WO2021151345A1

WO2021151345A1 - Method and apparatus for parameter acquisition for recognition model, electronic device, and storage medium

Info

Publication number: WO2021151345A1
Application number: PCT/CN2020/131974
Authority: WO
Inventors: 凡金龙; 刘莉红; 刘玉宇
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-07-09
Filing date: 2020-11-26
Publication date: 2021-08-05
Also published as: CN111814962B; CN111814962A

Abstract

A method for parameter acquisition for a recognition model, relating to data processing technology, and comprising: acquiring a training dataset containing a noise tag, and performing data standardization processing on the training dataset to obtain a standard dataset (S1); on the basis of a multi-layer deep neural network, constructing a recognition model, and using the standard dataset to train the recognition model to obtain a standard recognition model containing initialized parameters (S2); constructing a noise probability transition matrix for the standard dataset (S3); on the basis of the noise probability transition matrix, constructing a loss function (S4); and using the loss function to calculate updated parameters for the standard recognition model, thereby obtaining more accurate model parameters (S5). The described method further relates to blockchain technology, and the training dataset can be stored in nodes of a blockchain. Further disclosed are an apparatus for parameter acquisition for a recognition model, an electronic device, and a storage medium, able to improve the precision of acquired model parameters.

Description

Method, device, electronic equipment and storage medium for acquiring parameters of recognition model

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on July 9, 2020, the application number is 202010656659.8, and the invention title is "Methods, devices, electronic equipment and storage media for obtaining parameters of identification models", and its entire contents Incorporated in this application by reference.

Technical field

This application relates to the field of data processing technology, and in particular to a method, device, electronic device, and computer-readable storage medium for acquiring parameters of an identification model.

Background technique

The inventor realized that with the rise of artificial intelligence, more and more technicians use labeled data to train and build models to obtain required model parameters, and then use the model parameters to enable the model to achieve specific functions. However, the training of a model often requires a large amount of labeled data. Manually labeling these data is not only inefficient in labeling, but also a large number of false labels, namely noisy labels, will appear during the labeling process. Instead, use the data with noisy labels. The model cannot be trained to obtain accurate model parameters.

Therefore, how to use these noisy label data to train the model to obtain more accurate model parameters has become the focus of more and more attention.

Summary of the invention

A method for acquiring parameters of a recognition model provided in this application includes:

Acquiring a training data set containing noise labels, and performing data standardization processing on the training data set to obtain a standard data set;

Establishing a recognition model based on a multi-layer deep neural network, and using the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

The loss function is used to calculate the update parameters of the standard recognition model, and the update parameters are replaced with the initialization parameters.

The present application also provides a device for acquiring parameters of a recognition model, and the device includes:

The training data acquisition module is used to acquire a training data set containing noise labels, and perform data standardization processing on the training data set to obtain a standard data set;

A recognition model building module, used to establish a recognition model based on a multi-layer deep neural network, and use the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

A transition matrix construction module, which is used to construct the noise probability transition matrix of the standard data set;

A loss function construction module, configured to construct a loss function based on the noise probability transition matrix;

The model parameter update module is used to calculate the update parameters of the standard recognition model by using the loss function, and replace the update parameters with the initialization parameters.

This application also provides an electronic device, which includes:

Memory, storing at least one instruction; and

The processor executes the instructions stored in the memory to implement the method for acquiring parameters of the recognition model as described below:

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

The present application also provides a computer-readable storage medium, including a storage data area and a storage program area, the storage data area stores created data, and the storage program area stores a computer program; wherein the computer program is executed by the processor as follows The parameter acquisition method of the recognition model:

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

Description of the drawings

FIG. 1 is a schematic flowchart of a method for acquiring parameters of a recognition model provided by an embodiment of this application;

2 is a schematic diagram of modules of an apparatus for acquiring parameters of a recognition model provided by an embodiment of the application;

3 is a schematic diagram of the internal structure of an electronic device that implements a method for acquiring parameters of a recognition model provided by an embodiment of the application;

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

Detailed ways

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

The execution subject of the method for acquiring parameters of the recognition model provided in the embodiment of the present application includes, but is not limited to, at least one of the electronic devices that can be configured to execute the method provided in the embodiment of the present application, such as a server and a terminal. In other words, the method for acquiring the parameters of the recognition model may be executed by software or hardware installed on the terminal device or the server device, and the software may 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, etc.

