CN109670623A - Neural net prediction method and device - Google Patents
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Abstract
The disclosure provides a kind of neural net prediction method and device, which comprises obtains the characteristic information of object to be predicted；It will be handled in the characteristic information input prediction model, obtain the prediction result for being directed to the object to be predicted；Export the prediction result of the object to be predicted, wherein the prediction model includes deep neural network DNN module and multi-task learning network MTL module.By utilizing the prediction model for including deep neural network DNN and multi-task learning network MTL, the characteristic information of object to be predicted is handled, obtain the prediction result for being directed to object to be predicted, the relevance between the characteristic information of object to be predicted can be captured, to improve the accuracy rate of prediction result.
Description
Technical field
This disclosure relates to nerual network technique field more particularly to a kind of neural net prediction method and device.
Background technique
In actual life, need to predict various events, for example, for will show film prediction box office,
For will show TV play prediction audience ratings, America, presidential elections, the winning digit of current lottery, football match victory or defeat
Deng, various events to be predicted the incidence relation that influence factor is complicated, different between each influence factor also result in it is different pre-
Survey as a result, but in traditional predicting means, limited data can only be directed to, carried out using related algorithms such as probability statistics pre-
It surveys, the accuracy rate of prediction result is low.
Summary of the invention
In view of this, the present disclosure proposes a kind of neural net prediction method and device, it can be to the spy of object to be predicted
Reference breath is handled, and exports one or more prediction results according to demand, improves the accuracy rate of prediction result.
According to the one side of the disclosure, a kind of neural net prediction method is provided, which comprises
Obtain the characteristic information of object to be predicted；
It will be handled in the characteristic information input prediction model, obtain the prediction knot for being directed to the object to be predicted
Fruit；
The prediction result of the object to be predicted is exported,
Wherein, the prediction model includes deep neural network DNN module and multi-task learning network MTL module.
In one possible implementation, it will be handled in the characteristic information input prediction model, acquisition is directed to
The prediction result of the object to be predicted, comprising:
The characteristic information is inputted in the DNN module and is handled, determines the depth information of the object to be predicted；
The depth information is inputted in the MTL module and is handled, determines the prediction for being directed to the object to be predicted
As a result.
In one possible implementation, the prediction model further includes insertion module,
Wherein, it will handle, obtained for the pre- of the object to be predicted in the characteristic information input prediction model
Survey result, further includes:
Initial information is inputted into the insertion module and carries out vectorization processing, determines the vector information of the initial information；
The initial information and the vector information are determined as the characteristic information.
In one possible implementation, the prediction result includes:
Main prediction result and correlation predictive as a result,
Wherein, in the objective function of the prediction model, parameters weighting associated with the main prediction result is greater than
Parameters weighting associated with the correlation predictive result.
In one possible implementation, the prediction result for exporting the object to be predicted, includes any of the following:
Export the main prediction result of the object to be predicted；
Export the main prediction result and correlation predictive result of the object to be predicted.
In one possible implementation, the method also includes:
Obtain the characteristic information of sample object；
It will be handled in the characteristic information input initial predicted model of the sample object, obtain and be directed to the sample pair
The training prediction result of elephant；
According to the training prediction result of the sample object and the expectation prediction result of the sample object, determine described in
The model of sample object loses；
It is lost according to the model, adjusts the parameters weighting in the initial predicted model, determine prediction mould adjusted
Type；
In the case where model loss meets training condition, prediction model adjusted is determined as to final prediction
Model.
In one possible implementation, it is lost according to the model, adjusts the parameter in the initial predicted model
Weight determines prediction model adjusted, comprising:
It is lost according to the sample object model, is sequentially adjusted in the MTL module, the DNN module and the insertion
Parameters weighting in module determines prediction model adjusted.
According to another aspect of the present disclosure, a kind of neural network prediction device is provided, which is characterized in that described device packet
It includes:
Characteristic acquisition unit, for obtaining the characteristic information of object to be predicted；
Prediction result acquiring unit obtains for will handle in the characteristic information input prediction model and is directed to institute
State the prediction result of object to be predicted；
Prediction result output unit, for exporting the prediction result of the object to be predicted,
Wherein, the prediction model includes deep neural network DNN module and multi-task learning network MTL module.
In one possible implementation, the prediction result acquiring unit includes:
DNN handles subelement, handle for inputting the characteristic information in the DNN module, determine it is described to
Predict the depth information of object；
MTL handles subelement, handles for inputting the depth information in the MTL module, determines and is directed to institute
State the prediction result of object to be predicted.
In one possible implementation, the prediction model further includes insertion module,
The prediction result acquiring unit further include:
It is embedded in subelement, carries out vectorization processing for initial information to be inputted the insertion module, is determined described initial
The vector information of information；
Information determines subelement, for the initial information and the vector information to be determined as the characteristic information.
In one possible implementation, the prediction result includes:
Main prediction result and correlation predictive as a result,
Wherein, in the objective function of the prediction model, parameters weighting associated with the main prediction result is greater than
Parameters weighting associated with the correlation predictive result.
In one possible implementation, the prediction result output unit for export it is following any one:
Export the main prediction result of the object to be predicted；
Export the main prediction result and correlation predictive result of the object to be predicted.
