WO2020215560A1 - Procédé et appareil de traitement de réseau neuronal à codage automatique, et dispositif d'ordinateur et support d'informations - Google Patents

Procédé et appareil de traitement de réseau neuronal à codage automatique, et dispositif d'ordinateur et support d'informations Download PDF

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WO2020215560A1
WO2020215560A1 PCT/CN2019/102671 CN2019102671W WO2020215560A1 WO 2020215560 A1 WO2020215560 A1 WO 2020215560A1 CN 2019102671 W CN2019102671 W CN 2019102671W WO 2020215560 A1 WO2020215560 A1 WO 2020215560A1
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neural network
encoding neural
sample
self
auto
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金戈
徐亮
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G06F16/353—Clustering; Classification into predefined classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G06F16/355—Class or cluster creation or modification

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  • This application relates to the computer field, and in particular to a method, device, computer equipment and storage medium for processing a self-encoding neural network.
  • the usual method is to use the bag-of-words model to extract text features, and then cluster the extracted features through a clustering algorithm.
  • the word bag model needs to ignore the word order, grammar, syntax and other elements of the text, and split the text into words one by one.
  • This approach often leads to the loss of text feature information due to the lack of neural network for feature extraction, which leads to text feature extraction
  • the accuracy rate is reduced, which affects the clustering accuracy rate. Therefore, how to determine a neural network that can improve the accuracy of clustering remains to be solved.
  • This application provides an auto-encoding neural network processing method, device, computer equipment, and storage medium to train an auto-encoding neural network model that can improve clustering accuracy.
  • a self-encoding neural network processing method including: obtaining a text sample; converting the text sample into a sample word vector; inputting the sample word vector into a pre-trained convolutional neural network model to compare the sample The word vector performs preliminary feature extraction to obtain preliminary hidden features of the sample; input the preliminary hidden features of the sample into multiple auto-encoding neural networks, and train the auto-encoding neural networks to obtain multiple auto-encoding neural network models , Wherein the number of hidden layers and hidden layer units of each self-encoding neural network is different; the preliminary hidden features of the samples are input into each of the self-encoding neural network models for feature extraction, and each of the The hidden features of the samples output by the self-encoding neural network model; clustering the feature samples of the hidden features of the samples extracted by each of the self-encoding neural network models by using a clustering algorithm to obtain each Encoding neural network model corresponding to the clustering results; according to the clustering results corresponding to each of the self-encoding neural network model to determine whether to rebuild the self-encoding neural network;
  • a self-encoding neural network processing device including: an acquisition module, used to obtain a text sample; a conversion module, used to convert the text sample into a sample word vector; a first feature extraction module, used to convert the sample word vector Input to the pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain preliminary hidden features of the sample; the training module is used to input the preliminary hidden features of the sample into multiple self In the coding neural network, the self-encoding neural network is trained to obtain multiple self-encoding neural network models, wherein the hidden layer and the number of hidden layer units of each self-encoding neural network are different; the second feature extraction module uses To input the preliminary hidden features of the sample into each of the auto-encoding neural network models for feature extraction, respectively, to obtain the sample hidden features output by each of the auto-encoding neural network models; the clustering module is used for Using a clustering algorithm to cluster the feature samples of the hidden features of the samples extracted by each of the auto-encoding neural network models, respectively, to obtain the clustering results
  • a computer device includes a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions: Get text Sample; convert the text sample into a sample word vector; input the sample word vector into a pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain preliminary hidden features of the sample Input the preliminary hidden features of the sample into multiple self-encoding neural networks, and train the self-encoding neural network to obtain multiple self-encoding neural network models, wherein the hidden layer of each self-encoding neural network Different from the number of hidden layer units; respectively input the preliminary hidden features of the sample into each of the auto-encoding neural network models for feature extraction, and obtain the sample hidden features output by each of the auto-encoding neural network models; Use a clustering algorithm to cluster the feature samples of the hidden features of the samples extracted by each of the auto-encoding neural network models, to obtain the clustering results corresponding to each of the auto-encoding neural network models; The clustering
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, cause the one or more processors to perform the following steps: obtain a text sample; The text sample is converted into a sample word vector; the sample word vector is input into a pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain preliminary hidden features of the sample; The preliminary hidden features of the sample are input into multiple auto-encoding neural networks, and the auto-encoding neural networks are trained to obtain multiple auto-encoding neural network models, wherein the hidden layer and the hidden layer of each of the auto-encoding neural networks The number of units is different; the preliminary hidden features of the samples are input into each of the auto-encoding neural network models for feature extraction, and the hidden features of the samples output by each of the auto-encoding neural network models are obtained respectively; clustering is adopted The algorithm clusters the feature samples of the hidden features of the samples extracted by each of the self-encoding neural network models to obtain the clustering results corresponding to each of the self-
  • FIG. 1 is a schematic diagram of an application environment of the self-encoding neural network processing method in an embodiment of the present application
  • Fig. 2 is a flowchart of a self-encoding neural network processing method in an embodiment of the present application
  • Fig. 3 is an example diagram of a self-encoding neural network processing method in an embodiment of the present application
  • Fig. 4 is an example diagram of a self-encoding neural network processing method in an embodiment of the present application.
