Cleanup, and device-aware training

.gitignore

@@ -1,2 +1,2 @@
-data/
+data/*
 __pycache__

README.md

@@ -1,4 +1,4 @@
-This is a multi-class, multi-label network that categorises text into one of ~160 categories, mostly relating to the African continent.
+This is a multi-class, multi-label NLP network that categorises text into one of ~160 categories, mostly relating to the African continent.
 
 The training dataset is a proprietry dataset from allAfrica.com, consisting of stories that have been manuially categorised according to AllAfrica's inhouse categorisation scheme.
 

africat/categorise.py

@@ -19,9 +19,9 @@ import tqdm
 import torch
 import torchdata.datapipes as dp
 import torchtext.transforms as T
-from torchtext.vocab import build_vocab_from_iterator
-from torch.utils.data import Dataset, DataLoader
 from torch import nn
+from torch.utils.data import Dataset, DataLoader
+from torchtext.vocab import build_vocab_from_iterator
 
 from models.rnn import RNN
 
@@ -29,22 +29,11 @@ all_categories = list()
 # XXX None for all stories
 #story_num = 128
 #story_num = 256
-story_num = 512
+#story_num = 512
 #story_num = 1024
+story_num = 4096
 #story_num = None
 
-# Hyperparameters
-EPOCHS = 10       # epoch
-#EPOCHS = 2       # epoch
-#LR = 5           # learning rate
-#LR = 0.5
-LR = 0.05
-#LR = 0.005        # initial learning rate; too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process -- perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate
-#BATCH_SIZE = 64   # batch size for training
-#BATCH_SIZE = 16   # batch size for training
-BATCH_SIZE = 8   # batch size for training
-#BATCH_SIZE = 4   # batch size for training
-
 def read_csv(input_csv, rows=None, verbose=0):
   if verbose > 0:
     with open(input_csv, 'r', encoding="utf-8") as f:
@@ -349,9 +338,9 @@ def tensor2cat(vocab, tensor):
     for idx, pred in enumerate(tensor):
       if idx >= len(all_cats):
         print(f"Idx {idx} not in {len(all_cats)} categories")
-      elif pred > 0: # XXX
+      #elif pred > 0: # XXX
         #print(idx, len(all_cats))
-        chance[all_cats[idx]] = pred.item()
+      chance[all_cats[idx]] = pred.item()
     #print(chance)
     return chance
   else:
@@ -383,15 +372,15 @@ def train(dataloader, dataset, model, optimizer, criterion, epoch=0):
 
     optimizer.step()
 
-    #print("train loss",loss)
+    print("train loss", loss)
 
     ##predicted = np.round(output)
     ##total_acc += (predicted == cats).sum().item()
 
     predictions = torch.zeros(output.shape)
-    predictions[output >= 0.25] = True
-    #predictions[output >= 0.5] = True
-    #predictions[output <  0.5] = False ## assign 0 label to those with less than 0.5
+    #predictions[output >= 0.25] = True
+    predictions[output >= 0.5] = True
+    predictions[output <  0.5] = False ## assign 0 label to those with less than 0.5
 
     batch.clear()
     for target, out, pred in list(zip(cats, output, predictions)):
@@ -548,6 +537,28 @@ def main():
   )
   print(f"Using {device} device")
 
+  # Hyperparameters
+  #epochs = 10      # epoch
+  epochs = 4       # epoch
+  #lr = 5           # learning rate
+  #lr = 0.5
+  #lr = 0.05
+  #lr = 0.005       # initial learning rate; too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process -- perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate
+  lr = 0.0001 
+  batch_size = 64  # batch size for training
+  #batch_size = 16  # batch size for training
+  #batch_size = 8   # batch size for training
+  #batch_size = 4   # batch size for training
+
+  #num_layers = 2 # 2-3 layers should be enough for LTSM
+  num_layers = 3 # 2-3 layers should be enough for LTSM
+  hidden_size = 128 # hidden size of rnn module, should be tweaked manually
+  #hidden_size = 8 # hidden size of rnn module, should be tweaked manually
+  mean_seq = True # use mean of rnn output
+  #mean_seq = False # use mean of rnn output
+  weight_decay = 1e-4 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
+  #weight_decay = 1e-3 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
+
   '''
   dataloader = DataLoader(dataset,
     batch_size=4,
@@ -558,14 +569,14 @@ def main():
   )
   '''
   train_dataloader = DataLoader(train_dataset,
-    batch_size=BATCH_SIZE,
+    batch_size=batch_size,
     drop_last=True,
     shuffle=True,
     num_workers=0,
     collate_fn=CollateBatch(cats=train_dataset.cats_vocab.get_stoi(), pad_idx=train_dataset.stoi['<pad>']),
   )
   valid_dataloader = DataLoader(valid_dataset,
-    batch_size=BATCH_SIZE,
+    batch_size=batch_size,
     drop_last=True,
     shuffle=True,
     num_workers=0,
@@ -582,10 +593,6 @@ def main():
 
   embed = torch.empty(input_size, len(train_dataset)) # tokens per sample x samples
   embedding_size = embed.size(1) # was 64 (should be: samples)
-  num_layers = 2 # 2-3 layers should be enough for LTSM
-  hidden_size = 128 # hidden size of rnn module, should be tweaked manually
-  mean_seq = True # use mean of rnn output
-  weight_decay = 1e-4 # helps the neural networks to learn smoother / simpler functions which most of the time generalizes better compared to spiky, noisy ones ; try 1e-3, 1e-4
 
   if args.verbose:
     #for i in train_dataset.text_vocab.get_itos():
@@ -611,22 +618,28 @@ def main():
     print(model)
 
   # optimizer and loss
-  #optimizer = torch.optim.SGD(model.parameters(), lr=LR)
   criterion = nn.BCEWithLogitsLoss()
-  optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=LR, weight_decay=weight_decay)
+  #optimizer = torch.optim.SGD(model.parameters(), lr=lr)
+  optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=weight_decay)
   if args.verbose:
     print(criterion)
     print(optimizer)
 
   total_accu = None
-  #for epoch in range(1, EPOCHS + 1):
-  e = tqdm.tqdm(range(1, EPOCHS + 1), unit="epoch")
+  #for epoch in range(1, epochs + 1):
+  e = tqdm.tqdm(range(1, epochs + 1), unit="epoch")
   for epoch in e:
     e.set_description(f"Epoch {epoch}")
+
+    train_dataset.to(device)
+    valid_dataset.to(device)
+    model.to(device)
+
     model.train()
     train(train_dataloader, train_dataset,  model, optimizer, criterion, epoch)
 
     accu_val = evaluate(valid_dataloader, valid_dataset, model, criterion, epoch)
+
     if total_accu is not None and total_accu > accu_val:
       optimizer.step()
     else: