train.py

import json
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

from nltk_utils import tokenizer, stem, bag_of_words

from model import NeuralNet

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

tags = []
all_words = []
xy = []
x_train = []
y_train = []
ignore_words = ['?', '!', '.', ',']

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

for (tokenized_sentence, tag) in xy:
    bag = bag_of_words(tokenized_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)


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

# Hyper parameters
batch_size = 8
hidden_size = 8
output_size = len(tags)
input_size = len(all_words)
learning_rate = 0.001
num_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(num_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() # empty the gradient
        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={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
}

FILE = "trained_model_data.pth"

torch.save(data, FILE)

print(f"training complete. Data saved to {FILE}")