  • Fig. 5 is an example diagram of a self-encoding neural network processing method in an embodiment of the present application.
  • Fig. 6 is an example diagram of a self-encoding neural network processing method in an embodiment of the present application.
  • FIG. 7 is an example diagram of a self-encoding neural network processing method in an embodiment of the present application.
  • Fig. 8 is a functional block diagram of a self-encoding neural network processing device in an embodiment of the present application.
  • Fig. 9 is a schematic diagram of a self-encoding neural network processing device in an embodiment of the present application.
  • Fig. 10 is a schematic diagram of a computer device in an embodiment of the present application.
  • the self-encoding neural network processing method provided by the embodiments of the present application can be applied to the network architecture as shown in FIG. 1, in which the server preprocesses the text sample after obtaining the text sample, and after obtaining the hidden features of the preliminary sample, Start training the auto-encoding neural network. After the trained auto-encoding neural network model is obtained, further feature extraction is performed, and the feature samples corresponding to the extracted features are clustered, and finally the contour coefficients of the clustering results are reconstructed Target self-encoding neural network.
  • the server can be implemented as an independent server or a server cluster composed of multiple servers.
  • a self-encoding neural network processing method is provided. Taking the method applied to the server in FIG. 1 as an example for description, the method includes the following steps:
  • a text sample refers to a combination of characters, words, sentences, or characters.
  • the text sample can be obtained from the Internet through crawler technology, and the method of obtaining the text sample is not limited here.
  • the sample word vector refers to the vector sample that can be recognized by the computer and used to train the neural network.
  • the word2vec algorithm can be used to convert a text sample into a sample word vector, and the sample word vector can be used as a sample word vector, or the sample word vector can be obtained through other sample word vector conversion methods.
  • the glove algorithm converts the text sample into The sample word vector is not specifically limited here.
  • S30 Input the sample word vector into the pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain preliminary hidden features of the sample.
  • the pre-trained convolutional neural network model (Text in Convolutional Neural Network, TextCNN) refers to a neural network that has been trained in advance for the convolutional neural network model and has achieved the expected feature extraction effect.
  • the sample word vector is used as the input sample of the convolutional neural network model, input to the input layer of the convolutional neural network model, and sequentially processed by the convolutional layer, activation function, pooling layer, and fully connected layer.
  • the layer performs feature output to obtain the preliminary implicit features of the sample word vector.
  • the hidden feature of the sample is the feature vector extracted by the convolutional neural network model, and the preliminary hidden feature of the sample is a high-dimensional hidden feature, which usually contains a lot of interference information.
  • the sample word vector is input into a pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain preliminary hidden features of the sample.
  • the pre-trained convolutional neural network model is used to preprocess the sample word vectors, and the features of some sample word vectors are extracted to reduce the time required for subsequent training of the autoencoding neural network.
