{
"cells": [
{
"cell_type": "markdown",
"id": "0d8fea4f",
"metadata": {
"id": "0d8fea4f"
},
"source": [
"# **模型訓練（迴歸問題）**\n",
"此份程式碼會講解針對迴歸型任務在模型訓練上需要注意的細節。\n",
"\n",
"## 本章節內容大綱\n",
"* ### [創建資料集／載入資料集（Dataset Creating/ Loading）](#DatasetCreating/Loading)\n",
"* ### [資料前處理（Data Preprocessing）](#DataPreprocessing)\n",
"* ### [模型建置（Model Building）](#ModelBuilding)\n",
"* ### [模型訓練（Model Training）](#ModelTraining)\n",
"* ### [模型評估（Model Evaluation）](#ModelEvaluation)\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "bba873c7",
"metadata": {
"id": "bba873c7"
},
"source": [
"## 匯入套件"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "108ce7ba",
"metadata": {
"id": "108ce7ba"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm.auto import tqdm\n",
"\n",
"# PyTorch 相關套件\n",
"import torch\n",
"import torch.nn as nn"
]
},
{
"cell_type": "markdown",
"id": "c32bbd73",
"metadata": {
"id": "c32bbd73"
},
"source": [
"\n",
"## 創建資料集／載入資料集（Dataset Creating / Loading）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "rfM_DLvfFBMZ",
"metadata": {
"id": "rfM_DLvfFBMZ"
},
"outputs": [],
"source": [
"# 下載資料集\n",
"!wget -q https://github.com/TA-aiacademy/course_3.0/releases/download/DL/Data_part2.zip\n",
"!unzip -q Data_part2.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d408f0e",
"metadata": {
"id": "0d408f0e"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('./Data/FilmRating_train.csv')\n",
"test_df = pd.read_csv('./Data/FilmRating_test.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c6d4a50",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "4c6d4a50",
"outputId": "c91ee620-6812-441a-9a60-aa8de6247adc"
},
"outputs": [],
"source": [
"train_df.head()"
]
},
{
"cell_type": "markdown",
"id": "e7d33563",
"metadata": {
"id": "e7d33563"
},
"source": [
"* #### 電影評價資料集\n",
"資料集總共 2612 筆，\n",
"欄位包括預算 (budget)、電影類型 (genres)、關鍵字詞 (keywords)、知名度 (popularity)、製作公司 (production_companies)、國家 (production_countries)、收入 (revenue)、時長 (runtime)、卡司 (cast)、導演 (director)、距離發布時間 (n_days)、評分 (score)，多項欄位是以 leave-one-out encoding 方式轉換數值。\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8b89aa8",
"metadata": {
"id": "a8b89aa8"
},
"outputs": [],
"source": [
"X_df = train_df.iloc[:, :-1].values\n",
"y_df = train_df.score.values"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "916c3e24",
"metadata": {
"id": "916c3e24"
},
"outputs": [],
"source": [
"X_test = test_df.iloc[:, :-1].values\n",
"y_test = test_df.score.values"
]
},
{
"cell_type": "markdown",
"id": "1c94fd50",
"metadata": {
"id": "1c94fd50"
},
"source": [
"\n",
"## 資料前處理（Data Preprocessing）"
]
},
{
"cell_type": "markdown",
"id": "089f1a3a",
"metadata": {
"id": "089f1a3a"
},
"source": [
"* ### 資料正規化（Data Normalization）\n",
" - 減少過度關注的特徵（由特定數字範圍造成的影響）\n",
" - 避免更新方向偏離，較容易收斂"
]
},
{
"cell_type": "markdown",
"id": "6154aada",
"metadata": {
"id": "6154aada"
},
"source": [
"對於測試資料，需使用「訓練資料」的統計量去做轉換，避免改變兩組資料間的分布關係\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9d0e878",
"metadata": {
"id": "f9d0e878"
},
"outputs": [],
"source": [
"'''Normalize'''\n",
"X_scale = (X_df-X_df.min(axis=0)) / (X_df.max(axis=0)-X_df.min(axis=0))\n",
"X_test_scale = (X_test-X_df.min(axis=0)) / (X_df.max(axis=0)-X_df.min(axis=0))\n",
"\n",
"# 其他寫法\n",
"# from sklearn.preprocessing import MinMaxScaler\n",
"# sc = MinMaxScaler(feature_range=(0, 1))\n",
"# X_scale = sc.fit_transform(X_df)\n",
"# X_test_scale = sc.transform(X_test)\n",
"\n",
"# '''Standardize'''\n",
"# X_scale = (X_df-X_df.mean(axis=0)) / (X_df.std(axis=0))\n",
