point_history_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\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/point_history_classifier/point_history.csv'\n",
    "model_save_path = 'model/point_history_classifier/point_history_classifier.hdf5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分類数設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_CLASSES = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 入力長"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIME_STEPS = 16\n",
    "DIMENSION = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習データ読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (TIME_STEPS * DIMENSION) + 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.InputLayer(input_shape=(TIME_STEPS * DIMENSION, )),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(24, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.5),\n",
    "    tf.keras.layers.Dense(10, activation='relu'),\n",
    "    tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dropout (Dropout)           (None, 32)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 24)                792       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 24)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                250       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,075\n",
      "Trainable params: 1,075\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()  # tf.keras.utils.plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      " 1/24 [>.............................] - ETA: 8s - loss: 1.1037 - accuracy: 0.3984\n",
      "Epoch 1: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 1s 9ms/step - loss: 1.0946 - accuracy: 0.4967 - val_loss: 1.0639 - val_accuracy: 0.4956\n",
      "Epoch 2/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 1.0575 - accuracy: 0.5312\n",
      "Epoch 2: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 1.0491 - accuracy: 0.5282 - val_loss: 1.0288 - val_accuracy: 0.5634\n",
      "Epoch 3/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.9949 - accuracy: 0.5703\n",
      "Epoch 3: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 1.0164 - accuracy: 0.5518 - val_loss: 0.9962 - val_accuracy: 0.5792\n",
      "Epoch 4/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.9775 - accuracy: 0.6016\n",
      "Epoch 4: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.9835 - accuracy: 0.5692 - val_loss: 0.9645 - val_accuracy: 0.5900\n",
      "Epoch 5/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.9891 - accuracy: 0.5781\n",
      "Epoch 5: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.9516 - accuracy: 0.6007 - val_loss: 0.9326 - val_accuracy: 0.6391\n",
      "Epoch 6/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.9450 - accuracy: 0.5469\n",
      "Epoch 6: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.9220 - accuracy: 0.6318 - val_loss: 0.9028 - val_accuracy: 0.6863\n",
      "Epoch 7/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.9086 - accuracy: 0.6719\n",
      "Epoch 7: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.8963 - accuracy: 0.6616 - val_loss: 0.8749 - val_accuracy: 0.7748\n",
      "Epoch 8/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.8862 - accuracy: 0.6641\n",
      "Epoch 8: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.8642 - accuracy: 0.6993 - val_loss: 0.8471 - val_accuracy: 0.8083\n",
      "Epoch 9/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.8957 - accuracy: 0.7266\n",
      "Epoch 9: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.8407 - accuracy: 0.7236 - val_loss: 0.8171 - val_accuracy: 0.8250\n",
      "Epoch 10/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.8001 - accuracy: 0.8125\n",
      "Epoch 10: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.8169 - accuracy: 0.7446 - val_loss: 0.7843 - val_accuracy: 0.8387\n",
      "Epoch 11/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.8629 - accuracy: 0.6719\n",
      "Epoch 11: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.7905 - accuracy: 0.7531 - val_loss: 0.7505 - val_accuracy: 0.8437\n",
      "Epoch 12/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.8116 - accuracy: 0.6953\n",
      "Epoch 12: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.7592 - accuracy: 0.7633 - val_loss: 0.7151 - val_accuracy: 0.8496\n",
      "Epoch 13/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.7444 - accuracy: 0.7656\n",
      "Epoch 13: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.7316 - accuracy: 0.7705 - val_loss: 0.6782 - val_accuracy: 0.8496\n",
      "Epoch 14/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6404 - accuracy: 0.8125\n",
      "Epoch 14: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.6968 - accuracy: 0.7793 - val_loss: 0.6427 - val_accuracy: 0.8545\n",
      "Epoch 15/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6902 - accuracy: 0.7734\n",
      "Epoch 15: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.6721 - accuracy: 0.7807 - val_loss: 0.6083 - val_accuracy: 0.8525\n",
      "Epoch 16/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6941 - accuracy: 0.7422\n",
      "Epoch 16: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.6465 - accuracy: 0.7856 - val_loss: 0.5773 - val_accuracy: 0.8545\n",
