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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"for warn in [UserWarning, FutureWarning]: warnings.filterwarnings(\"ignore\", category = warn)\n",
"\n",
"import os\n",
"import time\n",
"import json\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"\n",
"# Импортируем классы моделей из нашего файла\n",
"from src.models.models import TransformerClassifier, MambaClassifier, LSTMClassifier\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"MODEL_TO_TRAIN = 'Transformer' \n",
"\n",
"# Гиперпараметры данных и модели\n",
"MAX_SEQ_LEN = 300\n",
"EMBEDDING_DIM = 128\n",
"BATCH_SIZE = 32\n",
"LEARNING_RATE = 1e-4\n",
"NUM_EPOCHS = 5 # Увеличим для лучшего результата\n",
"\n",
"# Пути для сохранения артефактов\n",
"SAVE_DIR = \"../pretrained\"\n",
"os.makedirs(SAVE_DIR, exist_ok=True)\n",
"MODEL_SAVE_PATH = os.path.join(SAVE_DIR, \"best_model.pth\")\n",
"VOCAB_SAVE_PATH = os.path.join(SAVE_DIR, \"vocab.json\")\n",
"CONFIG_SAVE_PATH = os.path.join(SAVE_DIR, \"config.json\")\n",
"DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from src.data_utils.dataset_generator import DatasetGenerator\n",
"from src.data_utils.dataset_params import DatasetName\n",
"\n",
"generator = DatasetGenerator(DatasetName.IMDB)\n",
"(X_train, y_train), (X_val, y_val), (X_test, y_test) = generator.generate_dataset()\n",
"X_train, y_train, X_val, y_val, X_test, y_test = X_train[:1000], y_train[:1000], X_val[:100], y_val[:100], X_test[:100], y_test[:100]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def create_dataloader(X, y, batch_size):\n",
" dataset = TensorDataset(torch.tensor(X, dtype=torch.long), torch.tensor(y, dtype=torch.long))\n",
" return DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
"train_loader = create_dataloader(X_train, y_train, BATCH_SIZE)\n",
"val_loader = create_dataloader(X_val, y_val, BATCH_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"model_params = {}\n",
"if MODEL_TO_TRAIN == 'Transformer':\n",
" model_params = {'vocab_size': len(generator.vocab), 'embed_dim': EMBEDDING_DIM, 'num_heads': 4, 'num_layers': 2, 'num_classes': 2, 'max_seq_len': MAX_SEQ_LEN}\n",
" model = TransformerClassifier(**model_params)\n",
"elif MODEL_TO_TRAIN == 'Mamba':\n",
" model_params = {'vocab_size': len(generator.vocab), 'embed_dim': EMBEDDING_DIM, 'mamba_d_state': 16, 'mamba_d_conv': 4, 'mamba_expand': 2, 'num_classes': 2}\n",
" model = MambaClassifier(**model_params)\n",
"elif MODEL_TO_TRAIN == 'LSTM':\n",
" model_params = {'vocab_size': len(generator.vocab), 'embed_dim': EMBEDDING_DIM, 'hidden_dim': 256, 'num_layers': 2, 'num_classes': 2, 'dropout': 0.5}\n",
" model = LSTMClassifier(**model_params)\n",
"else:\n",
" raise ValueError(\"Неизвестный тип модели. Выберите 'Transformer', 'Mamba' или 'LSTM'\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Начало обучения модели: Transformer ---\n",
"Эпоха 1/5 | Время: 17.06с | Train Loss: 0.7023 | Val Loss: 0.7095 | Val Acc: 0.4000\n",
" -> Модель сохранена, новая лучшая Val Loss: 0.7095\n",
"Эпоха 2/5 | Время: 16.40с | Train Loss: 0.6682 | Val Loss: 0.6937 | Val Acc: 0.4800\n",
" -> Модель сохранена, новая лучшая Val Loss: 0.6937\n",
"Эпоха 3/5 | Время: 16.13с | Train Loss: 0.6471 | Val Loss: 0.7075 | Val Acc: 0.4100\n",
"Эпоха 4/5 | Время: 16.36с | Train Loss: 0.6283 | Val Loss: 0.6917 | Val Acc: 0.5300\n",
" -> Модель сохранена, новая лучшая Val Loss: 0.6917\n",
"Эпоха 5/5 | Время: 16.39с | Train Loss: 0.6050 | Val Loss: 0.6871 | Val Acc: 0.5300\n",
" -> Модель сохранена, новая лучшая Val Loss: 0.6871\n"
]
}
],
"source": [
"model.to(DEVICE)\n",
"optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"best_val_loss = float('inf')\n",
"print(f\"--- Начало обучения модели: {MODEL_TO_TRAIN} ---\")\n",
"for epoch in range(NUM_EPOCHS):\n",
" model.train()\n",
" start_time = time.time()\n",
" total_train_loss = 0\n",
"\n",
" for batch_X, batch_y in train_loader:\n",
" batch_X, batch_y = batch_X.to(DEVICE), batch_y.to(DEVICE)\n",
" optimizer.zero_grad()\n",
" outputs = model(batch_X)\n",
" loss = criterion(outputs, batch_y)\n",
" loss.backward()\n",
" optimizer.step()\n",
" total_train_loss += loss.item()\n",
" avg_train_loss = total_train_loss / len(train_loader)\n",
" \n",
" model.eval()\n",
" total_val_loss, correct_val, total_val = 0, 0, 0\n",
" with torch.no_grad():\n",
" for batch_X, batch_y in val_loader:\n",
" batch_X, batch_y = batch_X.to(DEVICE), batch_y.to(DEVICE)\n",
" outputs = model(batch_X)\n",
" loss = criterion(outputs, batch_y)\n",
" total_val_loss += loss.item()\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total_val += batch_y.size(0)\n",
" correct_val += (predicted == batch_y).sum().item()\n",
" avg_val_loss = total_val_loss / len(val_loader)\n",
" val_accuracy = correct_val / total_val\n",
"\n",
" epoch_time = time.time() - start_time\n",
" print(f\"Эпоха {epoch+1}/{NUM_EPOCHS} | Время: {epoch_time:.2f}с | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Acc: {val_accuracy:.4f}\")\n",
" \n",
" if avg_val_loss < best_val_loss:\n",
" best_val_loss = avg_val_loss\n",
" torch.save(model.state_dict(), MODEL_SAVE_PATH)\n",
" print(f\" -> Модель сохранена, новая лучшая Val Loss: {best_val_loss:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Конфигурация модели сохранена в: ../pretrained/config.json\n"
]
}
],
"source": [
"with open(VOCAB_SAVE_PATH, 'w', encoding='utf-8') as f:\n",
" json.dump(generator.vocab, f, ensure_ascii=False, indent=4)\n",
"\n",
"config = {\n",
" \"model_type\": MODEL_TO_TRAIN,\n",
" \"max_seq_len\": MAX_SEQ_LEN,\n",
" \"model_params\": model_params,\n",
"}\n",
"with open(CONFIG_SAVE_PATH, 'w', encoding='utf-8') as f:\n",
" json.dump(config, f, ensure_ascii=False, indent=4)\n",
"print(f\"Конфигурация модели сохранена в: {CONFIG_SAVE_PATH}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "monkey-coding-dl-project-OWiM8ypK-py3.12",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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