diff --git a/util.py b/util.py
new file mode 100644
index 0000000..f6192d2
--- /dev/null
+++ b/util.py
@@ -0,0 +1,56 @@
+from __future__ import print_function
+
+import torch
+import numpy as np
+
+
+def adjust_learning_rate(epoch, opt, optimizer):
+    """Sets the learning rate to the initial LR decayed by 0.2 every steep step"""
+    steps = np.sum(epoch > np.asarray(opt.lr_decay_epochs))
+    if steps > 0:
+        new_lr = opt.learning_rate * (opt.lr_decay_rate ** steps)
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = new_lr
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+
+if __name__ == '__main__':
+    meter = AverageMeter()
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