      "Epoch 17/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6318 - accuracy: 0.7969\n",
      "Epoch 17: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.6266 - accuracy: 0.7905 - val_loss: 0.5511 - val_accuracy: 0.8535\n",
      "Epoch 18/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6152 - accuracy: 0.8047\n",
      "Epoch 18: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5950 - accuracy: 0.8075 - val_loss: 0.5260 - val_accuracy: 0.8555\n",
      "Epoch 19/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6335 - accuracy: 0.7578\n",
      "Epoch 19: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5788 - accuracy: 0.7987 - val_loss: 0.5047 - val_accuracy: 0.8545\n",
      "Epoch 20/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5555 - accuracy: 0.7891\n",
      "Epoch 20: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5606 - accuracy: 0.8013 - val_loss: 0.4854 - val_accuracy: 0.8584\n",
      "Epoch 21/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5455 - accuracy: 0.8438\n",
      "Epoch 21: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5512 - accuracy: 0.8033 - val_loss: 0.4706 - val_accuracy: 0.8604\n",
      "Epoch 22/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.6009 - accuracy: 0.7578\n",
      "Epoch 22: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5329 - accuracy: 0.8111 - val_loss: 0.4541 - val_accuracy: 0.8584\n",
      "Epoch 23/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5272 - accuracy: 0.7891\n",
      "Epoch 23: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5163 - accuracy: 0.8180 - val_loss: 0.4403 - val_accuracy: 0.8604\n",
      "Epoch 24/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4456 - accuracy: 0.8594\n",
      "Epoch 24: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5176 - accuracy: 0.8134 - val_loss: 0.4293 - val_accuracy: 0.8643\n",
      "Epoch 25/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5633 - accuracy: 0.7578\n",
      "Epoch 25: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.5000 - accuracy: 0.8216 - val_loss: 0.4191 - val_accuracy: 0.8604\n",
      "Epoch 26/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3931 - accuracy: 0.8984\n",
      "Epoch 26: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4817 - accuracy: 0.8246 - val_loss: 0.4081 - val_accuracy: 0.8633\n",
      "Epoch 27/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4300 - accuracy: 0.8516\n",
      "Epoch 27: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4829 - accuracy: 0.8210 - val_loss: 0.4004 - val_accuracy: 0.8623\n",
      "Epoch 28/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4760 - accuracy: 0.8359\n",
      "Epoch 28: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4739 - accuracy: 0.8256 - val_loss: 0.3926 - val_accuracy: 0.8673\n",
      "Epoch 29/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4129 - accuracy: 0.8672\n",
      "Epoch 29: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4611 - accuracy: 0.8256 - val_loss: 0.3848 - val_accuracy: 0.8663\n",
      "Epoch 30/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4662 - accuracy: 0.8125\n",
      "Epoch 30: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4642 - accuracy: 0.8262 - val_loss: 0.3781 - val_accuracy: 0.8712\n",
      "Epoch 31/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3497 - accuracy: 0.8984\n",
      "Epoch 31: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4503 - accuracy: 0.8361 - val_loss: 0.3720 - val_accuracy: 0.8712\n",
      "Epoch 32/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4661 - accuracy: 0.8125\n",
      "Epoch 32: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4380 - accuracy: 0.8357 - val_loss: 0.3661 - val_accuracy: 0.8712\n",
      "Epoch 33/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5007 - accuracy: 0.8125\n",
      "Epoch 33: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4449 - accuracy: 0.8344 - val_loss: 0.3621 - val_accuracy: 0.8741\n",
      "Epoch 34/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4230 - accuracy: 0.8281\n",
      "Epoch 34: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4331 - accuracy: 0.8393 - val_loss: 0.3587 - val_accuracy: 0.8761\n",
      "Epoch 35/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4185 - accuracy: 0.8672\n",
      "Epoch 35: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4291 - accuracy: 0.8420 - val_loss: 0.3547 - val_accuracy: 0.8771\n",
      "Epoch 36/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4052 - accuracy: 0.8750\n",
      "Epoch 36: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4300 - accuracy: 0.8377 - val_loss: 0.3496 - val_accuracy: 0.8771\n",
      "Epoch 37/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3763 - accuracy: 0.8672\n",
      "Epoch 37: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4243 - accuracy: 0.8400 - val_loss: 0.3467 - val_accuracy: 0.8800\n",
      "Epoch 38/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2940 - accuracy: 0.9141\n",
      "Epoch 38: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4133 - accuracy: 0.8449 - val_loss: 0.3433 - val_accuracy: 0.8791\n",
