Task2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c684b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "# import keras_tuner\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import layers\n",
    "from keras import backend as K\n",
    "# from keras.engine import training\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
    "from sklearn.model_selection import train_test_split\n",
    "from functools import partial\n",
    "import tensorflow.experimental.numpy as tnp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61369df",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Loading data\n",
    "\n",
    "First, we load in the data and split it into a train and test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f91e477",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (14400, 150, 150, 1)\n",
      "14400 train samples\n",
      "3600 test samples\n"
     ]
    }
   ],
   "source": [
    "# load the grayscale images and two-integer labels\n",
    "images = np.load('images.npy')\n",
    "labels = np.load('labels.npy')\n",
    "images = images.astype('float32')\n",
    "images /= 255\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 150, 150\n",
    "\n",
    "# reshape the 2D input data to 4D to fit the conv2D layer\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    images = images.reshape(images.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    images = images.reshape(images.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "# the data, split between train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)\n",
    "\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bee7e6ad-cc50-48a4-bd36-19a4b8a9facc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(5):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(images[i],cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acde9e76",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad3a1e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic parameter settings\n",
    "batch_size = 32 # 32>64>128\n",
    "epochs = 160\n",
    "\n",
    "# change the two-interger labels to a float\n",
    "y_reg_train = y_train[:, 0] + y_train[:, 1] / 60.0\n",
    "y_reg_test = y_test[:, 0] + y_test[:, 1] / 60.0\n",
    "\n",
    "# define the custom mean absolute error function\n",
    "def custom_mae(y_true, y_pred):\n",
    "    differences = K.abs(y_true - y_pred)\n",
    "    diff = K.mean(differences, axis=-1)\n",
    "    return diff\n",
    "\n",
    "# the common sense error for regression model\n",
    "def common_sense_reg(y_true, y_pred):\n",
    "    differences = K.abs(y_true - y_pred)\n",
    "    diff = K.minimum(differences, tf.subtract(12.0, differences))\n",
    "    return diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88283b85-5b3b-4054-b607-5b42fbfa511b",
   "metadata": {},
   "outputs": [],
   "source": [
    "DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, activation='relu', padding=\"SAME\")\n",
    "reg_model = keras.models.Sequential([\n",
    "  DefaultConv2D(filters=64, kernel_size=7, input_shape=input_shape), # worse: kernel_size=3, filters=32\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  DefaultConv2D(filters=128),\n",
    "  DefaultConv2D(filters=128),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  DefaultConv2D(filters=256),\n",
    "  DefaultConv2D(filters=256),\n",
    "  keras.layers.MaxPooling2D(pool_size=2), \n",
    "  DefaultConv2D(filters=256), # add a 256-filter block here\n",
    "  DefaultConv2D(filters=256),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.Flatten(),\n",
    "  keras.layers.Dense(units=128, activation='relu'),\n",
    "  keras.layers.Dropout(0.15), # better having this\n",
    "  keras.layers.Dense(units=64, activation='relu'),\n",
    "  keras.layers.Dropout(0.15),\n",
    "  keras.layers.Dense(1, activation='linear'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ea13770-8623-461d-ba6d-c2fabdb48316",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_7 (Conv2D)           (None, 150, 150, 64)      3200      \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 75, 75, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 37, 37, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPooling  (None, 18, 18, 256)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_12 (Conv2D)          (None, 18, 18, 256)       590080    \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 18, 18, 256)       590080    \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPooling  (None, 9, 9, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 20736)             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 128)               2654336   \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,952,705\n",
      "Trainable params: 4,952,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(reg_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "350c008a-915b-4970-8a90-1a994393638a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/160\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-25 06:40:23.904952: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450/450 [==============================] - 24s 38ms/step - loss: 3.2492 - common_sense_reg: 2.9970 - val_loss: 2.9786 - val_common_sense_reg: 2.9689\n",
      "Epoch 2/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 3.0748 - common_sense_reg: 3.0096 - val_loss: 2.9776 - val_common_sense_reg: 2.9692\n",
      "Epoch 3/160\n",
      "450/450 [==============================] - 15s 33ms/step - loss: 3.0655 - common_sense_reg: 2.9999 - val_loss: 2.9982 - val_common_sense_reg: 2.9758\n",
      "Epoch 4/160\n",
      "450/450 [==============================] - 15s 33ms/step - loss: 3.0578 - common_sense_reg: 3.0036 - val_loss: 3.0096 - val_common_sense_reg: 2.9779\n",
      "Epoch 5/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0533 - common_sense_reg: 3.0024 - val_loss: 2.9739 - val_common_sense_reg: 2.9703\n",
      "Epoch 6/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0473 - common_sense_reg: 2.9994 - val_loss: 3.0608 - val_common_sense_reg: 2.9647\n",
      "Epoch 7/160\n",
      "450/450 [==============================] - 15s 33ms/step - loss: 3.0525 - common_sense_reg: 3.0089 - val_loss: 2.9768 - val_common_sense_reg: 2.9690\n",
      "Epoch 8/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0516 - common_sense_reg: 3.0121 - val_loss: 2.9710 - val_common_sense_reg: 2.9707\n",
      "Epoch 9/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0467 - common_sense_reg: 3.0077 - val_loss: 2.9715 - val_common_sense_reg: 2.9712\n",
      "Epoch 10/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0426 - common_sense_reg: 3.0060 - val_loss: 2.9703 - val_common_sense_reg: 2.9696\n",
      "Epoch 11/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0452 - common_sense_reg: 3.0130 - val_loss: 2.9727 - val_common_sense_reg: 2.9675\n",
      "Epoch 12/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0430 - common_sense_reg: 3.0110 - val_loss: 2.9899 - val_common_sense_reg: 2.9625\n",
      "Epoch 13/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 3.0375 - common_sense_reg: 3.0065 - val_loss: 2.9709 - val_common_sense_reg: 2.9528\n",
      "Epoch 14/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 2.9909 - common_sense_reg: 2.9465 - val_loss: 2.9040 - val_common_sense_reg: 2.8437\n",
      "Epoch 15/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 2.8877 - common_sense_reg: 2.7849 - val_loss: 3.0215 - val_common_sense_reg: 2.9200\n",
      "Epoch 16/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 2.5938 - common_sense_reg: 2.4708 - val_loss: 2.3198 - val_common_sense_reg: 2.2275\n",
      "Epoch 17/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 2.2923 - common_sense_reg: 2.1758 - val_loss: 2.0437 - val_common_sense_reg: 1.9667\n",
      "Epoch 18/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 2.0204 - common_sense_reg: 1.9251 - val_loss: 1.7621 - val_common_sense_reg: 1.6938\n",
      "Epoch 19/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 1.8018 - common_sense_reg: 1.7189 - val_loss: 1.6009 - val_common_sense_reg: 1.5226\n",
      "Epoch 20/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 1.6291 - common_sense_reg: 1.5605 - val_loss: 1.4564 - val_common_sense_reg: 1.3890\n",
      "Epoch 21/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 1.4784 - common_sense_reg: 1.4200 - val_loss: 1.2450 - val_common_sense_reg: 1.1730\n",
      "Epoch 22/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 1.3100 - common_sense_reg: 1.2633 - val_loss: 1.0504 - val_common_sense_reg: 1.0141\n",
      "Epoch 23/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 1.1936 - common_sense_reg: 1.1636 - val_loss: 0.9643 - val_common_sense_reg: 0.9417\n",
      "Epoch 24/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 1.0946 - common_sense_reg: 1.0686 - val_loss: 0.9601 - val_common_sense_reg: 0.9374\n",
      "Epoch 25/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.9819 - common_sense_reg: 0.9640 - val_loss: 0.7904 - val_common_sense_reg: 0.7711\n",
      "Epoch 26/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.9127 - common_sense_reg: 0.8983 - val_loss: 0.6729 - val_common_sense_reg: 0.6591\n",
      "Epoch 27/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.8324 - common_sense_reg: 0.8218 - val_loss: 0.6990 - val_common_sense_reg: 0.6798\n",
      "Epoch 28/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.7773 - common_sense_reg: 0.7687 - val_loss: 0.5857 - val_common_sense_reg: 0.5700\n",
      "Epoch 29/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.7304 - common_sense_reg: 0.7239 - val_loss: 0.5911 - val_common_sense_reg: 0.5768\n",
      "Epoch 30/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.6902 - common_sense_reg: 0.6849 - val_loss: 0.6951 - val_common_sense_reg: 0.6704\n",
      "Epoch 31/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.6565 - common_sense_reg: 0.6513 - val_loss: 0.5349 - val_common_sense_reg: 0.5199\n",
      "Epoch 32/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.6286 - common_sense_reg: 0.6236 - val_loss: 0.4792 - val_common_sense_reg: 0.4694\n",
      "Epoch 33/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.6069 - common_sense_reg: 0.6040 - val_loss: 0.4678 - val_common_sense_reg: 0.4604\n",
      "Epoch 34/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5816 - common_sense_reg: 0.5788 - val_loss: 0.5089 - val_common_sense_reg: 0.5015\n",
      "Epoch 35/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5711 - common_sense_reg: 0.5681 - val_loss: 0.5126 - val_common_sense_reg: 0.4982\n",
      "Epoch 36/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5451 - common_sense_reg: 0.5425 - val_loss: 0.4959 - val_common_sense_reg: 0.4836\n",
      "Epoch 37/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5303 - common_sense_reg: 0.5284 - val_loss: 0.4074 - val_common_sense_reg: 0.3988\n",
      "Epoch 38/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5208 - common_sense_reg: 0.5192 - val_loss: 0.4167 - val_common_sense_reg: 0.4071\n",
      "Epoch 39/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5083 - common_sense_reg: 0.5077 - val_loss: 0.4058 - val_common_sense_reg: 0.3985\n",
      "Epoch 40/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.5000 - common_sense_reg: 0.4992 - val_loss: 0.4217 - val_common_sense_reg: 0.4063\n",
      "Epoch 41/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4853 - common_sense_reg: 0.4848 - val_loss: 0.3905 - val_common_sense_reg: 0.3828\n",
      "Epoch 42/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4750 - common_sense_reg: 0.4737 - val_loss: 0.3991 - val_common_sense_reg: 0.3882\n",
      "Epoch 43/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4667 - common_sense_reg: 0.4660 - val_loss: 0.3409 - val_common_sense_reg: 0.3346\n",
      "Epoch 44/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 0.4575 - common_sense_reg: 0.4575 - val_loss: 0.3441 - val_common_sense_reg: 0.3329\n",
      "Epoch 45/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4370 - common_sense_reg: 0.4368 - val_loss: 0.3752 - val_common_sense_reg: 0.3732\n",
      "Epoch 46/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4427 - common_sense_reg: 0.4427 - val_loss: 0.3820 - val_common_sense_reg: 0.3756\n",
      "Epoch 47/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4339 - common_sense_reg: 0.4336 - val_loss: 0.3205 - val_common_sense_reg: 0.3130\n",
      "Epoch 48/160\n",
      "450/450 [==============================] - 18s 41ms/step - loss: 0.4305 - common_sense_reg: 0.4305 - val_loss: 0.3714 - val_common_sense_reg: 0.3650\n",
      "Epoch 49/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4311 - common_sense_reg: 0.4308 - val_loss: 0.3483 - val_common_sense_reg: 0.3416\n",
      "Epoch 50/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4221 - common_sense_reg: 0.4220 - val_loss: 0.3273 - val_common_sense_reg: 0.3213\n",
      "Epoch 51/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4133 - common_sense_reg: 0.4130 - val_loss: 0.3238 - val_common_sense_reg: 0.3173\n",
      "Epoch 52/160\n",
      "450/450 [==============================] - 18s 41ms/step - loss: 0.4099 - common_sense_reg: 0.4096 - val_loss: 0.3168 - val_common_sense_reg: 0.3112\n",
      "Epoch 53/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.4125 - common_sense_reg: 0.4121 - val_loss: 0.3272 - val_common_sense_reg: 0.3203\n",
      "Epoch 54/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3996 - common_sense_reg: 0.3996 - val_loss: 0.3306 - val_common_sense_reg: 0.3226\n",
      "Epoch 55/160\n",
      "450/450 [==============================] - 19s 43ms/step - loss: 0.4002 - common_sense_reg: 0.4000 - val_loss: 0.2972 - val_common_sense_reg: 0.2923\n",
      "Epoch 56/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3907 - common_sense_reg: 0.3906 - val_loss: 0.2946 - val_common_sense_reg: 0.2905\n",
      "Epoch 57/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3948 - common_sense_reg: 0.3948 - val_loss: 0.2943 - val_common_sense_reg: 0.2893\n",
      "Epoch 58/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3840 - common_sense_reg: 0.3839 - val_loss: 0.3292 - val_common_sense_reg: 0.3217\n",
      "Epoch 59/160\n",
      "450/450 [==============================] - 18s 39ms/step - loss: 0.3884 - common_sense_reg: 0.3883 - val_loss: 0.3241 - val_common_sense_reg: 0.3177\n",
      "Epoch 60/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3798 - common_sense_reg: 0.3798 - val_loss: 0.3146 - val_common_sense_reg: 0.3114\n",
      "Epoch 61/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3731 - common_sense_reg: 0.3731 - val_loss: 0.3113 - val_common_sense_reg: 0.3082\n",
      "Epoch 62/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3733 - common_sense_reg: 0.3733 - val_loss: 0.3079 - val_common_sense_reg: 0.2977\n",
      "Epoch 63/160\n",
      "450/450 [==============================] - 19s 41ms/step - loss: 0.3731 - common_sense_reg: 0.3731 - val_loss: 0.2764 - val_common_sense_reg: 0.2714\n",
      "Epoch 64/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3686 - common_sense_reg: 0.3686 - val_loss: 0.3131 - val_common_sense_reg: 0.3040\n",
      "Epoch 65/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3683 - common_sense_reg: 0.3683 - val_loss: 0.2717 - val_common_sense_reg: 0.2665\n",
      "Epoch 66/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 0.3624 - common_sense_reg: 0.3624 - val_loss: 0.2788 - val_common_sense_reg: 0.2708\n",
      "Epoch 67/160\n",
      "450/450 [==============================] - 16s 35ms/step - loss: 0.3578 - common_sense_reg: 0.3578 - val_loss: 0.3056 - val_common_sense_reg: 0.2994\n",
      "Epoch 68/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3617 - common_sense_reg: 0.3617 - val_loss: 0.2735 - val_common_sense_reg: 0.2683\n",
      "Epoch 69/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3531 - common_sense_reg: 0.3531 - val_loss: 0.3006 - val_common_sense_reg: 0.2937\n",
      "Epoch 70/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3583 - common_sense_reg: 0.3583 - val_loss: 0.2685 - val_common_sense_reg: 0.2610\n",
      "Epoch 71/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3493 - common_sense_reg: 0.3493 - val_loss: 0.3091 - val_common_sense_reg: 0.3043\n",
      "Epoch 72/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3495 - common_sense_reg: 0.3495 - val_loss: 0.3092 - val_common_sense_reg: 0.3020\n",
      "Epoch 73/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3450 - common_sense_reg: 0.3450 - val_loss: 0.2671 - val_common_sense_reg: 0.2616\n",
      "Epoch 74/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3432 - common_sense_reg: 0.3430 - val_loss: 0.2489 - val_common_sense_reg: 0.2432\n",
      "Epoch 75/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3474 - common_sense_reg: 0.3474 - val_loss: 0.2770 - val_common_sense_reg: 0.2721\n",
      "Epoch 76/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3412 - common_sense_reg: 0.3412 - val_loss: 0.2886 - val_common_sense_reg: 0.2805\n",
      "Epoch 77/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3437 - common_sense_reg: 0.3437 - val_loss: 0.2849 - val_common_sense_reg: 0.2814\n",
      "Epoch 78/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3404 - common_sense_reg: 0.3404 - val_loss: 0.2721 - val_common_sense_reg: 0.2657\n",
      "Epoch 79/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3369 - common_sense_reg: 0.3369 - val_loss: 0.2932 - val_common_sense_reg: 0.2860\n",
      "Epoch 80/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3362 - common_sense_reg: 0.3361 - val_loss: 0.2568 - val_common_sense_reg: 0.2521\n",
      "Epoch 81/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3349 - common_sense_reg: 0.3349 - val_loss: 0.2542 - val_common_sense_reg: 0.2482\n",
      "Epoch 82/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3313 - common_sense_reg: 0.3313 - val_loss: 0.2771 - val_common_sense_reg: 0.2703\n",
      "Epoch 83/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3305 - common_sense_reg: 0.3305 - val_loss: 0.2513 - val_common_sense_reg: 0.2456\n",
      "Epoch 84/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3317 - common_sense_reg: 0.3317 - val_loss: 0.2597 - val_common_sense_reg: 0.2546\n",
      "Epoch 85/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3287 - common_sense_reg: 0.3285 - val_loss: 0.2466 - val_common_sense_reg: 0.2423\n",
      "Epoch 86/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3302 - common_sense_reg: 0.3302 - val_loss: 0.2596 - val_common_sense_reg: 0.2544\n",
      "Epoch 87/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3315 - common_sense_reg: 0.3314 - val_loss: 0.2828 - val_common_sense_reg: 0.2787\n",
      "Epoch 88/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3261 - common_sense_reg: 0.3261 - val_loss: 0.2549 - val_common_sense_reg: 0.2487\n",
      "Epoch 89/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3247 - common_sense_reg: 0.3247 - val_loss: 0.2434 - val_common_sense_reg: 0.2378\n",
      "Epoch 90/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3243 - common_sense_reg: 0.3243 - val_loss: 0.2852 - val_common_sense_reg: 0.2796\n",