In one possible implementation, further includes:
Sample characteristics information acquisition unit, for obtaining the characteristic information of sample object；
Training prediction result acquiring unit, for by the characteristic information of sample object input initial predicted model into
Row processing, obtains the training prediction result for being directed to the sample object；
Model loses determination unit, for according to the training prediction result of the sample object and the sample object
It is expected that prediction result, determines the model loss of the sample object；
Weight adjustment unit adjusts the parameters weighting in the initial predicted model, really for losing according to the model
Fixed prediction model adjusted；
Prediction model determination unit, for the model loss meet training condition in the case where, will be adjusted pre-
It surveys model and is determined as final prediction model.
In one possible implementation, the weight adjustment unit includes:
Weight adjusts subelement, for losing according to the model of the sample object, is sequentially adjusted in the MTL module, institute
The parameters weighting in DNN module and the insertion module is stated, determines prediction model adjusted.
According to another aspect of the present disclosure, a kind of neural network prediction device is provided, comprising: processor；For storing
The memory of processor-executable instruction；Wherein, the processor is configured to executing the above method.
According to another aspect of the present disclosure, a kind of non-volatile computer readable storage medium storing program for executing is provided, is stored thereon with
Computer program instructions, wherein the computer program instructions realize the above method when being executed by processor.
According to the neural net prediction method and device of embodiment of the present disclosure various aspects, by using including depth nerve net
The prediction model of network DNN and multi-task learning network MTL, are handled the characteristic information of object to be predicted, acquisition is directed to
The prediction result of object to be predicted can capture the relevance between the characteristic information of object to be predicted, to improve prediction
As a result accuracy rate.
According to below with reference to the accompanying drawings to detailed description of illustrative embodiments, the other feature and aspect of the disclosure will become
It is clear.
Detailed description of the invention
Comprising in the description and constituting the attached drawing of part of specification and specification together illustrates the disclosure
Exemplary embodiment, feature and aspect, and for explaining the principles of this disclosure.
Fig. 1 is a kind of flow chart of neural net prediction method shown according to an exemplary embodiment.
Fig. 2 is the flow chart of step S12 in a kind of neural net prediction method shown according to an exemplary embodiment.
Fig. 3 is the flow chart of step S12 in a kind of neural net prediction method shown according to an exemplary embodiment.
Fig. 4 is a kind of flow chart of neural net prediction method shown according to an exemplary embodiment.
Fig. 5 is a kind of block diagram of prediction model shown according to an exemplary embodiment.
Fig. 6 is a kind of three-layer neural network structural schematic diagram shown according to an exemplary embodiment.
Fig. 7 is a kind of showing for the multi-task learning network of neural net prediction method shown according to an exemplary embodiment
It is intended to.
Fig. 8 is the input schematic diagram of vector information and initial information shown according to an exemplary embodiment.
Fig. 9 is a kind of schematic diagram of the application scenarios of neural net prediction method shown according to an exemplary embodiment.
Figure 10 is a kind of block diagram of neural network prediction device shown according to an exemplary embodiment.
Figure 11 is a kind of block diagram of neural network prediction device shown according to an exemplary embodiment.
Figure 12 is a kind of block diagram of device for neural network prediction shown according to an exemplary embodiment.
Specific embodiment
Various exemplary embodiments, feature and the aspect of the disclosure are described in detail below with reference to attached drawing.It is identical in attached drawing
Appended drawing reference indicate element functionally identical or similar.Although the various aspects of embodiment are shown in the attached drawings, remove
It non-specifically points out, it is not necessary to attached drawing drawn to scale.
Dedicated word " exemplary " means " being used as example, embodiment or illustrative " herein.Here as " exemplary "
Illustrated any embodiment should not necessarily be construed as preferred or advantageous over other embodiments.
In addition, giving numerous details in specific embodiment below to better illustrate the disclosure.
It will be appreciated by those skilled in the art that without certain details, the disclosure equally be can be implemented.In some instances, for
Method, means, element and circuit well known to those skilled in the art are not described in detail, in order to highlight the purport of the disclosure.
Fig. 1 is a kind of flow chart of neural net prediction method shown according to an exemplary embodiment.This method can answer
For in server, as shown in Figure 1, the neural net prediction method includes:
Step S11 obtains the characteristic information of object to be predicted.
For example, the object to be predicted may include that film, TV play, ball match, lottery ticket, presidential election etc. will occur
Event, the characteristic information include may to the various influence factors that the prediction result of object to be predicted has an impact, for example,
When predicting the box office etc. for the film that will be shown, object to be predicted be film, prediction result can for the film box office,
Viewing number etc., characteristic information may include the associated various information such as the subject matter of the film, working days, director and performer.
And when predicting the result of the match of ball match, object to be predicted be ball match, prediction result can for the ball match result (victory or defeat),
Score etc., characteristic information may include the associated various information such as both sides coach, sportsman, weather, history victory or defeat.For it is each to
It predicts object, a variety of (various dimensions) characteristic informations of the object can be obtained as much as possible, in actual prediction, each to pre-
The characteristic information for surveying object can be, for example, 100 dimensions to 200 dimensions.
In one possible implementation, characteristic information may include characteristic information itself and relevant to characteristic information
Numerical value, the numerical value may include the real number value of discrete type or continuous type.By taking the scene that the box office of film is predicted as an example, characteristic information can
Including subject matter and performer, wherein subject matter can indicate whether to belong to this feature of theme of love, 1 table of value with a Boolean
Show that current sample (film) belongs to theme of love, the expression of value 0 is not belonging to theme of love；And quantifiable index can be used in performer
Indicate, for example, using performer movie show for the previous period in volumes of searches in a search engine.
