Merge pull request #3 from ChujieChen/develop

finished LSTM for polarity

src/yews/models/polarity.py

@@ -150,7 +150,9 @@ def __init__(self, **kwargs):
         self.contains_unkown = kwargs["contains_unkown"]
         self.start = kwargs['start']
         self.end = kwargs['end']
-        self.lstm = nn.LSTM(input_size, self.hidden_size,bidirectional=self.bidirectional)
+        self.num_layers = kwargs['num_layers']
+        self.lstm = nn.LSTM(input_size, self.hidden_size, self.num_layers, 
+                            bidirectional=self.bidirectional)
         self.fc = nn.Linear(self.hidden_size * (2 if self.bidirectional else 1), 3 if self.contains_unkown else 2)
 
     def forward(self, x):
@@ -163,11 +165,16 @@ def forward(self, x):
         return out
 def polarity_lstm(**kwargs):
     r"""A LSTM based model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on Wenchuan)
-        progress (bool): If True, displays a progress bar of the download to stderr
+    Kwargs (form like a dict and should be pass like **kwargs):
+      hidden_size (default 64): recommended to be similar as the length of trimmed subsequence
+      num_layers (default 2): layers are stacked and results are from the final layer
+      start (default 250): start index of the subsequence
+      end (default 350): end index of the subsequence
+      bidirectional (default False): run lstm from left to right and from right to left
+      contains_unkown (default False): True if targets have 0,1,2
     """
     default_kwargs = {"hidden_size":64, 
+                      "num_layers":2,
                       "start": 250,
                       "end": 350,
                       "bidirectional":False, 
@@ -179,6 +186,6 @@ def polarity_lstm(**kwargs):
     print(default_kwargs)
     print("\n##########################")
     if(default_kwargs['end'] < default_kwargs['start']):
-      raise ValueError('<-- end must be largger than start -->')
+      raise ValueError('<-- end cannot be smaller than start -->')
     model = PolarityLSTM(**default_kwargs)
     return model