  • S40 Input the preliminary hidden features of the sample into multiple auto-encoding neural networks, and train the auto-encoding neural network to obtain multiple auto-encoding neural network models. Among them, the number of hidden layers and hidden layer units of each auto-encoding neural network different.
  • the auto-encoding neural network is a neural network for unsupervised learning.
  • the preliminary hidden features of the sample are input to the auto-encoding neural network, and the hidden layer of the self-encoding neural network and the hidden layer unit are calculated layer by layer.
  • Training is performed by minimizing the gap between input and output to ensure that the hidden features of the extracted samples can retain sufficient information.
  • the preliminary hidden features of the sample are input to the self-encoding neural network, and the back-propagation algorithm is used to perform unsupervised training on the self-encoding neural network so that the output is as equal to the input as possible.
  • the self-encoding neural network can be a sparse autoencoder.
  • the training process of the sparse autoencoder is to continuously adjust the hidden layer and hidden layer unit of the self-encoding neural network by calculating the error between the output of the self-encoding and the original input Finally, the required auto-encoding neural network model is trained.
  • S50 Input the preliminary hidden features of the sample into each auto-encoding neural network model for feature extraction, and obtain the sample hidden features output by each auto-encoding neural network model.
  • the preliminary hidden features of the sample are used as input and input to the auto-encoding neural network model.
  • the preliminary hidden features are extracted through the auto-encoding neural network model, and the dimensionality of the preliminary hidden features of the sample is reduced to obtain a low-dimensional sample hidden With features.
  • the preliminary hidden features of the sample are input into the auto-encoding network model for further feature extraction, which can compress the preliminary hidden features of the high-dimensional sample into low-dimensional hidden features of the sample.
  • the features are processed by the self-compiled neural network model, which can remove the interference information of the preliminary hidden features of the sample, and can prevent the loss of the information contained in the preliminary hidden features of the sample to the greatest extent.
  • S60 Use a clustering algorithm to cluster the feature samples of the hidden features of the samples extracted by each auto-encoding neural network model, respectively, to obtain a clustering result corresponding to each auto-encoding neural network model.
  • the clustering algorithm is a method of dividing the feature sample set into different clusters according to a certain standard (such as distance criterion), so that the sample implicit feature similarity of the feature samples in the same cluster is as large as possible, and at the same time, The difference of the sample hidden features of the feature samples in different clusters is also as large as possible, that is, the feature samples of the same type are gathered together as much as possible after clustering, and the feature samples of different types are separated as much as possible.
  • the clustering algorithm can be the k-means algorithm (k-means clustering algorithm), where the clustering algorithm can be the K-Means algorithm mentioned above, or the DBSCAN algorithm, which will not be specifically described here. limited.
  • the clustering result corresponding to the auto-encoding neural network model refers to the feature samples corresponding to the hidden features of the samples extracted by the auto-encoding neural network model to generate clustering.
  • Auto-encoding neural network model A extracts sample hidden feature A
  • auto-encoding neural network model B extracts sample hidden feature B, and the sample is hidden
  • the feature samples corresponding to feature A are clustered using a clustering algorithm, and clustering result A is obtained.
  • the clustering result corresponding to auto-encoding neural network model A is clustering result A
  • the feature sample corresponding to sample hidden feature B is clustered
  • the algorithm performs clustering and obtains the clustering result B.
  • the clustering result corresponding to the self-encoding neural network model B is the clustering result B.
  • the K-Means algorithm may be used to perform a clustering operation on the samples from which the hidden features of the samples are extracted.
  • the feature samples are clustered into multiple clusters (in some documents, clusters are also called classes) according to the hidden features of the samples, and the clusters are output
  • the clustering result after calculation each cluster includes one or more feature samples, and the feature samples of different clusters have different features.
  • S70 Determine whether to reconstruct the self-encoding neural network according to the clustering result corresponding to each self-encoding neural network model.
  • one self-encoding neural network can be rebuilt, or multiple self-encoding neural networks can be rebuilt, and how many self-encoding neural networks need to be rebuilt can be determined according to actual needs.
  • the auto-encoding neural network model with the largest contour coefficient of the clustering result is selected as the final model.
