[DL]基于Pytorch的Linear classified model

留个坑，后面再添加详细解释

data = [("我 的 家 乡 在 哪 里".split(), "CHINESE"),        ("Give it to me".split(), "ENGLISH"),        ("今 天 天 气 怎 么 样".split(), "CHINESE"),        ("No it is not a good idea to get lost at sea".split(), "ENGLISH")]test_data = [("天 气 在 哪 里".split(), "CHINESE"),             ("it is lost on me".split(), "ENGLISH")]word2index={}for sen,_ in data+test_data:    for word in sen:        if word not in word2index:            word2index[word]=len(word2index)print(word2index)VOCAB_SIZE=len(word2index)NUM_CLASSES=2label2index={'CHINESE':0,'ENGLISH':1}import torch.nn as nnimport torch.autograd as autogradimport torch.functional as Fclass BoWClassifier(torch.nn.Module):    def __init__(self,num_labels,vocab_size):        super(BoWClassifier,self).__init__()        self.linear=nn.Linear(vocab_size,num_labels)        self.softmax=nn.Softmax()    def forward(self,bow_vec):        return self.softmax(self.linear(bow_vec))def make_bow_vector(sentence,word2index):    vec=torch.zeros(len(word2index))    for word in sentence:        vec[word2index[word]]+=1    return vec.view(1,-1)def make_target(label,label2index):    return torch.LongTensor([label2index[label]])model=BoWClassifier(NUM_CLASSES,VOCAB_SIZE)# for param in model.parameters():#     print(param)log_prob=model(autograd.Variable(make_bow_vector(data[0][0],word2index)))print(log_prob)loss_function=nn.NLLLoss()optimizer=torch.optim.SGD(model.parameters(),lr=0.1)for epoch in range(100):    for instance,label in data:        model.zero_grad()        bow_vec=autograd.Variable(make_bow_vector(instance,word2index))        target=autograd.Variable(make_target(label,label2index))#         print(target)        log_prob=model(bow_vec)#         print(log_prob)        loss=loss_function(log_prob,target)        loss.backward()        optimizer.step()for instance,label in test_data:    bow_vec=autograd.Variable(make_bow_vector(instance,word2index))    log_prob=model(bow_vec)#     print(log_prob)print(model(autograd.Variable(make_bow_vector(['Give','good','good','good','good'],word2index))))print(model(autograd.Variable(make_bow_vector(['我','我','我','我','我'],word2index))))