      "Epoch 91/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3223 - common_sense_reg: 0.3223 - val_loss: 0.2544 - val_common_sense_reg: 0.2487\n",
      "Epoch 92/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3228 - common_sense_reg: 0.3228 - val_loss: 0.2520 - val_common_sense_reg: 0.2448\n",
      "Epoch 93/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3177 - common_sense_reg: 0.3177 - val_loss: 0.2655 - val_common_sense_reg: 0.2604\n",
      "Epoch 94/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3173 - common_sense_reg: 0.3173 - val_loss: 0.2556 - val_common_sense_reg: 0.2496\n",
      "Epoch 95/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3207 - common_sense_reg: 0.3205 - val_loss: 0.2496 - val_common_sense_reg: 0.2460\n",
      "Epoch 96/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3132 - common_sense_reg: 0.3132 - val_loss: 0.2465 - val_common_sense_reg: 0.2419\n",
      "Epoch 97/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3131 - common_sense_reg: 0.3131 - val_loss: 0.2426 - val_common_sense_reg: 0.2368\n",
      "Epoch 98/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3139 - common_sense_reg: 0.3139 - val_loss: 0.2462 - val_common_sense_reg: 0.2409\n",
      "Epoch 99/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3137 - common_sense_reg: 0.3137 - val_loss: 0.2358 - val_common_sense_reg: 0.2325\n",
      "Epoch 100/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3088 - common_sense_reg: 0.3088 - val_loss: 0.2654 - val_common_sense_reg: 0.2601\n",
      "Epoch 101/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3086 - common_sense_reg: 0.3086 - val_loss: 0.2556 - val_common_sense_reg: 0.2500\n",
      "Epoch 102/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3114 - common_sense_reg: 0.3114 - val_loss: 0.2319 - val_common_sense_reg: 0.2266\n",
      "Epoch 103/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3104 - common_sense_reg: 0.3104 - val_loss: 0.2415 - val_common_sense_reg: 0.2354\n",
      "Epoch 104/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3114 - common_sense_reg: 0.3114 - val_loss: 0.2312 - val_common_sense_reg: 0.2259\n",
      "Epoch 105/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3115 - common_sense_reg: 0.3115 - val_loss: 0.2592 - val_common_sense_reg: 0.2547\n",
      "Epoch 106/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3020 - common_sense_reg: 0.3020 - val_loss: 0.2662 - val_common_sense_reg: 0.2598\n",
      "Epoch 107/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3065 - common_sense_reg: 0.3065 - val_loss: 0.2497 - val_common_sense_reg: 0.2440\n",
      "Epoch 108/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3078 - common_sense_reg: 0.3078 - val_loss: 0.2249 - val_common_sense_reg: 0.2216\n",
      "Epoch 109/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3031 - common_sense_reg: 0.3031 - val_loss: 0.2533 - val_common_sense_reg: 0.2470\n",
      "Epoch 110/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3070 - common_sense_reg: 0.3070 - val_loss: 0.2485 - val_common_sense_reg: 0.2436\n",
      "Epoch 111/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3058 - common_sense_reg: 0.3058 - val_loss: 0.2298 - val_common_sense_reg: 0.2281\n",
      "Epoch 112/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3011 - common_sense_reg: 0.3011 - val_loss: 0.2304 - val_common_sense_reg: 0.2277\n",
      "Epoch 113/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3012 - common_sense_reg: 0.3012 - val_loss: 0.2549 - val_common_sense_reg: 0.2490\n",
      "Epoch 114/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3014 - common_sense_reg: 0.3014 - val_loss: 0.2553 - val_common_sense_reg: 0.2514\n",
      "Epoch 115/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3019 - common_sense_reg: 0.3019 - val_loss: 0.2386 - val_common_sense_reg: 0.2339\n",
      "Epoch 116/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2994 - common_sense_reg: 0.2994 - val_loss: 0.2394 - val_common_sense_reg: 0.2347\n",
      "Epoch 117/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.3013 - common_sense_reg: 0.3013 - val_loss: 0.2475 - val_common_sense_reg: 0.2422\n",
      "Epoch 118/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2982 - common_sense_reg: 0.2982 - val_loss: 0.2339 - val_common_sense_reg: 0.2293\n",
      "Epoch 119/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2940 - common_sense_reg: 0.2940 - val_loss: 0.2384 - val_common_sense_reg: 0.2335\n",
      "Epoch 120/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2976 - common_sense_reg: 0.2976 - val_loss: 0.2268 - val_common_sense_reg: 0.2210\n",
      "Epoch 121/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2972 - common_sense_reg: 0.2972 - val_loss: 0.2188 - val_common_sense_reg: 0.2136\n",
      "Epoch 122/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2947 - common_sense_reg: 0.2947 - val_loss: 0.2114 - val_common_sense_reg: 0.2039\n",
      "Epoch 123/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2905 - common_sense_reg: 0.2905 - val_loss: 0.2247 - val_common_sense_reg: 0.2212\n",
      "Epoch 124/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2934 - common_sense_reg: 0.2934 - val_loss: 0.2295 - val_common_sense_reg: 0.2245\n",
      "Epoch 125/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2934 - common_sense_reg: 0.2934 - val_loss: 0.2445 - val_common_sense_reg: 0.2384\n",
      "Epoch 126/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2941 - common_sense_reg: 0.2941 - val_loss: 0.2305 - val_common_sense_reg: 0.2255\n",
      "Epoch 127/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2919 - common_sense_reg: 0.2919 - val_loss: 0.2465 - val_common_sense_reg: 0.2399\n",
      "Epoch 128/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2930 - common_sense_reg: 0.2930 - val_loss: 0.2114 - val_common_sense_reg: 0.2058\n",
      "Epoch 129/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2896 - common_sense_reg: 0.2896 - val_loss: 0.2139 - val_common_sense_reg: 0.2109\n",
      "Epoch 130/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2877 - common_sense_reg: 0.2877 - val_loss: 0.2300 - val_common_sense_reg: 0.2264\n",
      "Epoch 131/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2908 - common_sense_reg: 0.2908 - val_loss: 0.2339 - val_common_sense_reg: 0.2278\n",
      "Epoch 132/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2910 - common_sense_reg: 0.2910 - val_loss: 0.2253 - val_common_sense_reg: 0.2208\n",
      "Epoch 133/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2869 - common_sense_reg: 0.2869 - val_loss: 0.2339 - val_common_sense_reg: 0.2296\n",
      "Epoch 134/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2909 - common_sense_reg: 0.2909 - val_loss: 0.2134 - val_common_sense_reg: 0.2084\n",
      "Epoch 135/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2882 - common_sense_reg: 0.2882 - val_loss: 0.2269 - val_common_sense_reg: 0.2215\n",
      "Epoch 136/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2879 - common_sense_reg: 0.2879 - val_loss: 0.2453 - val_common_sense_reg: 0.2383\n",
      "Epoch 137/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2863 - common_sense_reg: 0.2863 - val_loss: 0.2170 - val_common_sense_reg: 0.2112\n",
      "Epoch 138/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2878 - common_sense_reg: 0.2878 - val_loss: 0.2426 - val_common_sense_reg: 0.2384\n",
      "Epoch 139/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2836 - common_sense_reg: 0.2836 - val_loss: 0.2228 - val_common_sense_reg: 0.2186\n",
      "Epoch 140/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2857 - common_sense_reg: 0.2857 - val_loss: 0.2139 - val_common_sense_reg: 0.2106\n",
      "Epoch 141/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2861 - common_sense_reg: 0.2861 - val_loss: 0.2432 - val_common_sense_reg: 0.2367\n",
      "Epoch 142/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2835 - common_sense_reg: 0.2835 - val_loss: 0.2440 - val_common_sense_reg: 0.2382\n",
      "Epoch 143/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2834 - common_sense_reg: 0.2834 - val_loss: 0.2107 - val_common_sense_reg: 0.2080\n",
      "Epoch 144/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2858 - common_sense_reg: 0.2858 - val_loss: 0.2397 - val_common_sense_reg: 0.2363\n",
      "Epoch 145/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2841 - common_sense_reg: 0.2841 - val_loss: 0.2257 - val_common_sense_reg: 0.2224\n",
      "Epoch 146/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2836 - common_sense_reg: 0.2836 - val_loss: 0.2145 - val_common_sense_reg: 0.2100\n",
      "Epoch 147/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2835 - common_sense_reg: 0.2835 - val_loss: 0.2119 - val_common_sense_reg: 0.2095\n",
      "Epoch 148/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2803 - common_sense_reg: 0.2803 - val_loss: 0.2005 - val_common_sense_reg: 0.1979\n",
      "Epoch 149/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2844 - common_sense_reg: 0.2844 - val_loss: 0.2384 - val_common_sense_reg: 0.2315\n",
      "Epoch 150/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2836 - common_sense_reg: 0.2836 - val_loss: 0.2298 - val_common_sense_reg: 0.2252\n",
      "Epoch 151/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2834 - common_sense_reg: 0.2834 - val_loss: 0.2147 - val_common_sense_reg: 0.2108\n",
      "Epoch 152/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2801 - common_sense_reg: 0.2801 - val_loss: 0.2101 - val_common_sense_reg: 0.2057\n",
      "Epoch 153/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2755 - common_sense_reg: 0.2755 - val_loss: 0.2110 - val_common_sense_reg: 0.2060\n",
      "Epoch 154/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2772 - common_sense_reg: 0.2772 - val_loss: 0.2301 - val_common_sense_reg: 0.2253\n",
      "Epoch 155/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2779 - common_sense_reg: 0.2779 - val_loss: 0.2134 - val_common_sense_reg: 0.2075\n",
      "Epoch 156/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2792 - common_sense_reg: 0.2792 - val_loss: 0.2086 - val_common_sense_reg: 0.2051\n",
      "Epoch 157/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2767 - common_sense_reg: 0.2767 - val_loss: 0.2112 - val_common_sense_reg: 0.2071\n",
      "Epoch 158/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2802 - common_sense_reg: 0.2802 - val_loss: 0.1993 - val_common_sense_reg: 0.1950\n",
      "Epoch 159/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2792 - common_sense_reg: 0.2792 - val_loss: 0.2214 - val_common_sense_reg: 0.2174\n",
      "Epoch 160/160\n",
      "450/450 [==============================] - 15s 34ms/step - loss: 0.2755 - common_sense_reg: 0.2755 - val_loss: 0.2097 - val_common_sense_reg: 0.2062\n",
      "Test mean absolute error: 0.2097097933292389\n",
      "Test common sense error: 0.20621193945407867\n"
     ]
    }
   ],
   "source": [
    "# reg_model.compile(loss=\"mean_absolute_error\", optimizer='sgd', metrics=[tf.keras.losses.MeanAbsoluteError()])\n",
    "reg_model.compile(loss=custom_mae, optimizer='sgd', metrics=[common_sense_reg])\n",
    "\n",
    "reg_history = reg_model.fit(X_train, y_reg_train, \n",
    "          batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_reg_test))\n",
    "score = reg_model.evaluate(X_test, y_reg_test, verbose=0)\n",
    "print('Test mean absolute error:', score[0])\n",
    "print('Test common sense error:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4c2fbef-9692-4ff9-b2ee-7087ac784b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_model.save(\"regression.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67c54143-a572-4c56-96ca-cd294aa44235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(reg_history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 3.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b2ed151-3e05-4b26-8b4e-c2d0dd331270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(reg_history.history['loss'])\n",
    "plt.plot(reg_history.history['val_loss'])\n",
    "plt.plot(reg_history.history['common_sense_reg'])\n",
    "plt.plot(reg_history.history['val_common_sense_reg'])\n",
    "# plt.title('Regression')\n",
    "plt.ylabel('error')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_mae', 'test_mae', 'train_cse', 'test_cse'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18dccf52",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "811329a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic parameter settings\n",
    "batch_size = 64 # tuning this？\n",
    "num_classes = 720 # 24 or 720\n",
    "epochs = 60 # 24: 200， 720: 60\n",
    "\n",
    "# change the two-integer labels to 24 or 720 classes\n",
    "def classification_label(y, num_classes):\n",
    "    label = []\n",
    "    if num_classes == 24:\n",
    "        for i in y:\n",
    "            if i[1] <= 30:\n",
    "                label.append(2 * i[0])\n",
    "            else:\n",
    "                label.append(2 * i[0] + 1)\n",
    "    else: # num_classes == 720\n",
    "        for i in y:\n",
    "            label.append(60 * i[0] + i[1])\n",
    "    return label\n",
    "\n",
    "y_class_train = np.array(classification_label(y_train, num_classes))\n",
    "y_class_test = np.array(classification_label(y_test, num_classes))\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_class_train = keras.utils.to_categorical(y_class_train, num_classes)\n",
    "y_class_test = keras.utils.to_categorical(y_class_test, num_classes)\n",
    "\n",
    "# the common sense accuracy for classification model\n",
    "def common_sense_class(y_true, y_pred):\n",
    "    differences = K.abs(tf.math.argmax(y_true, 1) - tf.math.argmax(y_pred, 1))\n",
    "    # num_classes = tf.cast(num_classes, tf.int64)\n",
    "    differences = tf.cast(differences, tf.int32)\n",
    "    diff = tf.math.minimum(differences, tf.subtract(num_classes, differences))\n",
    "    return diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d35515c",
   "metadata": {},
   "outputs": [],
   "source": [
    "DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, activation='relu', padding=\"same\")\n",
    "class_model_1 = keras.models.Sequential([\n",
    "  DefaultConv2D(filters=32, input_shape=input_shape), # worse: kernel_size=3, filters=32\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "    \n",
    "  DefaultConv2D(filters=64),\n",
    "  DefaultConv2D(filters=64),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "    \n",
    "  # DefaultConv2D(filters=96),\n",
    "  DefaultConv2D(filters=96),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "\n",
    "  # DefaultConv2D(filters=128),\n",
    "  DefaultConv2D(filters=128),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "\n",
    "  keras.layers.Flatten(),\n",
    "  keras.layers.Dense(units=256, activation='relu'),\n",
    "  keras.layers.Dropout(0.2), # better having this\n",
    "  keras.layers.Dense(units=128, activation='relu'),\n",
    "  keras.layers.Dropout(0.2),\n",
    "  keras.layers.Dense(units=num_classes, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d7eb760-9306-4f48-9f04-c1c2f6cfc4a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "225/225 [==============================] - 8s 32ms/step - loss: 3.2613 - accuracy: 0.0444 - common_sense_class: 5.8911 - val_loss: 3.2477 - val_accuracy: 0.0453 - val_common_sense_class: 5.9372\n",
      "Epoch 2/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 3.1709 - accuracy: 0.0501 - common_sense_class: 5.6805 - val_loss: 3.2418 - val_accuracy: 0.0453 - val_common_sense_class: 5.9372\n",
      "Epoch 3/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 3.0730 - accuracy: 0.0720 - common_sense_class: 5.1961 - val_loss: 3.3718 - val_accuracy: 0.0431 - val_common_sense_class: 5.9817\n",
      "Epoch 4/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.9029 - accuracy: 0.1054 - common_sense_class: 4.6284 - val_loss: 3.0072 - val_accuracy: 0.0811 - val_common_sense_class: 4.9150\n",
      "Epoch 5/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.7115 - accuracy: 0.1404 - common_sense_class: 4.1619 - val_loss: 2.8391 - val_accuracy: 0.1158 - val_common_sense_class: 4.3117\n",
      "Epoch 6/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.5650 - accuracy: 0.1699 - common_sense_class: 3.8815 - val_loss: 2.5930 - val_accuracy: 0.1714 - val_common_sense_class: 3.6594\n",
      "Epoch 7/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.4149 - accuracy: 0.2029 - common_sense_class: 3.5660 - val_loss: 2.6307 - val_accuracy: 0.1658 - val_common_sense_class: 3.5222\n",
      "Epoch 8/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.2752 - accuracy: 0.2360 - common_sense_class: 3.3073 - val_loss: 2.6452 - val_accuracy: 0.1728 - val_common_sense_class: 3.4242\n",
      "Epoch 9/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.1287 - accuracy: 0.2755 - common_sense_class: 3.0479 - val_loss: 2.3967 - val_accuracy: 0.2258 - val_common_sense_class: 2.9967\n",
      "Epoch 10/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.0024 - accuracy: 0.3105 - common_sense_class: 2.7433 - val_loss: 2.2973 - val_accuracy: 0.2556 - val_common_sense_class: 2.7731\n",
      "Epoch 11/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.8689 - accuracy: 0.3530 - common_sense_class: 2.4758 - val_loss: 2.0970 - val_accuracy: 0.3200 - val_common_sense_class: 2.4261\n",
      "Epoch 12/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.7716 - accuracy: 0.3755 - common_sense_class: 2.3144 - val_loss: 1.7244 - val_accuracy: 0.4306 - val_common_sense_class: 1.9650\n",
      "Epoch 13/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.6646 - accuracy: 0.4080 - common_sense_class: 2.1182 - val_loss: 1.6500 - val_accuracy: 0.4339 - val_common_sense_class: 1.8050\n",
      "Epoch 14/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.5659 - accuracy: 0.4405 - common_sense_class: 1.9431 - val_loss: 1.6041 - val_accuracy: 0.4636 - val_common_sense_class: 1.6206\n",
      "Epoch 15/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.4594 - accuracy: 0.4711 - common_sense_class: 1.7785 - val_loss: 1.4698 - val_accuracy: 0.5181 - val_common_sense_class: 1.4569\n",
      "Epoch 16/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.3809 - accuracy: 0.4981 - common_sense_class: 1.6235 - val_loss: 1.4003 - val_accuracy: 0.5208 - val_common_sense_class: 1.3597\n",
      "Epoch 17/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.3048 - accuracy: 0.5212 - common_sense_class: 1.5303 - val_loss: 1.2013 - val_accuracy: 0.6108 - val_common_sense_class: 1.0842\n",
      "Epoch 18/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.2303 - accuracy: 0.5493 - common_sense_class: 1.3972 - val_loss: 1.2650 - val_accuracy: 0.5808 - val_common_sense_class: 1.1236\n",
      "Epoch 19/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.1565 - accuracy: 0.5775 - common_sense_class: 1.2675 - val_loss: 1.1079 - val_accuracy: 0.6267 - val_common_sense_class: 1.0019\n",
      "Epoch 20/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.1073 - accuracy: 0.5876 - common_sense_class: 1.1983 - val_loss: 1.0565 - val_accuracy: 0.6533 - val_common_sense_class: 0.9333\n",
      "Epoch 21/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.0277 - accuracy: 0.6173 - common_sense_class: 1.0936 - val_loss: 1.0800 - val_accuracy: 0.6372 - val_common_sense_class: 0.9411\n",
