train.py

from pickletools import optimize
from random import shuffle
import numpy as np
import json
import torch
import torch.nn as nn


from torch.utils.data import Dataset,DataLoader
from NeuralNetwork import bag_of_words,tokenize,stem
from Brain import NeuralNet

with open('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']:
        #print(pattern)
        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)



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

num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(x_train[0])
hidden_size = 65
output_size = len(tags)

print("trainning the model...")


class ChatDatasets(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


dataset = ChatDatasets()
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=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(dtype=torch.long).to(device)
        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.item():.4f}')

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

FILE = "trainData.pth"
torch.save(data,FILE)

print(f"Trainning complete,Filed saved to {FILE}")