Step S12 will be handled in the characteristic information input prediction model, be obtained for the object to be predicted
Prediction result, wherein the prediction model includes deep neural network DNN module and multi-task learning network MTL module.
For example, the prediction model includes trained neural network prediction model, and the neural network prediction
Model includes deep neural network DNN (Deep Neural Network) module and multi-task learning network MTL (Multi-
Task Learning) module.
There is complicated incidence relation between multiple/multidimensional characteristic information of same object to be predicted, and incidence relation is
It is no accurately to be captured, the accuracy of prediction result is directly influenced, the DNN module based on deep neural network is for capturing not
The integrated mode of same characteristic information.And more (depth is bigger) its learning ability of the number of plies of DNN module are stronger, eventually form
The stability of prediction model is higher, and capability of fitting is also stronger.
It is using MTL module that multiple prediction results are same for the multiple prediction results for exporting an object to be predicted simultaneously
Shi Jinhang processing.
By the characteristic information of object to be predicted, input after trained prediction model handled, it is to be predicted right to obtain
One or more prediction results of elephant, for example, after the trained prediction model of characteristic information input of film is handled, it can
The prediction result for obtaining the box office of film, can also obtain multiple prediction results such as box office and public praise of film simultaneously.
It is understood that can flexibly set obtaining for prediction result according to demand according to the characteristic information of object to be predicted
Situation is taken, a prediction result including obtaining object to be predicted, and multiple prediction results of object to be predicted are obtained, it is predicting
It is set in the training process of model.The disclosure to this with no restriction.
Step S13 exports the prediction result of the object to be predicted.
For example, what is got is multiple prediction results for object to be predicted, and according to demand, selection output is wherein
The prediction result of one object to be predicted.
In the present embodiment, by utilizing the prediction model including DNN module and MTL module, to object to be predicted
After characteristic information is handled, the prediction result obtained for the object to be predicted can be caught according to all aspects of this disclosure
The relevance between the characteristic information of object to be predicted is caught, while exporting one or more prediction results of object to be predicted, is mentioned
The high accuracy rate of prediction result.
Fig. 2 is the flow chart of step S12 in a kind of neural net prediction method shown according to an exemplary embodiment, should
Method can be applied in server, on the basis of the above embodiments, as shown in Fig. 2, step S12 includes:
The characteristic information is inputted in the DNN module and is handled, determines the object to be predicted by step S121
Depth information.
For example, Fig. 5 is a kind of block diagram of prediction model shown according to an exemplary embodiment.
In the embodiment that Fig. 5 is provided, DNN module includes the full articulamentum of multilayer, and DNN module is used based on coding certainly
(AutoEncoder) pre-training (Pre-training) solves the problems, such as the gradient disappearance of DNN.Fig. 6 is exemplary according to one
Implement a kind of three-layer neural network structural schematic diagram exemplified.As shown in fig. 6, being used first for each layer of input of DNN
Three-layer neural network structure in Fig. 6 is fitted itself, and wherein output layer is the duplication of input layer, the mesh of network structure study
Mark is that this two groups of weight parameters are most by " input layer-hidden layer " (code weight) and " hidden layer-output layer " (decoding weight)
Possibly it is fitted input layer itself.The nodal point number of hidden layer is identical as next layer in DNN of nodal point number.Finally use code weight
As the initial value of weight parameter between current input layer and next hidden layer, and so on, by each layer in DNN of weight
Parameter is all initialized by way of above-mentioned pre-training, and entire DNN may finally be made to obtain good convergence.
In one possible implementation, according to the DNN model after pre-training, DNN module can be constructed.It will be to pre-
Survey in the characteristic information input DNN model of object, can incidence relation between the multidimensional characteristic information to object to be predicted into
Row capture processing, so that it is determined that the depth information of object to be predicted.
The depth information is inputted in the MTL module and is handled by step S122, is determined for described to be predicted right
The prediction result of elephant.
For example, it is handled as shown in figure 5, the relation information of object to be predicted can be input in MTL module.
Wherein, for MTL module for realizing multi-task learning, multi-task learning is to indicate (shared based on shared
Representation), main task (main tasks) is allowed to use inter-related task (related tasks), Lai Tisheng main task
A kind of machine learning method of extensive effect.For example, the prediction for video flow, main task is accurately to predict video flow
VV (video view), but predict that multiple inter-related tasks can promote the predictablity rate of main task simultaneously, inter-related task includes view
Frequency playback volume, click volume, playback volume etc..Multi-task learning is divided into hard parameter sharing (hard parameter sharing) and soft
Two kinds of parameter sharing (soft parameter sharing), the present embodiment uses hard parameter sharing.
Fig. 7 is a kind of showing for the multi-task learning network of neural net prediction method shown according to an exemplary embodiment
It is intended to.As shown in fig. 7, the multi-task learning network can be realized simultaneously main task output and inter-related task output, obtain to
Predict the corresponding main prediction result and correlation predictive result of object.It will be understood by those skilled in the art that for multitask
The specific structure and parameter sharing mode for practising network can be configured as needed, as long as can carry out by the MTL module
Multi-task learning, the disclosure to this with no restriction.
In MTL module, inter-related task is equivalent to relative to main task introduces noise, and introduces noise and mould can be improved
The generalization ability of type.Canonical effect between main task and inter-related task prevents over-fitting, lift scheme generalization ability.And MTL
The attention mechanism (attention) of module, can aid forecasting model focus more on relevant feature.
