keypoint_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 各パス指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'model/keypoint_classifier/keypoint.csv'\n",
    "model_save_path = 'model/keypoint_classifier/keypoint_classifier.hdf5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分類数設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_CLASSES = 26"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習データ読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# モデル構築"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Input((21 * 2, )),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(20, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.4),\n",
    "    tf.keras.layers.Dense(10, activation='relu'),\n",
    "    tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dropout (Dropout)           (None, 42)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 20)                860       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                210       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 26)                286       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,356\n",
      "Trainable params: 1,356\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()  # tf.keras.utils.plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルチェックポイントのコールバック\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    model_save_path, verbose=1, save_weights_only=False)\n",
    "# 早期打ち切り用コールバック\n",
    "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルコンパイル\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# モデル訓練"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.3333 - accuracy: 0.0417 \n",
      "Epoch 1: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 3s 50ms/step - loss: 3.3200 - accuracy: 0.0407 - val_loss: 3.2649 - val_accuracy: 0.0366\n",
      "Epoch 2/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 3.2853 - accuracy: 0.0469\n",
      "Epoch 2: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 3.2704 - accuracy: 0.0479 - val_loss: 3.2375 - val_accuracy: 0.0528\n",
      "Epoch 3/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.2415 - accuracy: 0.0462\n",
      "Epoch 3: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 16ms/step - loss: 3.2378 - accuracy: 0.0493 - val_loss: 3.2199 - val_accuracy: 0.0935\n",
      "Epoch 4/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 3.2298 - accuracy: 0.0562\n",
      "Epoch 4: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 16ms/step - loss: 3.2264 - accuracy: 0.0506 - val_loss: 3.1997 - val_accuracy: 0.0949\n",
      "Epoch 5/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 3.2102 - accuracy: 0.0668\n",
      "Epoch 5: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 16ms/step - loss: 3.2041 - accuracy: 0.0660 - val_loss: 3.1803 - val_accuracy: 0.1111\n",
      "Epoch 6/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.1881 - accuracy: 0.0762\n",
      "Epoch 6: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 3.1847 - accuracy: 0.0709 - val_loss: 3.1619 - val_accuracy: 0.1260\n",
      "Epoch 7/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.1636 - accuracy: 0.0814\n",
      "Epoch 7: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 3.1602 - accuracy: 0.0840 - val_loss: 3.1387 - val_accuracy: 0.1314\n",
      "Epoch 8/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.1446 - accuracy: 0.0729\n",
      "Epoch 8: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 14ms/step - loss: 3.1438 - accuracy: 0.0773 - val_loss: 3.1087 - val_accuracy: 0.1314\n",
      "Epoch 9/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 3.1163 - accuracy: 0.0905\n",
      "Epoch 9: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 3.1069 - accuracy: 0.0931 - val_loss: 3.0742 - val_accuracy: 0.1233\n",
      "Epoch 10/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 3.0705 - accuracy: 0.1016\n",
      "Epoch 10: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 16ms/step - loss: 3.0706 - accuracy: 0.1017 - val_loss: 3.0304 - val_accuracy: 0.1328\n",
      "Epoch 11/1000\n",
      "15/18 [========================>.....] - ETA: 0s - loss: 3.0326 - accuracy: 0.0964\n",
      "Epoch 11: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 17ms/step - loss: 3.0284 - accuracy: 0.0967 - val_loss: 2.9779 - val_accuracy: 0.1287\n",
      "Epoch 12/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 3.0051 - accuracy: 0.1172\n",
      "Epoch 12: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 3.0024 - accuracy: 0.1152 - val_loss: 2.9247 - val_accuracy: 0.1396\n",
      "Epoch 13/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.9381 - accuracy: 0.1206\n",
      "Epoch 13: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.9380 - accuracy: 0.1207 - val_loss: 2.8751 - val_accuracy: 0.1558\n",
      "Epoch 14/1000\n",
      "18/18 [==============================] - ETA: 0s - loss: 2.8944 - accuracy: 0.1288\n",
