123

.gitignore

@@ -7,4 +7,6 @@
 .DS_Store
 .token
 .vscode
-fa/*
+fa/*
+*.pt
+*.pickle

main.py

@@ -4,6 +4,7 @@ import spacy
 import torch
 import numpy as np
 import tqdm
+import pickle
 
 def clean_string(str):
     str = str.replace('\n', '')
@@ -36,7 +37,7 @@ def compute_class_vector(mark, classes):
     result = np.zeros(len(classes))
     for x in range(len(classes)):
         if classes[x] == mark[0]:
-            result[x] == 1
+            result[x] = 1.0
     return torch.tensor(result, dtype=torch.long)
 
 def prepare_input(index, data, context_size):
@@ -65,7 +66,10 @@ mode = "train"
 data_paths = os.listdir(data_dir)
 data_paths = [data_dir + "/" + x for x in data_paths]
 
-classes = [',', '.', '?', '!', '-', ':', '...']
+data_no_punc_paths = os.listdir(data_nopunc_dir)
+data_no_punc_paths = [data_nopunc_dir + "/" + x for x in data_no_punc_paths]
+
+classes = [',', '.', '?', '!', '-', ':', '...', '']
 nlp = spacy.load("pl_core_news_sm")
 context_size = 5
 
@@ -93,8 +97,8 @@ if mode == "train":
     for epoch in range(epochs):
         for path in tqdm.tqdm(data_paths):
             with open(path, mode="r", encoding="utf-8") as file:
-                list = file.readlines()[:-1]
-                for i in range(0, len(list) - context_size - 1):
+                list = file.readlines()
+                for i in range(0, 10):
                     model.zero_grad()
                     x = list[i: i + context_size]
                     x = [line2word(y) for y in x]
@@ -109,7 +113,7 @@ if mode == "train":
                     x = x.to(device)
                     output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
                     output = output.squeeze(1)
-                    loss = loss_function(output, compute_class_vector(mark, classes).to(device))
+                    loss = loss_function(output, compute_class_vector([mark], classes).to(device))
                     loss.backward()
                     optimizer.step()
                     hidden_state = hidden_state.detach()
@@ -120,43 +124,47 @@ if mode == "train":
                 model.state_dict(),
                 os.path.join("./", f"{output_prefix}-{epoch}.pt"),
             )
+        with open("hidden_state.pickle", "wb") as hs:
+            pickle.dump(hidden_state, hs)
+        with open("cell_state.pickle", "wb") as cs:
+            pickle.dump(cell_state, cs)
 
 elif mode == "evaluate":
     correct = 0
     incorrect = 0
     threshold = 0.3
-    for pathA, pathB in zip(data_nopunc_dir, data_dir):
-        listA = []
-        listB = []
+    model.load_state_dict(torch.load("model-0.pt"))
+    model.eval()
+    for pathA, pathB in zip(data_no_punc_paths, data_paths):
         with open(pathA, mode="r", encoding='utf-8') as file:
-            listA = file.readlines()[:-1]
-        with open(pathB, mode="r", encoding='utf-8') as file:
-            listb = file.readlines()[:-1]
-        for i in range(0, len(listA) - context_size - 1):
-            model.zero_grad()
-            x = listA[i: i + context_size]
-            x = [line2word(y) for y in x]
-            x_1 = [line2word(listA[i + context_size + 1])]
+            with open(pathB, mode="r", encoding='utf-8') as file2:
+                listA = file.readlines()[:-1]
+                listB = file2.readlines()[:-1]
+                for i in range(0, len(listA) - context_size - 1):
+                    model.zero_grad()
+                    x = listA[i: i + context_size]
+                    x = [line2word(y) for y in x]
+                    x_1 = [line2word(listA[i + context_size + 1])]
 
-            x = x + x_1
-            x = words_to_vecs(x)
+                    x = x + x_1
+                    x = words_to_vecs(x)
 
-            y =  listB[i + context_size]
-            y = [line2word(x) for x in y]
-            mark_y = find_interpunction(y)
-            x = torch.tensor(x, dtype=torch.float)
+                    mark_y = find_interpunction(x[-1], classes)
+                    x = torch.tensor(x, dtype=torch.float).to(device)
 
-            output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
-            result_index = np.argmax(output)
-            if output[result_index] < threshold and len(mark_y) == 0:
-                correct += 1
-            if len(mark_y) > 0:
-                if classes[np.argmax(output)] == mark_y:
-                    correct += 1
-                else:
-                    incorrect += 1
-            else:
-                incorrect += 1
-            
-            accuracy = correct / (correct + incorrect)
-            print(accuracy)
+                    output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
+                    output = output.cpu()
+                    output = output.detach().numpy()
+                    result_index = np.argmax(output)
+                    if output[result_index] < threshold and len(mark_y) == 0:
+                        correct += 1
+                    if len(mark_y) > 0:
+                        if classes[np.argmax(output)] == mark_y:
+                            correct += 1
+                        else:
+                            incorrect += 1
+                    else:
+                        incorrect += 1
+                    
+                    accuracy = correct / (correct + incorrect)
+                    print(accuracy)

util.py

@@ -17,13 +17,14 @@ class Model(torch.nn.Module):
         2 num layers
         """
         self.lstm = torch.nn.LSTM(150, 300, 2)
-        self.dense2 = torch.nn.Linear(300, 7)
-        self.softmax = torch.nn.Softmax(dim=1)
+        self.dense2 = torch.nn.Linear(300, 8)
+        self.softmax = torch.nn.Softmax(dim=0)
 
     def forward(self, data, hidden_state, cell_state):
         data = self.dense1(data.T)
         data = self.tanh1(data)
         data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1), (hidden_state, cell_state))
+        # data, (hidden_state, cell_state) = self.lstm(data.unsqueeze(1))
         data = self.dense2(data)
         data = self.softmax(data)
         return data, (hidden_state, cell_state)