In the present embodiment, by utilizing the prediction model including DNN module, MTL module, to the feature of object to be predicted
After information is handled, obtain be directed to the object to be predicted prediction result, according to all aspects of this disclosure, can capture to
It predicts the relevance between the characteristic information of object, exports one or more prediction results of object to be predicted, improve prediction
As a result accuracy rate.
Fig. 3 is the flow chart of step S12 in a kind of neural net prediction method shown according to an exemplary embodiment, should
Method can be applied in server, and on the basis of the above embodiments, the prediction model further includes insertion module.Such as Fig. 3 institute
Show, step S12 further include:
Initial information is inputted the insertion module and carries out vectorization processing, determines the initial information by step S1211
Vector information.
For example, in actual prediction, characteristic information includes the history box office of director, directs and be good at subject matter, performer A
Baidu's index, the history box office of performer A etc., wherein the history box office directed and director, which are good at subject matter, belongs to this master of director
Body, and the history box office of Baidu's index of performer A, performer A belong to this main body of performer A.I.e. an object to be predicted includes
Multiple main bodys, multiple characteristic informations belong to the same main body.
Initial information is to handle the discrete type obtained or successional reality by simple mathematical after directly acquiring various information
Numerical value, but the simple characteristic information for relying on numerical value, are typically only capable to the influence from a dimension response feature information.For example, performer A
Movie show for the previous period in Baidu index of the volumes of searches as performer A in a search engine, for predicting film
Box office.What Baidu's index substantially reflected is the temperature of chief creating, and is difficult the had an impact flow of exhaustion by Feature Engineering merely
Dimension, in addition to this, numerical value is also more serious to the dependence of external data.
In insertion module, all characteristic informations for each main body being involved in are spliced, with a fixed length
The vector of degree indicates, obtains the vector information of each main body, and by taking volume forecasting as an example, each chief creating, subject matter are corresponding
The vector of one regular length.
In the input layer of prediction model, in addition to inputting initial information, also input is generated after being spliced using initial information
The vector information of each main body, the vector letter in the training process of embeding layer, as the node in hiding, in input layer
Breath also needs to update, and it is shared that the corresponding vector information of the characteristic information of the same main body carries out weight.
Fig. 8 is the input schematic diagram of vector information and initial information shown according to an exemplary embodiment, such as Fig. 8 institute
Show, by taking TV play volume forecasting as an example, inputs the multiple features 1, feature 2 and feature 3 of TV play, the numeric type mark sheet in figure
Show the traditional characteristic of TV play, increase embeding layer (embedding layer) input on this basis, i.e., by current television play
The features such as director, protagonist are indicated with the vector of a regular length respectively, and every one-dimensional in random initialization vector, output
The flow that can be current sample can increase output, such as scoring under the frame of multi-task learning.Middle layer can be used
Arbitrary neural network structure (being DNN used in figure), constantly adjusts nerve by Back Propagation Algorithm in learning process
The numerical value of vector in the parameter and embeding layer of network.After the completion of training, each feature obtains a vector and indicates, obtains simultaneously
The parameter of final neural network.It is in addition to traditional numeric type feature, the feature of current television play is corresponding special when being predicted
The vector of sign is spliced into embeding layer, and the parameter obtained together with numeric type feature based on training carries out propagated forward, final to calculate
Depth information Y1, Y2 and the Y3 of each feature in output layer out.
The vectorization of characteristic information indicates, more preferable compared to traditional numerical representation stability.Vector information is advantageous
In portraying the similitude between characteristic information, such as subject matter " magical " and " magic " have a degree of similitude, according to biography
Boolean's representation of system can not portray this similitude, and can use the similarity between vector after being embedded in portray
This similitude.Also, current signature is often contained in multiple dimensions for prediction by being embedded in the vector that training obtains
The information content of structure reduces the engineering burden for extracting characteristic information.
The initial information and the vector information are determined as the characteristic information by step S1212.
For example, the prediction model that joined embeding layer needs simultaneously to include initial information and vector information.
In the present embodiment, it due to joined embeding layer, so that the stability of numerical representation is more preferable, more conducively captures
Similitude between characteristic information has contained current characteristic information in multiple dimensions for pre- by the vector information after training
The information content for surveying result reduces the burden that information characteristics extract engineering.
In one possible implementation, the prediction result include main prediction result and correlation predictive as a result, its
In, in the objective function of the prediction model, to the main associated parameters weighting of prediction result be greater than with it is described related
The associated parameters weighting of prediction result.
Multiple prediction results of prediction model output, in the objective function of training pattern, the settable parameter power first closed
Weight, to adjust the accuracy between different prediction results.For example, theme to be predicted is film, main prediction result is box office, related
Prediction result is public praise, and in objective function, by adjusting separately the parameters weighting at box office and public praise, adjusting main prediction result is
Box office or public praise.
In the present embodiment, prediction result includes main prediction result and correlation predictive as a result, by adjusting prediction model
Parameters weighting in objective function can adjust the accuracy of main prediction result and correlation predictive result.
In one possible implementation, the prediction result for exporting the object to be predicted, includes any of the following:
Export the main prediction result of the object to be predicted；Export the main prediction result and correlation predictive knot of the object to be predicted
Fruit.
Since the accuracy of main prediction result is higher than correlation predictive as a result, when focusing more on the accuracy of prediction result,
Main prediction result can be only exported, the prediction result at box office is such as only exported；When paying close attention to the correlation between each prediction result simultaneously,
Main prediction result and correlation predictive are exported simultaneously as a result, exporting related prediction result while exporting main prediction result box office
Public praise.
