Train.py

from dataclasses import dataclass
from pickletools import optimize
from random import shuffle
import re
from tkinter import Y
from black import out
import numpy as np
import json
import torch
from NeauralNetwork import bag_of_words, tokenize, stem
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
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"]:
        w = tokenize(pattern)
        all_words.extend(w)
        xy.append((w, tag))  # must have to send set

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))

# create training data
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
    # X: bag of words for each pattern_sentence
    bag = bag_of_words(pattern_sentence, all_words)
    X_train.append(bag)
    # y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
    label = tags.index(tag)
    y_train.append(label)

X_train = np.array(X_train)
y_train = np.array(y_train)

# Hyper-parameters
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
print(input_size, output_size)


class ChatDataset(Dataset):
    def __init__(self):
        self.n_samples = len(X_train)
        self.x_data = X_train
        self.y_data = y_train

    # support indexing such that dataset[i] can be used to get i-th sample
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    # we can call len(dataset) to return the size
    def __len__(self):
        return self.n_samples


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)

# Train the model
for epoch in range(num_epochs):
    for (words, labels) in train_loader:
        words = words.to(device)
        labels = labels.to(dtype=torch.long).to(device)

        # Forward pass
        outputs = model(words)
        # if y would be one-hot, we must apply
        # labels = torch.max(labels, 1)[1]
        loss = criterion(outputs, labels)

        # Backward and optimize
        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 = "data.pth"
torch.save(data, FILE)

print(f"training complete. file saved to {FILE}")