# RuntimeError: Error(s) in loading state\_dict for DenseNet: Unexpected key(s) in state\_dict

**URL:** <https://discuss.pytorch.org/t/runtimeerror-error-s-in-loading-state-dict-for-densenet-unexpected-key-s-in-state-dict/31117>\
**Category:** Uncategorized\
**Created:** [December 3, 2018, 4:46am UTC](https://discuss.pytorch.org/t/runtimeerror-error-s-in-loading-state-dict-for-densenet-unexpected-key-s-in-state-dict/31117 "2018-12-03T04:46:54Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![Satyendra\_Pandit](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/satyendra_pandit/32/7934_2.png) [@Satyendra\_Pandit](https://discuss.pytorch.org/u/Satyendra_Pandit)\
**Post date:** [December 3, 2018, 4:46am UTC](https://discuss.pytorch.org/t/runtimeerror-error-s-in-loading-state-dict-for-densenet-unexpected-key-s-in-state-dict/31117/1 "2018-12-03T04:46:54Z")

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#My code:```  
import torch  
from torch import nn  
import torch.nn.functional as F  
from torch import optim  
from torchvision import models  
from collections import OrderedDict  
structures = {“vgg16”:25088,  
“densenet121” : 1024,  
“alexnet” : 9216 }  
def Network(structure=‘vgg16’,dropout=0.5, hidden\_layer1 = 120,lr = 0.001):  
if structure == ‘vgg16’:  
model = models.vgg16(pretrained=True)  
elif structure == ‘densenet121’:  
model = models.densenet121(pretrained=True)  
elif structure == ‘alexnet’:  
model = models.alexnet(pretrained = True)  
else:  
print("{} is not a valid model.Please pass vgg16,densenet121,or alexnet".format(structure))  
for param in model.parameters():  
param.requires\_grad = False

```
    classifier = nn.Sequential(OrderedDict([
        ('dropout',nn.Dropout(dropout)),
        ('inputs', nn.Linear(structures[structure], hidden_layer1)),
        ('relu1', nn.ReLU()),
        ('hidden_layer1', nn.Linear(hidden_layer1, 90)),
        ('relu2',nn.ReLU()),
        ('hidden_layer2',nn.Linear(90,80)),
        ('relu3',nn.ReLU()),
        ('hidden_layer3',nn.Linear(80,102)),
        ('output', nn.LogSoftmax(dim=1))
                      ]))
    model.classifier = classifier
    criterion = nn.NLLLoss()
    optimizer = optim.Adam(model.classifier.parameters(), lr )

    return model , optimizer ,criterion

```

model,optimizer,criterion = Network(‘densenet121’)

# TODO: Write a function that loads a checkpoint and rebuilds the model

def load\_model(path):  
checkpoint = torch.load(’/home/workspace/model\_classifier.pth’, map\_location=‘cpu’)  
structure = checkpoint[‘structure’]  
hidden\_layer1 = checkpoint[‘hidden\_layer1’]  
model,_,_ = Network(structure , 0.5,hidden\_layer1)  
model.class\_to\_idx = checkpoint[‘class\_to\_idx’]  
model.load\_state\_dict(checkpoint[‘state\_dict’])  
return model

# Load your model to this variable

model = load\_model(’/home/workspace/model\_classifier.pth’)