      "Epoch 22/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 0.9999 - accuracy: 0.6278 - common_sense_class: 1.0583 - val_loss: 0.9982 - val_accuracy: 0.6708 - val_common_sense_class: 0.8225\n",
      "Epoch 23/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 0.9385 - accuracy: 0.6544 - common_sense_class: 0.9680 - val_loss: 0.8719 - val_accuracy: 0.7103 - val_common_sense_class: 0.7183\n",
      "Epoch 24/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8860 - accuracy: 0.6742 - common_sense_class: 0.8897 - val_loss: 0.9057 - val_accuracy: 0.6975 - val_common_sense_class: 0.7475\n",
      "Epoch 25/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8483 - accuracy: 0.6806 - common_sense_class: 0.8539 - val_loss: 0.8136 - val_accuracy: 0.7375 - val_common_sense_class: 0.6186\n",
      "Epoch 26/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7935 - accuracy: 0.7090 - common_sense_class: 0.7715 - val_loss: 0.7569 - val_accuracy: 0.7644 - val_common_sense_class: 0.5656\n",
      "Epoch 27/200\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 0.7615 - accuracy: 0.7154 - common_sense_class: 0.7434 - val_loss: 0.7480 - val_accuracy: 0.7597 - val_common_sense_class: 0.5536\n",
      "Epoch 28/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 0.7322 - accuracy: 0.7276 - common_sense_class: 0.7217 - val_loss: 0.6968 - val_accuracy: 0.7722 - val_common_sense_class: 0.5269\n",
      "Epoch 29/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7066 - accuracy: 0.7365 - common_sense_class: 0.6763 - val_loss: 0.7073 - val_accuracy: 0.7758 - val_common_sense_class: 0.5239\n",
      "Epoch 30/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.6839 - accuracy: 0.7483 - common_sense_class: 0.6617 - val_loss: 0.6164 - val_accuracy: 0.8161 - val_common_sense_class: 0.4075\n",
      "Epoch 31/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.6306 - accuracy: 0.7667 - common_sense_class: 0.5910 - val_loss: 0.5863 - val_accuracy: 0.8197 - val_common_sense_class: 0.4272\n",
      "Epoch 32/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.6136 - accuracy: 0.7759 - common_sense_class: 0.5600 - val_loss: 0.5424 - val_accuracy: 0.8333 - val_common_sense_class: 0.3747\n",
      "Epoch 33/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.5851 - accuracy: 0.7841 - common_sense_class: 0.5437 - val_loss: 0.6086 - val_accuracy: 0.7997 - val_common_sense_class: 0.4644\n",
      "Epoch 34/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.5632 - accuracy: 0.7890 - common_sense_class: 0.5297 - val_loss: 0.5173 - val_accuracy: 0.8414 - val_common_sense_class: 0.3517\n",
      "Epoch 35/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.5516 - accuracy: 0.7976 - common_sense_class: 0.4932 - val_loss: 0.5260 - val_accuracy: 0.8358 - val_common_sense_class: 0.3619\n",
      "Epoch 36/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.5125 - accuracy: 0.8135 - common_sense_class: 0.4630 - val_loss: 0.5155 - val_accuracy: 0.8317 - val_common_sense_class: 0.3753\n",
      "Epoch 37/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.5001 - accuracy: 0.8139 - common_sense_class: 0.4522 - val_loss: 0.4907 - val_accuracy: 0.8425 - val_common_sense_class: 0.3428\n",
      "Epoch 38/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4796 - accuracy: 0.8247 - common_sense_class: 0.4157 - val_loss: 0.4525 - val_accuracy: 0.8519 - val_common_sense_class: 0.3219\n",
      "Epoch 39/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4644 - accuracy: 0.8299 - common_sense_class: 0.4036 - val_loss: 0.4687 - val_accuracy: 0.8514 - val_common_sense_class: 0.3094\n",
      "Epoch 40/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4430 - accuracy: 0.8419 - common_sense_class: 0.3777 - val_loss: 0.4666 - val_accuracy: 0.8442 - val_common_sense_class: 0.3303\n",
      "Epoch 41/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4339 - accuracy: 0.8439 - common_sense_class: 0.3810 - val_loss: 0.4801 - val_accuracy: 0.8417 - val_common_sense_class: 0.3439\n",
      "Epoch 42/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4123 - accuracy: 0.8509 - common_sense_class: 0.3606 - val_loss: 0.4290 - val_accuracy: 0.8667 - val_common_sense_class: 0.2619\n",
      "Epoch 43/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.4046 - accuracy: 0.8540 - common_sense_class: 0.3460 - val_loss: 0.4003 - val_accuracy: 0.8767 - val_common_sense_class: 0.2706\n",
      "Epoch 44/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3910 - accuracy: 0.8605 - common_sense_class: 0.3299 - val_loss: 0.3719 - val_accuracy: 0.8928 - val_common_sense_class: 0.2197\n",
      "Epoch 45/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3837 - accuracy: 0.8617 - common_sense_class: 0.3265 - val_loss: 0.3960 - val_accuracy: 0.8806 - val_common_sense_class: 0.2458\n",
      "Epoch 46/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3677 - accuracy: 0.8683 - common_sense_class: 0.3048 - val_loss: 0.3773 - val_accuracy: 0.8889 - val_common_sense_class: 0.2258\n",
      "Epoch 47/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3618 - accuracy: 0.8683 - common_sense_class: 0.3112 - val_loss: 0.3561 - val_accuracy: 0.8864 - val_common_sense_class: 0.2250\n",
      "Epoch 48/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.3473 - accuracy: 0.8766 - common_sense_class: 0.2889 - val_loss: 0.4135 - val_accuracy: 0.8753 - val_common_sense_class: 0.2431\n",
      "Epoch 49/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3226 - accuracy: 0.8850 - common_sense_class: 0.2667 - val_loss: 0.3885 - val_accuracy: 0.8781 - val_common_sense_class: 0.2378\n",
      "Epoch 50/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3201 - accuracy: 0.8838 - common_sense_class: 0.2778 - val_loss: 0.4220 - val_accuracy: 0.8683 - val_common_sense_class: 0.2800\n",
      "Epoch 51/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2987 - accuracy: 0.8924 - common_sense_class: 0.2476 - val_loss: 0.3653 - val_accuracy: 0.8822 - val_common_sense_class: 0.2283\n",
      "Epoch 52/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.3009 - accuracy: 0.8955 - common_sense_class: 0.2447 - val_loss: 0.3262 - val_accuracy: 0.9017 - val_common_sense_class: 0.1831\n",
      "Epoch 53/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2954 - accuracy: 0.8940 - common_sense_class: 0.2503 - val_loss: 0.3299 - val_accuracy: 0.9014 - val_common_sense_class: 0.1944\n",
      "Epoch 54/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2871 - accuracy: 0.8979 - common_sense_class: 0.2343 - val_loss: 0.2995 - val_accuracy: 0.9061 - val_common_sense_class: 0.1772\n",
      "Epoch 55/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2734 - accuracy: 0.9025 - common_sense_class: 0.2334 - val_loss: 0.2768 - val_accuracy: 0.9164 - val_common_sense_class: 0.1561\n",
      "Epoch 56/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2689 - accuracy: 0.9052 - common_sense_class: 0.2163 - val_loss: 0.2781 - val_accuracy: 0.9197 - val_common_sense_class: 0.1633\n",
      "Epoch 57/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2637 - accuracy: 0.9062 - common_sense_class: 0.2202 - val_loss: 0.3165 - val_accuracy: 0.9022 - val_common_sense_class: 0.1853\n",
      "Epoch 58/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.2531 - accuracy: 0.9101 - common_sense_class: 0.2065 - val_loss: 0.3127 - val_accuracy: 0.9008 - val_common_sense_class: 0.2003\n",
      "Epoch 59/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2482 - accuracy: 0.9133 - common_sense_class: 0.1934 - val_loss: 0.2761 - val_accuracy: 0.9139 - val_common_sense_class: 0.1689\n",
      "Epoch 60/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2402 - accuracy: 0.9177 - common_sense_class: 0.1890 - val_loss: 0.2701 - val_accuracy: 0.9131 - val_common_sense_class: 0.1694\n",
      "Epoch 61/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2390 - accuracy: 0.9181 - common_sense_class: 0.1855 - val_loss: 0.2658 - val_accuracy: 0.9183 - val_common_sense_class: 0.1583\n",
      "Epoch 62/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2293 - accuracy: 0.9209 - common_sense_class: 0.1823 - val_loss: 0.2512 - val_accuracy: 0.9211 - val_common_sense_class: 0.1500\n",
      "Epoch 63/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2210 - accuracy: 0.9216 - common_sense_class: 0.1822 - val_loss: 0.3296 - val_accuracy: 0.8908 - val_common_sense_class: 0.2003\n",
      "Epoch 64/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2211 - accuracy: 0.9229 - common_sense_class: 0.1786 - val_loss: 0.2624 - val_accuracy: 0.9211 - val_common_sense_class: 0.1442\n",
      "Epoch 65/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.2093 - accuracy: 0.9271 - common_sense_class: 0.1715 - val_loss: 0.3237 - val_accuracy: 0.9019 - val_common_sense_class: 0.1781\n",
      "Epoch 66/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2035 - accuracy: 0.9285 - common_sense_class: 0.1600 - val_loss: 0.2609 - val_accuracy: 0.9169 - val_common_sense_class: 0.1567\n",
      "Epoch 67/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2039 - accuracy: 0.9292 - common_sense_class: 0.1605 - val_loss: 0.2812 - val_accuracy: 0.9083 - val_common_sense_class: 0.1647\n",
      "Epoch 68/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.2000 - accuracy: 0.9306 - common_sense_class: 0.1582 - val_loss: 0.2741 - val_accuracy: 0.9175 - val_common_sense_class: 0.1564\n",
      "Epoch 69/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.1849 - accuracy: 0.9351 - common_sense_class: 0.1484 - val_loss: 0.2438 - val_accuracy: 0.9253 - val_common_sense_class: 0.1403\n",
      "Epoch 70/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1965 - accuracy: 0.9304 - common_sense_class: 0.1580 - val_loss: 0.2736 - val_accuracy: 0.9111 - val_common_sense_class: 0.1617\n",
      "Epoch 71/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1873 - accuracy: 0.9351 - common_sense_class: 0.1424 - val_loss: 0.2596 - val_accuracy: 0.9186 - val_common_sense_class: 0.1489\n",
      "Epoch 72/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1839 - accuracy: 0.9352 - common_sense_class: 0.1526 - val_loss: 0.2959 - val_accuracy: 0.9100 - val_common_sense_class: 0.1658\n",
      "Epoch 73/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1781 - accuracy: 0.9378 - common_sense_class: 0.1424 - val_loss: 0.2641 - val_accuracy: 0.9172 - val_common_sense_class: 0.1519\n",
      "Epoch 74/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1695 - accuracy: 0.9406 - common_sense_class: 0.1293 - val_loss: 0.2354 - val_accuracy: 0.9283 - val_common_sense_class: 0.1358\n",
      "Epoch 75/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1708 - accuracy: 0.9393 - common_sense_class: 0.1352 - val_loss: 0.2977 - val_accuracy: 0.9008 - val_common_sense_class: 0.1906\n",
      "Epoch 76/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1632 - accuracy: 0.9420 - common_sense_class: 0.1265 - val_loss: 0.2995 - val_accuracy: 0.9069 - val_common_sense_class: 0.1697\n",
      "Epoch 77/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.1638 - accuracy: 0.9413 - common_sense_class: 0.1349 - val_loss: 0.2103 - val_accuracy: 0.9328 - val_common_sense_class: 0.1294\n",
      "Epoch 78/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1611 - accuracy: 0.9413 - common_sense_class: 0.1351 - val_loss: 0.2822 - val_accuracy: 0.9097 - val_common_sense_class: 0.1639\n",
      "Epoch 79/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1553 - accuracy: 0.9449 - common_sense_class: 0.1221 - val_loss: 0.2328 - val_accuracy: 0.9269 - val_common_sense_class: 0.1336\n",
      "Epoch 80/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1593 - accuracy: 0.9452 - common_sense_class: 0.1271 - val_loss: 0.2335 - val_accuracy: 0.9258 - val_common_sense_class: 0.1303\n",
      "Epoch 81/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1542 - accuracy: 0.9475 - common_sense_class: 0.1226 - val_loss: 0.2169 - val_accuracy: 0.9250 - val_common_sense_class: 0.1389\n",
      "Epoch 82/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1476 - accuracy: 0.9498 - common_sense_class: 0.1187 - val_loss: 0.2621 - val_accuracy: 0.9200 - val_common_sense_class: 0.1522\n",
      "Epoch 83/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1505 - accuracy: 0.9478 - common_sense_class: 0.1184 - val_loss: 0.2072 - val_accuracy: 0.9367 - val_common_sense_class: 0.1158\n",
      "Epoch 84/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1437 - accuracy: 0.9495 - common_sense_class: 0.1150 - val_loss: 0.2229 - val_accuracy: 0.9314 - val_common_sense_class: 0.1189\n",
      "Epoch 85/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1424 - accuracy: 0.9514 - common_sense_class: 0.1110 - val_loss: 0.2090 - val_accuracy: 0.9347 - val_common_sense_class: 0.1339\n",
      "Epoch 86/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1383 - accuracy: 0.9533 - common_sense_class: 0.1043 - val_loss: 0.2019 - val_accuracy: 0.9389 - val_common_sense_class: 0.1086\n",
      "Epoch 87/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1362 - accuracy: 0.9519 - common_sense_class: 0.1122 - val_loss: 0.2491 - val_accuracy: 0.9275 - val_common_sense_class: 0.1294\n",
      "Epoch 88/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1431 - accuracy: 0.9510 - common_sense_class: 0.1072 - val_loss: 0.2189 - val_accuracy: 0.9331 - val_common_sense_class: 0.1331\n",
      "Epoch 89/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1340 - accuracy: 0.9541 - common_sense_class: 0.1057 - val_loss: 0.2240 - val_accuracy: 0.9297 - val_common_sense_class: 0.1275\n",
      "Epoch 90/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1336 - accuracy: 0.9547 - common_sense_class: 0.1013 - val_loss: 0.2138 - val_accuracy: 0.9358 - val_common_sense_class: 0.1147\n",
      "Epoch 91/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1257 - accuracy: 0.9562 - common_sense_class: 0.1020 - val_loss: 0.1902 - val_accuracy: 0.9422 - val_common_sense_class: 0.1081\n",
      "Epoch 92/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1216 - accuracy: 0.9585 - common_sense_class: 0.0908 - val_loss: 0.2108 - val_accuracy: 0.9314 - val_common_sense_class: 0.1231\n",
      "Epoch 93/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.1227 - accuracy: 0.9591 - common_sense_class: 0.0914 - val_loss: 0.2281 - val_accuracy: 0.9331 - val_common_sense_class: 0.1178\n",
      "Epoch 94/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1263 - accuracy: 0.9562 - common_sense_class: 0.0971 - val_loss: 0.2245 - val_accuracy: 0.9328 - val_common_sense_class: 0.1281\n",
      "Epoch 95/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.1145 - accuracy: 0.9614 - common_sense_class: 0.0868 - val_loss: 0.2789 - val_accuracy: 0.9194 - val_common_sense_class: 0.1472\n",
      "Epoch 96/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1197 - accuracy: 0.9569 - common_sense_class: 0.0997 - val_loss: 0.2432 - val_accuracy: 0.9300 - val_common_sense_class: 0.1303\n",
      "Epoch 97/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1181 - accuracy: 0.9598 - common_sense_class: 0.0870 - val_loss: 0.1935 - val_accuracy: 0.9306 - val_common_sense_class: 0.1394\n",
      "Epoch 98/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1115 - accuracy: 0.9621 - common_sense_class: 0.0864 - val_loss: 0.1991 - val_accuracy: 0.9381 - val_common_sense_class: 0.1153\n",
      "Epoch 99/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1087 - accuracy: 0.9637 - common_sense_class: 0.0867 - val_loss: 0.2190 - val_accuracy: 0.9317 - val_common_sense_class: 0.1250\n",
      "Epoch 100/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1102 - accuracy: 0.9617 - common_sense_class: 0.0899 - val_loss: 0.2029 - val_accuracy: 0.9356 - val_common_sense_class: 0.1131\n",
      "Epoch 101/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1136 - accuracy: 0.9602 - common_sense_class: 0.0890 - val_loss: 0.2274 - val_accuracy: 0.9325 - val_common_sense_class: 0.1314\n",
      "Epoch 102/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1089 - accuracy: 0.9631 - common_sense_class: 0.0784 - val_loss: 0.2042 - val_accuracy: 0.9361 - val_common_sense_class: 0.1114\n",
      "Epoch 103/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1110 - accuracy: 0.9613 - common_sense_class: 0.0819 - val_loss: 0.1960 - val_accuracy: 0.9408 - val_common_sense_class: 0.1067\n",
      "Epoch 104/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1073 - accuracy: 0.9617 - common_sense_class: 0.0891 - val_loss: 0.2310 - val_accuracy: 0.9294 - val_common_sense_class: 0.1242\n",
      "Epoch 105/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1117 - accuracy: 0.9603 - common_sense_class: 0.0936 - val_loss: 0.2160 - val_accuracy: 0.9344 - val_common_sense_class: 0.1097\n",
      "Epoch 106/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1054 - accuracy: 0.9626 - common_sense_class: 0.0849 - val_loss: 0.1935 - val_accuracy: 0.9367 - val_common_sense_class: 0.1142\n",
      "Epoch 107/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1024 - accuracy: 0.9644 - common_sense_class: 0.0787 - val_loss: 0.2610 - val_accuracy: 0.9197 - val_common_sense_class: 0.1431\n",
      "Epoch 108/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1041 - accuracy: 0.9629 - common_sense_class: 0.0840 - val_loss: 0.2089 - val_accuracy: 0.9308 - val_common_sense_class: 0.1250\n",
      "Epoch 109/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0995 - accuracy: 0.9656 - common_sense_class: 0.0767 - val_loss: 0.2138 - val_accuracy: 0.9414 - val_common_sense_class: 0.1025\n",
      "Epoch 110/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.1051 - accuracy: 0.9645 - common_sense_class: 0.0812 - val_loss: 0.1902 - val_accuracy: 0.9411 - val_common_sense_class: 0.1103\n",
      "Epoch 111/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0977 - accuracy: 0.9668 - common_sense_class: 0.0777 - val_loss: 0.2087 - val_accuracy: 0.9361 - val_common_sense_class: 0.1097\n",
      "Epoch 112/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0950 - accuracy: 0.9676 - common_sense_class: 0.0763 - val_loss: 0.2084 - val_accuracy: 0.9386 - val_common_sense_class: 0.1078\n",
      "Epoch 113/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0878 - accuracy: 0.9707 - common_sense_class: 0.0658 - val_loss: 0.1980 - val_accuracy: 0.9411 - val_common_sense_class: 0.1061\n",
      "Epoch 114/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0917 - accuracy: 0.9691 - common_sense_class: 0.0711 - val_loss: 0.1844 - val_accuracy: 0.9469 - val_common_sense_class: 0.1019\n",
      "Epoch 115/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.0930 - accuracy: 0.9684 - common_sense_class: 0.0704 - val_loss: 0.1911 - val_accuracy: 0.9431 - val_common_sense_class: 0.1008\n",
      "Epoch 116/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0862 - accuracy: 0.9703 - common_sense_class: 0.0712 - val_loss: 0.2640 - val_accuracy: 0.9242 - val_common_sense_class: 0.1247\n",
      "Epoch 117/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0879 - accuracy: 0.9688 - common_sense_class: 0.0731 - val_loss: 0.1787 - val_accuracy: 0.9431 - val_common_sense_class: 0.1000\n",
