119 results about "Cross entropy" patented technology

The invention discloses a wire drawing machine operation state evaluation method and system based on deep learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: deploying a multi-source sensor at a preset part of a wire drawing machine, and forming a multi-dimensional operation data flow; the deep learning evaluation model is trained based on the standardized time series data set, model parameters are optimized through a cross entropy loss function, the model learns the characteristic difference between a normal working condition and an abnormal working condition, and the deep learning evaluation model is trained based on the standardized time series data set. And inputting a multi-dimensional operation data flow collected in real time into the trained deep learning evaluation model to generate a dynamic evaluation report. According to the wire drawing machine operation state evaluation method and system based on deep learning provided by the invention, a visual operation state score can be output, detailed fault type probability distribution is given, and intelligent support is provided for equipment maintenance decision.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention discloses a bearing fault diagnosis method based on a causal decoupling network, and relates to the technical field of computers. The method comprises the following steps: extracting overall characteristics of a sample bearing vibration signal; extracting cross-domain consistent causal factors of the overall features, and carrying out bearing fault classification on the causal factors; an extractor extracts non-causal factors of the overall features, performs fault classification on the non-causal factors, and performs domain classification on the non-causal factors; and determining the loss of the causal decoupling model based on the cross entropy loss of the causal branch and the non-causal branch, the cross-branch decorrelation loss, the maximum entropy loss and the branch specialization loss, and adjusting the parameters of the causal decoupling model until the loss of the causal decoupling model is minimum. The false correlation between the non-causal factors and the fault classification labels is blocked, and a trained causal decoupling model is obtained; and carrying out fault diagnosis on the to-be-classified bearing vibration signals through the trained causal decoupling model. The method can improve the diagnosis precision of the bearing fault.

The invention discloses a method for endowing a large language model with implicit thinking, and the method comprises the steps: introducing a potential token encoder which is isomorphic initialized with a basic model for a training data sample with an explicit reasoning chain of the large language model, collecting key information in the explicit reasoning chain through a special attention mask, generating a potential token, and transmitting the potential token to the basic model; limiting the potential tokens in a word list column space by using vocabulary probability superposition; discarding the potential token encoder, training a basic large language model (LLM) to autonomously generate a potential token, and adopting a combined target of KL divergence and cross entropy to stably optimize the target, so that potential reasoning collapses into an explicit answer at the reasoning tail end; and inputting a given question for the large language model LLM, generating a potential sequence in the LLM, and collapsing into a final answer to realize external hiding of intermediate reasoning content. According to the method, the efficiency, the precision, the stability and the engineering feasibility of potential reasoning are comprehensively improved.

The invention discloses a face-changing video detection method and system based on illumination feature decoupling. The method comprises the following steps of: framing a video and extracting an RGB (Red, Green and Blue) image of a detection area; sending the RGB image into a learnable local gravity mode extraction module, and extracting a local gravity mode image; the RGB image and the local gravity mode image are sent to a double-branch high-semantic feature extraction module, and illumination related features and illumination invariant features are extracted respectively; the illumination related features are sent to a contrast learning module based on relighting; sending the classification features into a classifier to obtain prediction probability distribution, and carrying out dichotomy supervision by using cross entropy loss; training a model and storing the model; and the model test loading model outputs a detection result of the to-be-detected video. According to the method, the illumination features are decoupled by using the domain generalization strategy, interference of domain-related illumination information is effectively suppressed, the characterization capability of the detection features is improved, and the method has a relatively good detection effect and generalization capability.

The invention relates to the technical field of large language model privacy protection, in particular to a large language model black box forgetting method based on a double-auxiliary model, which comprises the following steps of: constructing the double-auxiliary model and generating output difference characteristics, analyzing parameter criticality through a Fisher information matrix to carry out self-matching fine tuning, calculating a weighted forgetting relevance score, and carrying out self-matching fine tuning on the weighted forgetting relevance score. And a forgetting response is generated based on score matching target model output. According to the method, the forgetting demand is judged through the output difference of the double-auxiliary model, the self-matching fine-tuning optimization parameter updating strategy is utilized, the forgetting relevance is calculated in combination with the weighted cross entropy and the target model loss, the target information is accurately forgotten in the black box environment, meanwhile, the performance of the model on the non-forgetting task is maintained, and the forgetting efficiency is improved. And the practicability and efficiency of privacy protection of the large language model are improved.

