def DirectPlotHistory(self):
    '\n        This method helps to plot a keras history containing losses, accuracy and possibly least learning rates.\n        '
    try:
        fig_num: int = 1
        self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_labels=['Loss', 'Epoch'], extender='loss_epoch_plot', train_val_lists=[self._losses, self._val_losses])
        fig_num += 1
        if (('top_k_categorical_accuracy' in self._history_keys) and isNotNone(self._acc_topkcc_list) and isNotNone(self._val_acc_topkcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Top k Categorical Accuracy', metric='top_k_categorical_accuracy', axis_labels=['train', 'validation'], history_labels=['Top k Categorical Accuracy', 'Epoch'], extender='top_k_categoriacal_epoch_plot', train_val_lists=[self._acc_topkcc_list, self._val_acc_topkcc_list])
            fig_num += 1
        if (('categorical_accuracy' in self._history_keys) and isNotNone(self._acc_stdcc_list) and isNotNone(self._val_acc_stdcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Categorical Accuracy', metric='categorical_accuracy', axis_labels=['train', 'validation'], history_labels=['Categorical Accuracy', 'Epoch'], extender='categoriacal_epoch_plot', train_val_lists=[self._acc_stdcc_list, self._val_acc_stdcc_list])
            fig_num += 1
        if (('acc' in self._history_keys) and isNotNone(self._acc_stdcc_list) and isNotNone(self._val_acc_stdcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Accuracy', metric='accuracy', axis_labels=['train', 'validation'], history_labels=['Accuracy', 'Epoch'], extender='accuracy_epoch_plot', train_val_lists=[self._acc_stdcc_list, self._val_acc_stdcc_list])
            fig_num += 1
        if (('lr' in self._history_keys) and isNotNone(self._learning_rates)):
            self.LearningPlot(fig_num=fig_num, title='Model Learning Rate')
            fig_num += 1
    except Exception as ex:
        template = 'An exception of type {0} occurred in [HistoryPlotter.DirectPlotHistory]. Arguments:\n{1!r}'
        message = template.format(type(ex).__name__, ex.args)
        print(message)
This method helps to plot a keras history containing losses, accuracy and possibly least learning rates.
def DirectPlotHistory(self):
    '\n        \n        '
    try:
        fig_num: int = 1
        self.AccOrLossPlot(fig_num=fig_num, title='Model loss', metric='loss', axis_labels=['train', 'validation'], history_labels=['Loss', 'Epoch'], extender='loss_epoch_plot', train_val_lists=[self._losses, self._val_losses])
        fig_num += 1
        if (('top_k_categorical_accuracy' in self._history_keys) and isNotNone(self._acc_topkcc_list) and isNotNone(self._val_acc_topkcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Top k Categorical Accuracy', metric='top_k_categorical_accuracy', axis_labels=['train', 'validation'], history_labels=['Top k Categorical Accuracy', 'Epoch'], extender='top_k_categoriacal_epoch_plot', train_val_lists=[self._acc_topkcc_list, self._val_acc_topkcc_list])
            fig_num += 1
        if (('categorical_accuracy' in self._history_keys) and isNotNone(self._acc_stdcc_list) and isNotNone(self._val_acc_stdcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Categorical Accuracy', metric='categorical_accuracy', axis_labels=['train', 'validation'], history_labels=['Categorical Accuracy', 'Epoch'], extender='categoriacal_epoch_plot', train_val_lists=[self._acc_stdcc_list, self._val_acc_stdcc_list])
            fig_num += 1
        if (('acc' in self._history_keys) and isNotNone(self._acc_stdcc_list) and isNotNone(self._val_acc_stdcc_list)):
            self.AccOrLossPlot(fig_num=fig_num, title='Model Accuracy', metric='accuracy', axis_labels=['train', 'validation'], history_labels=['Accuracy', 'Epoch'], extender='accuracy_epoch_plot', train_val_lists=[self._acc_stdcc_list, self._val_acc_stdcc_list])
            fig_num += 1
        if (('lr' in self._history_keys) and isNotNone(self._learning_rates)):
            self.LearningPlot(fig_num=fig_num, title='Model Learning Rate')
            fig_num += 1
    except Exception as ex:
        template = 'An exception of type {0} occurred in [HistoryPlotter.DirectPlotHistory]. Arguments:\n{1!r}'
        message = template.format(type(ex).__name__, ex.args)
        print(message)