train.py

import numpy as np
import random
import json

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

from nltk_utils import bag_of_words, tokenize, stem
from model import NeuralNet

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

words = []
tags = []
xy = []

# Process the intents data
for intent in intents['intents']:
    tag = intent['tag']
    tags.append(tag)
    for pattern in intent['patterns']:
        w = tokenize(pattern)
        words.extend(w)
        xy.append((w, tag))

# Stem and lower each word and remove duplicates
ignore_words = ['?', '!', '.', ',']
words = [stem(w) for w in words if w not in ignore_words]
words = sorted(set(words))
tags = sorted(set(tags))

# Create training data
X_train = []
y_train = []

for (pattern_sentence, tag) in xy:
    bag = bag_of_words(pattern_sentence, words)
    X_train.append(bag)
    label = tags.index(tag)
    y_train.append(label)

X_train = np.array(X_train, dtype=np.float32)
y_train = np.array(y_train, dtype=np.int64)

class ChatDataset(Dataset):
    def __init__(self, X_train, y_train):
        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

# Custom collate function
def custom_collate_fn(batch):
    words = torch.tensor([item[0] for item in batch], dtype=torch.float32)
    labels = torch.tensor([item[1] for item in batch], dtype=torch.long)
    return words, labels

# Hyperparameters
batch_size = 8
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
learning_rate = 0.001
num_epochs = 1000

dataset = ChatDataset(X_train, y_train)
train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, collate_fn=custom_collate_fn)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
if __name__ == '__main__':
    for epoch in range(num_epochs):
        for (words, labels) in train_loader:
            words = words.to(device)
            labels = labels.to(device)
            
            # Forward pass
            outputs = model(words)
            loss = criterion(outputs, labels)
            
            # Backward and optimize
            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": words,
"tags": tags
}

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

print(f'training complete. file saved to {FILE}')