      "Epoch 39/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4564 - accuracy: 0.8281\n",
      "Epoch 39: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4159 - accuracy: 0.8456 - val_loss: 0.3404 - val_accuracy: 0.8820\n",
      "Epoch 40/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3680 - accuracy: 0.8906\n",
      "Epoch 40: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4193 - accuracy: 0.8452 - val_loss: 0.3373 - val_accuracy: 0.8820\n",
      "Epoch 41/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3961 - accuracy: 0.8594\n",
      "Epoch 41: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4142 - accuracy: 0.8498 - val_loss: 0.3345 - val_accuracy: 0.8859\n",
      "Epoch 42/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3995 - accuracy: 0.8672\n",
      "Epoch 42: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4080 - accuracy: 0.8462 - val_loss: 0.3320 - val_accuracy: 0.8850\n",
      "Epoch 43/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.5158 - accuracy: 0.7812\n",
      "Epoch 43: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.4007 - accuracy: 0.8485 - val_loss: 0.3295 - val_accuracy: 0.8859\n",
      "Epoch 44/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.4301 - accuracy: 0.8359\n",
      "Epoch 44: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3915 - accuracy: 0.8515 - val_loss: 0.3251 - val_accuracy: 0.8869\n",
      "Epoch 45/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3148 - accuracy: 0.8984\n",
      "Epoch 45: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3869 - accuracy: 0.8548 - val_loss: 0.3221 - val_accuracy: 0.8869\n",
      "Epoch 46/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3896 - accuracy: 0.8359\n",
      "Epoch 46: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3876 - accuracy: 0.8551 - val_loss: 0.3178 - val_accuracy: 0.8889\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3994 - accuracy: 0.8511 - val_loss: 0.3162 - val_accuracy: 0.8909\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3840 - accuracy: 0.8541 - val_loss: 0.3162 - val_accuracy: 0.8869\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3882 - accuracy: 0.8515 - val_loss: 0.3136 - val_accuracy: 0.8879\n",
      "Epoch 51/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3872 - accuracy: 0.8564 - val_loss: 0.3121 - val_accuracy: 0.8899\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3824 - accuracy: 0.8561 - val_loss: 0.3108 - val_accuracy: 0.8889\n",
      "Epoch 53/1000\n"
     ]
    },
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      " 1/24 [>.............................] - ETA: 0s - loss: 0.3637 - accuracy: 0.8750\n",
      "Epoch 53: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3877 - accuracy: 0.8557 - val_loss: 0.3089 - val_accuracy: 0.8899\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3987 - accuracy: 0.8479 - val_loss: 0.3081 - val_accuracy: 0.8928\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3781 - accuracy: 0.8584 - val_loss: 0.3071 - val_accuracy: 0.8909\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3733 - accuracy: 0.8626 - val_loss: 0.3049 - val_accuracy: 0.8928\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3803 - accuracy: 0.8525 - val_loss: 0.3032 - val_accuracy: 0.8938\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3639 - accuracy: 0.8593 - val_loss: 0.3014 - val_accuracy: 0.8987\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3737 - accuracy: 0.8590 - val_loss: 0.3004 - val_accuracy: 0.8968\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3691 - accuracy: 0.8580 - val_loss: 0.3001 - val_accuracy: 0.8928\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3735 - accuracy: 0.8616 - val_loss: 0.2986 - val_accuracy: 0.8948\n",
      "Epoch 62/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3803 - accuracy: 0.8570 - val_loss: 0.2986 - val_accuracy: 0.8968\n",
      "Epoch 63/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3652 - accuracy: 0.8613 - val_loss: 0.2981 - val_accuracy: 0.8928\n",
      "Epoch 64/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3699 - accuracy: 0.8577 - val_loss: 0.2964 - val_accuracy: 0.8928\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3718 - accuracy: 0.8597 - val_loss: 0.2950 - val_accuracy: 0.9007\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3617 - accuracy: 0.8652 - val_loss: 0.2946 - val_accuracy: 0.8987\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3619 - accuracy: 0.8646 - val_loss: 0.2921 - val_accuracy: 0.9017\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3683 - accuracy: 0.8649 - val_loss: 0.2915 - val_accuracy: 0.9017\n",