"# X_test_scale = (X_test-X_df.mean(axis=0)) / (X_df.std(axis=0))\n",
"\n",
"# 其他寫法\n",
"# from sklearn.preprocessing import StandardScaler\n",
"# sc = StandardScaler()\n",
"# X_scale = sc.fit_transform(X_df)\n",
"# X_test_scale = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"id": "5d972154",
"metadata": {
"id": "5d972154"
},
"source": [
"* ### 資料切分（Data Splitting）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a1dee07",
"metadata": {
"id": "4a1dee07"
},
"outputs": [],
"source": [
"# train, valid/test dataset split\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_scale, y_df,\n",
" test_size=0.2,\n",
" random_state=5566)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e514c344",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e514c344",
"outputId": "fd1c73e1-06e6-4047-a140-44e661d2f295"
},
"outputs": [],
"source": [
"print(f'X_train shape: {X_train.shape}')\n",
"print(f'X_valid shape: {X_valid.shape}')\n",
"print(f'y_train shape: {y_train.shape}')\n",
"print(f'y_valid shape: {y_valid.shape}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "noG5-JXJWTqz",
"metadata": {
"id": "noG5-JXJWTqz"
},
"outputs": [],
"source": [
"# Build torch dataset and dataloader\n",
"from torch.utils.data import TensorDataset, DataLoader\n",
"\n",
"BATCH_SIZE = 16\n",
"\n",
"train_dataset = TensorDataset(torch.from_numpy(X_train).float(),\n",
" torch.from_numpy(y_train).unsqueeze(1).float())\n",
"valid_dataset = TensorDataset(torch.from_numpy(X_valid).float(),\n",
" torch.from_numpy(y_valid).unsqueeze(1).float())\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
"valid_loader = DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0D3rcIn-XnIn",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0D3rcIn-XnIn",
"outputId": "dcfaf495-eb35-4df2-d3a4-290d62171df3"
},
"outputs": [],
"source": [
"# 檢查批次輸出\n",
"for x, y in train_loader:\n",
" print(x.shape, y.shape)\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "ad193ada",
"metadata": {
"id": "ad193ada"
},
"source": [
"\n",
"## 模型建置（Model Building）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39rR0ySkWkav",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "39rR0ySkWkav",
"outputId": "6ca8ccd6-19d1-4cd5-c7ac-cc01a1f6b22b"
},
"outputs": [],
"source": [
"torch.manual_seed(5566)\n",
"\n",
"model = nn.Sequential(\n",
" nn.Linear(X_train.shape[1], 64),\n",
" nn.Sigmoid(),\n",
" nn.Linear(64, 32),\n",
" nn.Sigmoid(),\n",
" nn.Linear(32, 1)\n",
")\n",
"\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"id": "2fca07aa",
"metadata": {
"id": "2fca07aa"
},
"source": [
"\n",
"## 模型訓練（Model Training）"
]
},
{
"cell_type": "markdown",
"id": "cb38c731",
"metadata": {
"id": "cb38c731"
},
"source": [
"* ### 優化器 (optimizer)、損失函數 (loss function)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "yfeJpOLFW4sd",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yfeJpOLFW4sd",
"outputId": "218d7916-6cd2-4d6d-cd34-8d43786d40ab"
},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f'device: {device}')\n",
"model = model.to(device)\n",
"\n",
"optimizer = torch.optim.RMSprop(model.parameters(), lr=0.001)\n",
"loss_fn = nn.MSELoss()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "NN3a6L49XLaW",
"metadata": {
"id": "NN3a6L49XLaW"
},
"outputs": [],
"source": [
"def train_epoch(model, optimizer, loss_fn, train_dataloader, val_dataloader):\n",
" # 訓練一輪\n",
" model.train()\n",
" total_train_loss = 0\n",
" for x, y in tqdm(train_dataloader, leave=False):\n",
" x, y = x.to(device), y.to(device) # 將資料移至GPU\n",
" y_pred = model(x) # 計算預測值\n",
" loss = loss_fn(y_pred, y) # 計算誤差\n",
