International Business Machines Corporation

Abhinandan Kelgere Ramesh

Ramani Ranjan Routray

Claire Abu-Assal

Sheng Hua Bao

Mu Qiao

Cartic Ramakrishnan

Bruce Light Hillsberg

Sundari Voruganti

Pathirage Dinindu Sujan Udayanga Perera

Hanqing Chen

Robert Ip

Hanging Chen

Mechanisms are provided for training a computer implemented model. The mechanisms perform multiple instances of training of the computer implemented model, where each instance of training of the computer implemented model comprises training the computer implemented model using a different training data set to generate a different instance of a trained computer implemented model. The mechanisms generate computer implemented model results after each instance of training by executing the corresponding instance of the trained computer implemented model. The mechanisms record differences in the instances of training of the computer implemented model in association with corresponding identifiers of the instances of trained computer implemented model and corresponding computer implemented model results. The mechanisms analyze the recorded differences and the corresponding computer implemented model results, and generate an output indicating a correlation between recorded differences and corresponding computer implemented model results.

Computer model machine learning based on correlations of training data with performance trends

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a cognitive service for cognitive model tuning with rich deep learning knowledge. The mechanism performs a first model training operation and records training data set and hyperparameter information for the model in a database. The mechanism performs a model testing operation using a testing data set and records metric values that result from the model testing in the database. For a next model training operation for a given model, the mechanism performs an anomalies check for the given model. The mechanism performs a difference comparison on the training data set, hyperparameter information, and the metric values. The mechanism generates a recommendation of a training data set and hyperparameters for the next model training operation. The mechanism performs the next model training operation.

Cognitive model tuning with rich deep learning knowledge

A mechanism is provided in a data processing system for training a computer implemented model. The mechanism determines an operation for which the computer implemented model is to be trained. The mechanism performs a statistical analysis of an enterprise dataset for an enterprise to generate one or more statistical distributions of cases and features correlated with the operation for which the computer implemented model is to be trained. The mechanism selects a subset of cases in the enterprise dataset for annotation based on the one or more statistical distributions of cases and features. The mechanism annotates the selected subset of cases to generate an annotated training dataset. The mechanism trains the computer implemented model, using the annotated training dataset, to perform the operation.

Cognitive data preparation for deep learning model training

In training a new neural network, batches of the new training dataset are generated. An epoch of batches is passed through the new neural network using an initial weight (&#x3b8;). An area minimized (A) under an error function curve and an accuracy for the epoch are calculated. It is then determined whether a set of conditions are met, where the set of conditions includes whether Ais less than an average area (A_avg) from a training of an existing neural network and whether the accuracy is within a predetermined threshold. When the set of conditions are not met, a new &#x3b8; is calculated by modifying a dynamic learning rate (&#x3b2;) by an amount proportional to a ratio of A/A_avg and by calculating the new &#x3b8; using the modified &#x3b2; according to &#x3b8;:&#xb1;&#x3b8;&#x2212; The process is repeated a next epoch until the set of conditions are met.

Optimization of model generation in deep learning neural networks using smarter gradient descent calibration

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San Jose, CA, United States of America

Abhinandan Kelgere Ramesh