      "Epoch 14: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.8944 - accuracy: 0.1288 - val_loss: 2.8269 - val_accuracy: 0.1585\n",
      "Epoch 15/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.8610 - accuracy: 0.1396\n",
      "Epoch 15: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.8660 - accuracy: 0.1374 - val_loss: 2.7773 - val_accuracy: 0.1721\n",
      "Epoch 16/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.8332 - accuracy: 0.1391\n",
      "Epoch 16: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 18ms/step - loss: 2.8259 - accuracy: 0.1347 - val_loss: 2.7375 - val_accuracy: 0.1870\n",
      "Epoch 17/1000\n",
      "18/18 [==============================] - ETA: 0s - loss: 2.7903 - accuracy: 0.1500\n",
      "Epoch 17: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 18ms/step - loss: 2.7903 - accuracy: 0.1500 - val_loss: 2.7037 - val_accuracy: 0.1856\n",
      "Epoch 18/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 2.7598 - accuracy: 0.1357\n",
      "Epoch 18: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 18ms/step - loss: 2.7415 - accuracy: 0.1423 - val_loss: 2.6632 - val_accuracy: 0.1856\n",
      "Epoch 19/1000\n",
      "15/18 [========================>.....] - ETA: 0s - loss: 2.7267 - accuracy: 0.1479\n",
      "Epoch 19: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.7246 - accuracy: 0.1491 - val_loss: 2.6318 - val_accuracy: 0.1938\n",
      "Epoch 20/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.7077 - accuracy: 0.1450\n",
      "Epoch 20: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 21ms/step - loss: 2.7066 - accuracy: 0.1464 - val_loss: 2.5981 - val_accuracy: 0.2033\n",
      "Epoch 21/1000\n",
      "14/18 [======================>.......] - ETA: 0s - loss: 2.6821 - accuracy: 0.1607\n",
      "Epoch 21: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.6796 - accuracy: 0.1573 - val_loss: 2.5655 - val_accuracy: 0.2033\n",
      "Epoch 22/1000\n",
      "14/18 [======================>.......] - ETA: 0s - loss: 2.6473 - accuracy: 0.1451\n",
      "Epoch 22: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.6490 - accuracy: 0.1410 - val_loss: 2.5355 - val_accuracy: 0.2263\n",
      "Epoch 23/1000\n",
      "17/18 [===========================>..] - ETA: 0s - loss: 2.6213 - accuracy: 0.1682\n",
      "Epoch 23: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.6182 - accuracy: 0.1690 - val_loss: 2.4997 - val_accuracy: 0.2276\n",
      "Epoch 24/1000\n",
      "15/18 [========================>.....] - ETA: 0s - loss: 2.5939 - accuracy: 0.1682\n",
      "Epoch 24: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 23ms/step - loss: 2.5806 - accuracy: 0.1731 - val_loss: 2.4618 - val_accuracy: 0.2534\n",
      "Epoch 25/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.5599 - accuracy: 0.1831\n",
      "Epoch 25: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.5546 - accuracy: 0.1821 - val_loss: 2.4219 - val_accuracy: 0.2683\n",
      "Epoch 26/1000\n",
      "14/18 [======================>.......] - ETA: 0s - loss: 2.5299 - accuracy: 0.1808\n",
      "Epoch 26: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.5363 - accuracy: 0.1744 - val_loss: 2.3854 - val_accuracy: 0.2913\n",
      "Epoch 27/1000\n",
      "17/18 [===========================>..] - ETA: 0s - loss: 2.5052 - accuracy: 0.1907\n",
      "Epoch 27: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.5084 - accuracy: 0.1902 - val_loss: 2.3561 - val_accuracy: 0.3049\n",
      "Epoch 28/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 8/18 [============>.................] - ETA: 0s - loss: 2.4572 - accuracy: 0.1973\n",
      "Epoch 28: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.4963 - accuracy: 0.1898 - val_loss: 2.3216 - val_accuracy: 0.2913\n",
      "Epoch 29/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.4522 - accuracy: 0.1929\n",
      "Epoch 29: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 21ms/step - loss: 2.4504 - accuracy: 0.1939 - val_loss: 2.2814 - val_accuracy: 0.3306\n",
      "Epoch 30/1000\n",
      "15/18 [========================>.....] - ETA: 0s - loss: 2.4576 - accuracy: 0.1984\n",
      "Epoch 30: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 2.4488 - accuracy: 0.2006 - val_loss: 2.2515 - val_accuracy: 0.3388\n",
      "Epoch 31/1000\n",
      "17/18 [===========================>..] - ETA: 0s - loss: 2.4510 - accuracy: 0.1926\n",
      "Epoch 31: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 24ms/step - loss: 2.4455 - accuracy: 0.1948 - val_loss: 2.2211 - val_accuracy: 0.3333\n",
      "Epoch 32/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 2.3870 - accuracy: 0.2102\n",
      "Epoch 32: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 17ms/step - loss: 2.3917 - accuracy: 0.2133 - val_loss: 2.1914 - val_accuracy: 0.3537\n",
      "Epoch 33/1000\n",