      "Epoch 69/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3597 - accuracy: 0.8613 - val_loss: 0.2913 - val_accuracy: 0.8977\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3731 - accuracy: 0.8623 - val_loss: 0.2902 - val_accuracy: 0.9007\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3583 - accuracy: 0.8666 - val_loss: 0.2889 - val_accuracy: 0.9027\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3563 - accuracy: 0.8662 - val_loss: 0.2873 - val_accuracy: 0.9027\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3597 - accuracy: 0.8623 - val_loss: 0.2864 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3538 - accuracy: 0.8679 - val_loss: 0.2861 - val_accuracy: 0.9007\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3588 - accuracy: 0.8639 - val_loss: 0.2851 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3541 - accuracy: 0.8675 - val_loss: 0.2863 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3646 - accuracy: 0.8613 - val_loss: 0.2849 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3566 - accuracy: 0.8652 - val_loss: 0.2850 - val_accuracy: 0.9027\n",
      "Epoch 79/1000\n"
     ]
    },
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      " 1/24 [>.............................] - ETA: 0s - loss: 0.3570 - accuracy: 0.8516\n",
      "Epoch 79: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3616 - accuracy: 0.8639 - val_loss: 0.2834 - val_accuracy: 0.9056\n",
      "Epoch 80/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3494 - accuracy: 0.8682 - val_loss: 0.2841 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3484 - accuracy: 0.8705 - val_loss: 0.2830 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3532 - accuracy: 0.8672 - val_loss: 0.2820 - val_accuracy: 0.9036\n",
      "Epoch 83/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3663 - accuracy: 0.8610 - val_loss: 0.2814 - val_accuracy: 0.9036\n",
      "Epoch 84/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3451 - accuracy: 0.8672 - val_loss: 0.2812 - val_accuracy: 0.9036\n",
      "Epoch 85/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3466 - accuracy: 0.8702 - val_loss: 0.2814 - val_accuracy: 0.9027\n",
      "Epoch 86/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3566 - accuracy: 0.8646 - val_loss: 0.2801 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3316 - accuracy: 0.8813 - val_loss: 0.2792 - val_accuracy: 0.9036\n",
      "Epoch 88/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3481 - accuracy: 0.8698 - val_loss: 0.2784 - val_accuracy: 0.9036\n",
      "Epoch 89/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3486 - accuracy: 0.8682 - val_loss: 0.2781 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3604 - accuracy: 0.8639 - val_loss: 0.2781 - val_accuracy: 0.9036\n",
      "Epoch 91/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3537 - accuracy: 0.8698 - val_loss: 0.2782 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3525 - accuracy: 0.8662 - val_loss: 0.2775 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3486 - accuracy: 0.8705 - val_loss: 0.2771 - val_accuracy: 0.9046\n",
      "Epoch 94/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3385 - accuracy: 0.8721 - val_loss: 0.2774 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3368 - accuracy: 0.8761 - val_loss: 0.2760 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3455 - accuracy: 0.8711 - val_loss: 0.2760 - val_accuracy: 0.9036\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3518 - accuracy: 0.8734 - val_loss: 0.2754 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3408 - accuracy: 0.8702 - val_loss: 0.2756 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3511 - accuracy: 0.8728 - val_loss: 0.2752 - val_accuracy: 0.9046\n",
      "Epoch 100/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3418 - accuracy: 0.8728 - val_loss: 0.2745 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3557 - accuracy: 0.8711 - val_loss: 0.2757 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3387 - accuracy: 0.8754 - val_loss: 0.2746 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3367 - accuracy: 0.8764 - val_loss: 0.2732 - val_accuracy: 0.9036\n",
      "Epoch 104/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3334 - accuracy: 0.8725 - val_loss: 0.2718 - val_accuracy: 0.9056\n",
      "Epoch 105/1000\n"
     ]
    },
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      "Epoch 105: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3469 - accuracy: 0.8711 - val_loss: 0.2717 - val_accuracy: 0.9066\n",
      "Epoch 106/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3387 - accuracy: 0.8734 - val_loss: 0.2712 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3393 - accuracy: 0.8705 - val_loss: 0.2706 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3413 - accuracy: 0.8705 - val_loss: 0.2713 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3481 - accuracy: 0.8718 - val_loss: 0.2719 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3400 - accuracy: 0.8734 - val_loss: 0.2712 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3412 - accuracy: 0.8761 - val_loss: 0.2705 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3358 - accuracy: 0.8774 - val_loss: 0.2703 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3396 - accuracy: 0.8744 - val_loss: 0.2699 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3468 - accuracy: 0.8708 - val_loss: 0.2705 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3476 - accuracy: 0.8711 - val_loss: 0.2706 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3312 - accuracy: 0.8770 - val_loss: 0.2703 - val_accuracy: 0.9046\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3366 - accuracy: 0.8754 - val_loss: 0.2693 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3496 - accuracy: 0.8698 - val_loss: 0.2699 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3299 - accuracy: 0.8774 - val_loss: 0.2694 - val_accuracy: 0.9056\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3410 - accuracy: 0.8725 - val_loss: 0.2688 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3515 - accuracy: 0.8708 - val_loss: 0.2689 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3303 - accuracy: 0.8761 - val_loss: 0.2687 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3401 - accuracy: 0.8774 - val_loss: 0.2679 - val_accuracy: 0.9066\n",
