training.py

import random, json,pickle, numpy as np, nltk, torch, torch.nn as nn
from numpy.core.arrayprint import _leading_trailing
from torch.utils.data import Dataset, DataLoader, dataset
from nltk.stem import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
from libraries.nltk_utilities import tokenize,stem,bag_of_words
from libraries.model import NeuralNet


with open('static/jsons/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) #CrossEntropyLoss

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

class ChatDataset(Dataset):
    def __init__(self) :
        self.n_sample = 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_sample

batch_size = 8
hidden_size = 8
output_size = len(tags)
input_size = len(x_train[0])
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)
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(device)
        labels = labels.to(dtype=torch.long)
        
        outputs = model(words)
        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 = {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 = 'data.pth'
torch.save(data, FILE)

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