  • This embodiment uses a convolutional neural network model for preliminary sample hidden feature extraction, and then uses a trained autoencoding neural network model for sample hidden feature extraction.
  • the auto-encoding neural network model is trained, the auto-encoding neural network is reconstructed by the clustering results of the feature samples corresponding to the hidden features of the sample, and the auto-encoding neural network can be rounded by the clustering results. For verification, a self-encoding neural network with high clustering accuracy can be obtained.
  • step S70 determining whether to rebuild the auto-encoding neural network according to the clustering result corresponding to each auto-encoding neural network model specifically includes the following steps:
  • the contour coefficient is the evaluation method of the clustering result.
  • the value range of the contour coefficient is: [-1, 1]. The larger the contour coefficient, the better the clustering result.
  • the contour coefficient calculation formula is used to calculate the contour coefficient of each clustering result.
  • the preset condition refers to the minimum value that the contour coefficient of the clustering result needs to reach, and the preset condition can be determined according to actual clustering requirements. For example, if the requirements for clustering results are high, the preset condition can be set to 0.95; if the requirements for clustering results are not high, the preset condition can be set to 0.4.
  • any contour coefficient meets the preset condition, there is no need to construct a target self-encoding neural network.
  • the contour coefficient of each clustering result corresponding to each self-encoding neural network model is calculated, and it is judged whether the contour coefficient of each clustering result meets the preset condition, if the contour coefficient of each clustering result does not satisfy The preset conditions are determined to rebuild the self-encoding neural network.
  • the verification of the self-encoding neural network model can be standardized judgment (that is, the same parameter is used for judgment).
  • a clustering result includes multiple clusters, and each cluster includes one or more feature samples, as shown in FIG. 4, in step S71, the clustering results corresponding to each auto-encoding neural network model are calculated separately
  • the contour coefficient of includes the following steps:
  • S711 Calculate the average distance from each of the feature samples to other feature samples in the cluster to which the feature samples belong in the same clustering result.
  • S712 Calculate the average distance from each feature sample to other feature samples in the cluster to which the sample does not belong in the same clustering result.
  • i represents the feature sample i of the same clustering result
  • S(i) is the contour coefficient of the feature sample i
  • b(i) is the other clusters from the feature sample i to the cluster to which the feature sample i does not belong
  • a(i) is the average distance from the feature sample i to other feature samples in the cluster to which the feature sample i belongs
  • max ⁇ a(i),b(i) ⁇ means take a(i) And the maximum value in b(i).
  • the contour coefficient of the clustering result of each self-encoding network model will be calculated according to the calculation method of steps S711-S714, so as to obtain the contour coefficient of the clustering result corresponding to each self-encoding neural network .
  • step S80 if it is determined to rebuild the self-encoding neural network, constructing the target self-encoding neural network according to the contour coefficients includes the following steps:
  • S81 Determine the hidden law between the contour coefficients of all clustering results and the number of hidden layers and hidden layer units of all auto-encoding neural network models.
  • the contour coefficient of each clustering result can be determined first by which clustering result is calculated, and then the auto-encoding neural network model corresponding to the contour coefficient of the clustering result can be deduced. Obtain the number of hidden layers and hidden layer units of the auto-encoding neural network model corresponding to the contour coefficient, and then find out the relationship between the size of the contour coefficient and the number of hidden layers and hidden layer units of the auto-encoding neural network model to obtain the hidden layer With law.
  • the hidden law can be that the more the number of hidden layers and hidden layer units of the self-encoding neural network model, the larger the contour coefficient; it can also be the less the number of hidden layers and hidden layer units of the self-encoding neural network model , The larger the profile coefficient.
  • S82 Use the hidden law as the basis for setting the hidden layer and the number of hidden layer units of the target self-encoding neural network.
  • the hidden law is used as the basis for setting the hidden layer and the number of hidden layer units of the target self-encoding neural network.
  • the hidden law is that the more the number of hidden layers and hidden layer units of the self-encoding neural network model, the larger the contour coefficient, the setting basis can be to increase the number of hidden layers and hidden layer units; the hidden law is auto-encoding neural The smaller the number of hidden layers and hidden layer units of the network model, the larger the contour coefficient.