# If you used something other than 224x224 cropped images, set the correct size here

image\_size = 224

# Values you used for normalizing the images. Default here are for

# pretrained models from torchvision.

norm\_mean = [0.485, 0.456, 0.406]  
norm\_std = [0.229, 0.224, 0.225]

```auto
#Error:
RuntimeError: Error(s) in loading state_dict for DenseNet:
	Unexpected key(s) in state_dict: "features.norm0.num_batches_tracked", "features.denseblock1.denselayer1.norm1.num_batches_tracked", "features.denseblock1.denselayer1.norm2.num_batches_tracked", "features.denseblock1.denselayer2.norm1.num_batches_tracked", "features.denseblock1.denselayer2.norm2.num_batches_tracked", "features.denseblock1.denselayer3.norm1.num_batches_tracked", "features.denseblock1.denselayer3.norm2.num_batches_tracked", "features.denseblock1.denselayer4.norm1.num_batches_tracked", "features.denseblock1.denselayer4.norm2.num_batches_tracked", "features.denseblock1.denselayer5.norm1.num_batches_tracked", "features.denseblock1.denselayer5.norm2.num_batches_tracked", "features.denseblock1.denselayer6.norm1.num_batches_tracked", "features.denseblock1.denselayer6.norm2.num_batches_tracked", "features.transition1.norm.num_batches_tracked", "features.denseblock2.denselayer1.norm1.num_batches_tracked", "features.denseblock2.denselayer1.norm2.num_batches_tracked", "features.denseblock2.denselayer2.norm1.num_batches_tracked", "features.denseblock2.denselayer2.norm2.num_batches_tracked", "features.denseblock2.denselayer3.norm1.num_batches_tracked", "features.denseblock2.denselayer3.norm2.num_batches_tracked", "features.denseblock2.denselayer4.norm1.num_batches_tracked", "features.denseblock2.denselayer4.norm2.num_batches_tracked", "features.denseblock2.denselayer5.norm1.num_batches_tracked", "features.denseblock2.denselayer5.norm2.num_batches_tracked", "features.denseblock2.denselayer6.norm1.num_batches_tracked", "features.denseblock2.denselayer6.norm2.num_batches_tracked", "features.denseblock2.denselayer7.norm1.num_batches_tracked", "features.denseblock2.denselayer7.norm2.num_batches_tracked", "features.denseblock2.denselayer8.norm1.num_batches_tracked", "features.denseblock2.denselayer8.norm2.num_batches_tracked", "features.denseblock2.denselayer9.norm1.num_batches_tracked", "features.denseblock2.denselayer9.norm2.num_batches_tracked", "features.denseblock2.denselayer10.norm1.num_batches_tracked", "features.denseblock2.denselayer10.norm2.num_batches_tracked", "features.denseblock2.denselayer11.norm1.num_batches_tracked", "features.denseblock2.denselayer11.norm2.num_batches_tracked", "features.denseblock2.denselayer12.norm1.num_batches_tracked", "features.denseblock2.denselayer12.norm2.num_batches_tracked", "features.transition2.norm.num_batches_tracked", "features.denseblock3.denselayer1.norm1.num_batches_tracked", "features.denseblock3.denselayer1.norm2.num_batches_tracked", "features.denseblock3.denselayer2.norm1.num_batches_tracked", "features.denseblock3.denselayer2.norm2.num_batches_tracked", "features.denseblock3.denselayer3.norm1.num_batches_tracked", "features.denseblock3.denselayer3.norm2.num_batches_tracked", "features.denseblock3.denselayer4.norm1.num_batches_tracked", "features.denseblock3.denselayer4.norm2.num_batches_tracked", "features.denseblock3.denselayer5.norm1.num_batches_tracked", "features.denseblock3.denselayer5.norm2.num_batches_tracked", "features.denseblock3.denselayer6.norm1.num_batches_tracked", "features.denseblock3.denselayer6.norm2.num_batches_tracked", "features.denseblock3.denselayer7.norm1.num_batches_tracked", "features.denseblock3.denselayer7.norm2.num_batches_tracked", "features.denseblock3.denselayer8.norm1.num_batches_tracked", "features.denseblock3.denselayer8.norm2.num_batches_tracked", "features.denseblock3.denselayer9.norm1.num_batches_tracked", "features.denseblock3.denselayer9.norm2.num_batches_tracked", "features.denseblock3.denselayer10.norm1.num_batches_tracked", "features.denseblock3.denselayer10.norm2.num_batches_tracked", "features.denseblock3.denselayer11.norm1.num_batches_tracked", "features.denseblock3.denselayer11.norm2.num_batches_tracked", "features.denseblock3.denselayer12.norm1.num_batches_tracked", "features.denseblock3.denselayer12.norm2.num_batches_tracked", "features.denseblock3.denselayer13.norm1.num_batches_tracked", "features.denseblock3.denselayer13.norm2.num_batches_tracked", "features.denseblock3.denselayer14.norm1.num_batches_tracked", "features.denseblock3.denselayer14.norm2.num_batches_tracked", "features.denseblock3.denselayer15.norm1.num_batches_tracked", "features.denseblock3.denselayer15.norm2.num_batches_tracked", "features.denseblock3.denselayer16.norm1.num_batches_tracked", "features.denseblock3.denselayer16.norm2.num_batches_tracked", "features.denseblock3.denselayer17.norm1.num_batches_tracked", "features.denseblock3.denselayer17.norm2.num_batches_tracked", "features.denseblock3.denselayer18.norm1.num_batches_tracked", "features.denseblock3.denselayer18.norm2.num_batches_tracked", "features.denseblock3.denselayer19.norm1.num_batches_tracked", "features.denseblock3.denselayer19.norm2.num_batches_tracked", "features.denseblock3.denselayer20.norm1.num_batches_tracked", "features.denseblock3.denselayer20.norm2.num_batches_tracked", "features.denseblock3.denselayer21.norm1.num_batches_tracked", "features.denseblock3.denselayer21.norm2.num_batches_tracked", "features.denseblock3.denselayer22.norm1.num_batches_tracked", "features.denseblock3.denselayer22.norm2.num_batches_tracked", "features.denseblock3.denselayer23.norm1.num_batches_tracked", "features.denseblock3.denselayer23.norm2.num_batches_tracked", "features.denseblock3.denselayer24.norm1.num_batches_tracked", "features.denseblock3.denselayer24.norm2.num_batches_tracked", "features.transition3.norm.num_batches_tracked", "features.denseblock4.denselayer1.norm1.num_batches_tracked", "features.denseblock4.denselayer1.norm2.num_batches_tracked", "features.denseblock4.denselayer2.norm1.num_batches_tracked", "features.denseblock4.denselayer2.norm2.num_batches_tracked", "features.denseblock4.denselayer3.norm1.num_batches_tracked", "features.denseblock4.denselayer3.norm2.num_batches_tracked", "features.denseblock4.denselayer4.norm1.num_batches_tracked", "features.denseblock4.denselayer4.norm2.num_batches_tracked", "features.denseblock4.denselayer5.norm1.num_batches_tracked", "features.denseblock4.denselayer5.norm2.num_batches_tracked", "features.denseblock4.denselayer6.norm1.num_batches_tracked", "features.denseblock4.denselayer6.norm2.num_batches_tracked", "features.denseblock4.denselayer7.norm1.num_batches_tracked", "features.denseblock4.denselayer7.norm2.num_batches_tracked", "features.denseblock4.denselayer8.norm1.num_batches_tracked", "features.denseblock4.denselayer8.norm2.num_batches_tracked", "features.denseblock4.denselayer9.norm1.num_batches_tracked", "features.denseblock4.denselayer9.norm2.num_batches_tracked", "features.denseblock4.denselayer10.norm1.num_batches_tracked", "features.denseblock4.denselayer10.norm2.num_batches_tracked", "features.denseblock4.denselayer11.norm1.num_batches_tracked", "features.denseblock4.denselayer11.norm2.num_batches_tracked", "features.denseblock4.denselayer12.norm1.num_batches_tracked", "features.denseblock4.denselayer12.norm2.num_batches_tracked", "features.denseblock4.denselayer13.norm1.num_batches_tracked", "features.denseblock4.denselayer13.norm2.num_batches_tracked", "features.denseblock4.denselayer14.norm1.num_batches_tracked", "features.denseblock4.denselayer14.norm2.num_batches_tracked", "features.denseblock4.denselayer15.norm1.num_batches_tracked", "features.denseblock4.denselayer15.norm2.num_batches_tracked", "features.denseblock4.denselayer16.norm1.num_batches_tracked", "features.denseblock4.denselayer16.norm2.num_batches_tracked", "features.norm5.num_batches_tracked".
```

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 3, 2018, 3:41pm UTC](https://discuss.pytorch.org/t/runtimeerror-error-s-in-loading-state-dict-for-densenet-unexpected-key-s-in-state-dict/31117/2 "2018-12-03T15:41:57Z")

</div>

It looks like you are using an older PyTorch version to load the model.  
Have a look at [this thread](https://discuss.pytorch.org/t/unexpected-key-in-state-dict-bn1-num-batches-tracked/29454?u=ptrblck) for possible solutions.