This application provides a method for acquiring parameters of a recognition model. Referring to FIG. 1, it is a schematic flowchart of a method for acquiring parameters of a recognition model provided by an embodiment of this application. The method can be executed by a device, and the device can be implemented by software and/or hardware.

In this embodiment, the method for acquiring parameters of the recognition model includes:

S1. Obtain a training data set containing noise labels, and perform data standardization processing on the training data set to obtain a standard data set.

In the embodiment of the present application, the training data set containing noise labels means that there is some data in the training data set, but the preset standard label of the data does not correspond to the content of the data, that is, the preset standard label is data of the noise label.

In the embodiment of the present application, the training data set can be obtained from the blockchain node by using a python sentence with a data capture function, and the training data set can also be obtained from a database.

Preferably, the training data set is stored in different nodes of the blockchain, and the high data throughput of the blockchain can be used to improve the efficiency of obtaining the training data set.

Specifically, the performing data standardization processing on the training data set includes one or a combination of the following:

Removing the unique attribute value in the training data set;

Filling the training data set with missing values; and performing data normalization on the training data set.

In detail, the unique attribute value includes but is not limited to: data id and data number.

Since the unique attribute value cannot describe the distribution law of the data, it will increase the content of the data, so that more additional computing resources are needed to process the data, and the efficiency of data processing is reduced. Therefore, the training data is used in the embodiment of the application. The centralized and unique data is deleted to improve the efficiency of subsequent data processing.

Preferably, the embodiment of the present application uses a high-dimensional mapping method to map the data in the training data set to a pre-built high-dimensional space, and then use the one-hot encoding technique to fill in the missing data. Utilizing the multi-dimensionality of high-bit space can improve the efficiency of searching for missing data in the training data set, and using one-hot encoding technology can improve the accuracy of data filling.

Specifically, the embodiment of the present application uses the following standardized algorithm to perform data normalization on the training data set:

Wherein, x is the standard data of data normalization, S _old data as the training data set, S _max is the maximum value of S _old, S _min is the minimum value of the value S _old.

It should be emphasized that S _max and S _min are preset and used to limit the range of data in the training data set.

After completing the data standardization process, the standard data set is obtained.

S2. Establish a recognition model based on a multi-layer deep neural network, and use the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters.

In the embodiment of the present application, the multi-layer deep neural network is:

h=(h ⁽ⁿ⁾ ·h ^(n-1) ·…·h ⁽¹⁾ )

Wherein, h ⁽ⁿ⁾ represents the network structure of the nth layer of the multilayer deep neural network.

After the multi-layer deep neural network is activated by the softmax function, it can output the predicted value of the joint distribution p(x, y) of the preset standard label y corresponding to the standard data x and x

And get the predicted label of the standard data in the standard data set.

The softmax function is an activation function for transforming the output result of the multi-layer deep neural network into a preset form. In the embodiment of the present application, the output result of the multi-layer deep neural network is transformed into a probabilistic form (ie

). The output result of the multi-layer deep neural network transformed into a probabilistic form can intuitively see the difference between the preset standard label and the predicted label, and the model parameters are adjusted according to the difference, which is beneficial to improve the model’s performance. Training efficiency.

Specifically, in this embodiment of the application, the standard data set is input to the recognition model, and the recognition model is trained using the standard data set to obtain the initialization parameters of the recognition model, and it is determined that the recognition model including the initialization parameters is Standard recognition model.

Further, in the embodiment of the present application, before establishing a recognition model based on a multi-layer deep neural network, the method further includes:

Construct feature space:

Wherein, the feature space is used to store standard data sets;

Construct a label space corresponding to the feature space:

y={e ⁱ :i∈[c]}, where e is the preset standard label of the standard data in the standard data set, [c]={1...c}, which is any c positive integers, the number of which corresponds to the standard The number of data in the data set is the same, and the label space is used to store preset standard labels corresponding to standard data in the feature space.

In addition, the joint distribution of the standard data x stored in the feature space and the corresponding preset standard label y in the label space is p(x, y):

p(x,y)=p(y|x)p(x)

Wherein, p(x) is the frequency of any standard data x in the standard data set in the feature space, and p(y|x) is the frequency of the preset standard label in the label space when the standard data x appears.