      " 9/18 [==============>...............] - ETA: 0s - loss: 2.4040 - accuracy: 0.1962\n",
      "Epoch 33: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 17ms/step - loss: 2.4009 - accuracy: 0.2024 - val_loss: 2.1667 - val_accuracy: 0.3631\n",
      "Epoch 34/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.3872 - accuracy: 0.2352\n",
      "Epoch 34: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.3682 - accuracy: 0.2395 - val_loss: 2.1416 - val_accuracy: 0.4024\n",
      "Epoch 35/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.3539 - accuracy: 0.2250\n",
      "Epoch 35: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.3530 - accuracy: 0.2259 - val_loss: 2.1118 - val_accuracy: 0.4024\n",
      "Epoch 36/1000\n",
      "18/18 [==============================] - ETA: 0s - loss: 2.3331 - accuracy: 0.2395\n",
      "Epoch 36: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.3331 - accuracy: 0.2395 - val_loss: 2.0830 - val_accuracy: 0.3835\n",
      "Epoch 37/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.2820 - accuracy: 0.2453\n",
      "Epoch 37: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.3072 - accuracy: 0.2395 - val_loss: 2.0590 - val_accuracy: 0.3943\n",
      "Epoch 38/1000\n",
      " 9/18 [==============>...............] - ETA: 0s - loss: 2.3460 - accuracy: 0.2266\n",
      "Epoch 38: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 21ms/step - loss: 2.3110 - accuracy: 0.2273 - val_loss: 2.0333 - val_accuracy: 0.4106\n",
      "Epoch 39/1000\n",
      " 9/18 [==============>...............] - ETA: 0s - loss: 2.2831 - accuracy: 0.2309\n",
      "Epoch 39: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.2783 - accuracy: 0.2264 - val_loss: 2.0082 - val_accuracy: 0.3916\n",
      "Epoch 40/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.2911 - accuracy: 0.2383\n",
      "Epoch 40: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.2629 - accuracy: 0.2449 - val_loss: 1.9881 - val_accuracy: 0.4295\n",
      "Epoch 41/1000\n",
      " 9/18 [==============>...............] - ETA: 0s - loss: 2.2964 - accuracy: 0.2413\n",
      "Epoch 41: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.2905 - accuracy: 0.2517 - val_loss: 1.9727 - val_accuracy: 0.4404\n",
      "Epoch 42/1000\n",
      "17/18 [===========================>..] - ETA: 0s - loss: 2.2319 - accuracy: 0.2528\n",
      "Epoch 42: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.2309 - accuracy: 0.2521 - val_loss: 1.9513 - val_accuracy: 0.4417\n",
      "Epoch 43/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 2.2364 - accuracy: 0.2546\n",
      "Epoch 43: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.2336 - accuracy: 0.2481 - val_loss: 1.9327 - val_accuracy: 0.4146\n",
      "Epoch 44/1000\n",
      "16/18 [=========================>....] - ETA: 0s - loss: 2.2288 - accuracy: 0.2524\n",
      "Epoch 44: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 20ms/step - loss: 2.2319 - accuracy: 0.2544 - val_loss: 1.9102 - val_accuracy: 0.4417\n",
      "Epoch 45/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 2.2222 - accuracy: 0.2552\n",
      "Epoch 45: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 16ms/step - loss: 2.2002 - accuracy: 0.2603 - val_loss: 1.8975 - val_accuracy: 0.4309\n",
      "Epoch 46/1000\n",
      "18/18 [==============================] - ETA: 0s - loss: 2.2127 - accuracy: 0.2657\n",
      "Epoch 46: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 18ms/step - loss: 2.2127 - accuracy: 0.2657 - val_loss: 1.8757 - val_accuracy: 0.4336\n",
      "Epoch 47/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 2.1977 - accuracy: 0.2557\n",
      "Epoch 47: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.2018 - accuracy: 0.2594 - val_loss: 1.8629 - val_accuracy: 0.4458\n",
      "Epoch 48/1000\n",
      "10/18 [===============>..............] - ETA: 0s - loss: 2.1991 - accuracy: 0.2594\n",
      "Epoch 48: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
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      "Epoch 50/1000\n",
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      "Epoch 50: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 2.1853 - accuracy: 0.2535 - val_loss: 1.8273 - val_accuracy: 0.4390\n",
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     ]
    },
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     "text": [
      "Epoch 55/1000\n",
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      "Epoch 55: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
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      "Epoch 60/1000\n",
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      "18/18 [==============================] - 0s 21ms/step - loss: 2.1069 - accuracy: 0.2955 - val_loss: 1.7244 - val_accuracy: 0.4485\n",
      "Epoch 61/1000\n",
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     ]
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      "Epoch 82/1000\n",
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      "Epoch 87/1000\n",
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      "Epoch 87: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
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     ]
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      "Epoch 109/1000\n",
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     ]
    },
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     "text": [
      "Epoch 136/1000\n",
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      "Epoch 163/1000\n",
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     ]
    },
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     "text": [
      "Epoch 190/1000\n",
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      "18/18 [==============================] - 0s 17ms/step - loss: 1.7575 - accuracy: 0.3818 - val_loss: 1.2956 - val_accuracy: 0.5854\n",
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     ]
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      "Epoch 217/1000\n",
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     ]
    },
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     "text": [
      "Epoch 244/1000\n",
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      "18/18 [==============================] - 0s 17ms/step - loss: 1.6775 - accuracy: 0.3886 - val_loss: 1.1901 - val_accuracy: 0.6504\n",
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      "18/18 [==============================] - 0s 14ms/step - loss: 1.7158 - accuracy: 0.3945 - val_loss: 1.1893 - val_accuracy: 0.6396\n",
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      "18/18 [==============================] - 0s 15ms/step - loss: 1.7017 - accuracy: 0.3967 - val_loss: 1.1929 - val_accuracy: 0.6463\n",
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      "18/18 [==============================] - 0s 17ms/step - loss: 1.6745 - accuracy: 0.4135 - val_loss: 1.1979 - val_accuracy: 0.6409\n",
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      "18/18 [==============================] - 0s 21ms/step - loss: 1.6724 - accuracy: 0.4067 - val_loss: 1.1920 - val_accuracy: 0.6409\n",
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     ]
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     "text": [
      "Epoch 271/1000\n",
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      "Epoch 280/1000\n",
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      "Epoch 280: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 19ms/step - loss: 1.6571 - accuracy: 0.4094 - val_loss: 1.1721 - val_accuracy: 0.6640\n",
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     ]
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      "Epoch 298/1000\n",
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      "18/18 [==============================] - 0s 22ms/step - loss: 1.6304 - accuracy: 0.4275 - val_loss: 1.1195 - val_accuracy: 0.6653\n",
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     ]
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      "Epoch 325/1000\n",
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     ]
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      "Epoch 352/1000\n",
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      "Epoch 377/1000\n",
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      "Epoch 377: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 379/1000\n",
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     ]
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      "Epoch 406/1000\n",
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      "Epoch 414/1000\n",
      "11/18 [=================>............] - ETA: 0s - loss: 1.5656 - accuracy: 0.4453\n",
      "Epoch 414: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 1.5740 - accuracy: 0.4324 - val_loss: 1.0774 - val_accuracy: 0.6856\n",
      "Epoch 415/1000\n",
      "12/18 [===================>..........] - ETA: 0s - loss: 1.5320 - accuracy: 0.4342\n",
      "Epoch 415: saving model to model/keypoint_classifier\\keypoint_classifier.hdf5\n",
      "18/18 [==============================] - 0s 15ms/step - loss: 1.5505 - accuracy: 0.4333 - val_loss: 1.0678 - val_accuracy: 0.6951\n",
      "Epoch 415: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a40fccefd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    epochs=1000,\n",
    "    batch_size=128,\n",
    "    validation_data=(X_test, y_test),\n",
    "    callbacks=[cp_callback, es_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 5ms/step - loss: 1.0678 - accuracy: 0.6951\n"
     ]
    }
   ],
   "source": [
    "# モデル評価\n",
    "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存したモデルのロード\n",
    "model = tf.keras.models.load_model(model_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 318ms/step\n",