The invention provides a large voice model training method and device, equipment and a medium, and the method comprises the steps: inputting a training voice data subset into a large voice model, and obtaining the prediction probability distribution of the training voice data subset outputted by a large language model module in the large voice model; according to the prediction probability distribution and the real probability distribution, an entropy weighted cross entropy loss function is determined, and the entropy weight of the entropy weighted cross entropy loss function is determined based on the distribution entropy of the prediction probability distribution at the current moment; and with the purpose of minimizing an entropy weighted cross entropy loss function, updating parameters of the large language model module and training a voice recognition tag of the voice data subset. In the training method of the large voice model, entropy weight is added in an entropy weighted cross entropy loss function, the problem of uncertainty of prediction probability distribution is solved to a certain extent, iteration is performed by using a self-feedback signal of distribution entropy based on prediction probability distribution, and the voice recognition effect of the large voice model is continuously improved.

The invention relates to the technical field of intelligent fault prediction, and provides a multi-dimensional time sequence anomaly detection method for the process industry, which aims at historical and real-time multi-source data of the process industry, performs adaptive feature extraction to obtain better features through a feature selection module fusing mRMR and a self-attention mechanism, and improves the detection accuracy. Multivariable time series data double-fusion prediction calculation is completed through a sequence prediction model, the incidence relation of data at different time points and the incidence relation of different features at the same time are mined, and after cross entropy loss and optimizer training optimization are carried out, abnormal state judgment is finally achieved in combination with a dynamic threshold value. The invention further discloses a system used for the method, the method and the system can mine mechanism information implied by equipment from historical multi-source data in the process industry, time and features are subjected to relevance fusion respectively, and therefore the method and the system can be more suitable for the unsteady state and strong coupling conditions of the process industry; and particularly, a remarkable effect is achieved in a slag grinding system.

The invention discloses a temperature normalization knowledge distillation method and system based on multi-scale decoupling, and the method comprises the following steps: S1, inputting an image into a teacher model and a student model at the same time, respectively outputting logit feature maps by the teacher model and the student model, decomposing the global logit output of the teacher model into a plurality of local logit outputs, each local logit output corresponding to a specific region of the input image, extracting local logit information of the region from the feature map of the teacher model; the student model takes the local logit output by the teacher model as a learning result, and the learning result corresponds to the local logit of the teacher model on the same scale and the same area; s2, inputting the image into a pre-trained teacher model and a pre-trained student model respectively, calculating a standard deviation, performing local logit calculation on the teacher model and the student model according to an adaptive temperature mechanism to obtain probability distribution of the teacher model and probability distribution of the student model respectively, introducing a temperature scaling factor, and controlling the softening degree; and S3, combining the teacher and student model probability distribution with the normalized distillation loss, the semantic decoupling distillation loss and the cross entropy loss to calculate the total loss.

The application discloses a kind of multi-modal large model learning method, system and storage medium based on instantaneous detection and rebalancing, method includes: S1, according to input sample construction multi-modal large model;S2, extract each single mode feature from training sample and calculate its prediction probability;S3, fusion all single mode features and obtain initial multi-modal feature and calculate its prediction probability;S4, compare the prediction probability difference of single mode and initial multi-modal, calculate rebalancing feature fusion weight;S5, based on rebalancing feature fusion weight calculation balanced multi-modal feature and calculate balanced multi-modal prediction probability;S6, calculate cross-entropy loss and update model parameter;S7, adjust the initial weight of next round feature fusion, return step S2 until all samples have participated in model training;The application can perceive and dynamically adjust the fusion weight between modes in time, to solve the mode imbalance problem in model training, to improve the accuracy and robustness of multi-modal large model.

