train.py

print("Loading packages")
import json
from utils.tokenization import tokenize
from utils.preprocessing import stem, bag_of_words
import numpy as np

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

from train.model import NeuralNet

TRAINING_DATA_PATH = 'data/intents.json'
SAVE_MODEL_PATH =  'models/data.pth'

print("Reading training data")
with open(TRAINING_DATA_PATH, '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) # CrossEntropyLoss

x_train = np.array(x_train)
y_train = np.array(y_train)

class ChatDataset(Dataset):
    def __init__(self) -> None:
        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

print("Generating model")  
# Hyperparameters
batch_size = 8
input_size = len(x_train[0])
hidden_size = 8
output_size = len(tags)
learning_rate = 0.001
epochs = 1000

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)


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

for epoch in range(epochs):
    for (words, labels) in train_loader:
        words = words.to(device)
        labels = labels.to(dtype=torch.long).to(device)

        # forward
        outputs = model(words)
        loss = criterion(outputs, labels)

        # backward and optimizer step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    if (epoch+1) % 100 == 0:
        print(f'Epoch {epoch+1}/{epochs}, loss = {loss.item():.4f}')

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



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

torch.save(data, SAVE_MODEL_PATH)

print(f'Training completed. File saved to {SAVE_MODEL_PATH}')