Fig. 2

Proposed model architecture. The model was created and trained on the Deep Learning Studio by Deep Cognition AI. The platform simplifies and accelerates the process of working with deep learning across popular frameworks such as TensorFlow and MXNet. By using advanced pretrained networks such as the Mask RCNN, DenseNet, MobileNet, InceptionV3, ResNet, and Xception, complete custom networks can be created in seconds with an AI Wizard on Deep Learning Studio. The feature vector is directly fed into the input nodes of the network. Each node generates an output with an activation function, and the linear combinations of the outputs are linked to the next hidden layers. The activation functions among different layers are different. Then, the features are retrieved and the retrieved features are concatenated to form a new feature vector. The output layer’s activation function is the softmax. The softmax classifier uses the new feature vector to get the confidence of each relation. The dimension of the output vector is the number of classes, while the confidence of each classification equals the value of each dimension. The number of neurons in the input layer is the same as the input feature dimension, and the number of neurons in the output layer is the same as the output classes. For the neurons in the input layer, they receive a single value on their input and are sent to all of the hidden nodes. The nodes of the hidden and output layers are active, and each layer is fully interconnected. The output layer is responsible for producing and presenting the final network outputs, which are generated from the procedure performed by the neurons in the previous layers. During the training process, the input feature goes through the input nodes at the bottom of the deep learning network, where the weights are initialized with random values. After that, the weight vectors are fine-tuned in sequence. The main training goal is to minimize the loss function and maximize the accuracy function of the process