      "[2.4317658e-04 1.4968878e-01 1.0088294e-01 3.0780073e-05 1.2561157e-01\n",
      " 5.1875418e-01 5.0269320e-12 4.3802739e-11 3.2134149e-03 3.1530100e-17\n",
      " 9.8153227e-04 1.2912195e-05 2.6323690e-04 3.9468077e-07 3.2228898e-02\n",
      " 1.2777075e-10 1.6283144e-09 3.0158579e-03 4.4671302e-03 8.2946724e-07\n",
      " 3.0909136e-03 1.2000446e-03 5.5955354e-02 1.4795056e-07 3.5797106e-04\n",
      " 1.2816346e-14]\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# 推論テスト\n",
    "predict_result = model.predict(np.array([X_test[0]]))\n",
    "print(np.squeeze(predict_result))\n",
    "print(np.argmax(np.squeeze(predict_result)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 混同行列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.97      0.92        30\n",
      "           1       0.87      0.87      0.87        53\n",
      "           2       0.83      0.83      0.83        46\n",
      "           3       0.68      1.00      0.81        34\n",
      "           4       0.75      1.00      0.86        24\n",
      "           5       1.00      0.93      0.97        30\n",
      "           6       0.77      0.50      0.61        34\n",
      "           7       0.76      0.93      0.84        45\n",
      "           8       0.93      0.96      0.95        28\n",
      "          10       0.41      0.40      0.41        35\n",
      "          11       1.00      0.82      0.90        28\n",
      "          12       0.83      0.21      0.33        24\n",
      "          13       0.42      1.00      0.59        22\n",
      "          14       0.76      1.00      0.87        26\n",
      "          15       0.61      0.72      0.66        39\n",
      "          16       0.74      0.59      0.65        29\n",
      "          17       0.24      1.00      0.39        22\n",
      "          18       0.75      0.82      0.78        33\n",
      "          19       0.80      0.14      0.24        29\n",
      "          20       0.00      0.00      0.00        32\n",
      "          21       0.00      0.00      0.00        27\n",
      "          22       0.91      0.42      0.57        24\n",
      "          23       1.00      0.48      0.65        23\n",
      "          24       1.00      0.90      0.95        21\n",
      "\n",
      "    accuracy                           0.70       738\n",
      "   macro avg       0.71      0.69      0.65       738\n",
      "weighted avg       0.71      0.70      0.67       738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "def print_confusion_matrix(y_true, y_pred, report=True):\n",
    "    labels = sorted(list(set(y_true)))\n",
    "    cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n",
    "    \n",
    "    df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n",
    " \n",
    "    fig, ax = plt.subplots(figsize=(7, 6))\n",
    "    sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n",
    "    ax.set_ylim(len(set(y_true)), 0)\n",
    "    plt.show()\n",
    "    \n",
    "    if report:\n",
    "        print('Classification Report')\n",
    "        print(classification_report(y_test, y_pred))\n",
    "\n",
    "Y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "\n",
    "print_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow-Lite用のモデルへ変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 推論専用のモデルとして保存\n",
    "model.save(model_save_path, include_optimizer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\lamba\\AppData\\Local\\Temp\\tmpl12cpsmb\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\lamba\\AppData\\Local\\Temp\\tmpl12cpsmb\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7512"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# モデルを変換(量子化)\n",
    "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "tflite_quantized_model = converter.convert()\n",
    "\n",
    "open(tflite_save_path, 'wb').write(tflite_quantized_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 推論テスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 入出力テンソルを取得\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.92 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 推論実施\n",
    "interpreter.invoke()\n",
    "tflite_results = interpreter.get_tensor(output_details[0]['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.4317650e-04 1.4968866e-01 1.0088292e-01 3.0780051e-05 1.2561153e-01\n",
      " 5.1875430e-01 5.0269138e-12 4.3802583e-11 3.2134140e-03 3.1530103e-17\n",
      " 9.8153204e-04 1.2912173e-05 2.6323670e-04 3.9468085e-07 3.2228883e-02\n",
      " 1.2777028e-10 1.6283116e-09 3.0158574e-03 4.4671269e-03 8.2946741e-07\n",
      " 3.0909118e-03 1.2000437e-03 5.5955335e-02 1.4795057e-07 3.5797077e-04\n",
      " 1.2816323e-14]\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(np.squeeze(tflite_results))\n",
    "print(np.argmax(np.squeeze(tflite_results)))"
   ]
  }
 ],
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