WO2022126902A1

WO2022126902A1 - Model compression method and apparatus, electronic device, and medium

Info

Publication number: WO2022126902A1
Application number: PCT/CN2021/083080
Authority: WO
Inventors: 成冠举; 李葛; 曾婵; 高鹏
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-12-18
Filing date: 2021-03-25
Publication date: 2022-06-23
Also published as: CN112465141A

Abstract

A model compression method and apparatus, an electronic device and a storage medium, which relate to data processing technology. The method comprises: performing data fitting on random noise data by using a pre-constructed fitter to obtain simulation data; calculating activation loss values between the simulation data and the noise data, adjusting parameters of the fitter when the activation loss values are greater than a preset activation threshold until the activation loss values are less than or equal to the preset activation threshold; inputting the simulation data into a model to be compressed to obtain output data; and calculating sparse loss values between the output data and the simulation data, and adjusting internal parameters of the fitter when the sparse loss values are greater than a preset sparse threshold until the sparse loss values are less than or equal to the preset sparse threshold; and outputting the simulation data and compressing the model to obtain a compressed model. The method, apparatus, electronic device and storage medium can achieve model compression without acquiring training data, network structures and parameters.

Description

Model compression method, device, electronic device and medium

This application claims the priority of the Chinese patent application with the application number 202011501677.5 and the invention titled "Model Compression Method, Apparatus, Electronic Device and Medium" filed with the China Patent Office on December 18, 2020, the entire contents of which are incorporated by reference in in this application.

technical field

The present application relates to the field of data processing, and in particular, to a model compression method, apparatus, electronic device, and computer-readable storage medium.

Background technique

In the era of big data, deep learning models are used more and more frequently. In order to apply deep learning models to small devices such as mobile devices and sensors, sometimes deep learning models must be compressed and trimmed before they can be deployed to small devices.

The inventor realizes that the current mainstream deep learning compression methods need to compress models based on the original training data set, network structure, parameters, etc., such as the knowledge distillation method and the metadata-based method, the former requires a large amount of original training data, and then However, due to legal, privacy and other reasons, training data, network structure and parameters are usually difficult to obtain.

SUMMARY OF THE INVENTION

A model compression method provided by this application includes:

Use a pre-built fitter to perform data fitting operation on random noise data to obtain simulated data;

Use a preset first loss function to calculate the activation loss value between the simulation data and the noise data, when the activation loss value is greater than a preset activation threshold, adjust the parameters of the fitter and return to using The pre-built fitter performs a data fitting operation on random noise data to obtain simulation data, and until the activation loss value is less than or equal to a preset activation threshold, the simulation data is input into the model to be compressed, and the output is obtained data;

Use a preset second loss function to calculate the sparse loss value between the output data and the simulation data, when the sparse loss value is greater than the preset sparse threshold, adjust the internal parameters of the fitter and return Use a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data, and output the simulation data until the sparse loss value is less than or equal to a preset sparse threshold;

The to-be-compressed model is compressed according to the simulation data to obtain a compressed model.

The present application also provides a model compression device, the device comprising:

The data fitting module is used to perform data fitting operation on random noise data by using a pre-built fitter to obtain simulation data;

an activation loss module, configured to use a preset first loss function to calculate an activation loss value between the simulation data and the noise data, and adjust the fitting when the activation loss value is greater than a preset activation threshold until the activation loss value is less than or equal to the preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

a sparse loss module, configured to use a preset second loss function to calculate a sparse loss value between the output data and the simulation data, and adjust the fitting when the sparse loss value is greater than a preset sparse threshold the internal parameters of the generator, until the sparse loss value is less than or equal to the preset sparse threshold, output the simulation data;

A model compression module, configured to perform compression processing on the to-be-compressed model according to the simulation data to obtain a compressed model.

The present application also provides an electronic device, the electronic device comprising:

at least one processor; and,

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

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a model compression method as described below:

The present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the following model compression method:

Use a preset first loss function to calculate the activation loss value between the simulation data and the noise data, when the activation loss value is greater than a preset activation threshold, adjust the parameters of the fitter and return to using The pre-built fitter performs a data fitting operation on random noise data to obtain simulation data, and until the activation loss value is less than or equal to a preset activation threshold, the simulation data is input into the model to be compressed to obtain an output data;

Description of drawings

FIG. 1 is a schematic flowchart of a model compression method provided by an embodiment of the present application;

FIG. 2 is a schematic block diagram of a model compression apparatus provided by an embodiment of the present application;

3 is a schematic diagram of the internal structure of an electronic device for implementing a model compression method provided by 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.

