,
is the second fitness value, t is the first iteration number, σ is the second iteration number, and L is the position of the candidate architecture in the initial candidate architecture set in the initial candidate architecture set.
The electronic device according to claim 11, wherein the updating the candidate architecture corresponding to the adaptive change rate smaller than the fixed threshold value comprises:

Calculate the step size of the candidate architecture corresponding to the adaptive change rate smaller than the fixed threshold by using the preset step size formula;

The candidate architecture is replaced according to the size of the stride.
The electronic device according to any one of claims 9 to 14, wherein before the network architecture search is performed in the search space through the cuckoo algorithm according to the location information to obtain a multi-target network architecture set, the At least one processor also performs the following steps:

Combining all candidate architectures in the search space to obtain a large super network;

using the constructed training set to train the large hyper-network;

The parameters of the candidate architectures in the search space are updated according to the large super-network that has been trained.
A computer-readable storage medium storing a computer program, wherein the computer program implements the following steps when executed by a processor:

Obtain the location information of the search space;

According to the location information, a network architecture search is performed in the search space through the cuckoo algorithm to obtain a multi-objective network architecture set;

Perform evaluation processing on multiple target network architectures in the multi-target network architecture set according to a preset performance evaluation model, to obtain evaluation results;

Judging whether the evaluation result satisfies a preset evaluation condition;

If the evaluation result satisfies the evaluation condition, it is determined that the target network architecture corresponding to the evaluation result is the final network architecture.
The computer-readable storage medium according to claim 16, wherein, before the acquisition of the location information of the search space, the computer program further implements the following steps when executed by the processor:

Get the preset neural network unit;

The neural network units are arranged and combined in a preset connection manner to obtain a search space.
The computer-readable storage medium according to claim 16 , wherein, according to the location information, performing a network architecture search in the search space through a cuckoo algorithm to obtain a multi-objective network architecture set, comprising:

Obtain a candidate architecture set in the search space and related parameters of the candidate architecture set according to the location information;

calculating the fitness value of each candidate architecture in the candidate architecture set;

Summarize the candidate architectures whose fitness value is greater than the preset adaptation threshold in the candidate architecture set to obtain an initial candidate architecture set;

calculating the adaptive change rate of the candidate architectures in the initial candidate architecture set;

If there is no adaptive change rate less than a fixed threshold, determining the initial candidate architecture set as the multi-objective network architecture set;

If there is an adaptive change rate less than the fixed threshold, update the candidate architecture corresponding to the adaptive change rate less than the fixed threshold to obtain a standard network architecture set;

Obtain a preset random number, and compare the random number with the size of the target probability corresponding to any network architecture in the standard network architecture set;

The network architecture corresponding to the target probability smaller than the random number is deleted, the network architecture corresponding to the target probability greater than or equal to the random number is retained, and the multi-target network architecture set is obtained.
The computer-readable storage medium of claim 18, wherein the calculating an adaptive change rate of candidate architectures in the initial set of candidate architectures comprises:

calculating the first fitness value of the network architecture in the initial network architecture set at the preset first iteration number and the second fitness value at the preset second iteration number;

Calculate the adaptive change rate between the first fitness value and the second fitness value according to a preset rate of change formula;

The adaptive change rate is determined as an adaptive change rate of the candidate architecture.
The computer-readable storage medium of claim 19, wherein the preset rate-of-change formula comprises:

where Δf is the adaptive change rate,
is the first fitness value,
is the second fitness value, t is the first iteration number, σ is the second iteration number, and L is the position of the candidate architecture in the initial candidate architecture set in the initial candidate architecture set.