modify a5 origin

Assignment_origin/Assignment 5/char_decoder.py

@@ -27,11 +27,7 @@ class CharDecoder(nn.Module):
         ### Hint: - Use target_vocab.char2id to access the character vocabulary for the target language.
         ###       - Set the padding_idx argument of the embedding matrix.
         ###       - Create a new Embedding layer. Do not reuse embeddings created in Part 1 of this assignment.
-        super(CharDecoder, self).__init__()        
-        self.charDecoder = nn.LSTM(char_embedding_size,hidden_size,batch_first=True) #bias = True
-        self.char_output_projection = nn.Linear(hidden_size,len(target_vocab.char2id))
-        self.decoderCharEmb = nn.Embedding(len(target_vocab.char2id),char_embedding_size,padding_idx=target_vocab.char2id['<pad>']) 
-        self.target_vocab = target_vocab
+
 
         ### END YOUR CODE
 
@@ -48,20 +44,7 @@ class CharDecoder(nn.Module):
         """
         ### YOUR CODE HERE for part 2b
         ### TODO - Implement the forward pass of the character decoder.
-        #print('size of input is',input.size())
-        input = input.permute(1,0).contiguous()
-        ip_embedding=self.decoderCharEmb(input)# F.embedding(source_padded, self.model_embeddings.source.weight)
-        #X = nn.utils.rnn.pack_padded_sequence(src_padded_embedding,source_lengths)
-
-        #ip_embedding = ip_embedding.permute(1,0,2).contiguous()
-
-        output,(h_n,c_n) = self.charDecoder(ip_embedding,dec_hidden)
-        #print('shape of hidden is',h_n.size())
-        s_t = self.char_output_projection(output)
-        #print('shape of logits is',s_t.size())
-        s_t = s_t.permute(1,0,2).contiguous()
-        
-        return s_t,(h_n,c_n)
+   
         ### END YOUR CODE 
 
 
@@ -79,22 +62,6 @@ class CharDecoder(nn.Module):
         ### Hint: - Make sure padding characters do not contribute to the cross-entropy loss.
         ###       - char_sequence corresponds to the sequence x_1 ... x_{n+1} from the handout (e.g., <START>,m,u,s,i,c,<END>).
 
-        input = char_sequence[:-1,:]
-        output = char_sequence[1:,:]
-        #print(input)
-        #print(output)
-        target = output.reshape(-1)
-        #print('shape of target',target.shape)
-        s_t,(h_n,c_n) = self.forward(input,dec_hidden)
-        #print('shape of s_t',s_t.shape) 
-        s_t_shape = s_t.shape
-        s_t_re = s_t.reshape(-1,s_t.shape[2])
-
-
-        #print('shape of s_t_re',s_t_re.shape) 
-        loss = nn.CrossEntropyLoss(ignore_index=self.target_vocab.char2id['<pad>'],reduction='sum') 
-
-        return loss(s_t_re,target)
         ### END YOUR CODE
 
     def decode_greedy(self, initialStates, device, max_length=21):
@@ -114,43 +81,6 @@ class CharDecoder(nn.Module):
         ###      - Use torch.tensor(..., device=device) to turn a list of character indices into a tensor.
         ###      - We use curly brackets as start-of-word and end-of-word characters. That is, use the character '{' for <START> and '}' for <END>.
         ###        Their indices are self.target_vocab.start_of_word and self.target_vocab.end_of_word, respectively.
-        decodedWords = []
-        current_char = self.target_vocab.start_of_word
-        start_tensor = torch.tensor([current_char],device=device)
-        #print('size of start_tensor is',start_tensor.shape)
-        batch_size = initialStates[0].shape[1]
-        start_batch = start_tensor.repeat(batch_size,1)
-        #print('size of start_batch is',start_batch.shape)
-        embed_current_char = self.decoderCharEmb(start_batch)
-        #print('size of embed_current_char is',embed_current_char.shape)
-        h_n,c_n = initialStates
-        output_word = torch.zeros((batch_size,1),dtype=torch.long,device=device)      
-        for t in range(0,max_length):
-          #h_n,c_n = self.charDecoder(embed_current_char,(h_n,c_n))
-          # s_t,(h_n,c_n) = self.forward(embed_current_char,(h_n,c_n))
-          #print('shape of embed_current_char is',embed_current_char.shape)
-          output,(h_n,c_n) = self.charDecoder(embed_current_char,(h_n,c_n))
-          s_t = self.char_output_projection(output)
-          #print(s_t.shape)
-          st_smax = nn.Softmax(dim=2)(s_t) 
-          p_next = st_smax.argmax(2)
-          current_char = p_next
-          embed_current_char = self.decoderCharEmb(current_char)
-          #decodedWords.append(self.target_vocab.id2char[current_char])
-          #print('*** size of current_char is',current_char.size())
-          output_word = torch.cat((output_word,current_char),1)
-        #Convert output_word tensor to list and each element to char and put together in decodedWords
-        out_list = output_word.tolist()
-        out_list = [[self.target_vocab.id2char[x] for x in ilist[1:]] for ilist in out_list]
-        decodedWords = []
-        for string_list in out_list:
-           stringer = ''
-           for char in string_list:
-              if char!='}':
-                 stringer = stringer+char
-              else:
-                 break
-           decodedWords.append(stringer)
-        return decodedWords
+      
        ### END YOUR CODE
 