The application relates to the technical field of semi-supervised learning, and discloses a method for semi-supervised learning by using adaptive double thresholds, which comprises the following steps: S1, for the labeled data, the weakly augmented data is input into a model, and a cross-entropy loss is obtained by taking the predicted result and the corresponding label; S2, when the model is trained by using unlabeled data, an adaptive threshold is extracted for each class, the fixed threshold and the class adaptive threshold are combined to form an adaptive double threshold, in addition to the high-confidence unlabeled data confirmed by using the fixed threshold, there is also the unlabeled data whose predicted value is less than the fixed threshold but greater than the extracted class adaptive threshold, different learning strategies are designed for the two types of unlabeled data; and S3, a new similar loss is proposed to further mine the information between similar unlabeled data, and the effective information in the unlabeled data is fully utilized.

This invention discloses an accelerated safety assessment method and system for low-altitude navigation systems based on alternating cross-entropy and importance sampling, relating to the fields of computer simulation and safety assessment technology. The method includes: constructing a probabilistic risk assessment model and initializing a proposal distribution; entering an alternating iterative loop: first, performing importance sampling assessment, calculating accident rate estimates and statistical errors; if the error does not meet accuracy requirements, initiating cross-entropy optimization, updating the proposal distribution parameters using simulation data; the new distribution is fed back to the next round of assessment. This loop continues until the results converge, and an assessment report is output. This invention, through a closed-loop feedback mechanism of alternating assessment and optimization, enables the sampling distribution to adaptively approximate the optimal distribution, intelligently focusing simulation resources on high-risk scenarios, solving the computational efficiency bottleneck of assessing extremely low probability events, achieving an order-of-magnitude improvement in assessment efficiency, and possessing high adaptability and engineering practical value.

The invention relates to a collagen extraction equipment state monitoring method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring monitoring data of collagen extraction equipment; obtaining static parameters of collagen extraction equipment; the multi-sensor time sequence monitoring data are recombined into a three-dimensional tensor by calculating the adaptive slice length and the dynamic slice number; constructing an equipment state monitoring model; adopting cross entropy loss as a main loss item to calculate the difference between the equipment state probability distribution predicted by the model and a real labeling label; training the model by adopting a small-batch gradient descent strategy to obtain a trained model; newly collected time sequence monitoring data are processed to generate a recombined three-dimensional tensor, the recombined three-dimensional tensor and equipment static parameters are input into the trained model, equipment state probability distribution is output, the equipment state is judged, and early warning is carried out. According to the invention, the classification precision and robustness of the model can be improved.

The application discloses an energy model training method, a data security detection method and system. The energy model training method comprises the following steps: obtaining training sample data, sample labels and historical probability output results corresponding to historical data; inputting the training sample data into an energy model to be trained to obtain first probability output results corresponding to the training sample data; determining a target loss function based on the first probability output results, the sample labels and the historical probability output results, wherein the target loss function at least comprises a cross-entropy loss function and a contrast loss function; and adjusting model parameters of the energy model by using the target loss function to obtain a target energy model. The application solves the technical problem that the prediction result accuracy of a pre-trained natural language model used for data security detection is not high.

The application provides a few-shot bearing fault migration diagnosis method based on multi-working condition supervised contrast learning, which comprises the following steps: taking a plurality of vibration signals of bearings under different working conditions and having a large number of labels as source domain data and taking vibration signals under a working condition different from that of the source domain data and having only a small number of labels as target domain data; obtaining feature representation and prediction probability distribution of each fault category according to a pre-constructed contrast learning training classification model and the source domain data and the target domain data; then, respectively calculating a supervised contrast loss and a cross-entropy loss, and combining them into a total loss function by weighting; and finally, obtaining a trained contrast learning training classification model by updating model parameters through an adaptive learning rate optimization algorithm and back propagation according to the combined total loss function. The application extracts features by using a plurality of different source domain data through an improved supervised contrast loss function, and optimizes model parameters in combination with a cross-entropy loss function, so as to diagnose the target domain with scarce data.

