Alexander-Schiendorfer

 def compare_nearest_neighbors(self):

        """ get nearest neighbor for every wrong labeled datapoint and compare feature values in one DataFrame """

        results = []
        for index, row in self.wrong_predictions.iterrows():
            query_data_point = row[:-2]
            query_data = row[:]
            data_points = self.X_predictions.iloc[:, :-2]

Alexander-Schiendorfer

predictions = model.predict(test_images)
predictions = np.argmax(predictions, axis=1)
conf_matrix = confusion_matrix(np.argmax(test_labels, axis=1), predictions)

# just for better presentation - but store original values
default_figsize = plt.rcParams['figure.figsize'] 
default_dpi = plt.rcParams['figure.dpi']

plt.rcParams['figure.figsize'] = [19, 12]
plt.rcParams['figure.dpi'] = 150