rename example metrics

README.md

@@ -130,7 +130,7 @@ Config files are in `.json` format:
   },
   "loss": "nll_loss",                  // loss
   "metrics": [
-    "my_metric", "my_metric2"          // list of metrics to evaluate
+    "accuracy", "top_k_acc"          // list of metrics to evaluate
   ],                         
   "lr_scheduler": {
     "type": "StepLR",                   // learning rate scheduler
@@ -285,7 +285,7 @@ Metric functions are located in 'model/metric.py'.
 
 You can monitor multiple metrics by providing a list in the configuration file, e.g.:
   ```json
-  "metrics": ["my_metric", "my_metric2"],
+  "metrics": ["accuracy", "top_k_acc"],
   ```
 
 ### Additional logging

config.json

@@ -26,7 +26,7 @@
     },
     "loss": "nll_loss",
     "metrics": [
-        "my_metric", "my_metric2"
+        "accuracy", "top_k_acc"
     ],
     "lr_scheduler": {
         "type": "StepLR",

model/metric.py

@@ -1,7 +1,7 @@
 import torch
 
 
-def my_metric(output, target):
+def accuracy(output, target):
     with torch.no_grad():
         pred = torch.argmax(output, dim=1)
         assert pred.shape[0] == len(target)
@@ -10,7 +10,7 @@ def my_metric(output, target):
     return correct / len(target)
 
 
-def my_metric2(output, target, k=3):
+def top_k_acc(output, target, k=3):
     with torch.no_grad():
         pred = torch.topk(output, k, dim=1)[1]
         assert pred.shape[0] == len(target)