| class NeuralNet(): | |
| """ | |
| This class specifies the base NeuralNet class. To define your own neural | |
| network, subclass this class and implement the functions below. The neural | |
| network does not consider the current player, and instead only deals with | |
| the canonical form of the board. | |
| See othello/NNet.py for an example implementation. | |
| """ | |
| def __init__(self, game): | |
| pass | |
| def train(self, examples): | |
| """ | |
| This function trains the neural network with examples obtained from | |
| self-play. | |
| Input: | |
| examples: a list of training examples, where each example is of form | |
| (board, pi, v). pi is the MCTS informed policy vector for | |
| the given board, and v is its value. The examples has | |
| board in its canonical form. | |
| """ | |
| pass | |
| def predict(self, board): | |
| """ | |
| Input: | |
| board: current board in its canonical form. | |
| Returns: | |
| pi: a policy vector for the current board- a numpy array of length | |
| game.getActionSize | |
| v: a float in [-1,1] that gives the value of the current board | |
| """ | |
| pass | |
| def save_checkpoint(self, folder, filename): | |
| """ | |
| Saves the current neural network (with its parameters) in | |
| folder/filename | |
| """ | |
| pass | |
| def load_checkpoint(self, folder, filename): | |
| """ | |
| Loads parameters of the neural network from folder/filename | |
| """ | |
| pass | |