model_evalu_big.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df29fa17-5a32-4bab-8bf7-310d66c57909",
   "metadata": {
    "id": "df29fa17-5a32-4bab-8bf7-310d66c57909",
    "outputId": "14abfdcf-0a5c-4cbd-d49b-ce750feea842"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import Sequential, layers, Input\n",
    "from tensorflow.keras.layers import Dense, BatchNormalization, Dropout, LSTM\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "import tensorflow.keras.backend as K\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.initializers import Constant\n",
    "from tensorflow.keras.initializers import RandomNormal, HeNormal, GlorotNormal\n",
    "import cv2\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a183989-9dc9-4b81-9fdb-598b6f2fbc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#溶氧量點數\n",
    "def DO_value(num):\n",
    "  if (num >= 6.5):\n",
    "    return 1\n",
    "  elif(num >= 4.6):\n",
    "    return 3\n",
    "  elif(num >= 2.0):\n",
    "    return 6\n",
    "  else:\n",
    "    return 10\n",
    "\n",
    "#懸浮固體點數\n",
    "def TSS_value(num):\n",
    "  if (num <= 20.0):\n",
    "    return 1\n",
    "  elif(num <= 49.9):\n",
    "    return 3\n",
    "  elif(num <= 100):\n",
    "    return 6\n",
    "  else:\n",
    "    return 10\n",
    "\n",
    "#生化需氧量點數\n",
    "def BOD_value(num):\n",
    "  if (num <= 3.0):\n",
    "    return 1\n",
    "  elif(num <= 4.9):\n",
    "    return 3\n",
    "  elif(num <= 15.0):\n",
    "    return 6\n",
    "  else:\n",
    "    return 10\n",
    "\n",
    "#氨氣點數\n",
    "def NH3N_value(num):\n",
    "  if (num <= 0.5):\n",
    "    return 1\n",
    "  elif(num <= 0.99):\n",
    "    return 3\n",
    "  elif(num <= 3.0):\n",
    "    return 6\n",
    "  else:\n",
    "    return 10\n",
    "\n",
    "#計算RPI(River Pollution Index)\n",
    "def cal_RPI(DO, TSS, BOD, NH3N):\n",
    "  DO_num = DO_value(DO)\n",
    "  TSS_num = TSS_value(TSS)\n",
    "  BOD_num = BOD_value(BOD)\n",
    "  NH3N_num = NH3N_value(NH3N)\n",
    "  return (DO_num + TSS_num + BOD_num + NH3N_num)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbe2437e-a4af-4318-96b0-dddba084cde6",
   "metadata": {
    "id": "bbe2437e-a4af-4318-96b0-dddba084cde6"
   },
   "outputs": [],
   "source": [
    "# RPI = (DO + SS + BOD + NH3N) / 4\n",
    "# NH3-N / NO3-N = K  -->  NH3-N = NO3-N * K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68bc81a9-8139-4a24-9dac-3aa6b5f37255",
   "metadata": {
    "id": "68bc81a9-8139-4a24-9dac-3aa6b5f37255"
   },
   "outputs": [],
   "source": [
    "# def cal_RPI(DO, SS, BOD, NH3N):\n",
    "#     return (DO + SS + BOD + NH3N) / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea27c056-bc25-4afa-97f4-a1553827c33c",
   "metadata": {
    "id": "ea27c056-bc25-4afa-97f4-a1553827c33c"
   },
   "outputs": [],
   "source": [
    "APRIL_RPI = cal_RPI(9.0 ,15, 0 ,1.3/4.5)\n",
    "MAY_RPI = cal_RPI(8.4, 10, 0, 2.5/4.5)\n",
    "JUNE_RPI = cal_RPI(8.5, 16, 0, 1.7/4.5)\n",
    "JULY_RPI = cal_RPI(6.4, 24, 0, 2.6/4.5)\n",
    "AUGUST_RPI = cal_RPI(8.1, 143, 0, 1.2/4.5)\n",
    "SEPTEMBER_RPI = cal_RPI(7.7, 53, 0, 1.5/4.5)\n",
    "OCTOBER_RPI = cal_RPI(7.8, 23, 0, 1.2 / 4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30aeb04e-2848-48be-8cf8-d089dd47956b",
   "metadata": {
    "id": "30aeb04e-2848-48be-8cf8-d089dd47956b",
    "outputId": "060f1062-58f0-4603-ab0b-fdbdbe66bf08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "April RPI: 1.0\n",
      "May RPI: 1.5\n",
      "June RPI: 1.0\n",
      "July RPI: 2.5\n",
      "August RPI: 3.25\n",
      "September RPI: 2.25\n",
      "October PRI: 1.5\n"
     ]
    }
   ],
   "source": [
    "# Printing the calculated values\n",
    "print(\"April RPI:\", APRIL_RPI)\n",
    "print(\"May RPI:\", MAY_RPI)\n",
    "print(\"June RPI:\", JUNE_RPI)\n",
    "print(\"July RPI:\", JULY_RPI)\n",
    "print(\"August RPI:\", AUGUST_RPI)\n",
    "print(\"September RPI:\", SEPTEMBER_RPI)\n",
    "print(\"October PRI:\", OCTOBER_RPI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17cee9eb-68ea-447a-b9db-3cfc0be1d2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: change the label\n",
    "def get_label(val):\n",
    "    if val <= 2.0:\n",
    "        return \"NP\"\n",
    "    elif val <= 3.0:\n",
    "        return \"SP\"\n",
    "    elif val <= 6.0:\n",
    "        return \"MP\"\n",
    "    else:\n",
    "        return \"SEVERE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "031f260f-5929-4d6b-8e6e-adcd3699c6fa",
   "metadata": {
    "id": "031f260f-5929-4d6b-8e6e-adcd3699c6fa"
   },
   "outputs": [],
   "source": [
    "folder_paths = ['./水質檢測/Image_data/1_April',\n",
    "                './水質檢測/Image_data/2_May',\n",
    "                './水質檢測/Image_data/3_June',\n",
    "                './水質檢測/Image_data/4_July',\n",
    "                './水質檢測/Image_data/5_August',\n",
    "                './水質檢測/Image_data/6_September']\n",
    "labels = [get_label(APRIL_RPI), get_label(MAY_RPI), get_label(JUNE_RPI), \n",
    "          get_label(JULY_RPI), get_label(AUGUST_RPI), get_label(SEPTEMBER_RPI)]  # 对应文件夹1和文件夹2的标签\n",
    "\n",
    "\n",
    "oct_paths = [\"./水質檢測/Image_data/7_October\"]\n",
    "oct_label_Stirng = [get_label(OCTOBER_RPI)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09e666a5-c933-4693-917e-ae6851d0ab52",
   "metadata": {
    "id": "09e666a5-c933-4693-917e-ae6851d0ab52"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "def load_and_label_images(folder_path, label):\n",
    "    image_paths = []  # 存储图像文件的路径\n",
    "    labels = []       # 存储图像对应的标签\n",
    "\n",
    "    # 获取文件夹中的所有图像文件的路径\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith('.jpg') or filename.endswith('.png'):  # 假设只加载jpg和png格式的图像文件\n",
    "            image_paths.append(os.path.join(folder_path, filename))\n",
    "            labels.append(label)\n",
    "\n",
    "    # 加载图像并为其指定标签\n",
    "    images = [tf.io.read_file(image_path) for image_path in image_paths]\n",
    "    images = [tf.image.decode_image(image, channels=3) for image in images]\n",
    "\n",
    "    # 可选的数据预处理：这里假设对图像进行归一化\n",
    "    #images = [tf.cast(image, tf.float32) / 255.0 for image in images]\n",
    "\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c1275d6-df3d-4313-bc65-ea41b554f30b",
   "metadata": {
    "id": "9c1275d6-df3d-4313-bc65-ea41b554f30b"
   },
   "outputs": [],
   "source": [
    "# 你可以根据需要加载多个文件夹中的图像并为其指定不同的标签\n",
    "# folder_paths = ['/path/to/folder1', '/path/to/folder2']\n",
    "# labels = [1, 2]  # 对应文件夹1和文件夹2的标签\n",
    "\n",
    "# 加载所有文件夹中的图像和标签\n",
    "all_images = []\n",
    "all_labels = []\n",
    "for folder_path, label in zip(folder_paths, labels):\n",
    "    images, labels = load_and_label_images(folder_path, label)\n",
    "    all_images.extend(images)\n",
    "    all_labels.extend(labels)\n",
    "    \n",
    "\n",
    "oct_images = []\n",
    "oct_labels = []\n",
    "for oct_path, label in zip(oct_paths, oct_label_Stirng):\n",
    "    images, labels = load_and_label_images(oct_path, oct_label_Stirng)\n",
    "    images_resized = [tf.image.resize(img, (224, 224)) for img  in images]\n",
    "    oct_images.extend(images_resized)\n",
    "    oct_labels.extend(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "afb56706-c873-4e3e-af24-a6db9109ea4e",
   "metadata": {
    "id": "afb56706-c873-4e3e-af24-a6db9109ea4e"
   },
   "outputs": [],
   "source": [
    "# print(all_labels)\n",
    "# print(oct_labels)\n",
