train.py

import numpy as np 
import json 
import torch 
from nltk.stem.porter import PorterStemmer 
import torch.nn as nn 
from torch.utils.data import Dataset, DataLoader
import nltk
nltk.download('punkt')
from NeuralNetwork import bag_of_words, tokenize, stem 
from brain import NeuralNet

with open('intents.json', 'r') as f:
    intents = json.load(f) 

all_words = []    
tags = [] 
xy = [] 

for intent in intents['intents']:
    tag = intent['tag']
    tags.append(tag)
    
    for pattern in intent['patterns']:
        w = tokenize(pattern)
        all_words.extend(w)
        xy.append((w, tag))

ignore_words = [',', '?', '/', '.', '!'] 
all_words = [stem(w) for w in all_words if w not in ignore_words]     
all_words = sorted(set(all_words)) 
tags = sorted(set(tags)) 

x_train = [] 
y_train = [] 

for (pattern_sentence, tag) in xy:
    bag = bag_of_words(pattern_sentence, all_words)
    x_train.append(bag) 
    
    label = tags.index(tag)
    y_train.append(label) 
    
x_train = np.array(x_train)
y_train = np.array(y_train) 

num_epochs = 10000 
batch_size = 8 
learning_rate = 0.01 
input_size = len(x_train[0])
hidden_size = 18
output_size = len(tags)

class ChatDataset(Dataset):
    def __init__(self):
        self.n_samples = len(x_train) 
        self.x_data = x_train
        self.y_data = y_train 
         
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]  
    
    def __len__(self):
        return self.n_samples  
    
dataset = ChatDataset() 
train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0)
    
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device=device)
criterion = nn.CrossEntropyLoss() 
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
    
for epoch in range(num_epochs):
    for words, labels in train_loader:
        words = words.to(device)
        labels = labels.to(dtype=torch.long).to(device)
        
        # Filter out labels that are out of bounds
        valid_labels = labels < output_size
        words = words[valid_labels]
        labels = labels[valid_labels]
        
        if words.shape[0] == 0:
            continue
        
        outputs = model(words)
        
        # Filter out outputs and labels based on valid_labels mask
        outputs = outputs[valid_labels]
        labels = labels[valid_labels]
        
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    if (epoch + 1) % 100 == 0:
        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')

print(f'Final Loss: {loss.item():.4f}')

data={
"model_state":model.state_dict(),
"input_size":input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"tags":tags
    
}

FILE= "TrainData.pth"
torch.save(data,FILE)

print("training Complete,File saved to{FILE}")