Improved logging the ANN and SNN accuracy during a run.

Author: rbodo

snntoolbox/bin/utils.py

@@ -401,9 +401,10 @@ def update_setup(config_filepath):
             assert os.path.isfile(py_filepath), \
                 "File {} not found.".format(py_filepath)
         else:
-            print("For the specified input model library {}, no test is "
-                  "implemented to check if input model files exist in the "
-                  "specified working directory!".format(model_lib))
+            pass
+            # print("For the specified input model library {}, no test is "
+            #       "implemented to check if input model files exist in the "
+            #       "specified working directory!".format(model_lib))
 
     # Set default path if user did not specify it.
     if config.get('paths', 'dataset_path') == '':

snntoolbox/simulation/utils.py

@@ -531,6 +531,9 @@ def run(self, x_test=None, y_test=None, dataflow=None, **kwargs):
         path_acc = os.path.join(log_dir, 'accuracy.txt')
         if os.path.isfile(path_acc):
             os.remove(path_acc)
+        with open(path_acc, str('a')) as f_acc:
+            f_acc.write(str("# samples | SNN top-1 | top-{0} | ANN top-1 | "
+                            "top-{0}\n".format(self.top_k)))
 
         self.init_log_vars()
 
@@ -632,9 +635,6 @@ def run(self, x_test=None, y_test=None, dataflow=None, **kwargs):
                 batch_idx + 1, num_batches, (batch_idx + 1) / num_batches))
             print("Moving accuracy of SNN (top-1, top-{}): {:.2%}, {:.2%}."
                   "".format(self.top_k, top1acc_moving, top5acc_moving))
-            with open(path_acc, str('a')) as f_acc:
-                f_acc.write(str("{} {:.2%} {:.2%}\n".format(
-                    num_samples_seen, top1acc_moving, top5acc_moving)))
 
             # Evaluate ANN on the same batch as SNN for a direct comparison.
             score = self.parsed_model.evaluate(
@@ -647,6 +647,11 @@ def run(self, x_test=None, y_test=None, dataflow=None, **kwargs):
                   "\n".format(self.top_k, 1 - self.top1err_ann,
                               1 - self.top5err_ann))
 
+            with open(path_acc, str('a')) as f_acc:
+                f_acc.write(str("{} {:.2%} {:.2%} {:.2%} {:.2%}\n".format(
+                    num_samples_seen, top1acc_moving, top5acc_moving,
+                    1 - self.top1err_ann, 1 - self.top5err_ann)))
+
             # Plot input image.
             if 'input_image' in self._plot_keys:
                 snn_plt.plot_input_image(x_b_l[0], int(truth_b[0]), log_dir,