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import torch
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import torch.nn as nn
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import torch.optim as optim
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from SimpleRNN import SimpleRNN
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import os
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import json
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from tqdm import tqdm, trange
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import time
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training_text = open("train_data.txt", encoding="utf-8").read()
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chars = sorted(list(set(training_text)))
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char_to_idx = {ch: i for i, ch in enumerate(chars)}
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idx_to_char = {i: ch for i, ch in enumerate(chars)}
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parameters = json.loads(open("parameter.json").read())
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input_size = len(chars)
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hidden_size = parameters["hidden_size"]
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output_size = len(chars)
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sequence_length = parameters["sequence_length"]
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epochs = 1000
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learning_rate = parameters["learning_rate"]
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model_path = parameters["model_path"]
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train_data = []
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for i in range(len(training_text) - sequence_length):
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input_seq = training_text[i : i + sequence_length]
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target_char = training_text[i + sequence_length]
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train_data.append((torch.tensor([char_to_idx[ch] for ch in input_seq]), char_to_idx[target_char]))
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if os.path.exists(model_path):
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model = torch.load(model_path, weights_only=False)
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print("Loaded pre-trained model. Continue training...")
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else:
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print("Training new model...")
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model = SimpleRNN(input_size, hidden_size, output_size)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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for epoch in range(epochs):
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try:
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total_loss = 0
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hidden = torch.zeros(1, 1, hidden_size)
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pbar = tqdm(train_data, desc=f"Epoch={epoch}, Loss=N/A")
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count = 0
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for input_seq, target in pbar:
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count += 1
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optimizer.zero_grad()
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output, hidden = model(input_seq, hidden.detach())
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loss = criterion(output, torch.tensor([target]))
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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pbar.desc = f"Epoch={epoch}, Loss={total_loss / count:.12f}"
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pbar.close()
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time.sleep(1)
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except KeyboardInterrupt:
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break
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hidden = torch.zeros(1, 1, hidden_size)
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output, hidden = model(input_seq, hidden.detach())
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torch.save(model, model_path)
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with open("vocab.json", "w") as f:
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f.write(json.dumps(chars))
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print("Model saved.")
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