  • the setting basis may be to reduce the number of hidden layer and hidden layer units.
  • S83 Set the number of hidden layers and hidden layer units of the target self-encoding neural network according to the setting basis.
  • the hidden law is used as the reconstruction of the auto-encoding neural network.
  • the setting basis of the hidden layer and the number of hidden layer units of the network model, and finally the number of hidden layers and hidden layer units of the self-encoding neural network model is set according to the settings.
  • the target auto-encoding neural network model performs feature extraction, and the feature samples corresponding to the features extracted by the target auto-encoding neural network model are clustered, and then the contour coefficient of the clustering result is calculated. If the contour coefficient of the clustering result does not meet the expected Set the conditions, then rebuild the target neural network until the desired effect is achieved.
  • the expected effect means that the contour coefficient of the clustering result corresponding to the self-encoding neural network model meets the preset condition.
  • step S80 that is, if it is determined to rebuild the self-encoding neural network, after constructing the target self-encoding neural network according to the contour coefficients of the clustering result, the self-encoding neural network processes
  • the method also includes the following steps:
  • S90 Select the target auto-encoding neural network model corresponding to the target auto-encoding neural network that achieves the expected effect as the final model.
  • step S83 if it is determined to rebuild the self-encoding neural network, then Constructing a target self-encoding neural network according to the contour coefficients of the clustering results includes the following steps:
  • the number of hidden layers and hidden layer units of the auto-encoding neural network model corresponding to the maximum contour coefficient is selected as the setting base.
  • the number of hidden layers and hidden layer units of the auto-encoding neural network model A corresponding to the maximum contour coefficient in the auto-encoding neural network models A, B, and C is 3 hidden layers and 100 hidden layer units. set The basis is to increase the number of hidden layers and hidden layer units of the auto-encoding neural network model, the target auto-encoding neural network is set to 3 hidden layers and 100 hidden layer units.
  • S832 Increase the number of hidden layer and hidden layer units of the target self-encoding neural network on the set base.
  • the number of hidden layers and hidden layer units of the target auto-encoding neural network is based on the set base, and the number of hidden layers and hidden layer units of the target auto-encoding neural network is increased.
  • the setting base of the target self-encoding neural network is that the number of hidden layers and hidden layer units is 3 layers of hidden layers and 100 hidden layer units, then the setting base of 3 layers of hidden layers and 100 hidden layer units, Increase the number of hidden layers and hidden layer units of the target autoencoding neural network.
  • the number of hidden layers and hidden layer units of the auto-encoding neural network model corresponding to the maximum contour coefficient is selected as the setting base.
  • the setting base of the target auto-encoding neural network is 3 hidden layers and 100 hidden layer units.
  • S834 Reduce the number of hidden layers and hidden layer units of the target self-encoding neural network on the setting base.
  • the number of hidden layers and hidden layer units of the target auto-encoding neural network is based on the set base, and the number of hidden layers and hidden layer units of the target auto-encoding neural network is reduced.
  • the setting base of the target self-encoding neural network is that the number of hidden layers and hidden layer units is 3 layers of hidden layers and 100 hidden layer units, then the setting base of 3 layers of hidden layers and 100 hidden layer units, Reduce the number of hidden layers and hidden layer units of the target auto-encoding neural network.
  • a self-encoding neural network processing device corresponds to the self-encoding neural network processing method in the above-mentioned embodiment one-to-one.
  • the self-encoding neural network processing device includes an acquisition module 10, a transformation module 20, a first feature extraction module 30, a training module 40, a second feature extraction module 50, a clustering module 60, a determination module 70, and a re ⁇ module 80.
  • each functional module is as follows: the acquisition module 10 is used to obtain text samples; the conversion module 20 is used to convert text samples into sample word vectors; the first feature extraction module 30 is used to input the sample word vectors into the pre-trained In the convolutional neural network model, the initial feature extraction of the sample word vector is used to obtain the initial implicit feature of the sample; the training module 40 is used to input the initial implicit feature of the sample into multiple auto-encoding neural networks to The neural network is trained to obtain multiple auto-encoding neural network models.