The application belongs to the technical field of underwater unmanned vehicle route planning, and particularly relates to a UUV route planning method based on a cross-entropy method, which comprises the following steps: step 1), each feasible and optimized path of a UUV is divided into m connected line segments to form a parameter set; step 2), each feasible and optimized path of the UUV corresponds to a parameter set, the path cost corresponding to each feasible path is calculated, and each feasible path is sorted from large to small, 10% to 20% of the superior set is selected as an optimized set; step 3), the mean and variance of each group of parameters in the optimized set are calculated as the mean and variance of the next iteration sampling, and the updated mean and variance of the m groups of parameters are obtained to obtain the optimal solution of this time; and step 4), for the optimal solution of this time, N feasible paths are obtained again, and steps 1) to 3) are repeated, and through continuous iteration, an optimized feasible path of the UUV is obtained, and the optimized feasible path is taken as the final optimal solution.

Implementations relate to ascertaining to what extent predictions, generated using a machine learning model, can be effectively reconstructed from model updates, where the model updates are generated based on those predictions and based on applying a particular loss technique (e.g., a particular cross-entropy loss technique). Some implementations disclosed generate measures that each indicate a degree of conformity between a corresponding reconstruction, generated using a corresponding model update, and a corresponding prediction. In some of those implementations, the measures are utilized in determining whether to utilize the particular loss technique (utilized in generating the model updates) in federated learning of the machine learning model and / or of additional machine learning model(s).

The invention relates to the technical field of construction risk management of constructional engineering, in particular to a subway station deep foundation pit construction risk assessment method based on bilateral probability language, which comprises the following steps: step 1, constructing an evaluation set in a bilateral probability language term set form; 2, performing objective standardization processing on evaluation information; 3, determining a combination weight of subjective and objective combination; step 4, hierarchical information aggregation based on a weighted average operator; and step 5, risk quantification and grade determination. According to the method, a bilateral probability language term set (DPLTS) is introduced to completely describe expert group opinion distribution, and an LCM objective standardization method and a fuzzy entropy-cross entropy-BWM combined weighting model are combined to construct a risk assessment system which is complete in information, scientific in weight and robust in decision making; the defects of the prior art in the aspects of expressing complex uncertainty and fusing subjective and objective information are effectively overcome, and the reliability, the distinction degree and the practical value of a risk assessment result are remarkably improved.

The invention discloses a double-head feature separation federated learning method and system, and belongs to the technical field of federated learning and machine learning. The model uses a feature extractor to extract basic features, a feature separation module performs context enhancement on the basic features, a selection module and a gating module separate common and personalized features, a personalized head and a shared head respectively process the two features, and finally the prediction results of the two features are fused. Meanwhile, a composite loss function containing cross entropy, MMD and entropy minimization loss is adopted to guide updating; the client uploads related parameters after training, the server carries out aggregation distribution according to the weight, and a personalized model is obtained through iteration. According to the method, global and personalized information can be fully utilized, model drift is relieved, and model performance and generalization ability are improved.

The invention discloses a multi-modal large model learning method and system based on instantaneous detection and rebalance, and a storage medium. The method comprises the following steps: S1, constructing a multi-modal large model according to an input sample; s2, extracting each single-mode feature from the training sample and calculating the prediction probability of each single-mode feature; s3, fusing all the single-mode features to obtain initial multi-mode features, and calculating the prediction probability of the initial multi-mode features; s4, comparing prediction probability differences of a single mode and an initial multi-mode, and calculating a rebalance feature fusion weight; s5, based on the rebalance feature fusion weight, calculating a balance multi-modal feature and calculating a balance multi-modal prediction probability; s6, calculating cross entropy loss to update model parameters; s7, adjusting the initial weight of the next round of feature fusion, and returning to the step S2 until all samples participate in model training; according to the method, the fusion weight between modals can be perceived in real time and dynamically adjusted, so that the problem of modal imbalance in model training is solved, and the accuracy and robustness of a multi-modal large model are improved.

