torch.save(model.state_dict(), "./weight/vgg16_baseline.pth")

After save original vgg weight,
model = myVgg16()
model.load_state_dict(torch.load("./weight/vgg16_baseline.pth"))

This code makes under error.
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-12-6ceaad033204> in <module>
      1 model = myVgg16()
----> 2 model.load_state_dict(torch.load("./weight/vgg16_baseline.pth"))

~/.local/lib/python3.5/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
    767         if len(error_msgs) > 0:
    768             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
--> 769                                self.__class__.__name__, "\n\t".join(error_msgs)))
    770 
    771     def _named_members(self, get_members_fn, prefix='', recurse=True):

RuntimeError: Error(s) in loading state_dict for myVgg16:
	Missing key(s) in state_dict: "head.0.weight", "head.0.bias", "head.1.running_mean", "head.1.running_var", "head.1.weight", "head.1.bias", "features.0.weight", "features.0.bias", "features.1.running_mean", "features.1.running_var", "features.1.weight", "features.1.bias", "features.4.weight", "features.4.bias", "features.5.running_mean", "features.5.running_var", "features.5.weight", "features.5.bias", "features.7.weight", "features.7.bias", "features.8.running_mean", "features.8.running_var", "features.8.weight", "features.8.bias", "features.11.weight", "features.11.bias", "features.12.running_mean", "features.12.running_var", "features.12.weight", "features.12.bias", "features.14.weight", "features.14.bias", "features.15.running_mean", "features.15.running_var", "features.15.weight", "features.15.bias", "features.17.weight", "features.17.bias", "features.18.running_mean", "features.18.running_var", "features.18.weight", "features.18.bias", "features.21.weight", "features.21.bias", "features.22.running_mean", "features.22.running_var", "features.22.weight", "features.22.bias", "features.24.weight", "features.24.bias", "features.25.running_mean", "features.25.running_var", "features.25.weight", "features.25.bias", "features.27.weight", "features.27.bias", "features.28.running_mean", "features.28.running_var", "features.28.weight", "features.28.bias", "features.31.weight", "features.31.bias", "features.32.running_mean", "features.32.running_var", "features.32.weight", "features.32.bias", "features.34.weight", "features.34.bias", "features.35.running_mean", "features.35.running_var", "features.35.weight", "features.35.bias", "features.37.weight", "features.37.bias", "features.38.running_mean", "features.38.running_var", "features.38.weight", "features.38.bias", "classifier.0.weight", "classifier.0.bias", "classifier.1.weight", "classifier.1.bias", "classifier.2.weight", "classifier.2.bias". 
	Unexpected key(s) in state_dict: "module.features.0.weight", "module.features.0.bias", "module.features.1.weight", "module.features.1.bias", "module.features.1.running_mean", "module.features.1.running_var", "module.features.1.num_batches_tracked", "module.features.3.weight", "module.features.3.bias", "module.features.4.weight", "module.features.4.bias", "module.features.4.running_mean", "module.features.4.running_var", "module.features.4.num_batches_tracked", "module.features.7.weight", "module.features.7.bias", "module.features.8.weight", "module.features.8.bias", "module.features.8.running_mean", "module.features.8.running_var", "module.features.8.num_batches_tracked", "module.features.10.weight", "module.features.10.bias", "module.features.11.weight", "module.features.11.bias", "module.features.11.running_mean", "module.features.11.running_var", "module.features.11.num_batches_tracked", "module.features.14.weight", "module.features.14.bias", "module.features.15.weight", "module.features.15.bias", "module.features.15.running_mean", "module.features.15.running_var", "module.features.15.num_batches_tracked", "module.features.17.weight", "module.features.17.bias", "module.features.18.weight", "module.features.18.bias", "module.features.18.running_mean", "module.features.18.running_var", "module.features.18.num_batches_tracked", "module.features.20.weight", "module.features.20.bias", "module.features.21.weight", "module.features.21.bias", "module.features.21.running_mean", "module.features.21.running_var", "module.features.21.num_batches_tracked", "module.features.24.weight", "module.features.24.bias", "module.features.25.weight", "module.features.25.bias", "module.features.25.running_mean", "module.features.25.running_var", "module.features.25.num_batches_tracked", "module.features.27.weight", "module.features.27.bias", "module.features.28.weight", "module.features.28.bias", "module.features.28.running_mean", "module.features.28.running_var", "module.features.28.num_batches_tracked", "module.features.30.weight", "module.features.30.bias", "module.features.31.weight", "module.features.31.bias", "module.features.31.running_mean", "module.features.31.running_var", "module.features.31.num_batches_tracked", "module.features.34.weight", "module.features.34.bias", "module.features.35.weight", "module.features.35.bias", "module.features.35.running_mean", "module.features.35.running_var", "module.features.35.num_batches_tracked", "module.features.37.weight", "module.features.37.bias", "module.features.38.weight", "module.features.38.bias", "module.features.38.running_mean", "module.features.38.running_var", "module.features.38.num_batches_tracked", "module.features.40.weight", "module.features.40.bias", "module.features.41.weight", "module.features.41.bias", "module.features.41.running_mean", "module.features.41.running_var", "module.features.41.num_batches_tracked", "module.classifier.0.weight", "module.classifier.0.bias", "module.classifier.1.weight", "module.classifier.1.bias", "module.classifier.2.weight", "module.classifier.2.bias". 

Is this because the different head architecture?
Umm… then how can Ioad the weight?