I have recently started working on binary classification using Convolution Neural Network (CNN). While training i am getting two outputs a Binary error (BinErr) and the output of the loss function (Objective) of softmax classifier which is converging to zero.
Studying about the softmax classifier first, I understand that in the machine learning we have to minimize the objective function w.r.t to the parameters (weights and bias). The Objective function consists of loss function and regularization function. I found these slides for the understanding of softmax.
The Loss function of the Softmax classifier is defined as: \begin{equation} p_j = \frac{e^{o_j}}{\sum_k e^{o_k}} \end{equation}
I couldn't understand after that as in the slides $o$ is shown that it is the output of the final layer of the NN and first input to softmax classifier and the second input $y$ is the actual features.
I am totally confused how it works about that. Anyone can help me to understand how softmax works in the CNN.