      "Epoch 118/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0937 - accuracy: 0.9692 - common_sense_class: 0.0703 - val_loss: 0.2587 - val_accuracy: 0.9264 - val_common_sense_class: 0.1325\n",
      "Epoch 119/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0854 - accuracy: 0.9696 - common_sense_class: 0.0694 - val_loss: 0.2077 - val_accuracy: 0.9428 - val_common_sense_class: 0.1006\n",
      "Epoch 120/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0877 - accuracy: 0.9719 - common_sense_class: 0.0644 - val_loss: 0.1980 - val_accuracy: 0.9392 - val_common_sense_class: 0.1036\n",
      "Epoch 121/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.0903 - accuracy: 0.9680 - common_sense_class: 0.0723 - val_loss: 0.2109 - val_accuracy: 0.9411 - val_common_sense_class: 0.1022\n",
      "Epoch 122/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0853 - accuracy: 0.9690 - common_sense_class: 0.0697 - val_loss: 0.2245 - val_accuracy: 0.9322 - val_common_sense_class: 0.1200\n",
      "Epoch 123/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0810 - accuracy: 0.9737 - common_sense_class: 0.0585 - val_loss: 0.1981 - val_accuracy: 0.9422 - val_common_sense_class: 0.1044\n",
      "Epoch 124/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0823 - accuracy: 0.9708 - common_sense_class: 0.0646 - val_loss: 0.1994 - val_accuracy: 0.9417 - val_common_sense_class: 0.1039\n",
      "Epoch 125/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0872 - accuracy: 0.9705 - common_sense_class: 0.0658 - val_loss: 0.2006 - val_accuracy: 0.9414 - val_common_sense_class: 0.1006\n",
      "Epoch 126/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0878 - accuracy: 0.9708 - common_sense_class: 0.0672 - val_loss: 0.2030 - val_accuracy: 0.9383 - val_common_sense_class: 0.1014\n",
      "Epoch 127/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0802 - accuracy: 0.9722 - common_sense_class: 0.0645 - val_loss: 0.1883 - val_accuracy: 0.9414 - val_common_sense_class: 0.1039\n",
      "Epoch 128/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0797 - accuracy: 0.9735 - common_sense_class: 0.0574 - val_loss: 0.1967 - val_accuracy: 0.9436 - val_common_sense_class: 0.1014\n",
      "Epoch 129/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0771 - accuracy: 0.9737 - common_sense_class: 0.0614 - val_loss: 0.2073 - val_accuracy: 0.9425 - val_common_sense_class: 0.1028\n",
      "Epoch 130/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0859 - accuracy: 0.9711 - common_sense_class: 0.0628 - val_loss: 0.1917 - val_accuracy: 0.9467 - val_common_sense_class: 0.0939\n",
      "Epoch 131/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0769 - accuracy: 0.9738 - common_sense_class: 0.0612 - val_loss: 0.1928 - val_accuracy: 0.9403 - val_common_sense_class: 0.1022\n",
      "Epoch 132/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0767 - accuracy: 0.9744 - common_sense_class: 0.0553 - val_loss: 0.2616 - val_accuracy: 0.9286 - val_common_sense_class: 0.1275\n",
      "Epoch 133/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0766 - accuracy: 0.9758 - common_sense_class: 0.0531 - val_loss: 0.2149 - val_accuracy: 0.9433 - val_common_sense_class: 0.0972\n",
      "Epoch 134/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0767 - accuracy: 0.9740 - common_sense_class: 0.0624 - val_loss: 0.2286 - val_accuracy: 0.9297 - val_common_sense_class: 0.1347\n",
      "Epoch 135/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0720 - accuracy: 0.9739 - common_sense_class: 0.0599 - val_loss: 0.2142 - val_accuracy: 0.9431 - val_common_sense_class: 0.1061\n",
      "Epoch 136/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0705 - accuracy: 0.9756 - common_sense_class: 0.0523 - val_loss: 0.1985 - val_accuracy: 0.9375 - val_common_sense_class: 0.1081\n",
      "Epoch 137/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0702 - accuracy: 0.9753 - common_sense_class: 0.0557 - val_loss: 0.3023 - val_accuracy: 0.9117 - val_common_sense_class: 0.1653\n",
      "Epoch 138/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0753 - accuracy: 0.9746 - common_sense_class: 0.0561 - val_loss: 0.1822 - val_accuracy: 0.9447 - val_common_sense_class: 0.0997\n",
      "Epoch 139/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0724 - accuracy: 0.9753 - common_sense_class: 0.0570 - val_loss: 0.1844 - val_accuracy: 0.9497 - val_common_sense_class: 0.0889\n",
      "Epoch 140/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0709 - accuracy: 0.9754 - common_sense_class: 0.0552 - val_loss: 0.1939 - val_accuracy: 0.9453 - val_common_sense_class: 0.0939\n",
      "Epoch 141/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 0.0748 - accuracy: 0.9754 - common_sense_class: 0.0570 - val_loss: 0.2074 - val_accuracy: 0.9383 - val_common_sense_class: 0.1111\n",
      "Epoch 142/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0682 - accuracy: 0.9762 - common_sense_class: 0.0497 - val_loss: 0.1867 - val_accuracy: 0.9442 - val_common_sense_class: 0.0914\n",
      "Epoch 143/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0697 - accuracy: 0.9765 - common_sense_class: 0.0519 - val_loss: 0.1978 - val_accuracy: 0.9428 - val_common_sense_class: 0.0983\n",
      "Epoch 144/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0671 - accuracy: 0.9771 - common_sense_class: 0.0497 - val_loss: 0.1958 - val_accuracy: 0.9414 - val_common_sense_class: 0.0989\n",
      "Epoch 145/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0648 - accuracy: 0.9793 - common_sense_class: 0.0459 - val_loss: 0.1872 - val_accuracy: 0.9453 - val_common_sense_class: 0.0931\n",
      "Epoch 146/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0686 - accuracy: 0.9778 - common_sense_class: 0.0514 - val_loss: 0.2386 - val_accuracy: 0.9325 - val_common_sense_class: 0.1128\n",
      "Epoch 147/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0619 - accuracy: 0.9793 - common_sense_class: 0.0485 - val_loss: 0.2321 - val_accuracy: 0.9342 - val_common_sense_class: 0.1072\n",
      "Epoch 148/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0647 - accuracy: 0.9779 - common_sense_class: 0.0494 - val_loss: 0.2000 - val_accuracy: 0.9417 - val_common_sense_class: 0.1050\n",
      "Epoch 149/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0645 - accuracy: 0.9785 - common_sense_class: 0.0487 - val_loss: 0.2207 - val_accuracy: 0.9331 - val_common_sense_class: 0.1189\n",
      "Epoch 150/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0628 - accuracy: 0.9790 - common_sense_class: 0.0510 - val_loss: 0.2191 - val_accuracy: 0.9322 - val_common_sense_class: 0.1344\n",
      "Epoch 151/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0630 - accuracy: 0.9787 - common_sense_class: 0.0488 - val_loss: 0.2308 - val_accuracy: 0.9336 - val_common_sense_class: 0.1083\n",
      "Epoch 152/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0631 - accuracy: 0.9790 - common_sense_class: 0.0489 - val_loss: 0.1808 - val_accuracy: 0.9450 - val_common_sense_class: 0.0997\n",
      "Epoch 153/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0677 - accuracy: 0.9769 - common_sense_class: 0.0501 - val_loss: 0.1844 - val_accuracy: 0.9467 - val_common_sense_class: 0.0958\n",
      "Epoch 154/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0639 - accuracy: 0.9791 - common_sense_class: 0.0478 - val_loss: 0.1975 - val_accuracy: 0.9469 - val_common_sense_class: 0.0883\n",
      "Epoch 155/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0630 - accuracy: 0.9800 - common_sense_class: 0.0462 - val_loss: 0.1932 - val_accuracy: 0.9433 - val_common_sense_class: 0.0933\n",
      "Epoch 156/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0609 - accuracy: 0.9799 - common_sense_class: 0.0456 - val_loss: 0.1860 - val_accuracy: 0.9497 - val_common_sense_class: 0.0822\n",
      "Epoch 157/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0659 - accuracy: 0.9768 - common_sense_class: 0.0476 - val_loss: 0.1779 - val_accuracy: 0.9481 - val_common_sense_class: 0.0928\n",
      "Epoch 158/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0575 - accuracy: 0.9808 - common_sense_class: 0.0449 - val_loss: 0.1813 - val_accuracy: 0.9481 - val_common_sense_class: 0.0958\n",
      "Epoch 159/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0575 - accuracy: 0.9810 - common_sense_class: 0.0426 - val_loss: 0.1923 - val_accuracy: 0.9469 - val_common_sense_class: 0.0944\n",
      "Epoch 160/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0599 - accuracy: 0.9794 - common_sense_class: 0.0448 - val_loss: 0.1774 - val_accuracy: 0.9486 - val_common_sense_class: 0.0850\n",
      "Epoch 161/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0599 - accuracy: 0.9805 - common_sense_class: 0.0424 - val_loss: 0.1956 - val_accuracy: 0.9422 - val_common_sense_class: 0.1036\n",
      "Epoch 162/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0645 - accuracy: 0.9776 - common_sense_class: 0.0507 - val_loss: 0.2386 - val_accuracy: 0.9358 - val_common_sense_class: 0.1131\n",
      "Epoch 163/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0551 - accuracy: 0.9791 - common_sense_class: 0.0442 - val_loss: 0.2434 - val_accuracy: 0.9339 - val_common_sense_class: 0.1139\n",
      "Epoch 164/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0536 - accuracy: 0.9809 - common_sense_class: 0.0423 - val_loss: 0.2520 - val_accuracy: 0.9369 - val_common_sense_class: 0.1072\n",
      "Epoch 165/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0632 - accuracy: 0.9796 - common_sense_class: 0.0480 - val_loss: 0.1732 - val_accuracy: 0.9514 - val_common_sense_class: 0.0806\n",
      "Epoch 166/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0595 - accuracy: 0.9803 - common_sense_class: 0.0423 - val_loss: 0.1636 - val_accuracy: 0.9506 - val_common_sense_class: 0.0806\n",
      "Epoch 167/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0615 - accuracy: 0.9796 - common_sense_class: 0.0446 - val_loss: 0.2145 - val_accuracy: 0.9336 - val_common_sense_class: 0.1206\n",
      "Epoch 168/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0544 - accuracy: 0.9812 - common_sense_class: 0.0410 - val_loss: 0.1934 - val_accuracy: 0.9453 - val_common_sense_class: 0.0931\n",
      "Epoch 169/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0546 - accuracy: 0.9810 - common_sense_class: 0.0443 - val_loss: 0.2133 - val_accuracy: 0.9425 - val_common_sense_class: 0.0931\n",
      "Epoch 170/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0574 - accuracy: 0.9808 - common_sense_class: 0.0415 - val_loss: 0.2227 - val_accuracy: 0.9433 - val_common_sense_class: 0.1036\n",
      "Epoch 171/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0566 - accuracy: 0.9808 - common_sense_class: 0.0431 - val_loss: 0.1746 - val_accuracy: 0.9431 - val_common_sense_class: 0.0992\n",
      "Epoch 172/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0548 - accuracy: 0.9814 - common_sense_class: 0.0398 - val_loss: 0.1761 - val_accuracy: 0.9472 - val_common_sense_class: 0.0947\n",
      "Epoch 173/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0565 - accuracy: 0.9806 - common_sense_class: 0.0465 - val_loss: 0.1812 - val_accuracy: 0.9483 - val_common_sense_class: 0.0856\n",
      "Epoch 174/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0536 - accuracy: 0.9812 - common_sense_class: 0.0447 - val_loss: 0.2637 - val_accuracy: 0.9369 - val_common_sense_class: 0.1089\n",
      "Epoch 175/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0541 - accuracy: 0.9828 - common_sense_class: 0.0432 - val_loss: 0.2539 - val_accuracy: 0.9353 - val_common_sense_class: 0.1086\n",
      "Epoch 176/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0503 - accuracy: 0.9837 - common_sense_class: 0.0342 - val_loss: 0.2057 - val_accuracy: 0.9444 - val_common_sense_class: 0.0981\n",
      "Epoch 177/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0543 - accuracy: 0.9822 - common_sense_class: 0.0410 - val_loss: 0.1685 - val_accuracy: 0.9517 - val_common_sense_class: 0.0853\n",
      "Epoch 178/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0513 - accuracy: 0.9840 - common_sense_class: 0.0363 - val_loss: 0.1825 - val_accuracy: 0.9519 - val_common_sense_class: 0.0825\n",
      "Epoch 179/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0497 - accuracy: 0.9832 - common_sense_class: 0.0376 - val_loss: 0.1919 - val_accuracy: 0.9436 - val_common_sense_class: 0.0975\n",
      "Epoch 180/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0463 - accuracy: 0.9849 - common_sense_class: 0.0343 - val_loss: 0.1813 - val_accuracy: 0.9544 - val_common_sense_class: 0.0819\n",
      "Epoch 181/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0547 - accuracy: 0.9801 - common_sense_class: 0.0476 - val_loss: 0.1763 - val_accuracy: 0.9483 - val_common_sense_class: 0.0878\n",
      "Epoch 182/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0465 - accuracy: 0.9840 - common_sense_class: 0.0352 - val_loss: 0.1814 - val_accuracy: 0.9525 - val_common_sense_class: 0.0783\n",
      "Epoch 183/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0512 - accuracy: 0.9819 - common_sense_class: 0.0402 - val_loss: 0.2876 - val_accuracy: 0.9347 - val_common_sense_class: 0.1119\n",
      "Epoch 184/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0495 - accuracy: 0.9827 - common_sense_class: 0.0380 - val_loss: 0.1670 - val_accuracy: 0.9497 - val_common_sense_class: 0.0906\n",
      "Epoch 185/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0488 - accuracy: 0.9830 - common_sense_class: 0.0379 - val_loss: 0.1777 - val_accuracy: 0.9503 - val_common_sense_class: 0.0803\n",
      "Epoch 186/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0534 - accuracy: 0.9819 - common_sense_class: 0.0399 - val_loss: 0.1756 - val_accuracy: 0.9481 - val_common_sense_class: 0.0978\n",
      "Epoch 187/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0505 - accuracy: 0.9826 - common_sense_class: 0.0399 - val_loss: 0.1700 - val_accuracy: 0.9494 - val_common_sense_class: 0.0894\n",
      "Epoch 188/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0505 - accuracy: 0.9831 - common_sense_class: 0.0392 - val_loss: 0.1696 - val_accuracy: 0.9519 - val_common_sense_class: 0.0839\n",
      "Epoch 189/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0418 - accuracy: 0.9857 - common_sense_class: 0.0313 - val_loss: 0.2040 - val_accuracy: 0.9469 - val_common_sense_class: 0.0861\n",
      "Epoch 190/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0460 - accuracy: 0.9843 - common_sense_class: 0.0342 - val_loss: 0.2055 - val_accuracy: 0.9431 - val_common_sense_class: 0.0961\n",
      "Epoch 191/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0520 - accuracy: 0.9833 - common_sense_class: 0.0381 - val_loss: 0.1855 - val_accuracy: 0.9489 - val_common_sense_class: 0.0817\n",
      "Epoch 192/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0475 - accuracy: 0.9837 - common_sense_class: 0.0355 - val_loss: 0.1808 - val_accuracy: 0.9503 - val_common_sense_class: 0.0869\n",
      "Epoch 193/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0418 - accuracy: 0.9867 - common_sense_class: 0.0258 - val_loss: 0.1875 - val_accuracy: 0.9447 - val_common_sense_class: 0.0883\n",
      "Epoch 194/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0448 - accuracy: 0.9844 - common_sense_class: 0.0312 - val_loss: 0.1808 - val_accuracy: 0.9508 - val_common_sense_class: 0.0814\n",
      "Epoch 195/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.0451 - accuracy: 0.9850 - common_sense_class: 0.0331 - val_loss: 0.1787 - val_accuracy: 0.9517 - val_common_sense_class: 0.0822\n",
      "Epoch 196/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.0420 - accuracy: 0.9851 - common_sense_class: 0.0331 - val_loss: 0.1735 - val_accuracy: 0.9569 - val_common_sense_class: 0.0717\n",
      "Epoch 197/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0462 - accuracy: 0.9837 - common_sense_class: 0.0384 - val_loss: 0.1733 - val_accuracy: 0.9483 - val_common_sense_class: 0.0881\n",
      "Epoch 198/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0492 - accuracy: 0.9840 - common_sense_class: 0.0369 - val_loss: 0.1740 - val_accuracy: 0.9469 - val_common_sense_class: 0.0964\n",
      "Epoch 199/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0487 - accuracy: 0.9836 - common_sense_class: 0.0372 - val_loss: 0.2284 - val_accuracy: 0.9253 - val_common_sense_class: 0.1472\n",
      "Epoch 200/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.0427 - accuracy: 0.9857 - common_sense_class: 0.0377 - val_loss: 0.2584 - val_accuracy: 0.9450 - val_common_sense_class: 0.0967\n",
      "Test loss: 0.2584049105644226\n",
      "Test accuracy: 0.9449999928474426\n",
      "Test common sense error: 0.09666666388511658\n"
     ]
    }
   ],
   "source": [
    "class_model_1.compile(loss=\"categorical_crossentropy\", optimizer=keras.optimizers.Adadelta(learning_rate=0.1), metrics=[\"accuracy\", common_sense_class])\n",
    "# class_model_1.compile(loss=\"categorical_crossentropy\", optimizer=keras.optimizers.Adadelta(learning_rate=0.1), metrics=[\"accuracy\"])\n",
    "\n",
    "class_history_1 = class_model_1.fit(X_train, y_class_train,\n",
    "        batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_class_test))\n",
    "class_score_1 = class_model_1.evaluate(X_test, y_class_test, verbose=0)\n",
    "print('Test loss:', class_score_1[0])\n",
    "print('Test accuracy:', class_score_1[1])\n",
    "print('Test common sense error:', class_score_1[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ab612d3d-2d38-41ac-8312-8955c9c7a6f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_model_1.save(\"Classification_24.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b24ee97-05d9-4aa9-920d-ebc9e8699fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(class_history_1.history['loss'])\n",
    "plt.plot(class_history_1.history['val_loss'])\n",
    "plt.plot(class_history_1.history['accuracy'])\n",
    "plt.plot(class_history_1.history['val_accuracy'])\n",
    "plt.plot(class_history_1.history['common_sense_class'])\n",
    "plt.plot(class_history_1.history['val_common_sense_class'])\n",
    "# plt.title('Classification_24')\n",
    "# plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_loss', 'test_loss', 'train_accuracy', 'test_accuracy', 'train_cse', 'test_cse'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d16bd279-9b44-40e2-9fd5-8105cb356272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(class_history_1.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 3.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "740b8bc7-061a-4735-8302-b1223be38b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_model_2 = keras.models.Sequential([\n",
    "  DefaultConv2D(filters=32, input_shape=input_shape), # worse: kernel_size=3, filters=32\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "    \n",
    "  DefaultConv2D(filters=64),\n",
    "  DefaultConv2D(filters=64),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "    \n",
    "  DefaultConv2D(filters=96), #\n",
    "  DefaultConv2D(filters=96),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "\n",
    "  DefaultConv2D(filters=128), #\n",
    "  DefaultConv2D(filters=128),\n",
    "  keras.layers.MaxPooling2D(pool_size=2),\n",
    "  keras.layers.BatchNormalization(),\n",