The embodiment of the present application provides a model compression method. The execution body of the model compression method includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server and a terminal. In other words, the model compression 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 , a schematic flowchart of a model compression method provided by an embodiment of the present application is shown. In this embodiment, the model compression method includes:

S1. Use a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data.

In this embodiment of the present application, the random noise data is random Gaussian noise sampled from a Gaussian distribution. The fitter continuously performs linear fitting processing on the noise data to generate simulation data that is close to the real data.

Specifically, performing a data fitting operation on random noise data using a pre-built fitter to obtain simulation data, including:

Using the long short-term memory network in the fitter to predict the noise data to obtain a fitting data set;

Compress the fitted data set by using an activation function to obtain a compressed data set;

Perform vectorization processing on the compressed data set to obtain simulation data.

Wherein, the long short-term memory network can train the mapping of the random noise from Gaussian distribution to fitting distribution, and at the same time, in order to prevent the occurrence of over-fitting, a dropout mechanism is added to each layer of neural network of the long short-term memory network. The activation function may be a tanh function, and the tanh function is used to compress the data in the fitting data set between -1 and 1, so that the vectorization operation can be performed subsequently.

Further, performing vectorization processing on the compressed data set to obtain simulation data, including:

Utilize the Word2Vec algorithm to map the compressed data in the compressed data set into a feature vector;

The eigenvectors are spliced according to the sequence of the eigenvectors to obtain the simulation data.

Among them, the Word2Vec algorithm can map the data into a vector of uniform dimension, and the Word2Vec algorithm is suitable for the situation that there is a strong correlation between the data of a sequence and the local data of the sequence, and can be used to perform more generalization on the data. analysis.

In detail, by using a pre-built fitter to perform a data fitting operation on the random noise data, a simulation data close to the random noise data can be obtained, which can be used to perform subsequent model compression in place of the random noise data.

S2. Calculate an activation loss value between the simulation data and the noise data by using a preset first loss function.

In the embodiment of the present application, the first loss function:

in,

is the activation loss value, n is the number of samples of the noise data,

is the mth data in the simulation data, || ||1 is the L1 norm. The L1 norm is mainly to obtain sparsity, and the negative sign is added to try not to be sparse, let

as many as possible.

When the activation loss value is greater than the preset activation threshold, the embodiment of the present application adjusts the parameters of the fitter and returns to the above S1, and re-uses the pre-built fitter to perform a data fitting operation on random noise data, Get simulation data.

Preferably, the parameters of the fitter may be weights, gradients and the like of the fitter.

When the activation loss value is less than or equal to a preset activation threshold, perform S3, input the simulation data into the model to be compressed, and obtain output data.

Wherein, the first loss function calculates the activation loss value between the simulation data and the noise data, compares the activation loss value with a preset activation threshold, and then adjusts the parameters of the fitter, Until the activation loss value between the simulation data and the noise data converges, at this time, the adjusted fitter meets the standard, and it is not necessary to adjust its parameters.

S4. Calculate a sparse loss value between the output data and the simulation data by using a preset second loss function.

In this embodiment of the present application, the second loss function may be

in,

is the sparse loss value, x is the number of samples of the simulated data,

is the mth data in the output data, t ^m is a preset parameter,

is the softmax loss function.

When the sparse loss value is greater than the preset sparse threshold, the embodiment of the present application adjusts the internal parameters of the fitter and returns to the above S1, and uses the pre-built fitter to perform a data fitting operation on the random noise data again , to get the simulation data.

When the sparse loss value is less than or equal to a preset sparse threshold, perform S5, output the simulation data, and perform compression processing on the to-be-compressed model according to the simulation data to obtain a compressed model.

In the embodiment of the present application, performing compression processing on the to-be-compressed model according to the simulation data to obtain a compressed model includes:

Inputting the simulation data into a preset standard compression model to perform vector operations to obtain the first feature output by the standard compression model, and inputting the simulation data into the to-be-compressed model to perform vector operations to obtain the the second feature output by the model to be compressed;

Determine the loss function of the to-be-compressed model according to the first feature and the second feature;

The model to be compressed is back-propagated according to the loss function to obtain a compressed model.

