Hi,
Based on the information you have provided, the "Gradient" and "Mu" are hyperparameters of the neural network that you are training. The "Gradient" is the step size used to update the weights of the neural network during the backpropagation process. The "Mu" is the momentum parameter that controls how much the weight updates are influenced by the previous updates.
The choice of values for these parameters depends on the architecture of your neural network, the size of your dataset, and the type of problem you are trying to solve. In general, it is recommended to start with a small learning rate and gradually increase it as the training progresses. A common starting value for the "Gradient" is 0.01, and you can experiment with different values to see which one works best for your problem.
The "Mu" parameter is used to speed up the training process and prevent oscillations during the weight updates. A common value for "Mu" is between 0.9 and 0.99, but again, you can experiment with different values to see which one gives you the best results.
Regarding the issue of the outputs being all zeros, this could be caused by a number of factors, including a poorly chosen architecture, insufficient training data, or overfitting. You may want to try increasing the size of your neural network, collecting more data to train on, or implementing regularization techniques to prevent overfitting