In the present embodiment, by choose whether output prediction result in main prediction result and correlation predictive as a result, can
To get the combination of relevant prediction result according to different concern demands, so that the applicability of the present embodiment is more extensive.
Fig. 4 is a kind of flow chart of neural net prediction method shown according to an exemplary embodiment, and this method can answer
For in server, as shown in Figure 4, which comprises
Step S14 obtains the characteristic information of sample object.
For example, the characteristic information of a certain number of sample objects is used to train prediction model, such as will
The characteristic information of the film at the known box office shown, as the characteristic information of sample object, for initial prediction model into
Row training.
Step S15 will be handled in the characteristic information input initial predicted model of the sample object, be obtained and be directed to institute
State the training prediction result of sample object.
For example, it will handle, obtain in the characteristic information input initial predicted model of one of sample object
Training prediction result.
Step S16, according to the training prediction result of the sample object and the expectation prediction result of the sample object,
Determine the model loss of the sample object.
For example, by the expectation prediction result of known sample object, and the training for sample object got is predicted
As a result it is compared, obtains the model loss of the sample object.
Step S17 loses according to the model, adjusts the parameters weighting in the initial predicted model, after determining adjustment
Prediction model.
For example, gradient is asked to all-network parameter according to the loss of above-mentioned model, then according to back-propagation algorithm BP
(Backpropagation algorithm) adjusts gradient, updates the parameters weighting in initial predicted model.
In one possible implementation, it is lost according to the model, adjusts the parameter in the initial predicted model
Weight determines prediction model adjusted, comprising: is lost according to the model of the sample object, is sequentially adjusted in the MTL mould
Parameters weighting in block, the DNN module and the insertion module, determines prediction model adjusted.
Equally by taking above-mentioned training process as an example, calculated according to the loss of the model of the sample object got, using backpropagation
Method, the parameters weighting being sequentially adjusted in the MTL module, the DNN module and the insertion module, determines adjusted pre-
Survey model.
When prediction model carries out the repetitive exercise number for meeting setting, in each repetitive exercise, in repetition
State step.
Prediction model adjusted is determined as most by step S18 in the case where model loss meets training condition
Whole prediction model.
For example, training condition includes the repetitive exercise number of setting, and/or the condition of convergence of setting.
In the present embodiment, it is trained according to characteristic information and initial predicted model, obtains meeting the pre- of training condition
Model is surveyed, obtained prediction model can preferably reflect the relevance of the characteristic information of object to be predicted, and can obtain standard
True prediction result.
In the present embodiment, it is sequentially adjusted in the parameters weighting of the modules in the initial predicted model, is finally obtained
Meet the prediction model of training condition, the adjustment of the parameters weighting of modules ensure that the prediction result of prediction model output
Accuracy.
Using example
Below in conjunction with " predicting film A1 " property application scenarios as an example, provide according to the embodiment of the present disclosure
Application example, in order to understand the process of neural net prediction method.It will be understood by those skilled in the art that applying example below
Merely for the sake of the purpose for being easy to understand the embodiment of the present invention, it is not construed as the limitation to the embodiment of the present invention.
Fig. 9 is a kind of schematic diagram of the application scenarios of neural net prediction method shown according to an exemplary embodiment.
As shown in figure 9, applying in example at this, prediction model (step 900) is trained.It is applied in example at this, obtains the spy of film A1
Reference breath.
In application example of the film A1 as object to be predicted, by the characteristic information input initial predicted model in into
Row processing, obtains the training prediction result for being directed to the sample object.
It is applied in example at this, according to the training prediction result of the sample object and expectation prediction result, determines institute
State the model loss of sample object.
It applies in example at this, is lost according to the model, the initial predicted model is adjusted using back-propagation algorithm
In parameters weighting, determine prediction model adjusted.For example, being lost according to the model of film A1 sample object, it is sequentially adjusted in
Parameters weighting in the MTL module, the DNN module and the insertion module, determines prediction model adjusted.
This using in example, in the case where model loss meets training condition, by prediction model adjusted
It is determined as final prediction model.For example, training condition includes the repetitive exercise number of setting, and/or the condition of convergence of setting.
When meeting repetitive exercise number and/or meeting the condition of convergence of setting, prediction model adjusted can be determined as finally
Prediction model.
This using in example, the characteristic information (step 901) of the available film A1 to be predicted of server, and will be special
Sign information input prediction model is handled (step 902), so that the prediction result of object to be predicted is got, for example, electric
The prediction result of shadow A1.It is applied in example at this, which may include main prediction result (box office) and correlation predictive
As a result (public praise, broadcasting rate etc.).
It, can be by the prediction model including multiple neural network modules, to object to be predicted according to the embodiment of the present disclosure
Characteristic information handled, thus obtain be directed to object to be predicted prediction result, improve the accuracy of the prediction result.
Figure 10 is a kind of block diagram of neural network prediction device shown according to an exemplary embodiment, as shown in Figure 10,
The neural network prediction device includes:
Characteristic acquisition unit 11, for obtaining the characteristic information of object to be predicted.
Prediction result acquiring unit 12, for will handle in the characteristic information input prediction model, acquisition is directed to
The prediction result of the object to be predicted.Wherein, the prediction model includes deep neural network DNN module and multitask
Practise network MTL module.
Prediction result output unit 13, for exporting the prediction result of the object to be predicted.
