We have used the ReLU activation function in hidden layers and the Softmax activation function in the output layer. The number of training epochs is 200, and the learning rate is 0.001. Adam optimizer has been used here. The loss function consists of binary cross-entropy with L2 ...
and its source of inspiration, has revealed that the algorithm can be further developed to address upcoming problems in the same domain of DL. One of these problems is optimizing the number of features passed on from the convolutional-pooling layers to the classification function, so that only d...
The Grad-CAM map is then a weighted combination of the feature maps with an applied ReLU: M=ReLU(∑kαckAk). The ReLU activation ensures you get only the features that have a positive contribution to the class of interest. The output is therefore a heatmap for the specified class, ...
Use of the ReLU activation function has allowed us to fit a much deeper model for this simple problem, but this capability does not extend infinitely. For example, increasing the number of layers results in slower learning to a point at about 20 layers where the model is no longer ...
Using the predictors, generated embedding, and adjacency matrix as input to a graph attention operation, the model computes graph embedding. Finally, the model uses ReLU activation, multiply operation, and two fully connected operations with a ReLU activation in between to compute predic...
The loss function to be minimized reflects the error between the transformed output values and the true values. By iteratively adjusting the weights and biases associated with each node, the loss function is minimized. We use the Rectified Linear Unit (ReLU) activation function, \(f(\cdot )=\...
The hyperbolic tangent, abbreviated tanh, is called the activation function. The tanh function accepts any value from negative infinity to positive infinity, and returns a value between -1.0 and +1.0. Important alternative activation functions include the logistic sigmoid and rectified linear (ReLU) ...
Finally, several available efficient optimization techniques also contributes the final success of deep learning, such as dropout, batch normalization, Adam optimizer and others, ReLU activation function and its variants, with that, we can update the weights and obtain the optimal performance. Motivated...
We used the Relu function in this project because it outperforms other functions like tanh. The matrix Y is obtained as follows after executing the convolution operation by using Eq. (14) $$Y = \left[ {y_{1,1} ,y_{1,2} , \ldots y_{q - e + 1,n - d + 1} } \right],Y...
The activation function used in this network is a rectified linear unit (ReLU). The ReLU can bring a certain sparsity to the network and prevent gradient dissipation. Its expression is given in Eq. (6): $$\begin{array}{c}F\left(x\right)=\mathrm{max}\left(0,x\right)\end{array}$$...