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 tokenize,stem,bag_of_words
from model import NeuralNet
with open('intents.json','r',encoding='utf-8') as f:
    intents=json.load(f)
    # print(intents)

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))
# print(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) #cross entropy loss(no need of hot encoding)
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

    #dataset[idx]
    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=1000

print(input_size,len(all_words))
print(output_size,tags)

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)

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

#training loop
for epoch in range(num_epochs):
    for (words,labels) in train_loader:
        words=words.to(device)
        labels= labels.to(device).long() # Convert labels to torch.LongTensor

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

        #backward and optimizer
        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}')