prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Training classification accuracy >1
  **Describe the bug**
  Hi, I am training a classification model on MRI brain scans using vgg16 in gandlf. We expect to have accuracy in the range of 0-1. But Training classification accuracy is displayed >1. Validation accuracy seems correct. Attaching the screenshot below. Can you please have a look?
  
  **Screenshots**
  ![Screenshot from 2022-03-17 14-13-02](https://user-images.githubusercontent.com/6376680/158874773-97c9a7cd-49a1-45fa-85c9-86b93533911b.png)
  
  **GaNDLF Version**
  0.0.14
  
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `GANDLF/metrics/regression.py`
  Content:
  ```
  1 """
  2 All the metrics are to be called from here
  3 """
  4 import torch
  5 from sklearn.metrics import balanced_accuracy_score
  6 import numpy as np
  7 
  8 
  9 def classification_accuracy(output, label, params):
  10     if params["problem_type"] == "classification":
  11         predicted_classes = torch.argmax(output, 1)
  12     else:
  13         predicted_classes = output
  14     acc = torch.sum(predicted_classes == label) / len(label)
  15     return acc
  16 
  17 
  18 def balanced_acc_score(output, label, params):
  19     if params["problem_type"] == "classification":
  20         predicted_classes = torch.argmax(output, 1)
  21     else:
  22         predicted_classes = output
  23 
  24     return torch.from_numpy(
  25         np.array(balanced_accuracy_score(predicted_classes.cpu(), label.cpu()))
  26     )
  27
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/GANDLF/metrics/regression.py b/GANDLF/metrics/regression.py
  --- a/GANDLF/metrics/regression.py
  +++ b/GANDLF/metrics/regression.py
  @@ -11,7 +11,8 @@
           predicted_classes = torch.argmax(output, 1)
       else:
           predicted_classes = output
  -    acc = torch.sum(predicted_classes == label) / len(label)
  +
  +    acc = torch.sum(predicted_classes == label.squeeze()) / len(label)
       return acc
  
verification_info: {"golden_diff": "diff --git a/GANDLF/metrics/regression.py b/GANDLF/metrics/regression.py\n--- a/GANDLF/metrics/regression.py\n+++ b/GANDLF/metrics/regression.py\n@@ -11,7 +11,8 @@\n         predicted_classes = torch.argmax(output, 1)\n     else:\n         predicted_classes = output\n-    acc = torch.sum(predicted_classes == label) / len(label)\n+\n+    acc = torch.sum(predicted_classes == label.squeeze()) / len(label)\n     return acc\n", "issue": "Training classification accuracy >1\n**Describe the bug**\r\nHi, I am training a classification model on MRI brain scans using vgg16 in gandlf. We expect to have accuracy in the range of 0-1. But Training classification accuracy is displayed >1. Validation accuracy seems correct. Attaching the screenshot below. Can you please have a look?\r\n\r\n**Screenshots**\r\n![Screenshot from 2022-03-17 14-13-02](https://user-images.githubusercontent.com/6376680/158874773-97c9a7cd-49a1-45fa-85c9-86b93533911b.png)\r\n\r\n**GaNDLF Version**\r\n0.0.14\r\n\r\n\n", "before_files": [{"content": "\"\"\"\nAll the metrics are to be called from here\n\"\"\"\nimport torch\nfrom sklearn.metrics import balanced_accuracy_score\nimport numpy as np\n\n\ndef classification_accuracy(output, label, params):\n    if params[\"problem_type\"] == \"classification\":\n        predicted_classes = torch.argmax(output, 1)\n    else:\n        predicted_classes = output\n    acc = torch.sum(predicted_classes == label) / len(label)\n    return acc\n\n\ndef balanced_acc_score(output, label, params):\n    if params[\"problem_type\"] == \"classification\":\n        predicted_classes = torch.argmax(output, 1)\n    else:\n        predicted_classes = output\n\n    return torch.from_numpy(\n        np.array(balanced_accuracy_score(predicted_classes.cpu(), label.cpu()))\n    )\n", "path": "GANDLF/metrics/regression.py"}], "after_files": [{"content": "\"\"\"\nAll the metrics are to be called from here\n\"\"\"\nimport torch\nfrom sklearn.metrics import balanced_accuracy_score\nimport numpy as np\n\n\ndef classification_accuracy(output, label, params):\n    if params[\"problem_type\"] == \"classification\":\n        predicted_classes = torch.argmax(output, 1)\n    else:\n        predicted_classes = output\n\n    acc = torch.sum(predicted_classes == label.squeeze()) / len(label)\n    return acc\n\n\ndef balanced_acc_score(output, label, params):\n    if params[\"problem_type\"] == \"classification\":\n        predicted_classes = torch.argmax(output, 1)\n    else:\n        predicted_classes = output\n\n    return torch.from_numpy(\n        np.array(balanced_accuracy_score(predicted_classes.cpu(), label.cpu()))\n    )\n", "path": "GANDLF/metrics/regression.py"}]}