I have a model I am using for sentence classification.  At the start I create a vocabulary of the words in the training and validation datasets and then load the Glove word vectors corresponding to these into an embedding layer (which is fixed and not updated during training)
After the embedding layery I have a relatively simple three layer net with a softmax output.
The model works fine on the training and test data and I get about 90% accuracy on both.
I then save the model using the command
torch.save(model.state_dict(), model_path)
Fine so far.
To use the model on new data which hasn’t been categorised in advance I then re-create the model and then read in the parameters as follows:
model.load_state_dict(torch.load(model_path))
model.eval()
The problem is that I now need to change the embedding layer since the vocabulary is different.  The approach I have taken is to create a new vocabulary and set of pre-trained word vectors from Glove and then update the embedding layer of the model as follows:
model.embeddings=nn.Embedding(vocab_size, word_vector_length)
model.embeddings.weight.data.copy_(v.vectors)
model.eval()
When I look at the model it looks to have done everything ok and the model will run, however, the resutls are clearly in error.  If I actually use the model on data I originally trained it on I get very different (and awful) results (even when the vocab and embedding layer loaded are the same as they were during training)
I have checked and the weights in all of the hidden layers still seem to be the same before and after the loading process.  Am I don’t something stupid?
Many thanks
John