def fit(self, features, labels, validation_split, epochs=50):
        """
        Trains the neural net on the data provided.

        :param features: Numpy array of training data.
        :param labels: Numpy array of target (label) data.
        :param validation_split: Float between 0 and 1. Percentage of training data to use for validation
        :param epochs: Max number of times to train over dataset.
        """
        self.model.fit(x=features, y=labels, epochs=epochs, verbose=1,
                       callbacks=[ReduceLROnPlateau(), EarlyStopping(patience=3)], validation_split=validation_split,
                       shuffle=True)

        for layer in self.model.layers[:self._NUM_BOTTOM_LAYERS_TO_RETRAIN]:
            layer.trainable = False
        for layer in self.model.layers[self._NUM_BOTTOM_LAYERS_TO_RETRAIN:]:
            layer.trainable = True

        self.model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
        self.model.fit(x=features, y=labels, epochs=50, verbose=1,
                       callbacks=[ReduceLROnPlateau(), EarlyStopping(patience=3)], validation_split=validation_split,
                       shuffle=True)
Trains the neural net on the data provided.

        :param features: Numpy array of training data.
        :param labels: Numpy array of target (label) data.
        :param validation_split: Float between 0 and 1. Percentage of training data to use for validation
        :param epochs: Max number of times to train over dataset.