      "Epoch 125/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3336 - accuracy: 0.8764 - val_loss: 0.2683 - val_accuracy: 0.9056\n",
      "Epoch 126/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3399 - accuracy: 0.8725 - val_loss: 0.2696 - val_accuracy: 0.9056\n",
      "Epoch 127/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3315 - accuracy: 0.8767 - val_loss: 0.2680 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3208 - accuracy: 0.8816 - val_loss: 0.2670 - val_accuracy: 0.9086\n",
      "Epoch 129/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3365 - accuracy: 0.8780 - val_loss: 0.2666 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3304 - accuracy: 0.8777 - val_loss: 0.2661 - val_accuracy: 0.9086\n",
      "Epoch 131/1000\n"
     ]
    },
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     "text": [
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3563 - accuracy: 0.8594\n",
      "Epoch 131: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3476 - accuracy: 0.8689 - val_loss: 0.2661 - val_accuracy: 0.9076\n",
      "Epoch 132/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3278 - accuracy: 0.8774 - val_loss: 0.2660 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3177 - accuracy: 0.8793 - val_loss: 0.2659 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3251 - accuracy: 0.8761 - val_loss: 0.2658 - val_accuracy: 0.9046\n",
      "Epoch 135/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3274 - accuracy: 0.8790 - val_loss: 0.2657 - val_accuracy: 0.9046\n",
      "Epoch 136/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3354 - accuracy: 0.8744 - val_loss: 0.2664 - val_accuracy: 0.9066\n",
      "Epoch 137/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3141 - accuracy: 0.8816 - val_loss: 0.2649 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3287 - accuracy: 0.8764 - val_loss: 0.2647 - val_accuracy: 0.9086\n",
      "Epoch 139/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3245 - accuracy: 0.8800 - val_loss: 0.2647 - val_accuracy: 0.9076\n",
      "Epoch 140/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3348 - accuracy: 0.8767 - val_loss: 0.2644 - val_accuracy: 0.9066\n",
      "Epoch 141/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3202 - accuracy: 0.8820 - val_loss: 0.2641 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3250 - accuracy: 0.8770 - val_loss: 0.2636 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3288 - accuracy: 0.8823 - val_loss: 0.2638 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3273 - accuracy: 0.8810 - val_loss: 0.2630 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8800 - val_loss: 0.2638 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3343 - accuracy: 0.8711 - val_loss: 0.2634 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3290 - accuracy: 0.8793 - val_loss: 0.2635 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3066 - accuracy: 0.8846 - val_loss: 0.2627 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3346 - accuracy: 0.8797 - val_loss: 0.2623 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3400 - accuracy: 0.8741 - val_loss: 0.2636 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3263 - accuracy: 0.8803 - val_loss: 0.2625 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3303 - accuracy: 0.8767 - val_loss: 0.2627 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3257 - accuracy: 0.8800 - val_loss: 0.2624 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3229 - accuracy: 0.8833 - val_loss: 0.2617 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3231 - accuracy: 0.8826 - val_loss: 0.2618 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3224 - accuracy: 0.8810 - val_loss: 0.2614 - val_accuracy: 0.9066\n",
      "Epoch 157/1000\n"
     ]
    },
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      " 1/24 [>.............................] - ETA: 0s - loss: 0.3632 - accuracy: 0.8359\n",
      "Epoch 157: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3115 - accuracy: 0.8793 - val_loss: 0.2611 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3409 - accuracy: 0.8744 - val_loss: 0.2608 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3418 - accuracy: 0.8721 - val_loss: 0.2628 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3273 - accuracy: 0.8770 - val_loss: 0.2632 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.8793 - val_loss: 0.2638 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3283 - accuracy: 0.8767 - val_loss: 0.2633 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.8810 - val_loss: 0.2623 - val_accuracy: 0.9066\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3190 - accuracy: 0.8820 - val_loss: 0.2610 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3208 - accuracy: 0.8790 - val_loss: 0.2600 - val_accuracy: 0.9076\n",
