Consider moving EarlyStopping into nolearn about nolearn HOT 9 CLOSED

class EarlyStopping(object):
    def __init__(self, patience=100):
        self.patience = patience
        self.best_valid = np.inf
        self.best_valid_epoch = 0
        self.best_weights = None

    def __call__(self, nn, train_history):
        current_valid = train_history[-1]['valid_loss']
        current_epoch = train_history[-1]['epoch']
        if current_valid < self.best_valid:
            self.best_valid = current_valid
            self.best_valid_epoch = current_epoch
            self.best_weights = nn.get_all_params_values()  # updated
        elif self.best_valid_epoch + self.patience < current_epoch:
            if nn.verbose:
                print("Early stopping.")
                print("Best valid loss was {:.6f} at epoch {}.".format(
                    self.best_valid, self.best_valid_epoch))
            nn.load_weights_from(self.best_weights)
            if nn.verbose:
                print("Weights set.")
            raise StopIteration()

    def load_best_weights(self, nn, train_history):
        nn.load_weights_from(self.best_weights)