checkpoint = torch.load(checkpoint)
start_epoch = checkpoint["epoch"] + 1
#best_score = checkpoint["best_score"]
best_score = 0
print("\t* Training will continue on existing model from epoch {}..."
.format(start_epoch))
model_now_dict = model.state_dict()
print(model_now_dict.keys())
load_pretrained_dict = (checkpoint["model"])
print(load_pretrained_dict.keys())
new_state_dict = {k: v for k, v in load_pretrained_dict.items() if k!="_word_embedding.weight"}
#new_state_dict = {k: v for k, v in load_pretrained_dict.items()}
# 1. filter out unnecessary keys
# 2. overwrite entries in the existing state dict
model_now_dict.update(new_state_dict)
model.load_state_dict(model_now_dict)
經過了好幾個關卡。