In this embodiment, the feature space and the label space are constructed, and the joint distribution of the standard data x and its corresponding label y in the label space is calculated as p(x, y). The standard data in the standard data set can be compared with the standard data. The relationship between the tags corresponding to the data is better displayed, and the efficiency of data processing is improved.

S3. Construct a noise probability transition matrix of the standard data set.

In the embodiment of the present application, the noise probability transition matrix of the standard data set can be expressed as:

Q∈[0,1] ^c×c

Among them, the size of c is the same as the number of standard data in the standard data set.

The noise probability transition matrix represents the distribution of noise tags in the data.

Specifically, the element in the i-th row and j-th column in the noise probability transition matrix Q represents the probability of the occurrence of a noise label.

In detail, in the embodiment of the present application, the noise probability transition matrix of the standard data set is as follows:

Where Q is the noise probability transition matrix, α is any standard data in the standard data set, and β ⁱ is a preset standard label corresponding to α,

Is the predicted label generated by the standard recognition model for α, and β ^j is the noise label of α.

S4. Construct a loss function based on the noise probability transition matrix.

In the embodiment of the present application, the loss function includes but is not limited to: a backward loss function and an preceding loss function.

Specifically, the forward loss function is:

Wherein, QT is the transposed matrix of the noise probability transition matrix, ψ is the error factor of the recognition model, h is the multilayer deep neural network,

Is the loss value of the forward loss function.

Specifically, the latter loss function is:

Where l ^→ (h) is the loss value of the backward loss function, and y is the preset standard label of any standard data x in the standard data set,

Is the predicted label of x by the standard recognition model, Q is the noise probability transition matrix, and p(x, y) is the joint distribution of standard data x and the preset standard label y corresponding to x,

Is the predicted value of p(x,y).

The backward loss function is used to calculate the probability value that the label corresponding to the standard data x in the label space is a noisy label, that is, the probability that the preset standard label of the standard data x is wrong.

S5. Calculate the update parameters of the standard recognition model by using the loss function, and replace the update parameters with the initialization parameters.

In the embodiment of the present application, the calculation of the update parameters of the standard recognition model by using the loss function includes:

Acquiring the preset standard label of the standard data in the standard data set, and the prediction label of the standard data in the standard data set by the standard recognition model;

Calculating a difference value between the predicted label and the standard label by using a loss function;

When the difference value is within a preset threshold interval, use a gradient descent algorithm to calculate the update parameters of the standard recognition model;

When the difference value is greater than the upper limit of the threshold interval, use the loss function to calculate the probability value that the standard label is a noise label;

When the probability value is less than the preset probability threshold, the gradient descent algorithm is used to calculate the update parameters of the standard recognition model.

In the embodiment of the present application, the loss function is a forward loss function and/or a backward loss function.

In the embodiment of the present application, when the difference value is within the preset threshold interval, it means that the recognition result of the standard recognition model is wrong. Then the gradient descent algorithm is used to update the parameters of the standard recognition model to improve the standard. Identify the accuracy of the model.

In this embodiment, the gradient descent algorithm includes, but is not limited to, a batch gradient descent algorithm, a stochastic gradient descent algorithm, and a small batch gradient descent algorithm.

When the difference value is greater than the upper limit of the threshold interval, it may not be due to a recognition error of the standard recognition model that the difference value is greater than the upper limit of the threshold interval. In practical applications, due to the existence of the noise label, the preset standard label of the standard data is wrong, which will also cause the difference between the preset standard label and the predicted label to be greater than the upper limit of the threshold interval. Therefore, when the difference value is greater than the upper limit of the threshold interval, the embodiment of the present application uses a loss function to calculate the probability value that the preset standard label of the standard data is a noise label, and when the probability value is less than the preset probability threshold, It indicates that the recognition result of the standard recognition model is wrong, and then the gradient descent algorithm is used to calculate the update parameters of the standard recognition model.

Further, when the probability value is greater than or equal to the probability threshold value, the embodiment of the present application corrects the preset standard label of the standard data.

Further, the embodiment of the present application uses the update parameters to replace the initialization parameters. After the initialization parameters are replaced, the final recognition model can be obtained. The final recognition model can be used to recognize input data, and the input data includes But it is not limited to image data.

