add rnn nlg

rnn-poetry/flask_demo.py

@@ -0,0 +1,11 @@
+from flask import Flask
+app = Flask(__name__)
+
+
+@app.route('/')
+def hello():
+    return 'Hello World!'
+
+
+if __name__ == '__main__':
+    app.run()

rnn-poetry/run.py

@@ -2,43 +2,28 @@ import re
 import tqdm
 import torch
 import collections
-import numpy
+import pickle
 from torch import nn
 
-from model import RNNModel
+from model import RNNModel,embed_size,hidden_dims,batch_size
 
-device=
+device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-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:
+    peotry_path='data/poetry.txt'
+    with open(peotry_path,'r',encoding='utf-8') as f:
+        for content in f:
+            content=content.strip()
             content = '[' + content + ']'
             poetrys.append(content)
-            except Exception as e:
-                pass
-    poetrys = sorted(poetrys, key=lambda line: len(line))
+
+    # poetrys = sorted(poetrys, key=lambda line: len(line))
     # 统计每个字出现次数
     all_words = []
     for poetry in poetrys:
@@ -50,41 +35,79 @@ def get_data():
     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)
+    ix2word = {v: k for k, v in word2ix.items()}
+    data = [[word2ix[c] for c in poetry ] for poetry in poetrys]
+    # data=numpy.array(data)
     return data,word2ix,ix2word
 
+def test(model):
+    start_idx=[word2ix['[']]
+    end_word=''
+    lens=0
+    hidden = None
+    ret=''
+    while end_word!=']' and len(ret)<100:
+        data_ = torch.tensor([start_idx],device=device).long()
+        # print("data size",data_.size())
+        output, hidden = model(data_, hidden)
+        # print("output size", output.size())
+        ouput_idx=output.view(-1).argmax().cpu()
+        # print('ouput_idx',ouput_idx)
+        # print('ouput_idx', ouput_idx.item())
+        ouput_idx=ouput_idx.item()
+        start_idx=[ouput_idx]
+        end_word=ix2word[ouput_idx]
+        ret+=end_word
+    return ret
+
+
+
+
 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)
+    model = RNNModel(len(word2ix), embed_size, hidden_dims)
     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)
+    model.train()
+    for epoch in (range(epochs)):
+        total_loss=0
+        count=0
+        for ii, data_ in tqdm.tqdm(enumerate(data)):
+            data_=torch.tensor(data_).long()
+            x = data_.unsqueeze(1).to(device)
             optimizer.zero_grad()
-            input_, target = data_[:-1, :], data_[1:, :]
-            output, _ = model(input_)
+            y = torch.zeros(x.shape).to(device).long()
+            y[:-1], y[-1] = x[1:], x[0]
+            output, _ = model(x)
+            loss = criterion(output, y.view(-1))
+            """
+            hidden=None
+            for k in range(2,max_lenth):
+                data1=data_[:k]
+                input_, target = data1[:-1, :], data1[1:, :]
+                output, hidden = model(input_,hidden)
                 loss = criterion(output, target.view(-1))
+                optimizer.step()
+            """
             loss.backward()
             optimizer.step()
-
-
-
+            total_loss+=(loss.item())
+            count+=1
+        print(epoch,'loss=',total_loss/count)
         torch.save(model.state_dict(), 'model.bin' )
-if __name__ == "__main__":
+        chars=test(model)
+        print(chars)
 
+
+if __name__ == "__main__":
+    # 获取数据
+    data, word2ix, ix2word = get_data()
+    with open("word2ix.pkl", 'wb') as outfile:
+        pickle.dump(word2ix,outfile)
+    with open("ix2word.pkl", 'wb') as outfile:
+        pickle.dump(ix2word,outfile)
+
+    data=data[:100]
     train()

rnn-poetry/run_server.py

@@ -0,0 +1,31 @@
+import pickle
+
+from flask import Flask,request
+app = Flask(__name__)
+import torch
+
+from model import RNNModel,embed_size,hidden_dims,generate_poetry
+
+with open("word2ix.pkl", 'rb') as outfile:
+    word2ix=pickle.load(outfile)
+with open("ix2word.pkl", 'rb') as outfile:
+    ix2word=pickle.load(outfile)
+
+device=torch.device('cpu')
+model = RNNModel(len(word2ix), embed_size, hidden_dims)
+init_checkpoint = 'model.bin'
+model.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
+
+@app.route('/')
+def hello():
+    return 'Hello World!'
+
+@app.route('/peom')
+def predict():
+    begin_word = request.args.get('text', '')
+    ret=generate_poetry(model,word2ix,ix2word,device,begin_word)
+
+    return ret
+
+if __name__ == '__main__':
+    app.run()