Specifically, the determining the loss function of the to-be-compressed model according to the first feature and the second feature includes:

Perform a difference calculation according to the first feature and the second feature to obtain a difference function;

The difference function is subjected to norm conversion processing and squared to obtain a loss function.

As shown in FIG. 2 , it is a schematic block diagram of the model compression device of the present application.

The model compression apparatus 100 described in this application can be installed in an electronic device. According to the implemented functions, the model compression apparatus 100 may include a data fitting module 101 , an activation loss module 102 , a sparse loss module 103 , and a model compression 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 data fitting module 101 is configured to perform a data fitting operation on random noise data by using a pre-built fitter to obtain simulation data;

The activation loss module 102 is configured to use a preset first loss function to calculate an activation loss value between the simulation data and the noise data, and adjust the activation loss value when the activation loss value is greater than a preset activation threshold. parameters of the fitter, until the activation loss value is less than or equal to a preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

The sparse loss module 103 is configured to use a preset second loss function to calculate a sparse loss value between the output data and the simulation data, and adjust the sparse loss value when the sparse loss value is greater than a preset sparse threshold. the internal parameters of the fitter, until the sparse loss value is less than or equal to a preset sparse threshold, output the simulation data;

The model compression module 104 is configured to compress the to-be-compressed model according to the simulation data to obtain a compressed model.

In detail, when each module in the model compression apparatus 100 is executed by a processor of an electronic device, a model compression method can be implemented, and the specific implementation steps of the model compression method are as follows:

Step 1: The data fitting module 101 uses a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data.

Specifically, the data fitting module 101 uses a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data, including:

Step 2: The activation loss module 102 uses a preset first loss function to calculate an activation loss value between the simulation data and the noise data.

In the embodiment of the present application, the first loss function:

in,

is the activation loss value, n is the number of samples of the noise data,

as many as possible.

When the activation loss value is greater than the preset activation threshold, the embodiment of the present application adjusts the parameters of the fitter and returns to the above step 1, and re-uses the pre-built fitter to perform a data fitting operation on random noise data , to get the simulation data.

When the activation loss value is less than or equal to a preset activation threshold, step 3 is performed to input the simulation data into the model to be compressed to obtain output data.

Step 4: The sparse loss module 103 uses a preset second loss function to calculate a sparse loss value between the output data and the simulation data.

In this embodiment of the present application, the second loss function may be

in,

is the sparse loss value, x is the number of samples of the simulated data,

is the mth data in the output data, t ^m is a preset parameter,

is the softmax loss function.

When the sparse loss value is greater than the preset sparse threshold, the embodiment of the present application adjusts the internal parameters of the fitter and returns to the above-mentioned step 1, and uses the pre-built fitter to re-fit the random noise data operation to obtain simulation data.

When the sparse loss value is less than or equal to a preset sparse threshold, step 5 is performed, the simulation data is output, and the to-be-compressed model is compressed according to the simulation data to obtain a compressed model.

As shown in FIG. 3 , it is a schematic structural diagram of an electronic device implementing the model compression 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 model compression program 12 .

Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium may be volatile or non-volatile. Specifically, the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (eg, SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. 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, SD) equipped on the electronic device 1. card, flash memory card (FlashCard), 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 types of data, such as the code of the model compression 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 (Central Processing unit, CPU), microprocessor, digital processing chip, graphics processor and combination of various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and by running or executing programs or modules (such as execution models) stored in the memory 11. Compression program, etc.), and call data stored in the memory 11 to perform 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 model compression program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can realize:

Use the preset first loss function to calculate the activation loss value between the simulation data and the noise data, when the activation loss value is greater than the preset activation threshold, adjust the parameters of the fitter and return to using The pre-built fitter performs a data fitting operation on random noise data to obtain simulation data, and until the activation loss value is less than or equal to a preset activation threshold, the simulation data is input into the model to be compressed to obtain an output data;

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, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

The present application also provides a computer-readable storage medium. The readable storage medium stores a computer program. When executed by a processor of an electronic device, the computer program can realize:

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 and not to limit them. 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 model compression method, wherein the method comprises:

Step A: use a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data;

