progress

main.py

@@ -2,6 +2,7 @@ from os import sep
 from nltk import word_tokenize
 import pandas as pd
 import torch
+from torch._C import device
 from tqdm import tqdm
 from torchtext.vocab import vocab
 from collections import Counter, OrderedDict
@@ -29,7 +30,15 @@ class Model(torch.nn.Module):
         # out = self.dense1(out.squeeze(0).T)
         out = self.hidden2tag(out)
         out = self.crf(out, tags.T)
-        out = self.sigm(self.fc1(torch.tensor([out])))
+        # out = self.sigm(self.fc1(torch.tensor([out])))
+        return out
+
+    def decode(self, data):
+        emb = self.relu(self.emb(data))
+        out, h_n = self.gru(emb)
+        # out = self.dense1(out.squeeze(0).T)
+        out = self.hidden2tag(out)
+        out = self.crf.decode(out)
         return out
 
 
@@ -74,31 +83,53 @@ labels_vocab = {
 
 train_tokens_ids = data_process(data["text"])
 train_labels = labels_process(data["labels"])
+
 num_tags = 9
 NUM_EPOCHS = 5
 seq_length = 15
 
 model = Model(num_tags, seq_length)
+device = torch.device("cuda")
+model.to(device)
+model.cuda(0)
+
 criterion = torch.nn.CrossEntropyLoss()
 optimizer = torch.optim.Adam(model.parameters())
 
 train_dataloader = DataLoader(list(zip(train_tokens_ids, train_labels)), batch_size=64, shuffle=True)
 # test_dataloader = DataLoader(train_labels, batch_size=64, shuffle=True)
 
-for i in range(NUM_EPOCHS):
+mode = "train"
-    model.train()
+# mode = "eval"
-    #for i in tqdm(range(500)):
+# mode = "generate"
+
+if mode == "train":
+    for i in range(NUM_EPOCHS):
+        model.train()
+        #for i in tqdm(range(500)):
+        for i in tqdm(range(len(train_labels))):
+            for k in range(0, len(train_tokens_ids[i]) - seq_length, seq_length):
+                batch_tokens = train_tokens_ids[i][k: k + seq_length].unsqueeze(0)
+                tags = train_labels[i][k: k + seq_length].unsqueeze(1)
+                
+                predicted_tags = model(batch_tokens.to(device), tags.to(device))
+
+                optimizer.zero_grad()
+                # tags = torch.tensor([x[0] for x in tags])
+                # loss  = criterion(predicted_tags.unsqueeze(0),tags.T)
+                
+                predicted_tags.backward()
+                optimizer.step()
+                model.zero_grad()
+
+    torch.save(model.state_dict(), "model.torch")
+
+if mode == "eval":
+    model.eval()
     for i in tqdm(range(len(train_labels))):
         for k in range(0, len(train_tokens_ids[i]) - seq_length, seq_length):
             batch_tokens = train_tokens_ids[i][k: k + seq_length].unsqueeze(0)
             tags = train_labels[i][k: k + seq_length].unsqueeze(1)
             
-            predicted_tags = model(batch_tokens, tags)
+            predicted_tags = model.decode(batch_tokens.to(device))
-
+            print('dupa')
-            optimizer.zero_grad()
-            tags = torch.tensor([x[0] for x in tags])
-            loss  = criterion(predicted_tags.unsqueeze(0),tags.T)
-            
-            loss.backward()
-            optimizer.step()
-            model.zero_grad()