{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32dc1769",
   "metadata": {},
   "source": [
    "# A9 Report\n",
    "\n",
    "Predict 13 Z-coordinates from 26 input features (X, Y for each joint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecdba5a",
   "metadata": {},
   "source": [
    "## 1. Data\n",
    "\n",
    "| Column Range | Data |\n",
    "|--------------|------|\n",
    "| 0 | FrameNo |\n",
    "| 1-3 | head_x, head_y, head_z |\n",
    "| 4-6 | left_shoulder_x/y/z |\n",
    "| 7-9 | left_elbow_x/y/z |\n",
    "| 10-12 | right_shoulder_x/y/z |\n",
    "| 13-15 | right_elbow_x/y/z |\n",
    "| 16-18 | left_hand_x/y/z |\n",
    "| 19-21 | right_hand_x/y/z |\n",
    "| 22-24 | left_hip_x/y/z |\n",
    "| 25-27 | right_hip_x/y/z |\n",
    "| 28-30 | left_knee_x/y/z |\n",
    "| 31-33 | right_knee_x/y/z |\n",
    "| 34-36 | left_foot_x/y/z |\n",
    "| 37-39 | right_foot_x/y/z |\n",
    "\n",
    "**Input**: 13 joints × 2 dimensions (x, y) = **26 features**\n",
    "\n",
    "**Output**: 13 joints × 1 dimension (z) = **13 targets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1ad3f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 14 experiment results\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "# Load aggregated results\n",
    "RESULTS_CSV = Path('results_summary.csv')\n",
    "if RESULTS_CSV.exists():\n",
    "    df = pd.read_csv(RESULTS_CSV)\n",
    "    print(f\"Loaded {len(df)} experiment results\")\n",
    "else:\n",
    "    print(\"Run aggregate_results.py first to generate results_summary.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da8874c",
   "metadata": {},
   "source": [
    "## 2. Deep Learning Steps Overview\n",
    "\n",
    "| Step | Description | Implementation |\n",
    "|------|-------------|----------------|\n",
    "| 1 | Load Data | `load_all_sequences()` in models.py |\n",
    "| 2 | Define Network | Dense, Conv1D, LSTM, GRU architectures |\n",
    "| 3 | Compile Model | Optimizer (SGD/Adam/RMSprop), Loss (MSE/MAE) |\n",
    "| 4 | Split Data | 5-fold cross-validation, 90/10 train/test |\n",
    "| 5 | Train Model | Early stopping, 100 epochs max |\n",
    "| 6 | Evaluate | MSE, MAE, R² on test set |\n",
    "| 7 | Use Model | Load saved .h5 weights for inference |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f90334",
   "metadata": {},
   "source": [
    "## 3. Model Architectures\n",
    "\n",
    "### Dense\n",
    "- Fully-connected layers\n",
    "- Treats each frame independently\n",
    "- Configuration: (256, 128, 64) hidden units, ReLU, dropout 0.3\n",
    "\n",
    "### Conv1D\n",
    "- 1D convolutional layers for temporal patterns\n",
    "- Window size: 30 frames\n",
    "- Best variant (v3): filters=(128, 256), kernel=3, pool=3, dense=(256, 128, 64)\n",
    "\n",
    "### LSTM\n",
    "- Recurrent network for sequences\n",
    "- Units: (64, 32), tanh activation\n",
    "\n",
    "### GRU\n",
    "- Gated Recurrent Unit variant\n",
    "- Units: (64, 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04dfdfd",
   "metadata": {},
   "source": [
    "## 4. Training & Hyperparameter Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a49b682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARCHITECTURE COMPARISON\n",
      "==================================================\n",
      " model  val_r2_mean  val_rmse_mean  val_mae_mean\n",
      "conv1d     0.932681       0.037704      0.027598\n",
      " dense     0.820046       0.058055      0.042731\n",
      "  lstm     0.768530       0.069834      0.052217\n"
     ]
    }
   ],
   "source": [
    "# Architecture comparison\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    arch_df = df[df['experiment'] == 'different_models'][['model', 'val_r2_mean', 'val_rmse_mean', 'val_mae_mean']]\n",
    "    arch_df = arch_df.sort_values('val_r2_mean', ascending=False)\n",
    "    print(\"ARCHITECTURE COMPARISON\")\n",
    "    print(\"=\"*50)\n",
    "    print(arch_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b5578e0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONV1D VARIANTS\n",
      "==================================================\n",
      "    model  val_r2_mean  val_rmse_mean  test_r2  test_rmse\n",
      "   conv1d     0.922923       0.040316 0.928322   0.039097\n",