In the embodiment of the present application, after obtaining the training data set containing the noise label, the training data set is standardized to improve the efficiency of processing the training data; after obtaining the standard recognition model containing the initialization parameters, the standard data set is constructed The noise probability transition matrix is beneficial to improve the applicability of the loss function constructed from the noise transition matrix to the model, so that the loss function can be used to train more accurate model parameters; the loss function is constructed based on the noise probability transition matrix, and The loss function calculates the updated parameters of the standard recognition model, so that more accurate model parameters can be obtained, and the purpose of improving the accuracy of obtaining the model parameters is achieved. Therefore, the method for acquiring parameters of a recognition model proposed in this application can provide a method for improving the accuracy of acquiring model parameters.

As shown in Figure 2, it is a schematic diagram of the module of the parameter acquisition device of the identification model of the present application.

The device 100 for acquiring the parameters of the recognition model described in this application can be installed in an electronic device. According to the realized function, the parameter acquisition device of the recognition model may include a training data acquisition module 101, a recognition model construction module 102, a transition matrix construction module 103, a loss function construction module 104, and a model parameter update module 105. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can complete 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 training data acquisition module 101 is configured to acquire a training data set containing noise labels, and perform data standardization processing on the training data set to obtain a standard data set;

The recognition model construction module 102 is configured to establish a recognition model based on a multi-layer deep neural network, and use the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

The transition matrix construction module 103 is used to construct the noise probability transition matrix of the standard data set;

The loss function construction module 104 is configured to construct a loss function based on the noise probability transition matrix;

The model parameter update module 105 is configured to use the loss function to calculate update parameters of the standard recognition model, and replace the update parameters with the initialization parameters.

In detail, the specific implementation of each module of the device for acquiring parameters of the recognition model is as follows:

The training data acquisition module 101 is configured to acquire a training data set containing noise labels, and perform data standardization processing on the training data set to obtain a standard data set.

Specifically, the training data acquisition module 101 performs data standardization processing on the training data set, including one or a combination of the following:

Removing the unique attribute value in the training data set;

Filling in missing values on the training data set;

Perform data normalization on the training data set.

Preferably, in the embodiment of the present application, high-dimensional mapping is used to map the data in the training data set to a pre-built high-dimensional space, and then the missing data is filled with data using a one-hot encoding technique. Utilizing the multi-dimensionality of high-bit space can improve the efficiency of searching for missing data in the training data set, and using one-hot encoding technology can improve the accuracy of data filling.

Specifically, this application uses the following standardized algorithm to normalize the training data set:

The recognition model construction module 102 is configured to establish a recognition model based on a multi-layer deep neural network, and use the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters.

h=(h ⁽ⁿ⁾ ·h ^(n-1) ·…·h ⁽¹⁾ )

After the multi-layer deep neural network is activated by the softmax function, it can output the predicted value of the joint distribution p(x,y) of the preset standard label y corresponding to the standard data x and x

And get the predicted label of the standard data in the standard data set.

Construct feature space:

Wherein, the feature space is used to store standard data sets;

Construct a label space corresponding to the feature space:

p(x,y)=p(y|x)p(x)

The transition matrix construction module 103 is used to construct the noise probability transition matrix of the standard data set.

Q∈[0,1] ^c×c

The loss function construction module 104 is configured to construct a loss function based on the noise probability transition matrix.

Specifically, the forward loss function is:

Is the loss value of the forward loss function.

Specifically, the latter loss function is:

Is the predicted value of p(x,y).

In the embodiment of the present application, the model parameter update module 105 uses the loss function to calculate the update parameters of the standard recognition model, including:

When the probability value is less than the preset probability threshold, a gradient descent algorithm is used to calculate the update parameters of the standard recognition model.

In the embodiment of the present application, after obtaining the training data set containing the noise label, the training data set is standardized to improve the efficiency of processing the training data; after obtaining the standard recognition model containing the initialization parameters, the standard data set is constructed The noise probability transition matrix is beneficial to improve the applicability of the loss function constructed from the noise transition matrix to the model, so that the loss function can be used to train more accurate model parameters; the loss function is constructed based on the noise probability transition matrix, and The loss function calculates the updated parameters of the standard recognition model, so that more accurate model parameters can be obtained, and the purpose of improving the accuracy of obtaining the model parameters is achieved. Therefore, the device for acquiring parameters of a recognition model proposed in this application can provide a method for improving the accuracy of acquiring model parameters.

As shown in FIG. 3, it is a schematic diagram of the structure of an electronic device that implements the method for acquiring parameters of a recognition model according to 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 running on the processor 10, such as a parameter acquisition program 12 of an identification model.

