modify a5 origin

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()