def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs): """ Perform prediction of the neural network for samples `x`. :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2). :param batch_size: Batch size. :return: Predictions. :rtype: Format as expected by the `model` """ return NeuralNetworkMixin.predict(self, x, batch_size=128, **kwargs)
def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None: """ Fit the model of the estimator on the training data `x` and `y`. :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2, nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2). :param y: Target values. :type y: Format as expected by the `model` :param batch_size: Batch size. :param nb_epochs: Number of training epochs. """ NeuralNetworkMixin.fit(self, x, y, batch_size=128, nb_epochs=20, **kwargs)