Step B: Calculate the activation loss value between the simulation data and the noise data by using a preset first loss function, and adjust the parameters of the fitter when the activation loss value is greater than a preset activation threshold And return to the above-mentioned step A, until the activation loss value is less than or equal to the preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

Step C: Calculate a sparse loss value between the output data and the simulation data by using a preset second loss function, and adjust the internal part of the fitter when the sparse loss value is greater than a preset sparse threshold. parameters and return to the above step A, until the sparse loss value is less than or equal to the preset sparse threshold, output the simulation data;

Step D: compressing the to-be-compressed model according to the simulation data to obtain a compressed model.
The model compression method according to claim 1, wherein, performing a data fitting operation on random noise data by using a pre-built fitter to obtain simulation data, comprising:

Using the long short-term memory network in the fitter to predict the noise data to obtain a fitting data set;

Compress the fitted data set by using an activation function to obtain a compressed data set;

Perform vectorization processing on the compressed data set to obtain simulation data.
The model compression method according to claim 2, wherein, performing vectorization processing on the compressed data set to obtain simulation data, comprising:

Utilize the Word2Vec algorithm to map the compressed data in the compressed data set into a feature vector;

The eigenvectors are spliced according to the sequence of the eigenvectors to obtain the simulation data.
The model compression method according to claim 1, wherein calculating an activation loss value between the simulation data and the noise data by using a preset first loss function comprises:

The activation loss value between the simulated data and the noisy data is calculated using the following first loss function:

in,
is the activation loss value, n is the number of samples of the noise data,
is the mth data in the simulation data, ||||1 is the L1 norm.
The model compression method according to claim 1, wherein calculating a sparse loss value between the output data and the simulation data by using a preset second loss function comprises:

A sparse loss value between the output data and the simulated data is calculated using the following second loss function:

in,
is the sparse loss value, x is the number of samples of the simulated data,
is the mth data in the output data, t m is a preset parameter,
is the softmax loss function.
The model compression method according to any one of claims 1 to 5, wherein the compressing the to-be-compressed model according to the simulation data to obtain a compressed model, comprising:

Inputting the simulation data into a preset standard compression model to perform vector operations to obtain the first feature output by the standard compression model, and inputting the simulation data into the to-be-compressed model to perform vector operations to obtain the the second feature output by the model to be compressed;

Determine the loss function of the to-be-compressed model according to the first feature and the second feature;

The model to be compressed is back-propagated according to the loss function to obtain a compressed model.
The model compression method according to claim 6, wherein the determining the loss function of the to-be-compressed model according to the first feature and the second feature comprises:

Perform a difference calculation according to the first feature and the second feature to obtain a difference function;

The difference function is subjected to norm conversion processing and squared to obtain a loss function.
A model compression device, wherein the device comprises:

The data fitting module is used to perform data fitting operation on random noise data by using a pre-built fitter to obtain simulation data;

an activation loss module, configured to use a preset first loss function to calculate an activation loss value between the simulation data and the noise data, and adjust the fitting when the activation loss value is greater than a preset activation threshold until the activation loss value is less than or equal to the preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

a sparse loss module, configured to use a preset second loss function to calculate a sparse loss value between the output data and the simulation data, and adjust the fitting when the sparse loss value is greater than a preset sparse threshold the internal parameters of the generator, until the sparse loss value is less than or equal to the preset sparse threshold, output the simulation data;

A model compression module, configured to perform compression processing on the to-be-compressed model according to the simulation data to obtain a compressed model.
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 instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a model compression method as described below:

Step A: use a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data;

Step B: Calculate the activation loss value between the simulation data and the noise data by using a preset first loss function, and adjust the parameters of the fitter when the activation loss value is greater than a preset activation threshold And return to the above-mentioned step A, until the activation loss value is less than or equal to the preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

Step C: Calculate a sparse loss value between the output data and the simulation data by using a preset second loss function, and adjust the internal part of the fitter when the sparse loss value is greater than a preset sparse threshold. parameters and return to the above step A, until the sparse loss value is less than or equal to the preset sparse threshold, output the simulation data;

Step D: compressing the to-be-compressed model according to the simulation data to obtain a compressed model.
The electronic device according to claim 9, wherein, performing a data fitting operation on random noise data by using a pre-built fitter to obtain simulation data, comprising:

Using the long short-term memory network in the fitter to predict the noise data to obtain a fitting data set;

