refactor: merge multiple project into one and create new project

dl-exp/exp3/modified/model.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn.functional as F
+
+class TimeDistributed(torch.nn.Module):
+    """模拟tensorflow中的TimeDistributed包装层，因为pytorch似乎不提供这个。"""
+
+    layer: torch.nn.Module
+    """内部节点"""
+
+    def __init__(self, layer: torch.nn.Module):
+        super(TimeDistributed, self).__init__()
+        self.layer = layer
+
+    def forward(self, x: torch.Tensor):
+        # 获取批次大小，时间步个数，特征个数
+        batch_size, time_steps, features = x.size()
+        # 把时间步维度合并到批次维度中然后运算，这样在其他层看来这就是不同的批次而已。
+        x = x.reshape(-1, features)
+        outputs: torch.Tensor = self.layer(x)
+        # 再把时间步维度还原出来
+        outputs = outputs.reshape(batch_size, time_steps, -1)
+        return outputs
+
+
+class Rnn(torch.nn.Module):
+    """循环神经网络"""
+
+    def __init__(self, vocab_size: int):
+        super(Rnn, self).__init__()
+        self.embedding = torch.nn.Embedding(vocab_size, 128)
+        self.lstm1 = torch.nn.LSTM(128, 128, batch_first=True, dropout=0.5)
+        self.lstm2 = torch.nn.LSTM(128, 128, batch_first=True, dropout=0.5)
+        self.timedfc = TimeDistributed(torch.nn.Linear(128, vocab_size))
+
+    def forward(self, x):
+        x = self.embedding(x)
+        x, _ = self.lstm1(x)
+        x, _ = self.lstm2(x)
+        x = self.timedfc(x)
+        return x
+