In the present embodiment, by using including deep neural network DNN module and multi-task learning network MTL module
Prediction model, after handling the characteristic information of object to be predicted, obtain be directed to the object to be predicted prediction result,
According to all aspects of this disclosure, the relevance between the characteristic information of object to be predicted can be captured, exports object to be predicted
One or more prediction results improve the accuracy rate of prediction result.
Figure 11 is a kind of block diagram of neural network prediction device shown according to an exemplary embodiment, in above-described embodiment
On the basis of, in one possible implementation, as shown in figure 11, prediction result acquiring unit 12 includes:
DNN handles subelement 121, handle for inputting the characteristic information in the DNN module, described in determination
The depth information of object to be predicted；
MTL handles subelement 122, handles for inputting the depth information in the MTL module, and determination is directed to
The prediction result of the object to be predicted.
On the basis of the above embodiments, in one possible implementation, as shown in figure 11, the characteristic information packet
Include initial information, the prediction result acquiring unit 12 further include:
It is embedded in subelement 123, vectorization processing is carried out for the initial information to be inputted the insertion module, determines institute
State the vector information of initial information；
Information determines subelement 124, for the initial information and the vector information to be determined as the characteristic information.
In the present embodiment, it due to joined embeding layer, so that the stability of numerical representation is more preferable, more conducively captures
Similitude between characteristic information has contained current characteristic information in multiple dimensions for pre- by the vector information after training
The information content for surveying result reduces the burden that information characteristics extract engineering.
In one possible implementation, the prediction result include main prediction result and correlation predictive as a result, its
In, in the objective function of the prediction model, to the main associated parameters weighting of prediction result be greater than with it is described related
The associated parameters weighting of prediction result.
In one possible implementation, the prediction result output unit, for export it is following any one: output
The main prediction result of the object to be predicted；Export the main prediction result and correlation predictive result of the object to be predicted.
On the basis of the above embodiments, in one possible implementation, as shown in figure 11, the neural network is pre-
Survey device, further includes:
Sample characteristics information acquisition unit 14, for obtaining the characteristic information of sample object.
Training prediction result acquiring unit 15, in the characteristic information input initial predicted model by the sample object
It is handled, obtains the training prediction result for being directed to the sample object.
Model loses determination unit 16, for the training prediction result and the sample object according to the sample object
Expectation prediction result, determine the sample object model loss.
Weight adjustment unit 17, for adjusting the parameters weighting in the initial predicted model according to model loss,
Determine prediction model adjusted.
Prediction model determination unit 18, for the model loss meet training condition in the case where, will be adjusted
Prediction model is determined as final prediction model.
In the present embodiment, it is trained according to characteristic information and initial predicted model, obtains meeting the pre- of training condition
Model is surveyed, obtained prediction model can preferably reflect the characteristic information relevance of object to be predicted, and can obtain accurately
Prediction result.
In one possible implementation, the weight adjustment unit includes: weight adjustment subelement, for according to institute
The loss of sample object model is stated, the parameter power being sequentially adjusted in the MTL module, the DNN module and the insertion module
Weight, determines prediction model adjusted.
In one possible implementation, a kind of neural network prediction device is also provided, comprising:
Processor；
Memory for storage processor executable instruction；
Wherein, the processor is configured to: execute the nerve net of any one of the claim of this application the method
Network prediction steps.
In one possible implementation, a kind of non-volatile computer readable storage medium storing program for executing is also provided, is stored thereon
There are computer program instructions, any one of the claim of this application is realized when the computer program instructions are executed by processor
The method.
Figure 12 is a kind of block diagram of device 1900 for neural network prediction shown according to an exemplary embodiment.Example
Such as, device 1900 may be provided as a server.Referring to Fig.1 2, device 1900 includes processing component 1922, is further wrapped
One or more processors and memory resource represented by a memory 1932 are included, it can be by processing component for storing
The instruction of 1922 execution, such as application program.The application program stored in memory 1932 may include one or one with
On each correspond to one group of instruction module.In addition, processing component 1922 is configured as executing instruction, to execute above-mentioned side
Method.
Device 1900 can also include that a power supply module 1926 be configured as the power management of executive device 1900, and one
Wired or wireless network interface 1950 is configured as device 1900 being connected to network and input and output (I/O) interface
1958.Device 1900 can be operated based on the operating system for being stored in memory 1932, such as Windows ServerTM, Mac
OS XTM, UnixTM, LinuxTM, FreeBSDTM or similar.
In the exemplary embodiment, a kind of non-volatile computer readable storage medium storing program for executing is additionally provided, for example including calculating
The memory 1932 of machine program instruction, above-mentioned computer program instructions can be executed by the processing component 1922 of device 1900 to complete
The above method.
The disclosure can be system, method and/or computer program product.Computer program product may include computer
Readable storage medium storing program for executing, containing for making processor realize the computer-readable program instructions of various aspects of the disclosure.
Computer readable storage medium, which can be, can keep and store the tangible of the instruction used by instruction execution equipment
Equipment.Computer readable storage medium for example can be-- but it is not limited to-- storage device electric, magnetic storage apparatus, optical storage
Equipment, electric magnetic storage apparatus, semiconductor memory apparatus or above-mentioned any appropriate combination.Computer readable storage medium
More specific example (non exhaustive list) includes: portable computer diskette, hard disk, random access memory (RAM), read-only deposits
It is reservoir (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable
Compact disk read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding equipment, for example thereon
It is stored with punch card or groove internal projection structure and the above-mentioned any appropriate combination of instruction.Calculating used herein above
Machine readable storage medium storing program for executing is not interpreted that instantaneous signal itself, the electromagnetic wave of such as radio wave or other Free propagations lead to
It crosses the electromagnetic wave (for example, the light pulse for passing through fiber optic cables) of waveguide or the propagation of other transmission mediums or is transmitted by electric wire
Electric signal.