Compress the fitted data set by using an activation function to obtain a compressed data set;

Perform vectorization processing on the compressed data set to obtain simulation data.
The electronic device according to claim 10, wherein, performing vectorization processing on the compressed data set to obtain simulation data, comprising:

Utilize the Word2Vec algorithm to map the compressed data in the compressed data set into a feature vector;

The eigenvectors are spliced according to the sequence of the eigenvectors to obtain the simulation data.
The electronic device according to claim 9, wherein calculating an activation loss value between the simulation data and the noise data using a preset first loss function comprises:

The activation loss value between the simulated data and the noisy data is calculated using the following first loss function:

in,
is the activation loss value, n is the number of samples of the noise data,
is the mth data in the simulation data, ||||1 is the L1 norm.
The electronic device according to claim 9, wherein, calculating a sparse loss value between the output data and the simulation data by using a preset second loss function, comprising:

A sparse loss value between the output data and the simulated data is calculated using the following second loss function:

in,
is the sparse loss value, x is the number of samples of the simulated data,
is the mth data in the output data, t m is a preset parameter,
is the softmax loss function.
The electronic device according to any one of claims 9 to 13, wherein the compressing the to-be-compressed model according to the simulation data to obtain a compressed model comprises:

Inputting the simulation data into a preset standard compression model to perform vector operations to obtain the first feature output by the standard compression model, and inputting the simulation data into the to-be-compressed model to perform vector operations to obtain the the second feature output by the model to be compressed;

Determine the loss function of the to-be-compressed model according to the first feature and the second feature;

The model to be compressed is back-propagated according to the loss function to obtain a compressed model.
A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the following model compression method is implemented:

Step A: use a pre-built fitter to perform a data fitting operation on random noise data to obtain simulation data;

Step B: Calculate the activation loss value between the simulation data and the noise data by using a preset first loss function, and adjust the parameters of the fitter when the activation loss value is greater than a preset activation threshold And return to the above-mentioned step A, until the activation loss value is less than or equal to the preset activation threshold, input the simulation data into the model to be compressed to obtain output data;

Step C: Calculate a sparse loss value between the output data and the simulation data by using a preset second loss function, and adjust the internal part of the fitter when the sparse loss value is greater than a preset sparse threshold. parameters and return to the above step A, until the sparse loss value is less than or equal to the preset sparse threshold, output the simulation data;

Step D: compressing the to-be-compressed model according to the simulation data to obtain a compressed model.
The computer-readable storage medium according to claim 15, wherein the data fitting operation performed on random noise data by using a pre-built fitter to obtain simulation data comprises:

Using the long short-term memory network in the fitter to predict the noise data to obtain a fitting data set;

Compress the fitted data set by using an activation function to obtain a compressed data set;

Perform vectorization processing on the compressed data set to obtain simulation data.
The computer-readable storage medium according to claim 16, wherein the performing vectorization processing on the compressed data set to obtain simulation data comprises:

Utilize the Word2Vec algorithm to map the compressed data in the compressed data set into a feature vector;

The eigenvectors are spliced according to the sequence of the eigenvectors to obtain the simulation data.
The computer-readable storage medium of claim 15, wherein calculating an activation loss value between the simulation data and the noise data using a preset first loss function comprises:

The activation loss value between the simulated data and the noisy data is calculated using the following first loss function:

in,
is the activation loss value, n is the number of samples of the noise data,
is the mth data in the simulation data, ||||1 is the L1 norm.
The computer-readable storage medium of claim 15, wherein calculating a sparse loss value between the output data and the simulation data using a preset second loss function comprises:

A sparse loss value between the output data and the simulated data is calculated using the following second loss function:

in,
is the sparse loss value, x is the number of samples of the simulated data,
is the mth data in the output data, t m is a preset parameter,
is the softmax loss function.
The computer-readable storage medium according to any one of claims 15 to 19, wherein the performing compression processing on the to-be-compressed model according to the simulation data to obtain a compressed model comprises:

Inputting the simulation data into a preset standard compression model to perform vector operations to obtain the first feature output by the standard compression model, and inputting the simulation data into the to-be-compressed model to perform vector operations to obtain the the second feature output by the model to be compressed;

Determine the loss function of the to-be-compressed model according to the first feature and the second feature;

The model to be compressed is back-propagated according to the loss function to obtain a compressed model.