Impact of the compilation method on determining the accuracy of the error loss in neural network learning

Abstract

In the field of NLP (Natural Language Processing) research, the use of a neural network has become important. The neural network is widely used in the semantic analysis of texts in different languages. In connection with the actualization of the processing of big data in the Kazakh language, a neural network was built for deep learning. In this study, the object is the learning process of a deep neural network, which evaluates the algorithm for constructing an LDA model. One of the most problematic places is determining the correct arguments, which, when compiling the model, will give an estimate of the algorithm’s performance. During the research, the compile () method from the Keras modular library was used, the main arguments of which are the loss function, optimizers, and metrics. The neural network is implemented in the Python programming language. The main arguments of the neural network deep learning compiler for evaluating the LDA model is the selection of arguments to obtain the correct evaluation of the algorithm of the constructed model using deep learning of the neural network. A corpus of text in the Kazakh language with no more than 8000 words is presented as learning data. Using the above methods, an experiment was carried out on the selection of arguments for the model compiler when learning a text corpus in the Kazakh language. As a result, the optimizer – SGD, the loss function – binary_crossentropy, and the estimation metric – ‘cosine_proximity’ were chosen as the optimal arguments, which, as a result of learning, showed a tendency to 0 loss (errors)=0.1984, and cosine_proximity (learning accuracy)=0.2239, which is considered acceptable learning measures. The results indicate the correct choice of compilation arguments. These arguments can be applied when conducting deep learning of a neural network, where the sample data is a pair of «topic and keywords».