Computer-readable program instructions as described herein can be downloaded to from computer readable storage medium it is each calculate/
Processing equipment, or outer computer or outer is downloaded to by network, such as internet, local area network, wide area network and/or wireless network
Portion stores equipment.Network may include copper transmission cable, optical fiber transmission, wireless transmission, router, firewall, interchanger, gateway
Computer and/or Edge Server.Adapter or network interface in each calculating/processing equipment are received from network to be counted
Calculation machine readable program instructions, and the computer-readable program instructions are forwarded, for the meter being stored in each calculating/processing equipment
In calculation machine readable storage medium storing program for executing.
Computer program instructions for executing disclosure operation can be assembly instruction, instruction set architecture (ISA) instructs,
Machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data or with one or more programming languages
The source code or object code that any combination is write, the programming language include the programming language-of object-oriented such as
Smalltalk, C++ etc., and conventional procedural programming languages-such as " C " language or similar programming language.Computer
Readable program instructions can be executed fully on the user computer, partly execute on the user computer, be only as one
Vertical software package executes, part executes on the remote computer or completely in remote computer on the user computer for part
Or it is executed on server.In situations involving remote computers, remote computer can pass through network-packet of any kind
It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit
It is connected with ISP by internet).In some embodiments, by utilizing computer-readable program instructions
Status information carry out personalized customization electronic circuit, such as programmable logic circuit, field programmable gate array (FPGA) or can
Programmed logic array (PLA) (PLA), the electronic circuit can execute computer-readable program instructions, to realize each side of the disclosure
Face.
Referring herein to according to the flow chart of the method, apparatus (system) of the embodiment of the present disclosure and computer program product and/
Or block diagram describes various aspects of the disclosure.It should be appreciated that flowchart and or block diagram each box and flow chart and/
Or in block diagram each box combination, can be realized by computer-readable program instructions.
These computer-readable program instructions can be supplied to general purpose computer, special purpose computer or other programmable datas
The processor of processing unit, so that a kind of machine is produced, so that these instructions are passing through computer or other programmable datas
When the processor of processing unit executes, function specified in one or more boxes in implementation flow chart and/or block diagram is produced
The device of energy/movement.These computer-readable program instructions can also be stored in a computer-readable storage medium, these refer to
It enables so that computer, programmable data processing unit and/or other equipment work in a specific way, thus, it is stored with instruction
Computer-readable medium then includes a manufacture comprising in one or more boxes in implementation flow chart and/or block diagram
The instruction of the various aspects of defined function action.
Computer-readable program instructions can also be loaded into computer, other programmable data processing units or other
In equipment, so that series of operation steps are executed in computer, other programmable data processing units or other equipment, to produce
Raw computer implemented process, so that executed in computer, other programmable data processing units or other equipment
Instruct function action specified in one or more boxes in implementation flow chart and/or block diagram.
The flow chart and block diagram in the drawings show system, method and the computer journeys according to multiple embodiments of the disclosure
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
One module of table, program segment or a part of instruction, the module, program segment or a part of instruction include one or more use
The executable instruction of the logic function as defined in realizing.In some implementations as replacements, function marked in the box
It can occur in a different order than that indicated in the drawings.For example, two continuous boxes can actually be held substantially in parallel
Row, they can also be executed in the opposite order sometimes, and this depends on the function involved.It is also noted that block diagram and/or
The combination of each box in flow chart and the box in block diagram and or flow chart, can the function as defined in executing or dynamic
The dedicated hardware based system made is realized, or can be realized using a combination of dedicated hardware and computer instructions.
The presently disclosed embodiments is described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes are obvious for the those of ordinary skill in art field.The selection of term used herein, purport
In the principle, practical application or technological improvement to the technology in market for best explaining each embodiment, or lead this technology
Other those of ordinary skill in domain can understand each embodiment disclosed herein.
Claims (16)
1. a kind of neural net prediction method, which is characterized in that the described method includes:
Obtain the characteristic information of object to be predicted；
It will be handled in the characteristic information input prediction model, obtain the prediction result for being directed to the object to be predicted；
The prediction result of the object to be predicted is exported,
Wherein, the prediction model includes deep neural network DNN module and multi-task learning network MTL module.
2. the method according to claim 1, wherein by the characteristic information input prediction model
Reason obtains the prediction result for being directed to the object to be predicted, comprising:
The characteristic information is inputted in the DNN module and is handled, determines the depth information of the object to be predicted；
The depth information is inputted in the MTL module and is handled, determines the prediction result for being directed to the object to be predicted.
3. according to the method described in claim 2, it is characterized in that, the prediction model further include insertion module,
Wherein, it will be handled in the characteristic information input prediction model, obtain the prediction knot for being directed to the object to be predicted
Fruit, further includes:
Initial information is inputted into the insertion module and carries out vectorization processing, determines the vector information of the initial information；
The initial information and the vector information are determined as the characteristic information.
