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', 'rb') as f:
    intents = json.load(f);

# Tokenize
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))

# Lowercase + Stemming + Ignore Punctuations:
ignore_words = ['?', '!', '.',',']
# print(all_words)
all_words = [stem(w) for w in all_words if w not in ignore_words]
# print(all_words)
# sorting and removing duplicates :  
all_words = sorted(set(all_words))
tags = sorted(set(tags))
# print(all_words)
# print(tags)

# Creating Bag of words and Training Data
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)
    # Cross Entropy Loss
    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

    # 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).to(device)

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

for epoch in range(num_epochs):
    for (words, labels) in train_loader:
        words = words.to(device)
        labels = labels.to(device = device, dtype = torch.int64)

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

        # backward and optimizer step
        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}')

# Saving Data from the model
data = {
    "model_state" : model.state_dict(),
    "input_size" : input_size,
    "output_size": output_size,
    "hidden_size": hidden_size,
    "all_words": all_words,
    "tags": tags
}

# for pytorch
FILE = "data.pth"
torch.save(data, FILE)

print(f'Training Complete, file saved to {FILE}')