" optimizer.zero_grad() # 梯度歸零\n",
" loss.backward() # 反向傳播計算梯度\n",
" optimizer.step() # 更新模型參數\n",
"\n",
" total_train_loss += loss.item()\n",
" # 驗證一輪\n",
" model.eval()\n",
" total_val_loss = 0\n",
" # 關閉梯度計算以加速\n",
" with torch.no_grad():\n",
" for x, y in val_dataloader:\n",
" x, y = x.to(device), y.to(device)\n",
" y_pred = model(x)\n",
" loss = loss_fn(y_pred, y)\n",
" total_val_loss += loss.item()\n",
"\n",
" return total_train_loss / len(train_dataloader), total_val_loss / len(val_dataloader)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "WzhE-fTzX_MY",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 414,
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},
"id": "WzhE-fTzX_MY",
"outputId": "52f2fd6d-8495-4cf8-f629-164410c078c0"
},
"outputs": [],
"source": [
"train_loss_log = []\n",
"val_loss_log = []\n",
"for epoch in tqdm(range(20)):\n",
" train_loss, val_loss = train_epoch(model, optimizer, loss_fn, train_loader, valid_loader)\n",
" train_loss_log.append(train_loss)\n",
" val_loss_log.append(val_loss)\n",
" print(f'Epoch: {epoch}, Train Loss: {train_loss:.3f}, Val Loss: {val_loss:.3f}')"
]
},
{
"cell_type": "markdown",
"id": "60eed35b",
"metadata": {
"id": "60eed35b"
},
"source": [
"\n",
"## 模型評估（Model Evaluation）"
]
},
{
"cell_type": "markdown",
"id": "bfd3fee3",
"metadata": {
"id": "bfd3fee3"
},
"source": [
"* ### 視覺化訓練過程的評估指標 （Visualization）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25cc2846",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "25cc2846",
"outputId": "dc0cf3d8-67c3-4e0f-aec6-7f24978bee65"
},
"outputs": [],
"source": [
"plt.figure(figsize=(15, 4))\n",
"plt.yscale('log')\n",
"plt.plot(range(len(train_loss_log)), train_loss_log, label='train_loss')\n",
"plt.plot(range(len(val_loss_log)), val_loss_log, label='valid_loss')\n",
"\n",
"plt.legend()\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "505ba915",
"metadata": {
"id": "505ba915"
},
"source": [
"* ### 模型預測（Model predictions）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "267aabab",
"metadata": {
"id": "267aabab"
},
"outputs": [],
"source": [
"# y_pred = model(X_valid)\n",
"# print(f'預測結果： {y_pred[:5, 0]}')\n",
"# print(f'目標值： {y_valid[:5]}')\n",
"\n",
"# predict all validation data\n",
"model.eval()\n",
"\n",
"y_pred = []\n",
"y_true = []\n",
"with torch.no_grad():\n",
" for x, y in valid_loader:\n",
" x, y = x.to(device), y.to(device)\n",
" y_pred.append(model(x).cpu())\n",
" y_true.append(y.cpu())\n",
"\n",
"y_pred = torch.cat(y_pred).numpy()\n",
"y_true = torch.cat(y_true).numpy()"
]
},
{
"cell_type": "markdown",
"id": "39dca3ca",
"metadata": {
"id": "39dca3ca"
},
"source": [
"* ### 視覺化結果"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abb92318",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 388
},
"id": "abb92318",
"outputId": "7e64d68b-8639-4399-f16e-a0c53a457a49"
},
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"plt.figure(figsize=(15, 4))\n",
"plt.plot(range(len(y_pred)), y_pred, label='prediction')\n",
"plt.plot(range(len(y_valid)), y_valid, label='groundtruth')\n",
"plt.plot(range(len(y_pred)), y_pred[:, 0]-y_valid, label='difference')\n",
"\n",
"plt.legend()\n",
"plt.xlabel('Samples')\n",
"plt.ylabel('Values')\n",
"plt.show()"
]
},
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"cell_type": "code",
"execution_count": null,
"id": "b3e3e6ad",
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"id": "b3e3e6ad"
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}
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