4. the method according to claim 1, wherein the prediction result includes:
Main prediction result and correlation predictive as a result,
Wherein, in the objective function of the prediction model, parameters weighting associated with the main prediction result is greater than and institute
State the associated parameters weighting of correlation predictive result.
5. according to the method described in claim 4, it is characterized in that, the prediction result of the output object to be predicted, including with
Descend any one:
Export the main prediction result of the object to be predicted；
Export the main prediction result and correlation predictive result of the object to be predicted.
6. method according to claim 1 or 3, which is characterized in that the method also includes:
Obtain the characteristic information of sample object；
It will handle, obtained for the sample object in the characteristic information input initial predicted model of the sample object
Training prediction result；
According to the training prediction result of the sample object and the expectation prediction result of the sample object, the sample is determined
The model of object loses；
It is lost according to the model, adjusts the parameters weighting in the initial predicted model, determine prediction model adjusted；
In the case where model loss meets training condition, prediction model adjusted is determined as to final prediction mould
Type.
7. according to the method described in claim 6, it is characterized in that, adjusting the initial predicted mould according to model loss
Parameters weighting in type determines prediction model adjusted, comprising:
It is lost according to the sample object model, is sequentially adjusted in the MTL module, the DNN module and the insertion module
In parameters weighting, determine prediction model adjusted.
8. a kind of neural network prediction device, which is characterized in that described device includes:
Characteristic acquisition unit, for obtaining the characteristic information of object to be predicted；
Prediction result acquiring unit, for will be handled in the characteristic information input prediction model, obtain for it is described to
Predict the prediction result of object；
Prediction result output unit, for exporting the prediction result of the object to be predicted,
Wherein, the prediction model includes deep neural network DNN module and multi-task learning network MTL module.
9. device according to claim 8, which is characterized in that the prediction result acquiring unit includes:
DNN handles subelement, handles, determines described to be predicted for inputting the characteristic information in the DNN module
The depth information of object；
MTL handles subelement, handle for inputting the depth information in the MTL module, determine for it is described to
Predict the prediction result of object.
10. device according to claim 9, which is characterized in that the prediction model further includes insertion module,
The prediction result acquiring unit further include:
It is embedded in subelement, vectorization processing is carried out for initial information to be inputted the insertion module, determines the initial information
Vector information；
Information determines subelement, for the initial information and the vector information to be determined as the characteristic information.
11. device according to claim 8, which is characterized in that the prediction result includes:
Main prediction result and correlation predictive as a result,
Wherein, in the objective function of the prediction model, parameters weighting associated with the main prediction result is greater than and institute
State the associated parameters weighting of correlation predictive result.
12. device according to claim 11, which is characterized in that the prediction result output unit is for exporting following
It anticipates one kind:
Export the main prediction result of the object to be predicted；
Export the main prediction result and correlation predictive result of the object to be predicted.
13. the device according to claim 8 or 10, which is characterized in that further include:
Sample characteristics information acquisition unit, for obtaining the characteristic information of sample object；
Training prediction result acquiring unit, in the characteristic information input initial predicted model by the sample object
Reason obtains the training prediction result for being directed to the sample object；
Model loses determination unit, for the expectation according to the training prediction result and the sample object of the sample object
Prediction result determines the model loss of the sample object；
Weight adjustment unit adjusts the parameters weighting in the initial predicted model, determines and adjust for being lost according to the model
Prediction model after whole；
Prediction model determination unit, for the model loss meet training condition in the case where, by prediction mould adjusted
Type is determined as final prediction model.
14. device according to claim 13, which is characterized in that the weight adjustment unit includes:
Weight adjusts subelement, for losing according to the model of the sample object, is sequentially adjusted in the MTL module, the DNN
Parameters weighting in module and the insertion module, determines prediction model adjusted.
15. a kind of neural network prediction device characterized by comprising
Processor；
Memory for storage processor executable instruction；
Wherein, the processor is configured to: perform claim require any one of 1 to 7 described in neural net prediction method.
16. a kind of non-volatile computer readable storage medium storing program for executing, is stored thereon with computer program instructions, which is characterized in that institute
It states and realizes method described in any one of claim 1 to 7 when computer program instructions are executed by processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113298283A (en) * | 2020-10-19 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Content object prediction method and device and content object recommendation method |
| CN113496304A (en) * | 2020-04-03 | 2021-10-12 | 北京达佳互联信息技术有限公司 | Network media information delivery control method, device, equipment and storage medium |
| CN113518962A (en) * | 2019-05-15 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Hybrid learning neural network architecture |
| CN113538030A (en) * | 2020-10-21 | 2021-10-22 | 腾讯科技（深圳）有限公司 | Content pushing method and device and computer storage medium |
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2017
- 2017-10-16 CN CN201710963263.6A patent/CN109670623A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113518962A (en) * | 2019-05-15 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Hybrid learning neural network architecture |
| CN113496304A (en) * | 2020-04-03 | 2021-10-12 | 北京达佳互联信息技术有限公司 | Network media information delivery control method, device, equipment and storage medium |
| CN113496304B (en) * | 2020-04-03 | 2024-03-08 | 北京达佳互联信息技术有限公司 | Method, device, equipment and storage medium for controlling delivery of network medium information |
| CN113298283A (en) * | 2020-10-19 | 2021-08-24 | 阿里巴巴集团控股有限公司 | Content object prediction method and device and content object recommendation method |
| CN113538030A (en) * | 2020-10-21 | 2021-10-22 | 腾讯科技（深圳）有限公司 | Content pushing method and device and computer storage medium |
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