    "  keras.layers.Dropout(0.2),\n",
    "\n",
    "  keras.layers.Flatten(),\n",
    "  keras.layers.Dense(units=256, activation='relu'), #\n",
    "  keras.layers.Dropout(0.2), # better having this\n",
    "  keras.layers.Dense(units=128, activation='relu'), #\n",
    "  keras.layers.Dropout(0.2),\n",
    "  keras.layers.Dense(units=num_classes, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b57504d-5edf-4f6c-9d13-bf3b8f8b3f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "225/225 [==============================] - 13s 43ms/step - loss: 6.6246 - accuracy: 0.0012 - common_sense_class: 180.4107 - val_loss: 6.5890 - val_accuracy: 0.0011 - val_common_sense_class: 178.3508\n",
      "Epoch 2/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 6.5937 - accuracy: 0.0014 - common_sense_class: 178.4233 - val_loss: 6.5877 - val_accuracy: 0.0017 - val_common_sense_class: 180.2931\n",
      "Epoch 3/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5859 - accuracy: 0.0019 - common_sense_class: 180.8148 - val_loss: 6.5842 - val_accuracy: 0.0028 - val_common_sense_class: 177.7336\n",
      "Epoch 4/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5839 - accuracy: 0.0019 - common_sense_class: 180.3960 - val_loss: 6.5839 - val_accuracy: 0.0011 - val_common_sense_class: 181.1567\n",
      "Epoch 5/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5800 - accuracy: 0.0014 - common_sense_class: 179.1104 - val_loss: 6.5893 - val_accuracy: 5.5556e-04 - val_common_sense_class: 179.0583\n",
      "Epoch 6/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5790 - accuracy: 0.0013 - common_sense_class: 178.9851 - val_loss: 6.5847 - val_accuracy: 0.0011 - val_common_sense_class: 179.1789\n",
      "Epoch 7/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5774 - accuracy: 0.0019 - common_sense_class: 179.1214 - val_loss: 6.5946 - val_accuracy: 0.0011 - val_common_sense_class: 180.4508\n",
      "Epoch 8/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5762 - accuracy: 0.0018 - common_sense_class: 177.5977 - val_loss: 6.5901 - val_accuracy: 0.0028 - val_common_sense_class: 177.1292\n",
      "Epoch 9/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5733 - accuracy: 0.0020 - common_sense_class: 176.2410 - val_loss: 6.5923 - val_accuracy: 0.0017 - val_common_sense_class: 176.8297\n",
      "Epoch 10/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 6.5655 - accuracy: 0.0019 - common_sense_class: 174.9230 - val_loss: 6.5961 - val_accuracy: 0.0019 - val_common_sense_class: 177.6169\n",
      "Epoch 11/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5558 - accuracy: 0.0033 - common_sense_class: 170.7494 - val_loss: 6.6051 - val_accuracy: 0.0025 - val_common_sense_class: 169.4492\n",
      "Epoch 12/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5390 - accuracy: 0.0037 - common_sense_class: 168.1144 - val_loss: 6.6123 - val_accuracy: 0.0017 - val_common_sense_class: 172.1728\n",
      "Epoch 13/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.5171 - accuracy: 0.0038 - common_sense_class: 165.0658 - val_loss: 6.6022 - val_accuracy: 0.0028 - val_common_sense_class: 167.5953\n",
      "Epoch 14/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.4805 - accuracy: 0.0051 - common_sense_class: 162.8424 - val_loss: 6.6341 - val_accuracy: 0.0019 - val_common_sense_class: 165.7061\n",
      "Epoch 15/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.4320 - accuracy: 0.0065 - common_sense_class: 157.4864 - val_loss: 6.6144 - val_accuracy: 0.0017 - val_common_sense_class: 162.0219\n",
      "Epoch 16/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.3799 - accuracy: 0.0078 - common_sense_class: 153.7260 - val_loss: 6.6392 - val_accuracy: 0.0025 - val_common_sense_class: 162.4097\n",
      "Epoch 17/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.3084 - accuracy: 0.0075 - common_sense_class: 147.0535 - val_loss: 6.6780 - val_accuracy: 0.0019 - val_common_sense_class: 166.2353\n",
      "Epoch 18/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 6.2173 - accuracy: 0.0115 - common_sense_class: 142.4465 - val_loss: 6.6496 - val_accuracy: 0.0033 - val_common_sense_class: 160.6617\n",
      "Epoch 19/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 6.1164 - accuracy: 0.0141 - common_sense_class: 136.8554 - val_loss: 6.5978 - val_accuracy: 0.0061 - val_common_sense_class: 152.5011\n",
      "Epoch 20/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 6.0017 - accuracy: 0.0178 - common_sense_class: 132.3645 - val_loss: 6.7087 - val_accuracy: 0.0047 - val_common_sense_class: 155.2561\n",
      "Epoch 21/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.8750 - accuracy: 0.0218 - common_sense_class: 127.8453 - val_loss: 6.5169 - val_accuracy: 0.0053 - val_common_sense_class: 143.5086\n",
      "Epoch 22/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.7279 - accuracy: 0.0270 - common_sense_class: 122.5719 - val_loss: 6.9698 - val_accuracy: 0.0039 - val_common_sense_class: 149.7386\n",
      "Epoch 23/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.5794 - accuracy: 0.0319 - common_sense_class: 118.7049 - val_loss: 6.5190 - val_accuracy: 0.0061 - val_common_sense_class: 133.4642\n",
      "Epoch 24/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.4121 - accuracy: 0.0391 - common_sense_class: 114.9494 - val_loss: 7.0538 - val_accuracy: 0.0028 - val_common_sense_class: 142.3092\n",
      "Epoch 25/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.2351 - accuracy: 0.0460 - common_sense_class: 111.4423 - val_loss: 7.0816 - val_accuracy: 0.0036 - val_common_sense_class: 131.9144\n",
      "Epoch 26/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 5.0614 - accuracy: 0.0546 - common_sense_class: 109.9826 - val_loss: 6.6567 - val_accuracy: 0.0058 - val_common_sense_class: 121.4389\n",
      "Epoch 27/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 4.9032 - accuracy: 0.0644 - common_sense_class: 107.8081 - val_loss: 7.1026 - val_accuracy: 0.0056 - val_common_sense_class: 134.4331\n",
      "Epoch 28/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 4.7253 - accuracy: 0.0748 - common_sense_class: 105.5898 - val_loss: 6.1524 - val_accuracy: 0.0097 - val_common_sense_class: 117.2064\n",
      "Epoch 29/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 4.5706 - accuracy: 0.0824 - common_sense_class: 101.7225 - val_loss: 7.3547 - val_accuracy: 0.0056 - val_common_sense_class: 132.5014\n",
      "Epoch 30/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 4.4081 - accuracy: 0.0943 - common_sense_class: 101.5367 - val_loss: 5.6334 - val_accuracy: 0.0172 - val_common_sense_class: 109.5311\n",
      "Epoch 31/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 4.2827 - accuracy: 0.1020 - common_sense_class: 99.3487 - val_loss: 5.2411 - val_accuracy: 0.0272 - val_common_sense_class: 103.7147\n",
      "Epoch 32/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 4.1350 - accuracy: 0.1156 - common_sense_class: 96.8888 - val_loss: 5.8272 - val_accuracy: 0.0175 - val_common_sense_class: 119.2303\n",
      "Epoch 33/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 3.9952 - accuracy: 0.1306 - common_sense_class: 94.8178 - val_loss: 5.0293 - val_accuracy: 0.0347 - val_common_sense_class: 101.5247\n",
      "Epoch 34/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.8531 - accuracy: 0.1368 - common_sense_class: 92.7020 - val_loss: 4.8164 - val_accuracy: 0.0372 - val_common_sense_class: 99.1667\n",
      "Epoch 35/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 3.7195 - accuracy: 0.1551 - common_sense_class: 91.7853 - val_loss: 5.1262 - val_accuracy: 0.0300 - val_common_sense_class: 105.4553\n",
      "Epoch 36/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 3.6389 - accuracy: 0.1604 - common_sense_class: 89.6031 - val_loss: 5.8116 - val_accuracy: 0.0186 - val_common_sense_class: 115.2761\n",
      "Epoch 37/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.5286 - accuracy: 0.1774 - common_sense_class: 85.8572 - val_loss: 4.7158 - val_accuracy: 0.0475 - val_common_sense_class: 95.9728\n",
      "Epoch 38/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.4053 - accuracy: 0.1911 - common_sense_class: 85.2092 - val_loss: 4.2554 - val_accuracy: 0.0631 - val_common_sense_class: 89.8331\n",
      "Epoch 39/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.3251 - accuracy: 0.1977 - common_sense_class: 82.5215 - val_loss: 4.4532 - val_accuracy: 0.0511 - val_common_sense_class: 92.5775\n",
      "Epoch 40/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.2096 - accuracy: 0.2151 - common_sense_class: 80.9846 - val_loss: 4.7609 - val_accuracy: 0.0522 - val_common_sense_class: 100.6622\n",
      "Epoch 41/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.1228 - accuracy: 0.2333 - common_sense_class: 77.3157 - val_loss: 3.9808 - val_accuracy: 0.0747 - val_common_sense_class: 82.9303\n",
      "Epoch 42/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 3.0452 - accuracy: 0.2387 - common_sense_class: 76.4150 - val_loss: 4.3135 - val_accuracy: 0.0575 - val_common_sense_class: 92.8753\n",
      "Epoch 43/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.9201 - accuracy: 0.2635 - common_sense_class: 73.4860 - val_loss: 4.1080 - val_accuracy: 0.0703 - val_common_sense_class: 87.5775\n",
      "Epoch 44/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 2.8628 - accuracy: 0.2722 - common_sense_class: 72.5319 - val_loss: 3.7901 - val_accuracy: 0.0914 - val_common_sense_class: 79.9981\n",
      "Epoch 45/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.7778 - accuracy: 0.2891 - common_sense_class: 69.6319 - val_loss: 3.8032 - val_accuracy: 0.0911 - val_common_sense_class: 80.6017\n",
      "Epoch 46/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.6963 - accuracy: 0.3006 - common_sense_class: 68.5356 - val_loss: 4.0455 - val_accuracy: 0.0808 - val_common_sense_class: 85.7758\n",
      "Epoch 47/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.6383 - accuracy: 0.3119 - common_sense_class: 67.6944 - val_loss: 3.8913 - val_accuracy: 0.0889 - val_common_sense_class: 82.9589\n",
      "Epoch 48/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.5353 - accuracy: 0.3322 - common_sense_class: 64.3735 - val_loss: 3.7918 - val_accuracy: 0.0972 - val_common_sense_class: 78.8381\n",
      "Epoch 49/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.4690 - accuracy: 0.3481 - common_sense_class: 64.4148 - val_loss: 3.7648 - val_accuracy: 0.0919 - val_common_sense_class: 79.7283\n",
      "Epoch 50/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 2.3957 - accuracy: 0.3600 - common_sense_class: 61.9489 - val_loss: 3.6239 - val_accuracy: 0.1058 - val_common_sense_class: 75.2328\n",
      "Epoch 51/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.3291 - accuracy: 0.3747 - common_sense_class: 60.6059 - val_loss: 3.9213 - val_accuracy: 0.0817 - val_common_sense_class: 83.4319\n",
      "Epoch 52/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 2.2625 - accuracy: 0.3926 - common_sense_class: 57.3794 - val_loss: 3.5771 - val_accuracy: 0.1150 - val_common_sense_class: 74.1686\n",
      "Epoch 53/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.2059 - accuracy: 0.3958 - common_sense_class: 57.3613 - val_loss: 3.7361 - val_accuracy: 0.1044 - val_common_sense_class: 75.6561\n",
      "Epoch 54/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.1346 - accuracy: 0.4174 - common_sense_class: 55.3243 - val_loss: 3.5194 - val_accuracy: 0.1250 - val_common_sense_class: 71.2256\n",
      "Epoch 55/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 2.0671 - accuracy: 0.4342 - common_sense_class: 51.7531 - val_loss: 3.5152 - val_accuracy: 0.1256 - val_common_sense_class: 71.5275\n",
      "Epoch 56/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 2.0027 - accuracy: 0.4462 - common_sense_class: 50.1735 - val_loss: 3.8279 - val_accuracy: 0.0969 - val_common_sense_class: 73.5494\n",
      "Epoch 57/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 1.9268 - accuracy: 0.4641 - common_sense_class: 49.1432 - val_loss: 3.7890 - val_accuracy: 0.1033 - val_common_sense_class: 75.7136\n",
      "Epoch 58/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 1.8891 - accuracy: 0.4730 - common_sense_class: 47.7483 - val_loss: 3.5260 - val_accuracy: 0.1233 - val_common_sense_class: 67.5483\n",
      "Epoch 59/60\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 1.8126 - accuracy: 0.4969 - common_sense_class: 45.5124 - val_loss: 3.5288 - val_accuracy: 0.1233 - val_common_sense_class: 68.7575\n",
      "Epoch 60/60\n",
      "225/225 [==============================] - 7s 33ms/step - loss: 1.7724 - accuracy: 0.5027 - common_sense_class: 44.9441 - val_loss: 3.4529 - val_accuracy: 0.1344 - val_common_sense_class: 67.0703\n",
      "Test loss: 3.4529476165771484\n",
      "Test accuracy: 0.1344444453716278\n"
     ]
    }
   ],
   "source": [
    "class_model_2.compile(loss=\"categorical_crossentropy\", optimizer=keras.optimizers.Adadelta(learning_rate=0.08), metrics=[\"accuracy\", common_sense_class])\n",
    "# class_model_2.compile(loss=\"categorical_crossentropy\", optimizer=\"sgd\", metrics=[common_sense_class])\n",
    "\n",
    "class_history_2 = class_model_2.fit(X_train, y_class_train,\n",
    "        batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_class_test))\n",
    "class_score_2 = class_model_2.evaluate(X_test, y_class_test, verbose=0)\n",
    "print('Test loss:', class_score_2[0])\n",
    "print('Test accuracy:', class_score_2[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e912149e-2ec1-4cc5-be0a-8a615d5e0dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_model_2.save(\"Classification_720.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e3aef1b0-7445-4b76-9f03-a3f1b9af6482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(class_history_2.history['loss'])\n",
    "plt.plot(class_history_2.history['val_loss'])\n",
    "plt.plot(class_history_2.history['accuracy'])\n",
    "plt.plot(class_history_2.history['val_accuracy'])\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_loss', 'test_loss', 'train_accuracy', 'test_accuracy'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11389a0e-a217-4540-b1b0-6f12d12ca6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(class_history_2.history['loss'])\n",
    "# plt.plot(class_history_2.history['val_loss'])\n",
    "# plt.plot(class_history_2.history['accuracy'])\n",
    "# plt.plot(class_history_2.history['val_accuracy'])\n",
    "plt.plot(class_history_2.history['common_sense_class'])\n",
    "plt.plot(class_history_2.history['val_common_sense_class'])\n",
    "# plt.title('Classification_720')\n",
    "plt.ylabel('error')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_cse', 'test_cse'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b55d18",
   "metadata": {},
   "source": [
    "## Multi-head models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a91b35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic parameter settings\n",
    "batch_size = 64\n",
    "num_classes = 12\n",
    "epochs = 200\n",
    "\n",
    "# change the two-integer labels to two categories\n",
    "y_hours_train, y_minutes_train = np.split(y_train, 2, 1)\n",
    "y_hours_test, y_minutes_test = np.split(y_test, 2, 1)\n",
    "\n",
    "# convert hour vectors to binary hour matrices\n",
    "y_hours_train = keras.utils.to_categorical(y_hours_train, num_classes)\n",
    "y_hours_test = keras.utils.to_categorical(y_hours_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec3acc5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=(3, 3), activation=\"relu\", padding=\"same\")\n",
    "\n",
    "img_input = keras.layers.Input(shape=input_shape)\n",
    "block1_conv1 = DefaultConv2D(filters=32)(img_input)\n",
    "block1_pool = keras.layers.MaxPooling2D(pool_size=2)(block1_conv1)\n",
    "block1_norm = keras.layers.BatchNormalization()(block1_pool)\n",
    "block1_drop = keras.layers.Dropout(0.2)(block1_norm)\n",
    "\n",
    "block2_conv1 = DefaultConv2D(filters=64)(block1_drop)\n",
    "block2_conv2 = DefaultConv2D(filters=64)(block2_conv1)\n",
    "block2_pool = keras.layers.MaxPooling2D(pool_size=2)(block2_conv2)\n",
    "block2_norm = keras.layers.BatchNormalization()(block2_pool)\n",
    "block2_drop = keras.layers.Dropout(0.2)(block2_norm)\n",
    "\n",
    "block3_conv1 = DefaultConv2D(filters=96)(block2_drop)\n",
    "block3_pool = keras.layers.MaxPooling2D(pool_size=2)(block3_conv1)\n",
    "block3_norm = keras.layers.BatchNormalization()(block3_pool)\n",
    "block3_drop = keras.layers.Dropout(0.2)(block3_norm)\n",
    "\n",
    "block4_conv1 = DefaultConv2D(filters=128)(block3_drop)\n",
    "block4_pool = keras.layers.MaxPooling2D(pool_size=2)(block4_conv1)\n",
    "block4_norm = keras.layers.BatchNormalization()(block4_pool)\n",
    "block4_drop = keras.layers.Dropout(0.2)(block4_norm)\n",
    "\n",
    "flatten = keras.layers.Flatten()(block4_drop)\n",
    "fc1 = keras.layers.Dense(units=256, activation=\"relu\")(flatten)\n",
    "drop1 = keras.layers.Dropout(0.2)(fc1)\n",
    "fc2 = keras.layers.Dense(128, activation=\"relu\")(drop1)\n",
    "drop2 = keras.layers.Dropout(0.2)(fc2)\n",
    "\n",
    "hour_class = keras.layers.Dense(num_classes, activation=\"softmax\", name=\"hour_class\")(drop2)\n",
    "minute_reg = keras.layers.Dense(1, activation='linear', name=\"minute_reg\")(drop2)\n",
    "multi_model = keras.models.Model(inputs=[img_input], outputs=[hour_class, minute_reg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a092c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-25 19:10:05.358210: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8101\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 14s 34ms/step - loss: 6.7397 - hour_class_loss: 3.3074 - minute_reg_loss: 17.1616 - hour_class_accuracy: 0.0876 - val_loss: 8.0511 - val_hour_class_loss: 2.6058 - val_minute_reg_loss: 27.2263 - val_hour_class_accuracy: 0.0836\n",
      "Epoch 2/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 5.7931 - hour_class_loss: 2.9180 - minute_reg_loss: 14.3759 - hour_class_accuracy: 0.1081 - val_loss: 8.2019 - val_hour_class_loss: 2.6433 - val_minute_reg_loss: 27.7926 - val_hour_class_accuracy: 0.0858\n",
      "Epoch 3/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 5.2541 - hour_class_loss: 2.6592 - minute_reg_loss: 12.9746 - hour_class_accuracy: 0.1325 - val_loss: 7.6491 - val_hour_class_loss: 2.5678 - val_minute_reg_loss: 25.4067 - val_hour_class_accuracy: 0.1019\n",
      "Epoch 4/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 4.9463 - hour_class_loss: 2.4806 - minute_reg_loss: 12.3287 - hour_class_accuracy: 0.1648 - val_loss: 6.9242 - val_hour_class_loss: 2.3466 - val_minute_reg_loss: 22.8881 - val_hour_class_accuracy: 0.1489\n",
      "Epoch 5/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 4.7013 - hour_class_loss: 2.3242 - minute_reg_loss: 11.8856 - hour_class_accuracy: 0.1976 - val_loss: 5.7399 - val_hour_class_loss: 2.0975 - val_minute_reg_loss: 18.2122 - val_hour_class_accuracy: 0.2175\n",
      "Epoch 6/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 4.4770 - hour_class_loss: 2.1842 - minute_reg_loss: 11.4642 - hour_class_accuracy: 0.2305 - val_loss: 4.9225 - val_hour_class_loss: 1.9634 - val_minute_reg_loss: 14.7955 - val_hour_class_accuracy: 0.2494\n",