      "conv1d_v2     0.799991       0.064831 0.826827   0.060770\n"
     ]
    }
   ],
   "source": [
    "# Conv1D variants comparison\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    conv_df = df[df['experiment'] == 'conv1d_variants'][['model', 'val_r2_mean', 'val_rmse_mean', 'test_r2', 'test_rmse']]\n",
    "    conv_df = conv_df.sort_values('test_r2', ascending=False)\n",
    "    print(\"CONV1D VARIANTS\")\n",
    "    print(\"=\"*50)\n",
    "    print(conv_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7b21485a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIMIZER COMPARISON\n",
      "==================================================\n",
      "                model  val_r2_mean  val_rmse_mean  test_r2  test_rmse\n",
      "conv1d_v3_rmsprop_mse     0.946803       0.033498 0.954934   0.031001\n",
      "   conv1d_v3_adam_mse     0.949678       0.032558 0.953484   0.031496\n",
      "    conv1d_v3_sgd_mse     0.950863       0.032199 0.952354   0.031876\n"
     ]
    }
   ],
   "source": [
    "# Optimizer comparison\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    opt_df = df[df['experiment'] == 'optimizer'][['model', 'val_r2_mean', 'val_rmse_mean', 'test_r2', 'test_rmse']]\n",
    "    opt_df = opt_df.sort_values('test_r2', ascending=False)\n",
    "    print(\"OPTIMIZER COMPARISON\")\n",
    "    print(\"=\"*50)\n",
    "    print(opt_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b55c089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOSS FUNCTION COMPARISON\n",
      "==================================================\n",
      "                model  val_r2_mean  val_rmse_mean  test_r2  test_rmse\n",
      "conv1d_v3_rmsprop_mse     0.950038       0.032467 0.954592   0.031119\n",
      "   conv1d_v3_adam_mse     0.948571       0.032956 0.952860   0.031707\n",
      "    conv1d_v3_sgd_mse     0.950748       0.032241 0.952171   0.031937\n",
      "conv1d_v3_rmsprop_mae     0.943196       0.034623 0.944302   0.034465\n",
      "   conv1d_v3_adam_mae     0.939982       0.035601 0.934766   0.037298\n",
      "    conv1d_v3_sgd_mae     0.939909       0.035625 0.934060   0.037500\n"
     ]
    }
   ],
   "source": [
    "# Loss function comparison\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    loss_df = df[df['experiment'] == 'loss_optimizer'][['model', 'val_r2_mean', 'val_rmse_mean', 'test_r2', 'test_rmse']]\n",
    "    loss_df = loss_df.sort_values('test_r2', ascending=False)\n",
    "    print(\"LOSS FUNCTION COMPARISON\")\n",
    "    print(\"=\"*50)\n",
    "    print(loss_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e29b86d1",
   "metadata": {},
   "source": [
    "## 5. Evaluation & Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5128b8be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALL EXPERIMENTS RANKED BY TEST R²\n",
      "======================================================================\n",
      "      experiment                 model  val_r2_mean  test_r2  test_rmse\n",
      "       optimizer conv1d_v3_rmsprop_mse     0.946803 0.954934   0.031001\n",
      "  loss_optimizer conv1d_v3_rmsprop_mse     0.950038 0.954592   0.031119\n",
      "       optimizer    conv1d_v3_adam_mse     0.949678 0.953484   0.031496\n",
      "  loss_optimizer    conv1d_v3_adam_mse     0.948571 0.952860   0.031707\n",
      "       optimizer     conv1d_v3_sgd_mse     0.950863 0.952354   0.031876\n",
      "  loss_optimizer     conv1d_v3_sgd_mse     0.950748 0.952171   0.031937\n",
      "  loss_optimizer conv1d_v3_rmsprop_mae     0.943196 0.944302   0.034465\n",
      "  loss_optimizer    conv1d_v3_adam_mae     0.939982 0.934766   0.037298\n",
      "  loss_optimizer     conv1d_v3_sgd_mae     0.939909 0.934060   0.037500\n",
      " conv1d_variants                conv1d     0.922923 0.928322   0.039097\n",
      " conv1d_variants             conv1d_v2     0.799991 0.826827   0.060770\n",
      "different_models                conv1d     0.932681      NaN        NaN\n",
      "different_models                 dense     0.820046      NaN        NaN\n",
      "different_models                  lstm     0.768530      NaN        NaN\n"
     ]
    }
   ],
   "source": [
    "# All results ranked by test R²\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    print(\"ALL EXPERIMENTS RANKED BY TEST R²\")\n",