    "\n",
    "label_mapping = {\n",
    "    \"NP\":tf.constant([1,0,0], dtype=tf.float32),\n",
    "    \"MP\":tf.constant([0,1,0], dtype=tf.float32),\n",
    "    \"SP\":tf.constant([0,0,1], dtype=tf.float32)\n",
    "}\n",
    "\n",
    "# unique_labels_list = list(set(all_labels))\n",
    "# unique_labels_list.sort()\n",
    "# label_to_index = {label: index for index, label in enumerate(unique_labels_list)}\n",
    "# label_one_hot = [tf.one_hot(label_to_index[label], len(unique_labels_list)) for label in all_labels]\n",
    "label_one_hot = [label_mapping[label] for label in all_labels]\n",
    "oct_label_one_hot = [label_mapping[label[0]] for label in oct_labels]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f916f3f2-2c54-44fb-ab38-686d39e25d5a",
   "metadata": {
    "id": "f916f3f2-2c54-44fb-ab38-686d39e25d5a",
    "outputId": "8d52d023-5f1d-4c2c-8ab7-a31566b0a06d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9728\n",
      "(224, 224, 3)\n",
      "9728\n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>]\n",
      "=============================\n",
      "625\n",
      "(224, 224, 3)\n",
      "625\n",
      "[<tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>, <tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 0., 0.], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "print(len(all_images))\n",
    "print(all_images[0].shape)\n",
    "print(len(label_one_hot))\n",
    "print(label_one_hot[0:10])\n",
    "\n",
    "print(\"=============================\")\n",
    "\n",
    "print(len(oct_images))\n",
    "print(oct_images[0].shape)\n",
    "print(len(oct_label_one_hot))\n",
    "print(oct_label_one_hot[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c62937c-f7ff-4f53-aa11-fe829587d15b",
   "metadata": {
    "id": "8c62937c-f7ff-4f53-aa11-fe829587d15b"
   },
   "outputs": [],
   "source": [
    "\n",
    "train_images, validation_images, train_labels, validation_labels = train_test_split(all_images, label_one_hot, test_size=0.2, random_state=42)\n",
    "\n",
    "# 创建训练集数据集\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
    "train_dataset = train_dataset.shuffle(buffer_size=len(train_images)).batch(batch_size=32)\n",
    "\n",
    "# 创建验证集数据集\n",
    "validation_dataset = tf.data.Dataset.from_tensor_slices((validation_images, validation_labels))\n",
    "validation_dataset = validation_dataset.shuffle(buffer_size=len(validation_images)).batch(batch_size=32)\n",
    "\n",
    "# 創建測試資料集\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((oct_images, oct_label_one_hot))\n",
    "test_dataset = test_dataset.shuffle(buffer_size=len(oct_images)).batch(batch_size=32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a64943b7-3d2b-4b80-b0cc-e4e0db1f458e",
   "metadata": {
    "id": "a64943b7-3d2b-4b80-b0cc-e4e0db1f458e"
   },
   "outputs": [],
   "source": [
    "# # 构建数据集\n",
    "# dataset = tf.data.Dataset.from_tensor_slices((all_images, label_one_hot))\n",
    "\n",
    "# # 可选的打乱和分批处理\n",
    "# dataset = dataset.shuffle(buffer_size=len(all_images)).batch(batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee37a14c-20d5-4284-a915-218a5556fdd0",
   "metadata": {
    "id": "ee37a14c-20d5-4284-a915-218a5556fdd0",
    "outputId": "189ac0ae-1c52-4ea7-a218-08e95b8a2bc1"
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # 从数据集中获取并显示图像\n",
    "# fig, axs = plt.subplots(2, 5, figsize=(20, 8))  # 创建一个2x5的子图布局\n",
    "\n",
    "# for i, (image, label) in enumerate(dataset.take(10)):  # 取出前十张图像\n",
    "#     row = i // 5  # 计算当前图像应该位于的行索引\n",
    "#     col = i % 5   # 计算当前图像应该位于的列索引\n",
    "\n",
    "#     axs[row, col].imshow(image[0])  # 假设每个batch里只有一张图像\n",
    "#     axs[row, col].set_title('Label: {}'.format(label[0].numpy()))\n",