      "Epoch 7/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 4.2685 - hour_class_loss: 2.0644 - minute_reg_loss: 11.0205 - hour_class_accuracy: 0.2617 - val_loss: 4.7583 - val_hour_class_loss: 1.8819 - val_minute_reg_loss: 14.3822 - val_hour_class_accuracy: 0.2825\n",
      "Epoch 8/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 4.0859 - hour_class_loss: 1.9468 - minute_reg_loss: 10.6951 - hour_class_accuracy: 0.2937 - val_loss: 4.4048 - val_hour_class_loss: 1.8119 - val_minute_reg_loss: 12.9646 - val_hour_class_accuracy: 0.3164\n",
      "Epoch 9/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 3.8904 - hour_class_loss: 1.8207 - minute_reg_loss: 10.3485 - hour_class_accuracy: 0.3251 - val_loss: 4.7015 - val_hour_class_loss: 1.7645 - val_minute_reg_loss: 14.6848 - val_hour_class_accuracy: 0.3456\n",
      "Epoch 10/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 3.7110 - hour_class_loss: 1.7122 - minute_reg_loss: 9.9940 - hour_class_accuracy: 0.3680 - val_loss: 3.9218 - val_hour_class_loss: 1.6061 - val_minute_reg_loss: 11.5781 - val_hour_class_accuracy: 0.3864\n",
      "Epoch 11/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 3.5514 - hour_class_loss: 1.5994 - minute_reg_loss: 9.7598 - hour_class_accuracy: 0.3971 - val_loss: 3.5563 - val_hour_class_loss: 1.4328 - val_minute_reg_loss: 10.6172 - val_hour_class_accuracy: 0.4400\n",
      "Epoch 12/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 3.4027 - hour_class_loss: 1.5101 - minute_reg_loss: 9.4633 - hour_class_accuracy: 0.4263 - val_loss: 3.6615 - val_hour_class_loss: 1.4629 - val_minute_reg_loss: 10.9930 - val_hour_class_accuracy: 0.4383\n",
      "Epoch 13/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 3.2757 - hour_class_loss: 1.4265 - minute_reg_loss: 9.2461 - hour_class_accuracy: 0.4501 - val_loss: 3.2585 - val_hour_class_loss: 1.3023 - val_minute_reg_loss: 9.7814 - val_hour_class_accuracy: 0.4939\n",
      "Epoch 14/200\n",
      "225/225 [==============================] - 7s 30ms/step - loss: 3.1472 - hour_class_loss: 1.3430 - minute_reg_loss: 9.0210 - hour_class_accuracy: 0.4758 - val_loss: 3.3171 - val_hour_class_loss: 1.2474 - val_minute_reg_loss: 10.3486 - val_hour_class_accuracy: 0.5172\n",
      "Epoch 15/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 3.0182 - hour_class_loss: 1.2667 - minute_reg_loss: 8.7574 - hour_class_accuracy: 0.5155 - val_loss: 3.0405 - val_hour_class_loss: 1.1800 - val_minute_reg_loss: 9.3024 - val_hour_class_accuracy: 0.5608\n",
      "Epoch 16/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.9160 - hour_class_loss: 1.1989 - minute_reg_loss: 8.5856 - hour_class_accuracy: 0.5349 - val_loss: 3.0481 - val_hour_class_loss: 1.0577 - val_minute_reg_loss: 9.9522 - val_hour_class_accuracy: 0.6017\n",
      "Epoch 17/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.8504 - hour_class_loss: 1.1459 - minute_reg_loss: 8.5226 - hour_class_accuracy: 0.5540 - val_loss: 2.9016 - val_hour_class_loss: 1.0531 - val_minute_reg_loss: 9.2424 - val_hour_class_accuracy: 0.5964\n",
      "Epoch 18/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.7113 - hour_class_loss: 1.0722 - minute_reg_loss: 8.1953 - hour_class_accuracy: 0.5819 - val_loss: 2.6763 - val_hour_class_loss: 0.9756 - val_minute_reg_loss: 8.5033 - val_hour_class_accuracy: 0.6353\n",
      "Epoch 19/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.6454 - hour_class_loss: 1.0196 - minute_reg_loss: 8.1289 - hour_class_accuracy: 0.5956 - val_loss: 2.7903 - val_hour_class_loss: 0.9201 - val_minute_reg_loss: 9.3511 - val_hour_class_accuracy: 0.6581\n",
      "Epoch 20/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 2.5476 - hour_class_loss: 0.9702 - minute_reg_loss: 7.8869 - hour_class_accuracy: 0.6174 - val_loss: 2.7065 - val_hour_class_loss: 0.9718 - val_minute_reg_loss: 8.6739 - val_hour_class_accuracy: 0.6350\n",
      "Epoch 21/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.4694 - hour_class_loss: 0.9179 - minute_reg_loss: 7.7576 - hour_class_accuracy: 0.6394 - val_loss: 2.7200 - val_hour_class_loss: 0.8635 - val_minute_reg_loss: 9.2825 - val_hour_class_accuracy: 0.6781\n",
      "Epoch 22/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.4353 - hour_class_loss: 0.8718 - minute_reg_loss: 7.8176 - hour_class_accuracy: 0.6554 - val_loss: 2.5957 - val_hour_class_loss: 0.8292 - val_minute_reg_loss: 8.8322 - val_hour_class_accuracy: 0.6950\n",
      "Epoch 23/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.3520 - hour_class_loss: 0.8383 - minute_reg_loss: 7.5682 - hour_class_accuracy: 0.6706 - val_loss: 2.5246 - val_hour_class_loss: 0.7655 - val_minute_reg_loss: 8.7951 - val_hour_class_accuracy: 0.7269\n",
      "Epoch 24/200\n",
      "225/225 [==============================] - 8s 36ms/step - loss: 2.2914 - hour_class_loss: 0.7988 - minute_reg_loss: 7.4633 - hour_class_accuracy: 0.6866 - val_loss: 2.2542 - val_hour_class_loss: 0.7418 - val_minute_reg_loss: 7.5621 - val_hour_class_accuracy: 0.7358\n",
      "Epoch 25/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.2353 - hour_class_loss: 0.7733 - minute_reg_loss: 7.3100 - hour_class_accuracy: 0.7004 - val_loss: 2.1760 - val_hour_class_loss: 0.6774 - val_minute_reg_loss: 7.4928 - val_hour_class_accuracy: 0.7675\n",
      "Epoch 26/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.1799 - hour_class_loss: 0.7265 - minute_reg_loss: 7.2670 - hour_class_accuracy: 0.7170 - val_loss: 2.1930 - val_hour_class_loss: 0.6362 - val_minute_reg_loss: 7.7843 - val_hour_class_accuracy: 0.7872\n",
      "Epoch 27/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.1091 - hour_class_loss: 0.6956 - minute_reg_loss: 7.0677 - hour_class_accuracy: 0.7299 - val_loss: 2.0994 - val_hour_class_loss: 0.6069 - val_minute_reg_loss: 7.4623 - val_hour_class_accuracy: 0.7983\n",
      "Epoch 28/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.0625 - hour_class_loss: 0.6673 - minute_reg_loss: 6.9758 - hour_class_accuracy: 0.7367 - val_loss: 1.9144 - val_hour_class_loss: 0.5359 - val_minute_reg_loss: 6.8925 - val_hour_class_accuracy: 0.8244\n",
      "Epoch 29/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 2.0406 - hour_class_loss: 0.6460 - minute_reg_loss: 6.9732 - hour_class_accuracy: 0.7504 - val_loss: 1.9723 - val_hour_class_loss: 0.5648 - val_minute_reg_loss: 7.0372 - val_hour_class_accuracy: 0.8172\n",
      "Epoch 30/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.9864 - hour_class_loss: 0.6137 - minute_reg_loss: 6.8635 - hour_class_accuracy: 0.7606 - val_loss: 2.2372 - val_hour_class_loss: 0.7388 - val_minute_reg_loss: 7.4919 - val_hour_class_accuracy: 0.7264\n",
      "Epoch 31/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.9405 - hour_class_loss: 0.5986 - minute_reg_loss: 6.7096 - hour_class_accuracy: 0.7703 - val_loss: 1.8725 - val_hour_class_loss: 0.5106 - val_minute_reg_loss: 6.8094 - val_hour_class_accuracy: 0.8331\n",
      "Epoch 32/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.9101 - hour_class_loss: 0.5707 - minute_reg_loss: 6.6972 - hour_class_accuracy: 0.7802 - val_loss: 1.7501 - val_hour_class_loss: 0.4610 - val_minute_reg_loss: 6.4457 - val_hour_class_accuracy: 0.8489\n",
      "Epoch 33/200\n",
      "225/225 [==============================] - 8s 34ms/step - loss: 1.8732 - hour_class_loss: 0.5518 - minute_reg_loss: 6.6070 - hour_class_accuracy: 0.7902 - val_loss: 1.8535 - val_hour_class_loss: 0.5226 - val_minute_reg_loss: 6.6543 - val_hour_class_accuracy: 0.8217\n",
      "Epoch 34/200\n",
      "225/225 [==============================] - 7s 31ms/step - loss: 1.8395 - hour_class_loss: 0.5263 - minute_reg_loss: 6.5660 - hour_class_accuracy: 0.8015 - val_loss: 2.0321 - val_hour_class_loss: 0.5542 - val_minute_reg_loss: 7.3898 - val_hour_class_accuracy: 0.8003\n",
      "Epoch 35/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.8150 - hour_class_loss: 0.5207 - minute_reg_loss: 6.4716 - hour_class_accuracy: 0.8039 - val_loss: 2.0739 - val_hour_class_loss: 0.5080 - val_minute_reg_loss: 7.8291 - val_hour_class_accuracy: 0.8333\n",
      "Epoch 36/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.7735 - hour_class_loss: 0.4940 - minute_reg_loss: 6.3973 - hour_class_accuracy: 0.8099 - val_loss: 1.7237 - val_hour_class_loss: 0.3910 - val_minute_reg_loss: 6.6637 - val_hour_class_accuracy: 0.8758\n",
      "Epoch 37/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.7565 - hour_class_loss: 0.4754 - minute_reg_loss: 6.4053 - hour_class_accuracy: 0.8184 - val_loss: 1.6259 - val_hour_class_loss: 0.4262 - val_minute_reg_loss: 5.9986 - val_hour_class_accuracy: 0.8642\n",
      "Epoch 38/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.7181 - hour_class_loss: 0.4587 - minute_reg_loss: 6.2969 - hour_class_accuracy: 0.8272 - val_loss: 1.6738 - val_hour_class_loss: 0.3808 - val_minute_reg_loss: 6.4648 - val_hour_class_accuracy: 0.8842\n",
      "Epoch 39/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.6843 - hour_class_loss: 0.4454 - minute_reg_loss: 6.1946 - hour_class_accuracy: 0.8333 - val_loss: 1.7463 - val_hour_class_loss: 0.4608 - val_minute_reg_loss: 6.4279 - val_hour_class_accuracy: 0.8478\n",
      "Epoch 40/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.6517 - hour_class_loss: 0.4329 - minute_reg_loss: 6.0940 - hour_class_accuracy: 0.8372 - val_loss: 1.7679 - val_hour_class_loss: 0.3949 - val_minute_reg_loss: 6.8650 - val_hour_class_accuracy: 0.8756\n",
      "Epoch 41/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.6339 - hour_class_loss: 0.4147 - minute_reg_loss: 6.0960 - hour_class_accuracy: 0.8447 - val_loss: 1.8032 - val_hour_class_loss: 0.5324 - val_minute_reg_loss: 6.3540 - val_hour_class_accuracy: 0.8111\n",
      "Epoch 42/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.5926 - hour_class_loss: 0.4050 - minute_reg_loss: 5.9381 - hour_class_accuracy: 0.8490 - val_loss: 1.7426 - val_hour_class_loss: 0.3594 - val_minute_reg_loss: 6.9164 - val_hour_class_accuracy: 0.8983\n",
      "Epoch 43/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.5845 - hour_class_loss: 0.3884 - minute_reg_loss: 5.9808 - hour_class_accuracy: 0.8544 - val_loss: 1.5627 - val_hour_class_loss: 0.2910 - val_minute_reg_loss: 6.3588 - val_hour_class_accuracy: 0.9192\n",
      "Epoch 44/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.5658 - hour_class_loss: 0.3790 - minute_reg_loss: 5.9341 - hour_class_accuracy: 0.8599 - val_loss: 1.4654 - val_hour_class_loss: 0.3273 - val_minute_reg_loss: 5.6904 - val_hour_class_accuracy: 0.9014\n",
      "Epoch 45/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.5453 - hour_class_loss: 0.3784 - minute_reg_loss: 5.8349 - hour_class_accuracy: 0.8585 - val_loss: 1.5237 - val_hour_class_loss: 0.3310 - val_minute_reg_loss: 5.9634 - val_hour_class_accuracy: 0.8958\n",
      "Epoch 46/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.5427 - hour_class_loss: 0.3752 - minute_reg_loss: 5.8374 - hour_class_accuracy: 0.8587 - val_loss: 1.4128 - val_hour_class_loss: 0.3069 - val_minute_reg_loss: 5.5295 - val_hour_class_accuracy: 0.9086\n",
      "Epoch 47/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.5080 - hour_class_loss: 0.3550 - minute_reg_loss: 5.7649 - hour_class_accuracy: 0.8670 - val_loss: 1.8077 - val_hour_class_loss: 0.3047 - val_minute_reg_loss: 7.5150 - val_hour_class_accuracy: 0.9067\n",
      "Epoch 48/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4960 - hour_class_loss: 0.3454 - minute_reg_loss: 5.7530 - hour_class_accuracy: 0.8722 - val_loss: 1.6645 - val_hour_class_loss: 0.3050 - val_minute_reg_loss: 6.7973 - val_hour_class_accuracy: 0.9114\n",
      "Epoch 49/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4757 - hour_class_loss: 0.3411 - minute_reg_loss: 5.6731 - hour_class_accuracy: 0.8752 - val_loss: 1.5523 - val_hour_class_loss: 0.3426 - val_minute_reg_loss: 6.0488 - val_hour_class_accuracy: 0.8825\n",
      "Epoch 50/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4595 - hour_class_loss: 0.3279 - minute_reg_loss: 5.6576 - hour_class_accuracy: 0.8781 - val_loss: 1.4103 - val_hour_class_loss: 0.2838 - val_minute_reg_loss: 5.6325 - val_hour_class_accuracy: 0.9083\n",
      "Epoch 51/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4492 - hour_class_loss: 0.3176 - minute_reg_loss: 5.6579 - hour_class_accuracy: 0.8831 - val_loss: 1.2905 - val_hour_class_loss: 0.2527 - val_minute_reg_loss: 5.1889 - val_hour_class_accuracy: 0.9242\n",
      "Epoch 52/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4194 - hour_class_loss: 0.3073 - minute_reg_loss: 5.5605 - hour_class_accuracy: 0.8878 - val_loss: 1.5304 - val_hour_class_loss: 0.3012 - val_minute_reg_loss: 6.1462 - val_hour_class_accuracy: 0.9033\n",
      "Epoch 53/200\n",
      "225/225 [==============================] - 7s 32ms/step - loss: 1.4364 - hour_class_loss: 0.3088 - minute_reg_loss: 5.6381 - hour_class_accuracy: 0.8867 - val_loss: 1.5756 - val_hour_class_loss: 0.3144 - val_minute_reg_loss: 6.3063 - val_hour_class_accuracy: 0.8994\n",
      "Epoch 54/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.4050 - hour_class_loss: 0.3044 - minute_reg_loss: 5.5030 - hour_class_accuracy: 0.8883 - val_loss: 1.2527 - val_hour_class_loss: 0.2434 - val_minute_reg_loss: 5.0467 - val_hour_class_accuracy: 0.9239\n",
      "Epoch 55/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3756 - hour_class_loss: 0.2891 - minute_reg_loss: 5.4328 - hour_class_accuracy: 0.8919 - val_loss: 1.2934 - val_hour_class_loss: 0.2552 - val_minute_reg_loss: 5.1910 - val_hour_class_accuracy: 0.9192\n",
      "Epoch 56/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3703 - hour_class_loss: 0.2869 - minute_reg_loss: 5.4170 - hour_class_accuracy: 0.8953 - val_loss: 1.4129 - val_hour_class_loss: 0.2177 - val_minute_reg_loss: 5.9760 - val_hour_class_accuracy: 0.9308\n",
      "Epoch 57/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3659 - hour_class_loss: 0.2816 - minute_reg_loss: 5.4215 - hour_class_accuracy: 0.8946 - val_loss: 1.3525 - val_hour_class_loss: 0.3140 - val_minute_reg_loss: 5.1923 - val_hour_class_accuracy: 0.8839\n",
      "Epoch 58/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3397 - hour_class_loss: 0.2766 - minute_reg_loss: 5.3154 - hour_class_accuracy: 0.8983 - val_loss: 1.2562 - val_hour_class_loss: 0.2614 - val_minute_reg_loss: 4.9739 - val_hour_class_accuracy: 0.9092\n",
      "Epoch 59/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3153 - hour_class_loss: 0.2589 - minute_reg_loss: 5.2820 - hour_class_accuracy: 0.9061 - val_loss: 1.5746 - val_hour_class_loss: 0.2412 - val_minute_reg_loss: 6.6670 - val_hour_class_accuracy: 0.9206\n",
      "Epoch 60/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3248 - hour_class_loss: 0.2650 - minute_reg_loss: 5.2988 - hour_class_accuracy: 0.9032 - val_loss: 1.1811 - val_hour_class_loss: 0.2248 - val_minute_reg_loss: 4.7815 - val_hour_class_accuracy: 0.9325\n",
      "Epoch 61/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.3165 - hour_class_loss: 0.2611 - minute_reg_loss: 5.2773 - hour_class_accuracy: 0.9040 - val_loss: 1.3008 - val_hour_class_loss: 0.2579 - val_minute_reg_loss: 5.2142 - val_hour_class_accuracy: 0.9156\n",
      "Epoch 62/200\n",
      "225/225 [==============================] - 9s 41ms/step - loss: 1.2890 - hour_class_loss: 0.2530 - minute_reg_loss: 5.1798 - hour_class_accuracy: 0.9089 - val_loss: 1.3034 - val_hour_class_loss: 0.2122 - val_minute_reg_loss: 5.4559 - val_hour_class_accuracy: 0.9297\n",
      "Epoch 63/200\n",
      "225/225 [==============================] - 7s 30ms/step - loss: 1.2867 - hour_class_loss: 0.2460 - minute_reg_loss: 5.2032 - hour_class_accuracy: 0.9099 - val_loss: 1.2777 - val_hour_class_loss: 0.2228 - val_minute_reg_loss: 5.2747 - val_hour_class_accuracy: 0.9322\n",
      "Epoch 64/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2786 - hour_class_loss: 0.2503 - minute_reg_loss: 5.1413 - hour_class_accuracy: 0.9088 - val_loss: 1.0933 - val_hour_class_loss: 0.1867 - val_minute_reg_loss: 4.5333 - val_hour_class_accuracy: 0.9406\n",
      "Epoch 65/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2655 - hour_class_loss: 0.2438 - minute_reg_loss: 5.1085 - hour_class_accuracy: 0.9090 - val_loss: 1.2962 - val_hour_class_loss: 0.2078 - val_minute_reg_loss: 5.4422 - val_hour_class_accuracy: 0.9400\n",
      "Epoch 66/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2347 - hour_class_loss: 0.2340 - minute_reg_loss: 5.0035 - hour_class_accuracy: 0.9181 - val_loss: 1.2962 - val_hour_class_loss: 0.1941 - val_minute_reg_loss: 5.5107 - val_hour_class_accuracy: 0.9394\n",
      "Epoch 67/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2282 - hour_class_loss: 0.2276 - minute_reg_loss: 5.0028 - hour_class_accuracy: 0.9170 - val_loss: 1.0841 - val_hour_class_loss: 0.1751 - val_minute_reg_loss: 4.5450 - val_hour_class_accuracy: 0.9431\n",
      "Epoch 68/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2280 - hour_class_loss: 0.2257 - minute_reg_loss: 5.0117 - hour_class_accuracy: 0.9170 - val_loss: 1.4364 - val_hour_class_loss: 0.2518 - val_minute_reg_loss: 5.9228 - val_hour_class_accuracy: 0.9156\n",
      "Epoch 69/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2274 - hour_class_loss: 0.2271 - minute_reg_loss: 5.0014 - hour_class_accuracy: 0.9170 - val_loss: 1.0719 - val_hour_class_loss: 0.1741 - val_minute_reg_loss: 4.4892 - val_hour_class_accuracy: 0.9433\n",
      "Epoch 70/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2066 - hour_class_loss: 0.2193 - minute_reg_loss: 4.9366 - hour_class_accuracy: 0.9184 - val_loss: 1.3342 - val_hour_class_loss: 0.2054 - val_minute_reg_loss: 5.6438 - val_hour_class_accuracy: 0.9397\n",
      "Epoch 71/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.2024 - hour_class_loss: 0.2185 - minute_reg_loss: 4.9198 - hour_class_accuracy: 0.9203 - val_loss: 1.1049 - val_hour_class_loss: 0.1772 - val_minute_reg_loss: 4.6387 - val_hour_class_accuracy: 0.9494\n",
      "Epoch 72/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.1838 - hour_class_loss: 0.2169 - minute_reg_loss: 4.8346 - hour_class_accuracy: 0.9204 - val_loss: 1.2871 - val_hour_class_loss: 0.2087 - val_minute_reg_loss: 5.3921 - val_hour_class_accuracy: 0.9278\n",
      "Epoch 73/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1778 - hour_class_loss: 0.2109 - minute_reg_loss: 4.8348 - hour_class_accuracy: 0.9227 - val_loss: 1.1860 - val_hour_class_loss: 0.2187 - val_minute_reg_loss: 4.8368 - val_hour_class_accuracy: 0.9281\n",
      "Epoch 74/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1736 - hour_class_loss: 0.2072 - minute_reg_loss: 4.8319 - hour_class_accuracy: 0.9254 - val_loss: 1.1416 - val_hour_class_loss: 0.1792 - val_minute_reg_loss: 4.8117 - val_hour_class_accuracy: 0.9425\n",
      "Epoch 75/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1687 - hour_class_loss: 0.2033 - minute_reg_loss: 4.8271 - hour_class_accuracy: 0.9265 - val_loss: 1.3368 - val_hour_class_loss: 0.2019 - val_minute_reg_loss: 5.6745 - val_hour_class_accuracy: 0.9358\n",
      "Epoch 76/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1627 - hour_class_loss: 0.2019 - minute_reg_loss: 4.8041 - hour_class_accuracy: 0.9256 - val_loss: 1.3607 - val_hour_class_loss: 0.1886 - val_minute_reg_loss: 5.8603 - val_hour_class_accuracy: 0.9389\n",
