# Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks
###### Abstract  
Through the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have the inherent capabilities of humans, such as pattern recognition in images and signals. However, conventional methods are based on deterministic models, which cannot express the epistemic uncertainty of their predictions. The alternative consists of probabilistic models, although these are considerably more difficult to develop. To address the problems related to the development of probabilistic networks and the choice of network architecture, this article proposes the development of an application that allows the user to choose the desired architecture with the trained model for the given data. This application, named \"Graphical User Interface for Probabilistic Neural Networks\", allows the user to develop or to use a standard convolutional neural network for the provided data, with networks already adapted to implement a probabilistic model. Contrary to the existing models for generic use, which are deterministic and already pre-trained on databases to be used in transfer learning, the approach followed in this work creates the network layer by layer, with training performed on the provided data, originating a specific model for the data in question. artificial intelligence; graphical interface; probabilistic convolutional neural network; no-code development platform 2023202330/signals202320230/02320230/02320230/02320230/02320/023  
each neuron. In this case, the computational load added makes the network unfeasible for image analysis [4]. Convolutional neural networks (CNNs) have made pattern recognition in images feasible. When considering an image, the proximity of the pixels has a strong correlation, and the CNNs specifically use this relationship. Nearby pixels have a higher probability of being related than pixels further apart. Convolution solves the problem of the need for a high number of connections that occurs in conventional ANNs, making it possible to process images by filtering connections by proximity. In a given layer, instead of connecting each input to each neuron, CNNs restrict the connections intentionally so that any neuron accepts inputs only from a small part of the previous layer (such as \(5\times 5\) or \(3\times 3\) pixels). The evolution of graphics processing units (GPUs) and the availability of libraries, such as TensorFlow (TF) [5] and Keras [6], have substantially facilitated the development of CNNs. Despite the high accuracy that DL-based models can achieve, the problem associated with the level of confidence that the user has in the prediction of the network still remains. This can be solved through the use of probabilistic models (PMs), as they allow the expression of epistemic uncertainty in the predictions generated by the models. These models are computationally demanding and have only recently become feasible with the development of variational inference (VI) [7]. The creation of probabilistic CNNs became simpler through the emergence of models based on the estimators, such as Flipout, and their inclusion in TF probability [8]. Pre-trained deterministic models (DMs) with conventional architectures are available with optimization performed on large data sets, allowing transfer techniques to be used to achieve high accuracy. However, PMs do not yet have this transfer option because, typically, the distributions that these models learn are strongly associated with the data used to train the models. Thus, it is usually necessary to fully train the models on the train data sets, whose distributions should be learned. One of the biggest barriers to the use of DL models by inexperienced users is the complexity associated with programming the components. Actually, the need for tools that can help the development of ML models has been identified since the beginning of this field.