    "#     axs[row, col].axis('off')  # 关闭坐标轴\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d11fadea-067a-41f5-93f3-e1dd204bbf40",
   "metadata": {
    "id": "d11fadea-067a-41f5-93f3-e1dd204bbf40"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "# 32, 64, 64\n",
    "# 定义一个简单的卷积神经网络模型\n",
    "def create_model():\n",
    "    model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    # tf.keras.layers.Dropout(0.25),  # 添加 Dropout 層，丟棄 25% 的神經元\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "    # tf.keras.layers.Dropout(0.25),  # 添加 Dropout 層，丟棄 25% 的神經元\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    # tf.keras.layers.Dropout(0.5),   # 添加 Dropout 層，丟棄 50% 的神經元\n",
    "    tf.keras.layers.Dense(3, activation='softmax')\n",
    "])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "318dd38c-5f37-4242-992d-057dacd8da05",
   "metadata": {
    "id": "318dd38c-5f37-4242-992d-057dacd8da05"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/future-outlier/miniconda3/envs/dev/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.categorical_crossentropy,\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e51c502-7231-4196-922e-9c83e809b414",
   "metadata": {},
   "outputs": [
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21632</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,384,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │         \u001b[38;5;34m4,624\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m8\u001b[0m)      │         \u001b[38;5;34m1,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21632\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │     \u001b[38;5;34m1,384,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m195\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,391,387</span> (5.31 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,391,387\u001b[0m (5.31 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,391,387</span> (5.31 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,391,387\u001b[0m (5.31 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3708dceb-452d-4180-bdef-45468222aefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath='best_model.keras',  # 保存模型的文件路径\n",
    "    monitor='val_accuracy',    # 监控的指标，可以是'val_loss'或'val_accuracy'\n",
    "    save_best_only=True,       # 仅保存最好的模型\n",
    "    mode='max',                # 对于准确率，使用'max'模式\n",
    "    verbose=1                  # 输出详细日志信息\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb945a98-1fc8-4c0e-bc09-5fa2171e459d",
   "metadata": {
    "id": "bb945a98-1fc8-4c0e-bc09-5fa2171e459d",
    "outputId": "11fa26b6-c422-466a-e69f-321ed66f8461"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m243/244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - accuracy: 0.6283 - loss: 16.1942\n",
      "Epoch 1: val_accuracy improved from -inf to 0.96557, saving model to best_model.keras\n",
      "\u001b[1m244/244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 120ms/step - accuracy: 0.6295 - loss: 16.0944 - val_accuracy: 0.9656 - val_loss: 0.1355\n",
      "Epoch 2/2\n",
      "\u001b[1m243/244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - accuracy: 0.9889 - loss: 0.0649\n",
      "Epoch 2: val_accuracy improved from 0.96557 to 0.97739, saving model to best_model.keras\n",
      "\u001b[1m244/244\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 118ms/step - accuracy: 0.9890 - loss: 0.0648 - val_accuracy: 0.9774 - val_loss: 0.1489\n"
     ]
    }
   ],
   "source": [
    "# 這裡建議把epoch設小一點\n",
    "# 我跑的時候差不多一個epoch要5~7分鐘\n",
    "\n",
    "result = model.fit(train_dataset, \n",
    "                   validation_data=validation_dataset, \n",
    "                   epochs=2,verbose=1, \n",