      "Epoch 77/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1506 - hour_class_loss: 0.2073 - minute_reg_loss: 4.7164 - hour_class_accuracy: 0.9244 - val_loss: 1.1798 - val_hour_class_loss: 0.1953 - val_minute_reg_loss: 4.9223 - val_hour_class_accuracy: 0.9375\n",
      "Epoch 78/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1373 - hour_class_loss: 0.1934 - minute_reg_loss: 4.7193 - hour_class_accuracy: 0.9277 - val_loss: 1.2609 - val_hour_class_loss: 0.2117 - val_minute_reg_loss: 5.2461 - val_hour_class_accuracy: 0.9267\n",
      "Epoch 79/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1350 - hour_class_loss: 0.1893 - minute_reg_loss: 4.7282 - hour_class_accuracy: 0.9324 - val_loss: 1.0282 - val_hour_class_loss: 0.1512 - val_minute_reg_loss: 4.3852 - val_hour_class_accuracy: 0.9542\n",
      "Epoch 80/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.1257 - hour_class_loss: 0.1915 - minute_reg_loss: 4.6710 - hour_class_accuracy: 0.9319 - val_loss: 1.1331 - val_hour_class_loss: 0.1736 - val_minute_reg_loss: 4.7978 - val_hour_class_accuracy: 0.9433\n",
      "Epoch 81/200\n",
      "225/225 [==============================] - 6s 29ms/step - loss: 1.1128 - hour_class_loss: 0.1886 - minute_reg_loss: 4.6210 - hour_class_accuracy: 0.9330 - val_loss: 1.2338 - val_hour_class_loss: 0.1614 - val_minute_reg_loss: 5.3619 - val_hour_class_accuracy: 0.9489\n",
      "Epoch 82/200\n",
      "225/225 [==============================] - 8s 33ms/step - loss: 1.1150 - hour_class_loss: 0.1862 - minute_reg_loss: 4.6440 - hour_class_accuracy: 0.9344 - val_loss: 0.9975 - val_hour_class_loss: 0.1403 - val_minute_reg_loss: 4.2859 - val_hour_class_accuracy: 0.9583\n",
      "Epoch 83/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0882 - hour_class_loss: 0.1794 - minute_reg_loss: 4.5440 - hour_class_accuracy: 0.9353 - val_loss: 1.1775 - val_hour_class_loss: 0.1906 - val_minute_reg_loss: 4.9346 - val_hour_class_accuracy: 0.9358\n",
      "Epoch 84/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0940 - hour_class_loss: 0.1792 - minute_reg_loss: 4.5740 - hour_class_accuracy: 0.9369 - val_loss: 1.0800 - val_hour_class_loss: 0.1707 - val_minute_reg_loss: 4.5466 - val_hour_class_accuracy: 0.9444\n",
      "Epoch 85/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0999 - hour_class_loss: 0.1790 - minute_reg_loss: 4.6044 - hour_class_accuracy: 0.9358 - val_loss: 1.1035 - val_hour_class_loss: 0.1679 - val_minute_reg_loss: 4.6780 - val_hour_class_accuracy: 0.9497\n",
      "Epoch 86/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.0747 - hour_class_loss: 0.1689 - minute_reg_loss: 4.5291 - hour_class_accuracy: 0.9376 - val_loss: 1.1407 - val_hour_class_loss: 0.2188 - val_minute_reg_loss: 4.6095 - val_hour_class_accuracy: 0.9283\n",
      "Epoch 87/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 1.0852 - hour_class_loss: 0.1789 - minute_reg_loss: 4.5314 - hour_class_accuracy: 0.9372 - val_loss: 1.5448 - val_hour_class_loss: 0.1830 - val_minute_reg_loss: 6.8089 - val_hour_class_accuracy: 0.9392\n",
      "Epoch 88/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0645 - hour_class_loss: 0.1701 - minute_reg_loss: 4.4723 - hour_class_accuracy: 0.9382 - val_loss: 1.0765 - val_hour_class_loss: 0.1721 - val_minute_reg_loss: 4.5221 - val_hour_class_accuracy: 0.9467\n",
      "Epoch 89/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0597 - hour_class_loss: 0.1710 - minute_reg_loss: 4.4432 - hour_class_accuracy: 0.9379 - val_loss: 1.2172 - val_hour_class_loss: 0.1487 - val_minute_reg_loss: 5.3423 - val_hour_class_accuracy: 0.9511\n",
      "Epoch 90/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0444 - hour_class_loss: 0.1662 - minute_reg_loss: 4.3909 - hour_class_accuracy: 0.9389 - val_loss: 1.0905 - val_hour_class_loss: 0.1485 - val_minute_reg_loss: 4.7099 - val_hour_class_accuracy: 0.9506\n",
      "Epoch 91/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0499 - hour_class_loss: 0.1724 - minute_reg_loss: 4.3875 - hour_class_accuracy: 0.9371 - val_loss: 1.1017 - val_hour_class_loss: 0.1397 - val_minute_reg_loss: 4.8098 - val_hour_class_accuracy: 0.9558\n",
      "Epoch 92/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0380 - hour_class_loss: 0.1610 - minute_reg_loss: 4.3850 - hour_class_accuracy: 0.9427 - val_loss: 1.1685 - val_hour_class_loss: 0.1708 - val_minute_reg_loss: 4.9885 - val_hour_class_accuracy: 0.9447\n",
      "Epoch 93/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0232 - hour_class_loss: 0.1584 - minute_reg_loss: 4.3240 - hour_class_accuracy: 0.9424 - val_loss: 1.0285 - val_hour_class_loss: 0.1470 - val_minute_reg_loss: 4.4075 - val_hour_class_accuracy: 0.9528\n",
      "Epoch 94/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0269 - hour_class_loss: 0.1588 - minute_reg_loss: 4.3406 - hour_class_accuracy: 0.9442 - val_loss: 1.0265 - val_hour_class_loss: 0.1443 - val_minute_reg_loss: 4.4108 - val_hour_class_accuracy: 0.9544\n",
      "Epoch 95/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0187 - hour_class_loss: 0.1489 - minute_reg_loss: 4.3486 - hour_class_accuracy: 0.9479 - val_loss: 1.4908 - val_hour_class_loss: 0.1686 - val_minute_reg_loss: 6.6110 - val_hour_class_accuracy: 0.9431\n",
      "Epoch 96/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0156 - hour_class_loss: 0.1528 - minute_reg_loss: 4.3144 - hour_class_accuracy: 0.9460 - val_loss: 1.0971 - val_hour_class_loss: 0.1356 - val_minute_reg_loss: 4.8072 - val_hour_class_accuracy: 0.9569\n",
      "Epoch 97/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0006 - hour_class_loss: 0.1507 - minute_reg_loss: 4.2495 - hour_class_accuracy: 0.9480 - val_loss: 1.1696 - val_hour_class_loss: 0.2105 - val_minute_reg_loss: 4.7954 - val_hour_class_accuracy: 0.9281\n",
      "Epoch 98/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0027 - hour_class_loss: 0.1491 - minute_reg_loss: 4.2678 - hour_class_accuracy: 0.9454 - val_loss: 0.9849 - val_hour_class_loss: 0.1306 - val_minute_reg_loss: 4.2714 - val_hour_class_accuracy: 0.9581\n",
      "Epoch 99/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 1.0010 - hour_class_loss: 0.1563 - minute_reg_loss: 4.2237 - hour_class_accuracy: 0.9426 - val_loss: 1.3802 - val_hour_class_loss: 0.1654 - val_minute_reg_loss: 6.0740 - val_hour_class_accuracy: 0.9481\n",
      "Epoch 100/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9985 - hour_class_loss: 0.1468 - minute_reg_loss: 4.2587 - hour_class_accuracy: 0.9450 - val_loss: 1.0260 - val_hour_class_loss: 0.1268 - val_minute_reg_loss: 4.4960 - val_hour_class_accuracy: 0.9625\n",
      "Epoch 101/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9821 - hour_class_loss: 0.1459 - minute_reg_loss: 4.1814 - hour_class_accuracy: 0.9476 - val_loss: 1.0057 - val_hour_class_loss: 0.1316 - val_minute_reg_loss: 4.3705 - val_hour_class_accuracy: 0.9572\n",
      "Epoch 102/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9921 - hour_class_loss: 0.1476 - minute_reg_loss: 4.2228 - hour_class_accuracy: 0.9464 - val_loss: 1.0352 - val_hour_class_loss: 0.1739 - val_minute_reg_loss: 4.3065 - val_hour_class_accuracy: 0.9444\n",
      "Epoch 103/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9923 - hour_class_loss: 0.1528 - minute_reg_loss: 4.1974 - hour_class_accuracy: 0.9451 - val_loss: 1.0353 - val_hour_class_loss: 0.1391 - val_minute_reg_loss: 4.4811 - val_hour_class_accuracy: 0.9564\n",
      "Epoch 104/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9674 - hour_class_loss: 0.1383 - minute_reg_loss: 4.1453 - hour_class_accuracy: 0.9506 - val_loss: 0.9271 - val_hour_class_loss: 0.1663 - val_minute_reg_loss: 3.8036 - val_hour_class_accuracy: 0.9453\n",
      "Epoch 105/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9629 - hour_class_loss: 0.1418 - minute_reg_loss: 4.1056 - hour_class_accuracy: 0.9487 - val_loss: 0.9649 - val_hour_class_loss: 0.1351 - val_minute_reg_loss: 4.1491 - val_hour_class_accuracy: 0.9603\n",
      "Epoch 106/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9662 - hour_class_loss: 0.1373 - minute_reg_loss: 4.1446 - hour_class_accuracy: 0.9490 - val_loss: 1.1556 - val_hour_class_loss: 0.1610 - val_minute_reg_loss: 4.9730 - val_hour_class_accuracy: 0.9494\n",
      "Epoch 107/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9397 - hour_class_loss: 0.1349 - minute_reg_loss: 4.0241 - hour_class_accuracy: 0.9520 - val_loss: 1.1311 - val_hour_class_loss: 0.1899 - val_minute_reg_loss: 4.7061 - val_hour_class_accuracy: 0.9336\n",
      "Epoch 108/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9561 - hour_class_loss: 0.1349 - minute_reg_loss: 4.1061 - hour_class_accuracy: 0.9505 - val_loss: 1.0102 - val_hour_class_loss: 0.1513 - val_minute_reg_loss: 4.2942 - val_hour_class_accuracy: 0.9494\n",
      "Epoch 109/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9531 - hour_class_loss: 0.1329 - minute_reg_loss: 4.1008 - hour_class_accuracy: 0.9515 - val_loss: 0.9595 - val_hour_class_loss: 0.1284 - val_minute_reg_loss: 4.1557 - val_hour_class_accuracy: 0.9583\n",
      "Epoch 110/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9367 - hour_class_loss: 0.1313 - minute_reg_loss: 4.0268 - hour_class_accuracy: 0.9519 - val_loss: 1.1977 - val_hour_class_loss: 0.1392 - val_minute_reg_loss: 5.2927 - val_hour_class_accuracy: 0.9569\n",
      "Epoch 111/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9389 - hour_class_loss: 0.1331 - minute_reg_loss: 4.0289 - hour_class_accuracy: 0.9514 - val_loss: 1.0544 - val_hour_class_loss: 0.1269 - val_minute_reg_loss: 4.6376 - val_hour_class_accuracy: 0.9611\n",
      "Epoch 112/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9339 - hour_class_loss: 0.1279 - minute_reg_loss: 4.0297 - hour_class_accuracy: 0.9544 - val_loss: 0.9961 - val_hour_class_loss: 0.1263 - val_minute_reg_loss: 4.3489 - val_hour_class_accuracy: 0.9586\n",
      "Epoch 113/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9301 - hour_class_loss: 0.1264 - minute_reg_loss: 4.0186 - hour_class_accuracy: 0.9539 - val_loss: 1.1246 - val_hour_class_loss: 0.1993 - val_minute_reg_loss: 4.6263 - val_hour_class_accuracy: 0.9339\n",
      "Epoch 114/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9241 - hour_class_loss: 0.1294 - minute_reg_loss: 3.9738 - hour_class_accuracy: 0.9534 - val_loss: 1.1679 - val_hour_class_loss: 0.2051 - val_minute_reg_loss: 4.8142 - val_hour_class_accuracy: 0.9331\n",
      "Epoch 115/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9295 - hour_class_loss: 0.1273 - minute_reg_loss: 4.0110 - hour_class_accuracy: 0.9547 - val_loss: 1.0258 - val_hour_class_loss: 0.1274 - val_minute_reg_loss: 4.4918 - val_hour_class_accuracy: 0.9558\n",
      "Epoch 116/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9152 - hour_class_loss: 0.1266 - minute_reg_loss: 3.9432 - hour_class_accuracy: 0.9554 - val_loss: 0.9731 - val_hour_class_loss: 0.1406 - val_minute_reg_loss: 4.1626 - val_hour_class_accuracy: 0.9522\n",
      "Epoch 117/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9127 - hour_class_loss: 0.1272 - minute_reg_loss: 3.9272 - hour_class_accuracy: 0.9539 - val_loss: 1.1499 - val_hour_class_loss: 0.1523 - val_minute_reg_loss: 4.9880 - val_hour_class_accuracy: 0.9497\n",
      "Epoch 118/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8930 - hour_class_loss: 0.1219 - minute_reg_loss: 3.8551 - hour_class_accuracy: 0.9572 - val_loss: 1.0614 - val_hour_class_loss: 0.1238 - val_minute_reg_loss: 4.6884 - val_hour_class_accuracy: 0.9586\n",
      "Epoch 119/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9018 - hour_class_loss: 0.1199 - minute_reg_loss: 3.9092 - hour_class_accuracy: 0.9570 - val_loss: 1.1024 - val_hour_class_loss: 0.1578 - val_minute_reg_loss: 4.7234 - val_hour_class_accuracy: 0.9492\n",
      "Epoch 120/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9061 - hour_class_loss: 0.1248 - minute_reg_loss: 3.9067 - hour_class_accuracy: 0.9553 - val_loss: 1.0271 - val_hour_class_loss: 0.1240 - val_minute_reg_loss: 4.5157 - val_hour_class_accuracy: 0.9606\n",
      "Epoch 121/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9015 - hour_class_loss: 0.1239 - minute_reg_loss: 3.8884 - hour_class_accuracy: 0.9550 - val_loss: 0.9523 - val_hour_class_loss: 0.1258 - val_minute_reg_loss: 4.1325 - val_hour_class_accuracy: 0.9586\n",
      "Epoch 122/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8859 - hour_class_loss: 0.1189 - minute_reg_loss: 3.8347 - hour_class_accuracy: 0.9577 - val_loss: 0.9202 - val_hour_class_loss: 0.1336 - val_minute_reg_loss: 3.9331 - val_hour_class_accuracy: 0.9583\n",
      "Epoch 123/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8838 - hour_class_loss: 0.1186 - minute_reg_loss: 3.8258 - hour_class_accuracy: 0.9578 - val_loss: 1.0624 - val_hour_class_loss: 0.1115 - val_minute_reg_loss: 4.7545 - val_hour_class_accuracy: 0.9639\n",
      "Epoch 124/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.9016 - hour_class_loss: 0.1247 - minute_reg_loss: 3.8848 - hour_class_accuracy: 0.9540 - val_loss: 0.9382 - val_hour_class_loss: 0.1175 - val_minute_reg_loss: 4.1035 - val_hour_class_accuracy: 0.9611\n",
      "Epoch 125/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8858 - hour_class_loss: 0.1177 - minute_reg_loss: 3.8407 - hour_class_accuracy: 0.9588 - val_loss: 0.9892 - val_hour_class_loss: 0.1290 - val_minute_reg_loss: 4.3010 - val_hour_class_accuracy: 0.9608\n",
      "Epoch 126/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8655 - hour_class_loss: 0.1134 - minute_reg_loss: 3.7603 - hour_class_accuracy: 0.9598 - val_loss: 0.9153 - val_hour_class_loss: 0.1293 - val_minute_reg_loss: 3.9300 - val_hour_class_accuracy: 0.9608\n",
      "Epoch 127/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8693 - hour_class_loss: 0.1099 - minute_reg_loss: 3.7969 - hour_class_accuracy: 0.9619 - val_loss: 1.0542 - val_hour_class_loss: 0.1204 - val_minute_reg_loss: 4.6693 - val_hour_class_accuracy: 0.9636\n",
      "Epoch 128/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8716 - hour_class_loss: 0.1144 - minute_reg_loss: 3.7860 - hour_class_accuracy: 0.9592 - val_loss: 0.9049 - val_hour_class_loss: 0.1025 - val_minute_reg_loss: 4.0119 - val_hour_class_accuracy: 0.9667\n",
      "Epoch 129/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8664 - hour_class_loss: 0.1084 - minute_reg_loss: 3.7899 - hour_class_accuracy: 0.9617 - val_loss: 0.9722 - val_hour_class_loss: 0.1334 - val_minute_reg_loss: 4.1940 - val_hour_class_accuracy: 0.9572\n",
      "Epoch 130/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8683 - hour_class_loss: 0.1087 - minute_reg_loss: 3.7982 - hour_class_accuracy: 0.9605 - val_loss: 0.9923 - val_hour_class_loss: 0.1457 - val_minute_reg_loss: 4.2333 - val_hour_class_accuracy: 0.9544\n",
      "Epoch 131/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8609 - hour_class_loss: 0.1149 - minute_reg_loss: 3.7301 - hour_class_accuracy: 0.9576 - val_loss: 0.8717 - val_hour_class_loss: 0.1025 - val_minute_reg_loss: 3.8463 - val_hour_class_accuracy: 0.9678\n",
      "Epoch 132/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8468 - hour_class_loss: 0.1099 - minute_reg_loss: 3.6848 - hour_class_accuracy: 0.9606 - val_loss: 0.8161 - val_hour_class_loss: 0.1146 - val_minute_reg_loss: 3.5071 - val_hour_class_accuracy: 0.9617\n",
      "Epoch 133/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8497 - hour_class_loss: 0.1109 - minute_reg_loss: 3.6938 - hour_class_accuracy: 0.9607 - val_loss: 1.2452 - val_hour_class_loss: 0.1582 - val_minute_reg_loss: 5.4350 - val_hour_class_accuracy: 0.9492\n",
      "Epoch 134/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8466 - hour_class_loss: 0.1116 - minute_reg_loss: 3.6749 - hour_class_accuracy: 0.9616 - val_loss: 0.9301 - val_hour_class_loss: 0.1137 - val_minute_reg_loss: 4.0818 - val_hour_class_accuracy: 0.9639\n",
      "Epoch 135/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8409 - hour_class_loss: 0.1041 - minute_reg_loss: 3.6838 - hour_class_accuracy: 0.9641 - val_loss: 1.0474 - val_hour_class_loss: 0.1085 - val_minute_reg_loss: 4.6943 - val_hour_class_accuracy: 0.9644\n",
      "Epoch 136/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8425 - hour_class_loss: 0.1079 - minute_reg_loss: 3.6726 - hour_class_accuracy: 0.9612 - val_loss: 1.2541 - val_hour_class_loss: 0.1231 - val_minute_reg_loss: 5.6552 - val_hour_class_accuracy: 0.9636\n",
      "Epoch 137/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8350 - hour_class_loss: 0.1016 - minute_reg_loss: 3.6672 - hour_class_accuracy: 0.9653 - val_loss: 0.9764 - val_hour_class_loss: 0.1315 - val_minute_reg_loss: 4.2244 - val_hour_class_accuracy: 0.9586\n",
      "Epoch 138/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8337 - hour_class_loss: 0.1058 - minute_reg_loss: 3.6395 - hour_class_accuracy: 0.9631 - val_loss: 0.9175 - val_hour_class_loss: 0.1304 - val_minute_reg_loss: 3.9353 - val_hour_class_accuracy: 0.9564\n",
      "Epoch 139/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8282 - hour_class_loss: 0.1011 - minute_reg_loss: 3.6353 - hour_class_accuracy: 0.9647 - val_loss: 0.9211 - val_hour_class_loss: 0.1105 - val_minute_reg_loss: 4.0528 - val_hour_class_accuracy: 0.9633\n",
      "Epoch 140/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8350 - hour_class_loss: 0.1016 - minute_reg_loss: 3.6670 - hour_class_accuracy: 0.9644 - val_loss: 1.0916 - val_hour_class_loss: 0.1272 - val_minute_reg_loss: 4.8219 - val_hour_class_accuracy: 0.9581\n",
      "Epoch 141/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8281 - hour_class_loss: 0.1040 - minute_reg_loss: 3.6204 - hour_class_accuracy: 0.9624 - val_loss: 0.8084 - val_hour_class_loss: 0.1115 - val_minute_reg_loss: 3.4846 - val_hour_class_accuracy: 0.9597\n",
      "Epoch 142/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8288 - hour_class_loss: 0.1092 - minute_reg_loss: 3.5982 - hour_class_accuracy: 0.9608 - val_loss: 0.8384 - val_hour_class_loss: 0.1044 - val_minute_reg_loss: 3.6702 - val_hour_class_accuracy: 0.9689\n",
      "Epoch 143/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8209 - hour_class_loss: 0.1028 - minute_reg_loss: 3.5903 - hour_class_accuracy: 0.9645 - val_loss: 0.8622 - val_hour_class_loss: 0.1020 - val_minute_reg_loss: 3.8013 - val_hour_class_accuracy: 0.9714\n",
      "Epoch 144/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8164 - hour_class_loss: 0.0962 - minute_reg_loss: 3.6007 - hour_class_accuracy: 0.9669 - val_loss: 0.9333 - val_hour_class_loss: 0.1354 - val_minute_reg_loss: 3.9892 - val_hour_class_accuracy: 0.9608\n",
      "Epoch 145/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8113 - hour_class_loss: 0.1010 - minute_reg_loss: 3.5514 - hour_class_accuracy: 0.9626 - val_loss: 1.1215 - val_hour_class_loss: 0.1137 - val_minute_reg_loss: 5.0388 - val_hour_class_accuracy: 0.9647\n",
      "Epoch 146/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8173 - hour_class_loss: 0.0997 - minute_reg_loss: 3.5880 - hour_class_accuracy: 0.9636 - val_loss: 0.8957 - val_hour_class_loss: 0.1039 - val_minute_reg_loss: 3.9590 - val_hour_class_accuracy: 0.9653\n",
      "Epoch 147/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8043 - hour_class_loss: 0.0967 - minute_reg_loss: 3.5383 - hour_class_accuracy: 0.9644 - val_loss: 0.9354 - val_hour_class_loss: 0.1160 - val_minute_reg_loss: 4.0967 - val_hour_class_accuracy: 0.9653\n",