    "    print(\"=\"*70)\n",
    "    ranked = df.sort_values('test_r2', ascending=False, na_position='last')\n",
    "    print(ranked[['experiment', 'model', 'val_r2_mean', 'test_r2', 'test_rmse']].to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "38efd1c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "if 'df' in dir() and len(df) > 0:\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "    \n",
    "    # Plot 1: R² comparison\n",
    "    plot_df = df[df['test_r2'].notna()].sort_values('test_r2', ascending=True)\n",
    "    colors = ['green' if r > 0.95 else 'orange' if r > 0.90 else 'red' for r in plot_df['test_r2']]\n",
    "    axes[0].barh(plot_df['model'], plot_df['test_r2'], color=colors)\n",
    "    axes[0].set_xlabel('Test R²')\n",
    "    axes[0].set_title('Model Performance (Test R²)')\n",
    "    axes[0].axvline(0.95, color='green', linestyle='--', alpha=0.5, label='95%')\n",
    "    axes[0].axvline(0.90, color='orange', linestyle='--', alpha=0.5, label='90%')\n",
    "    axes[0].legend()\n",
    "    \n",
    "    # Plot 2: RMSE comparison\n",
    "    axes[1].barh(plot_df['model'], plot_df['test_rmse'], color='steelblue')\n",
    "    axes[1].set_xlabel('Test RMSE')\n",
    "    axes[1].set_title('Model Error (Test RMSE)')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be01d215",
   "metadata": {},
   "source": [
    "## 6. Champion Model\n",
    "\n",
    "### Conv1D_v3 + RMSprop + MSE\n",
    "\n",
    "| Parameter | Value |\n",
    "|-----------|-------|\n",
    "| Architecture | Conv1D |\n",
    "| Filters | (128, 256) |\n",
    "| Kernel size | 3 |\n",
    "| Pool size | 3 |\n",
    "| Dense layers | (256, 128, 64) |\n",
    "| Activation | ReLU |\n",
    "| Dropout | 0.2 |\n",
    "| Optimizer | RMSprop (lr=0.001) |\n",
    "| Loss | MSE |\n",
    "| Window size | 30 frames |\n",
    "| Batch size | 32 |\n",
    "| Epochs | 100 (early stopping) |\n",
    "\n",
    "### Performance\n",
    "\n",
    "| Metric | Validation | Test |\n",
    "|--------|------------|------|\n",
    "| R² | 0.9468 ± 0.0049 | **0.9549** |\n",
    "| RMSE | 0.0335 ± 0.0018 | **0.0310** |\n",
    "| MAE | 0.0241 ± 0.0013 | 0.0229 |\n",
    "\n",
    "1. **Conv1D captures temporal patterns** - The 30-frame windows allow the model to learn motion dynamics\n",
    "2. **Deeper architecture (v3)** - More filters and dense layers provide sufficient capacity\n",
    "3. **RMSprop optimizer** - Adaptive learning rate works well with this data distribution\n",
    "4. **MSE loss** - Better than MAE for regression with continuous targets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf697d8",
   "metadata": {},
   "source": [
    "## 7. Dead Ends\n",
    "\n",
    "### What Did NOT Work\n",
    "\n",
    "| Configuration | R² | Issue |\n",
    "|---------------|------|-------|\n",
    "| LSTM | 0.77 | Overfitting, slow training |\n",
    "| GRU | 0.74 | Similar to LSTM, underperformed |\n",
    "| Dense MLP | 0.82 | Cannot capture temporal patterns |\n",
    "| Conv1D_v2 (high dropout) | 0.83 | Too much regularization |\n",
    "| MAE loss | ~0.93-0.94 | Lower R² than MSE |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68fd0e3",
   "metadata": {},
   "source": [
    "## 8. Usage Guide\n",
    "\n",
    "### Loading the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "82cdb3ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from cv_results_loss_optimizer/conv1d_v3_rmsprop_mse_fold4_best.h5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"Conv1DModel\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Conv1DModel\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ xy_seq_input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,112</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ pool_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_conv_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv1D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">98,560</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ pool_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling1D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_conv_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gap (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ z_output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">845</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ xy_seq_input (\u001b[38;5;33mInputLayer\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m26\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_1 (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │        \u001b[38;5;34m10,112\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ pool_1 (\u001b[38;5;33mMaxPooling1D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_conv_1 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_2 (\u001b[38;5;33mConv1D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m256\u001b[0m)        │        \u001b[38;5;34m98,560\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ pool_2 (\u001b[38;5;33mMaxPooling1D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_conv_2 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gap (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_1 (\u001b[38;5;33mDense\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m65,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_2 (\u001b[38;5;33mDense\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ fc_3 (\u001b[38;5;33mDense\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ drop_fc_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ z_output (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m)             │           \u001b[38;5;34m845\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">216,461</span> (845.55 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m216,461\u001b[0m (845.55 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">216,461</span> (845.55 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m216,461\u001b[0m (845.55 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example: Load and use the best model\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Best model path\n",
    "BEST_MODEL_PATH = 'cv_results_loss_optimizer/conv1d_v3_rmsprop_mse_fold4_best.h5'\n",
    "\n",
    "if os.path.exists(BEST_MODEL_PATH):\n",
    "    # Rebuild the model architecture and load weights (avoids Keras version mismatch)\n",
    "    from models import build_conv1d_model\n",
    "    model = build_conv1d_model(\n",
    "        filters=(128, 256),\n",
    "        kernel_size=3,\n",
    "        pool_size=3,\n",
    "        dense_units=(256, 128, 64),\n",
    "        dropout_rate=0.2\n",
    "    )\n",
    "    model.load_weights(BEST_MODEL_PATH)\n",
    "    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "    \n",
    "    print(f\"Model loaded from {BEST_MODEL_PATH}\")\n",
    "    model.summary()\n",
    "else:\n",
    "    print(f\"Model not found at {BEST_MODEL_PATH}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "15765a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (1, 30, 26)\n",
      "Output shape: (1, 13)\n",
      "Predicted Z values: [ 0.05649754  0.03968733 -0.03916807  0.05462698  0.00482728]...\n"
     ]
    }
   ],
   "source": [
    "# Example inference\n",
    "def predict_z_coordinates(model, xy_sequence):\n",
    "    \"\"\"\n",
    "    Predict Z coordinates from X/Y sequence.\n",
    "    \n",
    "    Args:\n",
    "        model: Loaded Keras model\n",
    "        xy_sequence: numpy array of shape (batch, 30, 26)\n",
    "                     30 = window size, 26 = 13 joints × 2 (x, y)\n",
    "    \n",
    "    Returns:\n",
    "        z_predictions: numpy array of shape (batch, 13)\n",
    "                       13 z-coordinates for each joint\n",
    "    \"\"\"\n",
    "    return model.predict(xy_sequence, verbose=0)\n",
    "\n",
    "# Example with dummy data\n",
    "if 'model' in dir():\n",
    "    dummy_input = np.random.randn(1, 30, 26).astype(np.float32)\n",
    "    z_pred = predict_z_coordinates(model, dummy_input)\n",
    "    print(f\"Input shape: {dummy_input.shape}\")\n",
    "    print(f\"Output shape: {z_pred.shape}\")\n",
    "    print(f\"Predicted Z values: {z_pred[0][:5]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb36696",
   "metadata": {},
   "source": [
    "### Retraining the Model\n",
    "\n",
    "```bash\n",
    "# Run the cross-validation training script\n",
    "cd A9\n",
    "python cv_training.py\n",
    "```\n",
    "\n",
    "Or modify `all_models_config.py` to change architecture parameters."
   ]
  }
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