    "                   callbacks=[checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06465f2b-9774-4fc1-8257-070ecebde2b4",
   "metadata": {
    "id": "06465f2b-9774-4fc1-8257-070ecebde2b4",
    "outputId": "750dd20a-5bcd-4cab-f81d-c5fe127af6f4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history['accuracy'], label='accuracy')\n",
    "plt.plot(result.history['val_accuracy'], label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0, 1])\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b81bd23-cd48-45a9-bdaf-a632cb19c23c",
   "metadata": {
    "id": "0b81bd23-cd48-45a9-bdaf-a632cb19c23c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# from time import datetime\n",
    "# model_name = \"model\" + datetime.now().strftime(\"%Y-%m-%d\") + \".h5\"\n",
    "model_name = \"big_model.h5\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5fd48e60-4a6e-46cb-967e-f5bdb9a610ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6c6aa3f-295a-4ba5-a917-3bfdaffbbc52",
   "metadata": {
    "id": "c6c6aa3f-295a-4ba5-a917-3bfdaffbbc52"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step\n",
      "Confusion Matrix:\n",
      "[[538  20  67]\n",
      " [  0   0   0]\n",
      " [  0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 使用model預測\n",
    "predictions = model.predict(test_dataset)\n",
    "# 轉換成class index\n",
    "predicted_classes_index = np.argmax(predictions, axis=1)\n",
    "true_classes_index = np.argmax([label.numpy() for label in oct_label_one_hot], axis=1)\n",
    "\n",
    "# 計算confusion matrix\n",
    "conf_matrix = confusion_matrix(true_classes_index, predicted_classes_index)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91fada5d-efcd-40bc-80b3-ea3985ad6397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'86.08%'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str((538) / (538 + 20 + 67) * 100) + \"%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec35fcf9-11b9-4a00-8fb2-d71debaa2758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">222</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)      │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21632</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,384,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m222\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │         \u001b[38;5;34m4,624\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m54\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m52\u001b[0m, \u001b[38;5;34m8\u001b[0m)      │         \u001b[38;5;34m1,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21632\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │     \u001b[38;5;34m1,384,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)              │           \u001b[38;5;34m195\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,391,389</span> (5.31 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,391,389\u001b[0m (5.31 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,391,387</span> (5.31 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,391,387\u001b[0m (5.31 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (12.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2\u001b[0m (12.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "0b71e5ec-e0f8-44c6-9863-3d00313fb321",
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "0dbb9eaf-dc54-4de8-85ba-40cecd36ada6",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4957c536-3c36-480b-af5f-48f7d20664ea",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d34a2b-0817-4092-9bd8-6a1766017035",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce090505-344e-4008-a513-d1700e92a114",
   "metadata": {},
   "outputs": [],
   "source": []
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