      "Epoch 166/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3056 - accuracy: 0.8885 - val_loss: 0.2605 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3144 - accuracy: 0.8839 - val_loss: 0.2598 - val_accuracy: 0.9086\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3120 - accuracy: 0.8846 - val_loss: 0.2593 - val_accuracy: 0.9076\n",
      "Epoch 169/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3196 - accuracy: 0.8826 - val_loss: 0.2585 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.8787 - val_loss: 0.2583 - val_accuracy: 0.9076\n",
      "Epoch 171/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3351 - accuracy: 0.8767 - val_loss: 0.2596 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3198 - accuracy: 0.8813 - val_loss: 0.2595 - val_accuracy: 0.9076\n",
      "Epoch 173/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3240 - accuracy: 0.8793 - val_loss: 0.2591 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3235 - accuracy: 0.8823 - val_loss: 0.2589 - val_accuracy: 0.9076\n",
      "Epoch 175/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3307 - accuracy: 0.8754 - val_loss: 0.2585 - val_accuracy: 0.9076\n",
      "Epoch 176/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3135 - accuracy: 0.8803 - val_loss: 0.2583 - val_accuracy: 0.9076\n",
      "Epoch 177/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3215 - accuracy: 0.8787 - val_loss: 0.2585 - val_accuracy: 0.9076\n",
      "Epoch 178/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3215 - accuracy: 0.8810 - val_loss: 0.2586 - val_accuracy: 0.9076\n",
      "Epoch 179/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3279 - accuracy: 0.8774 - val_loss: 0.2583 - val_accuracy: 0.9076\n",
      "Epoch 180/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3280 - accuracy: 0.8780 - val_loss: 0.2591 - val_accuracy: 0.9066\n",
      "Epoch 181/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3209 - accuracy: 0.8810 - val_loss: 0.2588 - val_accuracy: 0.9076\n",
      "Epoch 182/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3247 - accuracy: 0.8793 - val_loss: 0.2589 - val_accuracy: 0.9076\n",
      "Epoch 183/1000\n"
     ]
    },
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     "text": [
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2771 - accuracy: 0.9062\n",
      "Epoch 183: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8770 - val_loss: 0.2585 - val_accuracy: 0.9076\n",
      "Epoch 184/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3338 - accuracy: 0.8787 - val_loss: 0.2580 - val_accuracy: 0.9076\n",
      "Epoch 185/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3149 - accuracy: 0.8823 - val_loss: 0.2578 - val_accuracy: 0.9076\n",
      "Epoch 186/1000\n",
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      "Epoch 186: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3240 - accuracy: 0.8826 - val_loss: 0.2579 - val_accuracy: 0.9076\n",
      "Epoch 187/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3145 - accuracy: 0.8797 - val_loss: 0.2582 - val_accuracy: 0.9076\n",
      "Epoch 188/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3107 - accuracy: 0.8872 - val_loss: 0.2570 - val_accuracy: 0.9076\n",
      "Epoch 189/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3221 - accuracy: 0.8810 - val_loss: 0.2572 - val_accuracy: 0.9076\n",
      "Epoch 190/1000\n",
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      "Epoch 190: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3190 - accuracy: 0.8790 - val_loss: 0.2569 - val_accuracy: 0.9076\n",
      "Epoch 191/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.8830 - val_loss: 0.2573 - val_accuracy: 0.9076\n",
      "Epoch 192/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3151 - accuracy: 0.8833 - val_loss: 0.2569 - val_accuracy: 0.9076\n",
      "Epoch 193/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3196 - accuracy: 0.8793 - val_loss: 0.2571 - val_accuracy: 0.9076\n",
      "Epoch 194/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3199 - accuracy: 0.8807 - val_loss: 0.2566 - val_accuracy: 0.9076\n",
      "Epoch 195/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3281 - accuracy: 0.8790 - val_loss: 0.2564 - val_accuracy: 0.9076\n",
      "Epoch 196/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3239 - accuracy: 0.8800 - val_loss: 0.2569 - val_accuracy: 0.9076\n",
      "Epoch 197/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3167 - accuracy: 0.8833 - val_loss: 0.2568 - val_accuracy: 0.9076\n",
      "Epoch 198/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3229 - accuracy: 0.8813 - val_loss: 0.2567 - val_accuracy: 0.9076\n",