  • the number of hidden layer and hidden layer unit of each auto-encoding neural network is different; the second feature extraction module 50 is used to input the preliminary hidden features of the sample into each Feature extraction is performed in each auto-encoding neural network model, and the hidden features of the samples output by each auto-encoding neural network model are obtained respectively; the clustering module 60 is used to use a clustering algorithm to extract samples from each auto-encoding neural network model The feature samples of the hidden features are clustered separately to obtain the clustering results corresponding to each auto-encoding neural network model; the determining module 70 is used to determine whether to reconstruct the self-encoding neural network model according to the clustering results corresponding to each auto-encoding neural network model. Encoding neural network; the reconstruction module 80 is used to construct the target self-encoding neural network according to the contour coefficients of the clustering result if it is determined to rebuild the self-encoding neural network.
  • the determination module 70 includes: a calculation sub-module 71, a judgment sub-module 72, and a determination sub-module 73.
  • the detailed function descriptions between the modules are as follows: the calculation sub-module 71, It is used to calculate the contour coefficients of the clustering results corresponding to each self-encoding neural network model; the judging sub-module 72 is used to judge whether the contour coefficients of each clustering result meets preset conditions; the determining sub-module 73 is used to determine If the contour coefficient of each clustering result does not meet the preset condition, it is determined to rebuild the self-encoding neural network.
  • the calculation sub-module 71 includes: a first calculation unit, a second calculation unit, a third calculation unit, and a fourth calculation unit.
  • the detailed function descriptions of each unit are as follows: , Used to calculate the average distance from each feature sample to other feature samples in the cluster to which the feature sample belongs in the same clustering result; the second calculation unit is used to calculate the same clustering result from each feature sample to the sample The average distance of other feature samples in the cluster to which it belongs; the third calculation unit is used to calculate the contour coefficient of each feature sample in the same clustering result, where the contour coefficient is:
  • i represents the feature sample i of the same clustering result
  • S(i) is the contour coefficient of the feature sample i
  • b(i) is the average distance from the feature sample i to other feature samples in the cluster to which the feature sample i does not belong
  • a(i) is the average distance between the feature sample i and the other feature samples in the cluster to which the feature sample i belongs
  • max ⁇ a(i),b(i) ⁇ represents the largest of a(i) and b(i) value
  • the fourth calculation unit is used to take the average of the contour coefficients of all feature samples in the same clustering result as the contour coefficient of the same clustering result.
  • the reconstruction module 80 includes: a determining unit, a setting basis determining unit, and a setting unit.
  • the functions of each unit are described as follows: the determining unit is used to determine the contour coefficients of all clustering results and The hidden law between the hidden layer and the number of hidden layer units of all auto-encoding neural network models; the setting basis determines the unit, which is used to set the hidden law as the target hidden layer and the number of hidden layer units in the auto-encoding neural network Basis; setting unit, used to set the hidden layer and the number of hidden layer units of the target self-encoding neural network according to the setting basis.
  • the auto-encoding neural network processing device further includes a selection module.
  • the detailed function description of the module is as follows: the selection module is used to select the target auto-encoding nerve that achieves the desired effect
  • the target auto-encoding neural network model corresponding to the network is used as the final model.
  • the setting unit includes: selecting subunits and setting subunits, and the detailed description of each unit is as follows:
  • Select subunits if the setting basis is to increase the number of hidden layers and hidden layer units, then select the number of hidden layers and hidden layer units of the auto-encoding neural network model corresponding to the largest contour coefficient as the setting base of the target auto-encoding neural network ;
  • the setting basis is to reduce the number of hidden layer and hidden layer units, select the number of hidden layers and hidden layer units of the auto-encoding neural network model corresponding to the largest contour coefficient as the setting base of the target auto-encoding neural network.
  • each module in the above-mentioned self-encoding neural network processing device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the foregoing modules may be embedded in the form of hardware or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 10.
