You don't have to possess a profound understanding of how neural networks work to effectively use Stonito Lotto.
Understanding a basic configuration options will suffice.
Input values count
This value represents the number of inputs in the neural networks. This number is calculated based on the network settings and what types of input data it will use.
Every Neural Network has at least two layers: one is the input layer, and the second is the output layer. They are implied and not seen here. Hidden layers are the layers interconnecting those input and output layers. Each hidden layer is represented only by the number of nodes it comprises. Every number represents the hidden layer with a particular number of nodes, starting from the input layer. The more layers you add, the network will be more complex. A complex network needs more time to train but can catch more intricate relationships between input and output data.
This is also related to the training. When the minimal error is reached, the training is stopped. The error doesn't converge to zero because the prediction is not a deterministic problem.
Defines how many previous rounds are used as inputs for training and inference. For example, if the numbers pool is 39, value of 3 meaning that in training for every round three previous will be used as inputs, that makes 87 input values in total.
History may be quite large, so including all the draws in training would lead to a lot of processing burden. It makes sense to limit the training to the last number of drawings. It's up to the user to find out the optimal number for a particular game.
Similar as previous, but only limit the date after which the draws are considered. This date does not limit the number of drawings actually included in training otherwise.
If checked, the new network will not be created, but the existing will be updated. Otherwise, new network will be created and current network will be saved.
Initiate process of training network. It may take some time. During the process the current values of epoch and error are updated for each finished epoch.
You are advised to use multiple networks with various settings and keep track of how well they perform in the future games.
You can adjust them any time you want.
To add a new network to the particula game just uncheck the Update existing checkbox.
After the training is completed, newly created network will be selected as active.
You can opt to delete the selected network from main menu. Deleting network is necessary only if you want to decrease the number of networks. Otherwise you can easily update settings and name of the network and retrain it to replace existing network.
In the main menu there is also an option to Train all networks, which is used to retrain all the networks in a succession. The last that will be retrained is the network for patterns.
