selecting data to train

Author: wasimusu

config.py

@@ -8,7 +8,7 @@ class Config:
     def __init__(self):
         """Expirement configuration"""
 
-        self.mode = "train"  # TODO: Why do we want this in config?
+        self.mode = "dev"
 
         # Device params
         # self.use_cuda = torch.cuda.is_available()
@@ -17,7 +17,7 @@ def __init__(self):
         self.use_cuda = False
 
         # Global dimension params
-        self.embedding_dim = 100
+        self.embedding_dim = 50
         self.hidden_size = self.embedding_dim
         self.context_len = 200  # TODO: Why do we need this?
         self.question_len = 20  # TODO: Why do we need this?

data_utils.py

@@ -23,8 +23,7 @@ def get_data(batch, is_train=True):
     qn_seq_var = torch.from_numpy(batch.qn_ids).long()
     context_seq_var = torch.from_numpy(batch.context_ids).long()
 
-    if config.mode == "train":
-        span_var = torch.from_numpy(batch.ans_span).long()
+    span_var = torch.from_numpy(batch.ans_span).long()
 
     if is_train:
         qn_mask_var = qn_mask_var.to(config.device)
@@ -34,10 +33,7 @@ def get_data(batch, is_train=True):
         if is_train:
             span_var = span_var.to(config.device)
 
-    if config.mode == "train":
-        return qn_seq_var, qn_mask_var, context_seq_var, context_mask_var, span_var
-    else:
-        return qn_seq_var, qn_mask_var, context_seq_var, context_mask_var
+    return qn_seq_var, qn_mask_var, context_seq_var, context_mask_var, span_var
 
 
 import time

train.py

@@ -56,7 +56,7 @@ def evaluate():
     pass
 
 
-def train(*args, **kwargs):
+def train(context_path, qn_path, ans_path):
     """ Train the network """
 
     model = N.CoattentionNetwork(device=config.device,
@@ -109,8 +109,7 @@ def train(*args, **kwargs):
     # For each epoch
     for epoch in range(config.num_epochs):
         # For each batch
-        for i, batch in enumerate(get_batch_generator(word2index, train_context_path,
-                                                      train_qn_path, train_ans_path,
+        for i, batch in enumerate(get_batch_generator(word2index, context_path, qn_path, ans_path,
                                                       config.batch_size, config.context_len,
                                                       config.question_len, discard_long=True)):
             # Take step in training
@@ -136,4 +135,12 @@ def train(*args, **kwargs):
 
 
 if __name__ == '__main__':
-    train()
+    if config.mode == 'train':
+        context_path = train_context_path
+        qn_path = train_qn_path
+        ans_path = train_ans_path
+    else:
+        context_path = dev_context_path
+        qn_path = dev_qn_path
+        ans_path = dev_ans_path
+    train(context_path, qn_path, ans_path)