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)