      "Epoch 199/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3129 - accuracy: 0.8843 - val_loss: 0.2566 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3023 - accuracy: 0.8879 - val_loss: 0.2552 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3206 - accuracy: 0.8823 - val_loss: 0.2555 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3178 - accuracy: 0.8830 - val_loss: 0.2552 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3198 - accuracy: 0.8852 - val_loss: 0.2549 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3185 - accuracy: 0.8816 - val_loss: 0.2550 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3200 - accuracy: 0.8807 - val_loss: 0.2552 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3235 - accuracy: 0.8797 - val_loss: 0.2547 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3126 - accuracy: 0.8833 - val_loss: 0.2544 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3265 - accuracy: 0.8744 - val_loss: 0.2549 - val_accuracy: 0.9076\n",
      "Epoch 209/1000\n"
     ]
    },
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      " 1/24 [>.............................] - ETA: 0s - loss: 0.2930 - accuracy: 0.9062\n",
      "Epoch 209: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3169 - accuracy: 0.8826 - val_loss: 0.2551 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3084 - accuracy: 0.8872 - val_loss: 0.2547 - val_accuracy: 0.9076\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3105 - accuracy: 0.8852 - val_loss: 0.2551 - val_accuracy: 0.9076\n",
      "Epoch 212/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3189 - accuracy: 0.8859 - val_loss: 0.2549 - val_accuracy: 0.9076\n",
      "Epoch 213/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3367 - accuracy: 0.8757 - val_loss: 0.2553 - val_accuracy: 0.9076\n",
      "Epoch 214/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3095 - accuracy: 0.8869 - val_loss: 0.2547 - val_accuracy: 0.9076\n",
      "Epoch 215/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.2991 - accuracy: 0.8908 - val_loss: 0.2539 - val_accuracy: 0.9076\n",
      "Epoch 216/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3010 - accuracy: 0.8898 - val_loss: 0.2530 - val_accuracy: 0.9086\n",
      "Epoch 217/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3208 - accuracy: 0.8810 - val_loss: 0.2531 - val_accuracy: 0.9086\n",
      "Epoch 218/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3101 - accuracy: 0.8839 - val_loss: 0.2538 - val_accuracy: 0.9076\n",
      "Epoch 219/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3148 - accuracy: 0.8800 - val_loss: 0.2529 - val_accuracy: 0.9076\n",
      "Epoch 220/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3125 - accuracy: 0.8849 - val_loss: 0.2534 - val_accuracy: 0.9076\n",
      "Epoch 221/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3026 - accuracy: 0.8859 - val_loss: 0.2530 - val_accuracy: 0.9076\n",
      "Epoch 222/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3080 - accuracy: 0.8836 - val_loss: 0.2531 - val_accuracy: 0.9076\n",
      "Epoch 223/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3214 - accuracy: 0.8820 - val_loss: 0.2521 - val_accuracy: 0.9076\n",
      "Epoch 224/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3253 - accuracy: 0.8803 - val_loss: 0.2521 - val_accuracy: 0.9076\n",
      "Epoch 225/1000\n",
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      "24/24 [==============================] - 0s 3ms/step - loss: 0.3169 - accuracy: 0.8797 - val_loss: 0.2509 - val_accuracy: 0.9076\n",
      "Epoch 226/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3294 - accuracy: 0.8906\n",
      "Epoch 226: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3135 - accuracy: 0.8836 - val_loss: 0.2517 - val_accuracy: 0.9076\n",
      "Epoch 227/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3660 - accuracy: 0.8281\n",
      "Epoch 227: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3213 - accuracy: 0.8833 - val_loss: 0.2521 - val_accuracy: 0.9076\n",
      "Epoch 228/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2506 - accuracy: 0.9062\n",
      "Epoch 228: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3235 - accuracy: 0.8816 - val_loss: 0.2529 - val_accuracy: 0.9076\n",
      "Epoch 229/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3703 - accuracy: 0.8438\n",
      "Epoch 229: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.8807 - val_loss: 0.2531 - val_accuracy: 0.9076\n",
      "Epoch 230/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2894 - accuracy: 0.8828\n",
      "Epoch 230: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3021 - accuracy: 0.8898 - val_loss: 0.2526 - val_accuracy: 0.9076\n",
      "Epoch 231/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3031 - accuracy: 0.8828\n",
      "Epoch 231: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.2991 - accuracy: 0.8889 - val_loss: 0.2529 - val_accuracy: 0.9076\n",
      "Epoch 232/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2555 - accuracy: 0.9062\n",
      "Epoch 232: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3088 - accuracy: 0.8826 - val_loss: 0.2520 - val_accuracy: 0.9076\n",
      "Epoch 233/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3469 - accuracy: 0.8672\n",
      "Epoch 233: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3244 - accuracy: 0.8797 - val_loss: 0.2523 - val_accuracy: 0.9076\n",
      "Epoch 234/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2830 - accuracy: 0.8906\n",
