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- from model import *
- from train import *
-
-
- def evaluate(net, dataset, bactch_size=64, use_cuda=False):
- dataloader = DataLoader(dataset, batch_size=bactch_size, collate_fn=collate, num_workers=0)
- count = 0
- if use_cuda:
- net.cuda()
- for i, batch_samples in enumerate(dataloader):
- x, y = batch_samples
- doc_list = []
- for sample in x:
- doc = []
- for sent_vec in sample:
- if use_cuda:
- sent_vec = sent_vec.cuda()
- doc.append(Variable(sent_vec, volatile=True))
- doc_list.append(pack_sequence(doc))
- if use_cuda:
- y = y.cuda()
- predicts = net(doc_list)
- p, idx = torch.max(predicts, dim=1)
- idx = idx.data
- count += torch.sum(torch.eq(idx, y))
- return count
-
-
- if __name__ == '__main__':
- '''
- Evaluate the performance of models
- '''
- from gensim.models import Word2Vec
-
- embed_model = Word2Vec.load('yelp.word2vec')
- embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size)
- del embed_model
-
- net = HAN(input_size=200, output_size=5,
- word_hidden_size=50, word_num_layers=1, word_context_size=100,
- sent_hidden_size=50, sent_num_layers=1, sent_context_size=100)
- net.load_state_dict(torch.load('models.dict'))
- test_dataset = YelpDocSet('reviews', 199, 4, embedding)
- correct = evaluate(net, test_dataset, True)
- print('accuracy {}'.format(correct / len(test_dataset)))
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