The memory 11 includes at least one type of readable storage medium, and the readable storage medium may be volatile or nonvolatile. Specifically, 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, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, for example, 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 plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), and a secure digital (SecureDigital, SD) equipped on the electronic device 1. Card, flash 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 be used not only to store application software and various data installed in the electronic device 1, such as the code of the parameter acquisition program 12 of the identification model, etc., but also to temporarily store data that has been output or will be output.

The processor 10 may be composed of integrated circuits in some embodiments, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one or more Central Processing Unit (CPU), microprocessor, digital processing chip, graphics processor and a combination of various control chips, etc. The processor 10 is the control core (ControlUnit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and runs or executes programs or modules stored in the memory 11 (for example, perform identification The parameter acquisition program of the model, etc.), and call the 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 interconnection standard (PCI) bus or an extended industry standard architecture (EISA) 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 and 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 shown in the figure. Components, or combinations of certain components, or different component arrangements.

For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, thereby controlling power The device implements functions such as charge management, discharge management, and power consumption management. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also 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 also include a user interface. The user interface may be a display (Display) and an input unit (such as 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 liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light emitting diode) touch device, etc. Among them, the display can also be appropriately called a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visualized user interface.

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

The parameter acquisition program 12 of the recognition model 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:

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

Further, if the integrated module/unit of the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. The computer-readable storage medium may be volatile or non-volatile. Specifically, the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read -OnlyMemory).

Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function, etc.; the storage data area may store a block chain node Use the created data, etc.

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

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

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

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application.

Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any accompanying diagrams in the claims should not be regarded as limiting the claims involved.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The second class words are used to indicate names, and do not indicate any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the 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 application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

A method for acquiring parameters of a recognition model, wherein the method includes:

Acquiring a training data set containing noise labels, and performing data standardization processing on the training data set to obtain a standard data set;

Establishing a recognition model based on a multi-layer deep neural network, and using the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

The loss function is used to calculate the update parameters of the standard recognition model, and the update parameters are replaced with the initialization parameters.
The method for acquiring parameters of a recognition model according to claim 1, wherein said performing data standardization processing on said training data set includes one or a combination of the following:

Removing the unique attribute value in the training data set;

Filling in missing values on the training data set;

Perform data normalization on the training data set.
The method for acquiring parameters of a recognition model according to claim 2, wherein said performing data normalization on said training data set comprises:

Use the following standardized algorithm to normalize the training data set:

Wherein, x is the standard data of data normalization, S old data as the training data set, S max is the maximum value of S old, S min is the minimum value of the value S old.
The method for acquiring parameters of a recognition model according to claim 1, wherein the noise probability transition matrix comprises:

Q∈[0,1] c×c

Among them, the size of c is the same as the number of standard data in the standard data set.
The method for acquiring parameters of a recognition model according to claim 1, wherein the loss function includes a forward loss function, and the forward loss function is:

Wherein, QT is the transposed matrix of the noise probability transition matrix, ψ is the error factor of the recognition model, h is the multilayer deep neural network,
Is the loss value of the forward loss function.
The method for acquiring parameters of a recognition model according to claim 3, wherein the loss function further comprises a backward loss function, and the latter loss function is:

Where l → (h) is the loss value of the backward loss function, and y is the preset standard label of any standard data x in the standard data set,
Is the predicted label of x by the standard recognition model, Q is the noise probability transition matrix, and p(x, y) is the joint distribution of standard data x and the preset standard label y corresponding to x,
Is the predicted value of p(x,y).
The method for acquiring parameters of a recognition model according to any one of claims 1 to 6, wherein said using the loss function to calculate the updated parameters of the standard recognition model comprises:

Acquiring the preset standard label of the standard data in the standard data set, and the prediction label of the standard data in the standard data set by the standard recognition model;

Calculating a difference value between the predicted label and the standard label by using a loss function;

When the difference value is within a preset threshold interval, use a gradient descent algorithm to calculate the update parameters of the standard recognition model;

When the difference value is greater than the upper limit of the threshold interval, use the loss function to calculate the probability value that the standard label is a noise label;

When the probability value is less than the preset probability threshold, a gradient descent algorithm is used to calculate the update parameters of the standard recognition model.
A device for acquiring parameters of a recognition model, wherein the device includes:

