train.py

import json
from nltk_utils import tokenize,stem,bag_of_words
import numpy as np

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

from model import NeuralNet



with open('intents.json','r',encoding="utf-8") 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_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  

#HyperParameters
batch_size = 8
hidden_size = 8
output_size = len(tags)
input_size = len(X_train[0])
learning_rate = 0.001
num_epochs = 300



dataset = ChatDataset()
train_loader = DataLoader(dataset= dataset,batch_size=batch_size,shuffle=True,num_workers = 2)  


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

        #backwards
        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'finalloss = {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} ')