      "Epoch 234: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3141 - accuracy: 0.8803 - val_loss: 0.2526 - val_accuracy: 0.9076\n",
      "Epoch 235/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/24 [>.............................] - ETA: 0s - loss: 0.1988 - accuracy: 0.9375\n",
      "Epoch 235: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3065 - accuracy: 0.8859 - val_loss: 0.2525 - val_accuracy: 0.9076\n",
      "Epoch 236/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2528 - accuracy: 0.9062\n",
      "Epoch 236: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3127 - accuracy: 0.8830 - val_loss: 0.2521 - val_accuracy: 0.9076\n",
      "Epoch 237/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3381 - accuracy: 0.9062\n",
      "Epoch 237: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3154 - accuracy: 0.8849 - val_loss: 0.2528 - val_accuracy: 0.9076\n",
      "Epoch 238/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3063 - accuracy: 0.8906\n",
      "Epoch 238: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3122 - accuracy: 0.8869 - val_loss: 0.2530 - val_accuracy: 0.9076\n",
      "Epoch 239/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3015 - accuracy: 0.8828\n",
      "Epoch 239: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3301 - accuracy: 0.8767 - val_loss: 0.2537 - val_accuracy: 0.9076\n",
      "Epoch 240/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2629 - accuracy: 0.9062\n",
      "Epoch 240: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3071 - accuracy: 0.8852 - val_loss: 0.2532 - val_accuracy: 0.9076\n",
      "Epoch 241/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.3463 - accuracy: 0.8594\n",
      "Epoch 241: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3122 - accuracy: 0.8836 - val_loss: 0.2522 - val_accuracy: 0.9076\n",
      "Epoch 242/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2050 - accuracy: 0.9375\n",
      "Epoch 242: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3045 - accuracy: 0.8898 - val_loss: 0.2520 - val_accuracy: 0.9086\n",
      "Epoch 243/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2625 - accuracy: 0.9062\n",
      "Epoch 243: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3035 - accuracy: 0.8869 - val_loss: 0.2511 - val_accuracy: 0.9076\n",
      "Epoch 244/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2916 - accuracy: 0.8828\n",
      "Epoch 244: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3073 - accuracy: 0.8859 - val_loss: 0.2514 - val_accuracy: 0.9076\n",
      "Epoch 245/1000\n",
      " 1/24 [>.............................] - ETA: 0s - loss: 0.2130 - accuracy: 0.9297\n",
      "Epoch 245: saving model to model/point_history_classifier\\point_history_classifier.hdf5\n",
      "24/24 [==============================] - 0s 3ms/step - loss: 0.3035 - accuracy: 0.8908 - val_loss: 0.2514 - val_accuracy: 0.9076\n",
      "Epoch 245: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x226e8682370>"
      ]
     },
     "execution_count": 12,
     "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": 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 64ms/step\n",
      "[0.77480465 0.01290015 0.21229513]\n",
      "0\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": [
      "32/32 [==============================] - 0s 549us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      1.00      0.90       393\n",
      "           1       0.99      0.98      0.99       304\n",
      "           2       0.97      0.72      0.83       320\n",
      "\n",
      "    accuracy                           0.91      1017\n",
      "   macro avg       0.93      0.90      0.91      1017\n",
      "weighted avg       0.92      0.91      0.90      1017\n",
      "\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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "# 推論専用のモデルとして保存\n",
    "model.save(model_save_path, include_optimizer=False)\n",
    "model = tf.keras.models.load_model(model_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "tflite_save_path = 'model/point_history_classifier/point_history_classifier.tflite'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\mfaja\\AppData\\Local\\Temp\\tmpkw21_00_\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6388"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# モデルを変換(量子化\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)  # converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)\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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'serving_default_input_1:0', 'index': 0, 'shape': array([ 1, 32]), 'shape_signature': array([-1, 32]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n"
     ]
    }
   ],
   "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()\n",
    "print(input_details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.77480465 0.01290015 0.21229509]\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(np.squeeze(tflite_results))\n",
    "print(np.argmax(np.squeeze(tflite_results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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