rnn-poetry/run.py

@@ -0,0 +1,90 @@
+import re
+import tqdm
+import torch
+import collections
+import numpy
+from torch import nn
+
+from model import RNNModel
+
+device=
+
+batch_size=128
+embed_size=256
+
+epochs=100
+lr=0.001
+def get_data():
+    poetry_file = 'data/poetry.txt'
+    special_character_removal = re.compile(r'[^\w。， ]', re.IGNORECASE)
+    # 诗集
+    poetrys = []
+    with open(poetry_file, "r", encoding='utf-8', ) as f:
+        for line in f:
+            try:
+                title, content = line.strip().split(':')
+                content = special_character_removal.sub('', content)
+                content = content.replace(' ', '')
+                if len(content) < 5:
+                    continue
+                if (len(content) > 12 * 6):
+                    content_list = content.split("。")
+                    for i in range(0, len(content_list), 2):
+                        content_temp = '[' + content_list[i] + "。" + content_list[i + 1] + '。]'
+                        content_temp = content_temp.replace("。。", "。")
+                        poetrys.append(content_temp)
+                else:
+                    content = '[' + content + ']'
+                    poetrys.append(content)
+            except Exception as e:
+                pass
+    poetrys = sorted(poetrys, key=lambda line: len(line))
+    # 统计每个字出现次数
+    all_words = []
+    for poetry in poetrys:
+        all_words += [word for word in poetry]
+    counter = collections.Counter(all_words)
+    count_pairs = sorted(counter.items(), key=lambda x: -x[1])
+    words, _ = zip(*count_pairs)
+    # 取前多少个常用字
+    words = words[:len(words)] + (' ',)
+    # 每个字映射为一个数字ID
+    word2ix = dict(zip(words, range(len(words))))
+    ix2word = lambda word: word2ix.get(word, len(words))
+    data = [list(map(ix2word, poetry)) for poetry in poetrys]
+    data=numpy.array(data)
+    return data,word2ix,ix2word
+
+def train():
+
+    # 获取数据
+    data, word2ix, ix2word = get_data()
+    data = torch.from_numpy(data)
+    dataloader = torch.utils.data.DataLoader(data,
+                                         batch_size=batch_size,
+                                         shuffle=True,
+                                         num_workers=1)
+
+    # 模型定义
+    model = RNNModel(len(word2ix), batch_size, embed_size)
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
+    criterion = nn.CrossEntropyLoss()
+    model.to(device)
+
+    for epoch in range(epochs):
+        for ii, data_ in tqdm(enumerate(dataloader)):
+            data_ = data_.long().transpose(1, 0).contiguous()
+            data_ = data_.to(device)
+            optimizer.zero_grad()
+            input_, target = data_[:-1, :], data_[1:, :]
+            output, _ = model(input_)
+            loss = criterion(output, target.view(-1))
+            loss.backward()
+            optimizer.step()
+
+
+
+    torch.save(model.state_dict(), 'model.bin' )
+if __name__ == "__main__":
+
+    train()