  • the computer equipment includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a readable storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the database of the computer equipment is used to store the data required by the self-encoding neural network processing method.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize a self-encoding neural network processing method.
  • the readable storage medium provided in this embodiment includes a volatile storage medium and a non-volatile storage medium.
  • a computer device including a memory, a processor, and computer-readable instructions stored in the memory and running on the processor.
  • the processor executes the computer-readable instructions, the following steps are implemented: Get text Sample; convert the text sample into sample word vector; input the sample word vector into the pre-trained convolutional neural network model to perform preliminary feature extraction on the sample word vector to obtain the preliminary implicit features of the sample; initially implicit the sample
  • the features are input into multiple auto-encoding neural networks, and the auto-encoding neural network is trained to obtain multiple auto-encoding neural network models.
  • the number of hidden layers and hidden layer units of each auto-encoding neural network is different; the samples are initially hidden respectively Containing features are input to each auto-encoding neural network model for feature extraction, and the hidden features of the samples output by each auto-encoding neural network model are obtained respectively; the clustering algorithm is used for the hidden features of the samples extracted from each auto-encoding neural network model
  • the feature samples of the features are clustered separately to obtain the clustering results corresponding to each auto-encoding neural network model; according to the clustering results corresponding to each auto-encoding neural network model, determine whether to rebuild the auto-encoding neural network; if it is determined to be rebuilt
  • construct a target self-encoding neural network based on the contour coefficients.
  • one or more readable storage media storing computer readable instructions are provided, the readable storage medium storing computer readable instructions, and the computer readable instructions are executed by one or more processors At this time, one or more processors are made to implement the following steps: obtain text samples; convert the text samples into sample word vectors; input the sample word vectors into the pre-trained convolutional neural network model to perform preliminary sample word vectors Feature extraction to obtain the preliminary hidden features of the sample; input the preliminary hidden features of the sample into multiple auto-encoding neural networks, and train the auto-encoding neural network to obtain multiple auto-encoding neural network models, where each auto-encoding neural network The number of hidden layer and hidden layer unit is different; the preliminary hidden features of the sample are input into each auto-encoding neural network model for feature extraction, and the hidden features of the sample output by each auto-encoding neural network model are obtained respectively; clustering is adopted The algorithm clusters the feature samples of the hidden features of the samples extracted by each auto-encoding neural network model, and obtains the clustering results corresponding to each
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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  • Image Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un procédé et un appareil de traitement de réseau neuronal à codage automatique, ainsi qu'un dispositif d'ordinateur et un support d'informations. Le procédé consiste : à convertir un échantillon de texte en un vecteur de mot d'échantillon ; à entrer le vecteur de mot d'échantillon dans un modèle de réseau neuronal convolutionnel afin d'effectuer une extraction de caractéristique préliminaire sur le vecteur de mot d'échantillon de façon à obtenir une caractéristique implicite d'échantillon préliminaire ; à entrer la caractéristique implicite d'échantillon préliminaire dans une pluralité de réseaux neuronaux à codage automatique, et former les réseaux neuronaux à codage automatique à obtenir une pluralité de modèles de réseau neuronal à codage automatique ; à entrer la caractéristique implicite d'échantillon préliminaire dans les modèles de réseau neuronal à codage automatique afin d'effectuer une extraction de caractéristiques de façon à obtenir des caractéristiques implicites d'échantillon délivrées par les modèles de réseau neuronal à codage automatique ; à regrouper des échantillons de caractéristiques des caractéristiques implicites d'échantillon extraites pour obtenir un résultat de regroupement ; à déterminer, en fonction du résultat de regroupement, s'il faut reconstruire un réseau neuronal à codage automatique ; et s'il est déterminé que le réseau neuronal à codage automatique doit être reconstruit, à construire un réseau neuronal à codage automatique cible selon un coefficient de contour afin d'obtenir un réseau neuronal à codage automatique doté d'une précision de regroupement élevée.
PCT/CN2019/102671 2019-04-26 2019-08-27 Procédé et appareil de traitement de réseau neuronal à codage automatique, et dispositif d'ordinateur et support d'informations WO2020215560A1 (fr)

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