if args.embedding_type == 'linear':
model = EmbeddingModel(len(args.classes))
elif args.embedding_type == 'conv':
model = ConvolutionalEmbeddingModel(len(args.classes))
else:
print("Model type [{0}] not supported".format(args.embedding_type))
exit(1)
eval_dataset, eval_labels, _, __, ___ = generateData(
args.eval_file, eval_list, 1.0, args.load_embedding_dict_from_file,
args.save_embedding_dict, args.verbose,
'embedding_dicts/animal_embedding_dict.pkl', False, args.classes)
embedding_dict = {}
if len(args.model_checkpoint) > 0:
checkpoint = torch.load(args.model_checkpoint, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
embedding = checkpoint['model_state_dict']['l2.weight']
for i in range(len(args.classes)):
embedding_dict[args.classes[i]] = ([embedding[i, :]], [i])
x = eval_dataset
y = eval_labels
classes = eval_list
for i in range(len(x)):
inputs = x[i]
label = y[i]
if inputs.shape[1] < 2:
continue
embedding = model.embedding(inputs)
output = model(inputs)
_, predicted = torch.max(output.data, 1)
if classes[label] in embedding_dict:
df2 = pd.DataFrame(tempc, index=tempb, columns=['Test'])
df1.plot(ax=ax1, kind='line', rot=360, grid='on')
ax1.set_xticks(range(len(index_list)))
ax1.set_xticklabels(range(len(index_list)))
df2.plot(ax=ax2, kind='line', rot=360, grid='on')
ax2.set_xticks(range(Epoch))
ax2.set_xticklabels(range(Epoch))
plt.show()
"""
USE_Bi=True
"""
w2v = EmbeddingModel(vocab_size, embedding_dim)
checkpoint = torch.load('Model/checkpoint.pth2.tar')
w2v.load_state_dict(checkpoint['state_dict']) # 模型参数
print(w2v.state_dict()["in_embed.weight"])
"""
if USE_Bi:
print("Using BiLSTM")
model = BiLSTM_Match(w2v,embedding_dim, hidden_dim, vocab_size, target, Batchsize, stringlen)
model_path = "./Model/BiLSTMmodel.pth"
else:
print("Using LSTM")
model = LSTM_Match(embedding_dim, hidden_dim, vocab_size,target,Batchsize,stringlen)
model_path = "./Model/LSTMmodel.pth"
print(w2v.in_embed==model.word_embeddings)