The training data acquisition module is used to acquire a training data set containing noise labels, and perform data standardization processing on the training data set to obtain a standard data set;

A recognition model building module, used to establish a recognition model based on a multi-layer deep neural network, and use the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

A transition matrix construction module, which is used to construct the noise probability transition matrix of the standard data set;

A loss function construction module, configured to construct a loss function based on the noise probability transition matrix;

The model parameter update module is used to calculate the update parameters of the standard recognition model by using the loss function, and replace the update parameters with the initialization parameters.
An electronic device, wherein the electronic device includes:

At least one processor; and,

A memory communicatively connected with the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for acquiring parameters of the recognition model as described below:

Acquiring a training data set containing noise labels, and performing data standardization processing on the training data set to obtain a standard data set;

Establishing a recognition model based on a multi-layer deep neural network, and using the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

The loss function is used to calculate the update parameters of the standard recognition model, and the update parameters are replaced with the initialization parameters.
9. The electronic device according to claim 9, wherein said performing data standardization processing on said training data set comprises one or a combination of the following:

Removing the unique attribute value in the training data set;

Filling in missing values on the training data set;

Perform data normalization on the training data set.
11. The electronic device of claim 10, wherein said performing data normalization on said training data set comprises:

Use the following standardized algorithm to normalize the training data set:

Wherein, x is the standard data of data normalization, S old data as the training data set, S max is the maximum value of S old, S min is the minimum value of the value S old.
9. The electronic device of claim 9, wherein the noise probability transition matrix comprises:

Q∈[0,1] c×c

Among them, the size of c is the same as the number of standard data in the standard data set.
9. The electronic device of claim 9, wherein the loss function comprises a forward loss function, and the forward loss function is:

Wherein, QT is the transposed matrix of the noise probability transition matrix, ψ is the error factor of the recognition model, h is the multilayer deep neural network,
Is the loss value of the forward loss function.
11. The electronic device according to claim 11, wherein the loss function further comprises a backward loss function, and the latter loss function is:

Where l → (h) is the loss value of the backward loss function, and y is the preset standard label of any standard data x in the standard data set,
Is the predicted label of x by the standard recognition model, Q is the noise probability transition matrix, and p(x, y) is the joint distribution of standard data x and the preset standard label y corresponding to x,
Is the predicted value of p(x,y).
A computer-readable storage medium includes a storage data area and a storage program area. The storage data area stores created data, and the storage program area stores a computer program; wherein the computer program is executed by a processor to realize the following recognition The parameter acquisition method of the model:

Acquiring a training data set containing noise labels, and performing data standardization processing on the training data set to obtain a standard data set;

Establishing a recognition model based on a multi-layer deep neural network, and using the standard data set to train the recognition model to obtain a standard recognition model including initialization parameters;

Constructing the noise probability transition matrix of the standard data set;

Constructing a loss function based on the noise probability transition matrix;

The loss function is used to calculate the update parameters of the standard recognition model, and the update parameters are replaced with the initialization parameters.
15. The computer-readable storage medium according to claim 15, wherein said performing data standardization processing on said training data set comprises one or a combination of the following:

Removing the unique attribute value in the training data set;

Filling in missing values on the training data set;

Perform data normalization on the training data set.
15. The computer-readable storage medium of claim 16, wherein said performing data normalization on said training data set comprises:

Use the following standardized algorithm to normalize the training data set:

Wherein, x is the standard data of data normalization, S old data as the training data set, S max is the maximum value of S old, S min is the minimum value of the value S old.
15. The computer-readable storage medium of claim 15, wherein the noise probability transition matrix comprises:

Q∈[0,1] c×c

Among them, the size of c is the same as the number of standard data in the standard data set.
15. The computer-readable storage medium of claim 15, wherein the loss function comprises a forward loss function, and the forward loss function is:

Wherein, QT is the transposed matrix of the noise probability transition matrix, ψ is the error factor of the recognition model, h is the multilayer deep neural network,
Is the loss value of the forward loss function.
17. The computer-readable storage medium of claim 17, wherein the loss function further comprises a backward loss function, and the latter loss function is:

Where l → (h) is the loss value of the backward loss function, and y is the preset standard label of any standard data x in the standard data set,
Is the predicted label of x by the standard recognition model, Q is the noise probability transition matrix, and p(x, y) is the joint distribution of standard data x and the preset standard label y corresponding to x,
Is the predicted value of p(x,y).