The invention provides a substitution model training method and device and related equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: inputting first image data into a substitution model of a target model for countermeasure attack to generate a first countermeasure sample of the first image data, the target model being used for image processing; the first adversarial sample is processed through M teacher models of the target model, M cross entropy losses between output of the M teacher models and a first label are calculated, the M teacher models at least comprise two different types of teacher models, the first label indicates identification information of first image data, and the M cross entropy losses are calculated according to the identification information of the first image data; m is an integer greater than 1; weighting processing is carried out based on the M cross entropy losses, and target cross entropy losses are obtained; and on the basis of the target cross entropy loss, parameters of the substitution model are updated, and the substitution model is used for generating image data attacking the target model.

The invention relates to the technical field of natural language processing, in particular to an event extraction method based on entity representation and side information enhancement. The method comprises the following steps: fusing part-of-speech information, sequence information and an attention mechanism guided by an entity type through an entity representation enhancement module to optimize entity recognition, and solving sample imbalance and label transfer constraints by adopting a weighted cross entropy loss function; constructing a document graph comprising pseudo event nodes, entity nodes and context nodes, modeling a node relationship through three types of directed edges, and initializing edge feature vector explicit coding semantics; aggregating global information by using a graph attention mechanism, and dynamically integrating cross-sentence event elements through pseudo event nodes; the event decoding module adopts a Hausdorff distance loss function to measure the difference between a prediction event set and a real event set, and event type classification and argument role allocation are achieved. According to the method, the F1 value on a financial announcement data set reaches 83.9%, and the problem that document-level event elements are dispersed is effectively solved.

The application relates to the technical field of image classification, in particular to a hyperspectral open set classification method combining feature reconstruction and prototype constraint, which comprises the following steps: carrying out block division and label annotation on each original sample data in an original sample data set; inputting sample data blocks into a classification model composed of an encoder, a decoder, a prototype classification module and an unknown class detection module; constructing contrast prototype loss, reconstruction error loss and cross-entropy loss by using the encoder, the decoder and the prototype classification module; constructing a total loss function based on the contrast prototype loss, the reconstruction error loss and the cross-entropy loss, so that the classification model is iteratively trained until convergence, and a trained classification model is obtained; performing open set identification based on sample feature distribution and reconstruction error loss by using the unknown class detection module, combining prototype distance and reconstruction loss to evaluate the possibility score of the unknown class, so as to determine the unknown class. The method realizes high-precision hyperspectral open set classification.

The embodiment of the invention provides a classification model training method based on hierarchical knowledge migration. The method comprises the steps of obtaining a universal baseline model with a semantic understanding capability; according to the hierarchical classification structure of the target domain, an industry domain model containing a plurality of sub-classification output heads is constructed, and initialization is carried out by using a general baseline model parameter; constructing a hierarchical constraint loss function, and constraining the parent class prediction probability to be greater than or equal to the child class prediction probability; obtaining domain classification training samples, and respectively inputting the domain classification training samples into the general baseline model and the industry domain model to obtain first and second prediction category probability distributions; calculating domain cross entropy loss based on the second prediction distribution and the real label, calculating knowledge distillation loss based on the difference between the two distributions, and constructing a target loss function in combination with hierarchical constraint loss; and taking minimization of the target loss function as a target training industry domain model. According to the method, the model training efficiency is effectively improved, efficient migration of domain knowledge is realized, and the hierarchical classification accuracy is remarkably improved.

The invention relates to the technical field of power system reliability evaluation, in particular to a power grid reliability evaluation cross entropy important sampling method based on functional optimization. According to the method, system random variables are mapped to a standard normal space, conditional distribution is constructed, sampling estimation is carried out by using a sample re-sampling and conditional M-H sampling method, functional optimization is carried out on important sampling functions step by step by taking the conditional distribution as a target, the conditional distribution gradually approaches a theoretical optimal IS-PDF along with the increase of the number of iterations, and the optimal IS-PDF is obtained. And an important sampling function optimized by taking conditional distribution as a target gradually approaches the theoretical optimal IS-PDF, so that approximate estimation of the optimal zero-variance IS-PDF is realized. The problem that in a traditional cross entropy method, iterative optimization of IS-PDF parameters can only use fault samples, strict theoretical rules are lacked to serve as the basis for IS-PDF type selection, efficiency and performance are affected to different degrees, and serious problems occur in some occasions is solved.

The application provides a power system reliability evaluation method based on a lossless load state set, which adopts an extended cross-entropy importance sampling method and is divided into two stages of pre-sampling and formal sampling; in the loss load amount calculation state steps of the two stages, the lossless load state set is used to reduce the calculation amount. The application designs a lossless load state set realized by a hash table; by using the lossless load state set, the number of solving direct current load reduction models is reduced, and the efficiency of the reliability evaluation of the power system with continuous random variables in the state by using the extended cross-entropy importance sampling method is accelerated.

This paper proposes a method for extracting relationships in the field of power grid dispatching that combines semantic dependency and part-of-speech embedding. The method includes the following steps: S1, collecting power grid dispatching data, then performing entity and relationship annotation to obtain a data set in the field of power grid dispatching; S2, inputting the data set into the RoBERTa‑CE model for model training, and learning entity information and relationship information separately; S3, performing entity-relationship multi-task learning, first concatenating the entity context information to make entity predictions, and using cross-entropy loss to calculate the entity loss value, then concatenating the relationship context to make relationship predictions, and using cross-entropy loss to calculate the relationship loss value; adding the entity loss value and the relationship loss value, and jointly participating in the back propagation of the model, so that the overall loss value is optimized towards the minimum value until all tasks converge; S4, predicting the relationship through the fully connected layer. This method can deeply mine power grid dispatching data and quickly extract the relationship between entities in the field of power grid dispatching.

The embodiment of the application provides a target recognition model training method, a target recognition method and related equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: extracting a feature vector of a sonar image sequence through a backbone network, performing manifold mapping on the feature vector to obtain normalized features; performing frequency statistics on the normalized features to obtain a category distribution; calculating a dynamic interval component of each category based on the category distribution; determining a cross-entropy loss, a dynamic interval loss and an uncertainty loss based on the dynamic interval component, and determining a total loss function based on the cross-entropy loss, the dynamic interval loss and the uncertainty loss; and performing model training based on the total loss function through a sonar image training set to obtain a trained target recognition model. The target recognition model obtained by the embodiment of the application can effectively distinguish target categories, improve the long-tail target distinguishing degree, reduce the false alarm rate of unknown obstacles, and improve the survivability in a complex environment.

This invention discloses a crystal oscillator circuit fault classification technique based on a multi-source information fusion network, belonging to the field of analog circuit fault diagnosis technology. First, multi-source datasets from different measurement points of the crystal oscillator circuit are acquired. Then, a fast Fourier transform is used to convert the datasets from the time domain to the frequency domain. Next, a convolutional neural network is used to extract fault features from the data, and a softmax classifier is used to assign trust functions to the data from each source. Then, the D-S evidence theory is employed to fuse the different trust assignment functions under the multi-source data, obtaining the final diagnostic result and diagnostic probability output, and calculating the cross-entropy loss. Finally, the model parameters are trained through backpropagation to obtain the final diagnostic model. This invention combines time-frequency transformation algorithms, deep neural network algorithms, and information fusion algorithms to construct a multi-source information fusion network, overcoming the shortcomings of existing diagnostic models in crystal oscillator circuit fault classification and significantly improving the accuracy and stability of crystal oscillator circuit fault diagnosis.

A SAR image road segmentation method based on semi-supervised prototype representation learning and pseudo label balancing includes the following steps: step 1, dividing the SAR road data set into labeled data and unlabeled data; step 2, training the labeled data to calculate the cross-entropy loss and output the segmentation result; the segmentation network is optimized in the back propagation; step 3, obtaining feature prediction and feature mapping; step 4, dynamically adjusting the feature prediction based on the class frequency in the pseudo label; step 5, calculating the cross-entropy loss of the adjusted feature prediction; step 6, calculating the prototype loss of the unlabeled data; step 7, calculating the cross-supervision loss; step 8, performing prototype consistency regularization on the feature mapping and the unlabeled prototype; step 9, updating the prototype by exponential moving average; step 10, segmenting and predicting the road image. The present application utilizes labeled data and unlabeled data to improve the segmentation accuracy in the case of fewer labeled samples.