      "Epoch 148/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8035 - hour_class_loss: 0.0957 - minute_reg_loss: 3.5394 - hour_class_accuracy: 0.9663 - val_loss: 1.7413 - val_hour_class_loss: 0.2036 - val_minute_reg_loss: 7.6880 - val_hour_class_accuracy: 0.9339\n",
      "Epoch 149/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.8149 - hour_class_loss: 0.1010 - minute_reg_loss: 3.5698 - hour_class_accuracy: 0.9650 - val_loss: 1.1365 - val_hour_class_loss: 0.1818 - val_minute_reg_loss: 4.7732 - val_hour_class_accuracy: 0.9397\n",
      "Epoch 150/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7932 - hour_class_loss: 0.0982 - minute_reg_loss: 3.4749 - hour_class_accuracy: 0.9658 - val_loss: 0.8700 - val_hour_class_loss: 0.1115 - val_minute_reg_loss: 3.7927 - val_hour_class_accuracy: 0.9647\n",
      "Epoch 151/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7959 - hour_class_loss: 0.0992 - minute_reg_loss: 3.4836 - hour_class_accuracy: 0.9651 - val_loss: 1.0790 - val_hour_class_loss: 0.1080 - val_minute_reg_loss: 4.8549 - val_hour_class_accuracy: 0.9675\n",
      "Epoch 152/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7922 - hour_class_loss: 0.0932 - minute_reg_loss: 3.4946 - hour_class_accuracy: 0.9682 - val_loss: 0.9680 - val_hour_class_loss: 0.1096 - val_minute_reg_loss: 4.2921 - val_hour_class_accuracy: 0.9661\n",
      "Epoch 153/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.8003 - hour_class_loss: 0.0967 - minute_reg_loss: 3.5178 - hour_class_accuracy: 0.9660 - val_loss: 0.8941 - val_hour_class_loss: 0.1234 - val_minute_reg_loss: 3.8535 - val_hour_class_accuracy: 0.9597\n",
      "Epoch 154/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7902 - hour_class_loss: 0.0898 - minute_reg_loss: 3.5017 - hour_class_accuracy: 0.9676 - val_loss: 0.9787 - val_hour_class_loss: 0.1607 - val_minute_reg_loss: 4.0901 - val_hour_class_accuracy: 0.9503\n",
      "Epoch 155/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7864 - hour_class_loss: 0.0890 - minute_reg_loss: 3.4870 - hour_class_accuracy: 0.9676 - val_loss: 1.0126 - val_hour_class_loss: 0.1174 - val_minute_reg_loss: 4.4756 - val_hour_class_accuracy: 0.9647\n",
      "Epoch 156/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7994 - hour_class_loss: 0.1009 - minute_reg_loss: 3.4922 - hour_class_accuracy: 0.9647 - val_loss: 1.3053 - val_hour_class_loss: 0.1273 - val_minute_reg_loss: 5.8899 - val_hour_class_accuracy: 0.9581\n",
      "Epoch 157/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7780 - hour_class_loss: 0.0894 - minute_reg_loss: 3.4428 - hour_class_accuracy: 0.9694 - val_loss: 0.9175 - val_hour_class_loss: 0.1465 - val_minute_reg_loss: 3.8551 - val_hour_class_accuracy: 0.9536\n",
      "Epoch 158/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7721 - hour_class_loss: 0.0885 - minute_reg_loss: 3.4180 - hour_class_accuracy: 0.9708 - val_loss: 0.9393 - val_hour_class_loss: 0.1265 - val_minute_reg_loss: 4.0641 - val_hour_class_accuracy: 0.9594\n",
      "Epoch 159/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7763 - hour_class_loss: 0.0938 - minute_reg_loss: 3.4127 - hour_class_accuracy: 0.9683 - val_loss: 1.0886 - val_hour_class_loss: 0.1335 - val_minute_reg_loss: 4.7754 - val_hour_class_accuracy: 0.9581\n",
      "Epoch 160/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7723 - hour_class_loss: 0.0900 - minute_reg_loss: 3.4115 - hour_class_accuracy: 0.9682 - val_loss: 0.9241 - val_hour_class_loss: 0.0995 - val_minute_reg_loss: 4.1232 - val_hour_class_accuracy: 0.9683\n",
      "Epoch 161/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7729 - hour_class_loss: 0.0922 - minute_reg_loss: 3.4033 - hour_class_accuracy: 0.9676 - val_loss: 0.8911 - val_hour_class_loss: 0.1041 - val_minute_reg_loss: 3.9350 - val_hour_class_accuracy: 0.9642\n",
      "Epoch 162/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7739 - hour_class_loss: 0.0893 - minute_reg_loss: 3.4228 - hour_class_accuracy: 0.9682 - val_loss: 0.8809 - val_hour_class_loss: 0.1063 - val_minute_reg_loss: 3.8730 - val_hour_class_accuracy: 0.9661\n",
      "Epoch 163/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7660 - hour_class_loss: 0.0825 - minute_reg_loss: 3.4175 - hour_class_accuracy: 0.9703 - val_loss: 1.0249 - val_hour_class_loss: 0.1237 - val_minute_reg_loss: 4.5060 - val_hour_class_accuracy: 0.9628\n",
      "Epoch 164/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7779 - hour_class_loss: 0.0912 - minute_reg_loss: 3.4332 - hour_class_accuracy: 0.9681 - val_loss: 0.9730 - val_hour_class_loss: 0.0981 - val_minute_reg_loss: 4.3747 - val_hour_class_accuracy: 0.9669\n",
      "Epoch 165/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7626 - hour_class_loss: 0.0885 - minute_reg_loss: 3.3705 - hour_class_accuracy: 0.9685 - val_loss: 0.9775 - val_hour_class_loss: 0.1259 - val_minute_reg_loss: 4.2581 - val_hour_class_accuracy: 0.9594\n",
      "Epoch 166/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7653 - hour_class_loss: 0.0876 - minute_reg_loss: 3.3882 - hour_class_accuracy: 0.9691 - val_loss: 1.3571 - val_hour_class_loss: 0.1562 - val_minute_reg_loss: 6.0046 - val_hour_class_accuracy: 0.9497\n",
      "Epoch 167/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7638 - hour_class_loss: 0.0907 - minute_reg_loss: 3.3654 - hour_class_accuracy: 0.9665 - val_loss: 1.0512 - val_hour_class_loss: 0.0983 - val_minute_reg_loss: 4.7645 - val_hour_class_accuracy: 0.9667\n",
      "Epoch 168/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7534 - hour_class_loss: 0.0836 - minute_reg_loss: 3.3490 - hour_class_accuracy: 0.9706 - val_loss: 0.9360 - val_hour_class_loss: 0.1107 - val_minute_reg_loss: 4.1268 - val_hour_class_accuracy: 0.9642\n",
      "Epoch 169/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7625 - hour_class_loss: 0.0857 - minute_reg_loss: 3.3840 - hour_class_accuracy: 0.9692 - val_loss: 0.9858 - val_hour_class_loss: 0.1044 - val_minute_reg_loss: 4.4070 - val_hour_class_accuracy: 0.9658\n",
      "Epoch 170/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7429 - hour_class_loss: 0.0793 - minute_reg_loss: 3.3177 - hour_class_accuracy: 0.9727 - val_loss: 1.0610 - val_hour_class_loss: 0.1188 - val_minute_reg_loss: 4.7113 - val_hour_class_accuracy: 0.9631\n",
      "Epoch 171/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7600 - hour_class_loss: 0.0914 - minute_reg_loss: 3.3431 - hour_class_accuracy: 0.9688 - val_loss: 0.9949 - val_hour_class_loss: 0.0986 - val_minute_reg_loss: 4.4814 - val_hour_class_accuracy: 0.9672\n",
      "Epoch 172/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7469 - hour_class_loss: 0.0844 - minute_reg_loss: 3.3123 - hour_class_accuracy: 0.9709 - val_loss: 0.8525 - val_hour_class_loss: 0.1002 - val_minute_reg_loss: 3.7614 - val_hour_class_accuracy: 0.9719\n",
      "Epoch 173/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7438 - hour_class_loss: 0.0824 - minute_reg_loss: 3.3068 - hour_class_accuracy: 0.9705 - val_loss: 0.8565 - val_hour_class_loss: 0.0993 - val_minute_reg_loss: 3.7861 - val_hour_class_accuracy: 0.9697\n",
      "Epoch 174/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7541 - hour_class_loss: 0.0856 - minute_reg_loss: 3.3426 - hour_class_accuracy: 0.9696 - val_loss: 0.8333 - val_hour_class_loss: 0.1145 - val_minute_reg_loss: 3.5939 - val_hour_class_accuracy: 0.9642\n",
      "Epoch 175/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7436 - hour_class_loss: 0.0813 - minute_reg_loss: 3.3114 - hour_class_accuracy: 0.9708 - val_loss: 1.2706 - val_hour_class_loss: 0.1193 - val_minute_reg_loss: 5.7567 - val_hour_class_accuracy: 0.9622\n",
      "Epoch 176/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7465 - hour_class_loss: 0.0828 - minute_reg_loss: 3.3188 - hour_class_accuracy: 0.9720 - val_loss: 1.0041 - val_hour_class_loss: 0.1238 - val_minute_reg_loss: 4.4015 - val_hour_class_accuracy: 0.9608\n",
      "Epoch 177/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7436 - hour_class_loss: 0.0818 - minute_reg_loss: 3.3090 - hour_class_accuracy: 0.9713 - val_loss: 0.8795 - val_hour_class_loss: 0.1138 - val_minute_reg_loss: 3.8289 - val_hour_class_accuracy: 0.9658\n",
      "Epoch 178/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7341 - hour_class_loss: 0.0864 - minute_reg_loss: 3.2384 - hour_class_accuracy: 0.9710 - val_loss: 0.9970 - val_hour_class_loss: 0.1239 - val_minute_reg_loss: 4.3657 - val_hour_class_accuracy: 0.9608\n",
      "Epoch 179/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7276 - hour_class_loss: 0.0726 - minute_reg_loss: 3.2751 - hour_class_accuracy: 0.9743 - val_loss: 1.3360 - val_hour_class_loss: 0.1460 - val_minute_reg_loss: 5.9500 - val_hour_class_accuracy: 0.9553\n",
      "Epoch 180/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7340 - hour_class_loss: 0.0780 - minute_reg_loss: 3.2800 - hour_class_accuracy: 0.9728 - val_loss: 0.9173 - val_hour_class_loss: 0.1079 - val_minute_reg_loss: 4.0471 - val_hour_class_accuracy: 0.9669\n",
      "Epoch 181/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7311 - hour_class_loss: 0.0839 - minute_reg_loss: 3.2362 - hour_class_accuracy: 0.9710 - val_loss: 1.0930 - val_hour_class_loss: 0.1298 - val_minute_reg_loss: 4.8160 - val_hour_class_accuracy: 0.9619\n",
      "Epoch 182/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7246 - hour_class_loss: 0.0749 - minute_reg_loss: 3.2488 - hour_class_accuracy: 0.9744 - val_loss: 0.9904 - val_hour_class_loss: 0.1155 - val_minute_reg_loss: 4.3747 - val_hour_class_accuracy: 0.9656\n",
      "Epoch 183/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7235 - hour_class_loss: 0.0771 - minute_reg_loss: 3.2319 - hour_class_accuracy: 0.9735 - val_loss: 0.8603 - val_hour_class_loss: 0.1115 - val_minute_reg_loss: 3.7436 - val_hour_class_accuracy: 0.9639\n",
      "Epoch 184/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7299 - hour_class_loss: 0.0787 - minute_reg_loss: 3.2560 - hour_class_accuracy: 0.9724 - val_loss: 0.8341 - val_hour_class_loss: 0.0984 - val_minute_reg_loss: 3.6783 - val_hour_class_accuracy: 0.9714\n",
      "Epoch 185/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7339 - hour_class_loss: 0.0902 - minute_reg_loss: 3.2185 - hour_class_accuracy: 0.9690 - val_loss: 0.9474 - val_hour_class_loss: 0.1171 - val_minute_reg_loss: 4.1512 - val_hour_class_accuracy: 0.9650\n",
      "Epoch 186/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7153 - hour_class_loss: 0.0731 - minute_reg_loss: 3.2107 - hour_class_accuracy: 0.9751 - val_loss: 0.9898 - val_hour_class_loss: 0.0943 - val_minute_reg_loss: 4.4773 - val_hour_class_accuracy: 0.9708\n",
      "Epoch 187/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7142 - hour_class_loss: 0.0727 - minute_reg_loss: 3.2074 - hour_class_accuracy: 0.9742 - val_loss: 0.9619 - val_hour_class_loss: 0.1345 - val_minute_reg_loss: 4.1368 - val_hour_class_accuracy: 0.9592\n",
      "Epoch 188/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7302 - hour_class_loss: 0.0827 - minute_reg_loss: 3.2377 - hour_class_accuracy: 0.9712 - val_loss: 0.9986 - val_hour_class_loss: 0.1045 - val_minute_reg_loss: 4.4708 - val_hour_class_accuracy: 0.9675\n",
      "Epoch 189/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7138 - hour_class_loss: 0.0790 - minute_reg_loss: 3.1741 - hour_class_accuracy: 0.9723 - val_loss: 1.1710 - val_hour_class_loss: 0.1122 - val_minute_reg_loss: 5.2940 - val_hour_class_accuracy: 0.9706\n",
      "Epoch 190/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7144 - hour_class_loss: 0.0791 - minute_reg_loss: 3.1761 - hour_class_accuracy: 0.9731 - val_loss: 1.0580 - val_hour_class_loss: 0.1205 - val_minute_reg_loss: 4.6877 - val_hour_class_accuracy: 0.9606\n",
      "Epoch 191/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7171 - hour_class_loss: 0.0792 - minute_reg_loss: 3.1893 - hour_class_accuracy: 0.9729 - val_loss: 0.9040 - val_hour_class_loss: 0.0972 - val_minute_reg_loss: 4.0338 - val_hour_class_accuracy: 0.9694\n",
      "Epoch 192/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7115 - hour_class_loss: 0.0745 - minute_reg_loss: 3.1849 - hour_class_accuracy: 0.9737 - val_loss: 0.8688 - val_hour_class_loss: 0.1052 - val_minute_reg_loss: 3.8181 - val_hour_class_accuracy: 0.9661\n",
      "Epoch 193/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7038 - hour_class_loss: 0.0750 - minute_reg_loss: 3.1439 - hour_class_accuracy: 0.9732 - val_loss: 1.2331 - val_hour_class_loss: 0.1672 - val_minute_reg_loss: 5.3297 - val_hour_class_accuracy: 0.9500\n",
      "Epoch 194/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7191 - hour_class_loss: 0.0814 - minute_reg_loss: 3.1886 - hour_class_accuracy: 0.9725 - val_loss: 1.0961 - val_hour_class_loss: 0.1419 - val_minute_reg_loss: 4.7711 - val_hour_class_accuracy: 0.9594\n",
      "Epoch 195/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7079 - hour_class_loss: 0.0743 - minute_reg_loss: 3.1682 - hour_class_accuracy: 0.9733 - val_loss: 0.9612 - val_hour_class_loss: 0.1056 - val_minute_reg_loss: 4.2782 - val_hour_class_accuracy: 0.9694\n",
      "Epoch 196/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7113 - hour_class_loss: 0.0756 - minute_reg_loss: 3.1784 - hour_class_accuracy: 0.9738 - val_loss: 0.9759 - val_hour_class_loss: 0.1046 - val_minute_reg_loss: 4.3566 - val_hour_class_accuracy: 0.9669\n",
      "Epoch 197/200\n",
      "225/225 [==============================] - 6s 27ms/step - loss: 0.7085 - hour_class_loss: 0.0784 - minute_reg_loss: 3.1505 - hour_class_accuracy: 0.9737 - val_loss: 0.9090 - val_hour_class_loss: 0.1316 - val_minute_reg_loss: 3.8868 - val_hour_class_accuracy: 0.9586\n",
      "Epoch 198/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.6944 - hour_class_loss: 0.0704 - minute_reg_loss: 3.1204 - hour_class_accuracy: 0.9754 - val_loss: 1.1615 - val_hour_class_loss: 0.1347 - val_minute_reg_loss: 5.1337 - val_hour_class_accuracy: 0.9631\n",
      "Epoch 199/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.7040 - hour_class_loss: 0.0739 - minute_reg_loss: 3.1502 - hour_class_accuracy: 0.9738 - val_loss: 1.2118 - val_hour_class_loss: 0.1109 - val_minute_reg_loss: 5.5049 - val_hour_class_accuracy: 0.9700\n",
      "Epoch 200/200\n",
      "225/225 [==============================] - 6s 28ms/step - loss: 0.6917 - hour_class_loss: 0.0722 - minute_reg_loss: 3.0975 - hour_class_accuracy: 0.9751 - val_loss: 0.7894 - val_hour_class_loss: 0.1081 - val_minute_reg_loss: 3.4066 - val_hour_class_accuracy: 0.9661\n",
      "Test loss: 0.7893834114074707\n",
      "Test hour loss: 0.10806502401828766\n",
      "Test minute loss: 3.4065914154052734\n",
      "Test hour accuracy: 0.9661111235618591\n"
     ]
    }
   ],
   "source": [
    "multi_model.compile(loss={'hour_class': \"categorical_crossentropy\", 'minute_reg': \"mean_absolute_error\"},\n",
    "                    loss_weights={'hour_class': 1.0, 'minute_reg': 0.2},\n",
    "                    optimizer=keras.optimizers.Adadelta(learning_rate=0.1), \n",
    "                    metrics={'hour_class': \"accuracy\", 'minute_reg': []})\n",
    "# multi_model.compile(loss=[\"categorical_crossentropy\", custom_mae], optimizer=keras.optimizers.Adadelta(learning_rate=0.1), \n",
    "#               metrics=[\"accuracy\", common_sense_class, common_sense_reg]) # optimizer=[keras.optimizers.Adadelta(learning_rate=0.1), \"sgd\"]\n",
    "\n",
    "multi_history = multi_model.fit(X_train, (y_hours_train, y_minutes_train), batch_size=batch_size, epochs=epochs, verbose=1, \n",
    "                    validation_data=(X_test, (y_hours_test, y_minutes_test)))\n",
    "score = multi_model.evaluate(X_test, (y_hours_test, y_minutes_test), verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test hour loss:', score[1])\n",
    "print('Test minute loss:', score[2])\n",
    "print('Test hour accuracy:', score[3])\n",
    "# print('Test minute MeanAbsoluteError:', score[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50060813-9ba2-4b91-80f8-85c9e5223bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_model.save(\"Multi_head.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62453a0a-c96c-4a25-946e-762c066dc5a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'hour_class_loss', 'minute_reg_loss', 'hour_class_accuracy', 'val_loss', 'val_hour_class_loss', 'val_minute_reg_loss', 'val_hour_class_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "print(multi_history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "712366d0-69bb-4b84-ab20-b3905c828c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(figsize=(8, 5))\n",
    "plt.plot(multi_history.history['hour_class_loss'])\n",
    "plt.plot(multi_history.history['val_hour_class_loss'])\n",
    "plt.plot(multi_history.history['hour_class_accuracy'])\n",
    "plt.plot(multi_history.history['val_hour_class_accuracy'])\n",
    "plt.title('Output_hour')\n",
    "# plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_loss', 'test_loss', 'train_accuracy', 'test_accuracy'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1464340d-1ab4-47a6-864d-db21bf80b5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(multi_history.history['minute_reg_loss'])\n",
    "plt.plot(multi_history.history['val_minute_reg_loss'])\n",
    "plt.plot(multi_history.history['loss'])\n",
    "plt.plot(multi_history.history['val_loss'])\n",
    "plt.title('Output_minute')\n",
    "# plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_minute_loss', 'test_minute_loss', 'train_total_loss', 'test_total_loss'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5e91e3b-7e86-49e8-aaec-24c1d4b880b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(multi_history.history['minute_reg_loss'])\n",
    "plt.plot(multi_history.history['val_minute_reg_loss'])\n",
    "# plt.plot(multi_history.history['loss'])\n",
    "# plt.plot(multi_history.history['val_loss'])\n",
    "plt.title('Output_minute')\n",
    "# plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_loss', 'test_loss'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb9dd8fd-3fcf-4206-9309-c7ddc1dda65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(figsize=(8, 5))\n",
    "plt.plot(multi_history.history['hour_class_loss'])\n",
    "plt.plot(multi_history.history['val_hour_class_loss'])\n",
    "plt.plot(multi_history.history['hour_class_accuracy'])\n",
    "plt.plot(multi_history.history['val_hour_class_accuracy'])\n",
    "plt.plot(multi_history.history['minute_reg_loss'])\n",
    "plt.plot(multi_history.history['val_minute_reg_loss'])\n",
    "plt.plot(multi_history.history['loss'])\n",
    "plt.plot(multi_history.history['val_loss'])\n",
    "plt.title('Output_hour')\n",
    "# plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train_hour_loss', 'test_hour_loss', 'train_hour_accuracy', 'test_hour_accuracy', 'train_minute_loss', 'test_minute_loss', 'train_total_loss', 'test_total_loss'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74f27f47-a358-41d0-a7b8-e5edac61cb7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(multi_history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e7aa1f5-bff4-4780-8185-d3221d8a6368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113/113 [==============================] - 1s 4ms/step\n",
      "0.08255910437830069\n"
     ]
    }
   ],
   "source": [
    "# the common sense accuracy for Multi model\n",
    "y_pred = multi_model.predict(X_test)\n",
    "prediction = np.concatenate([np.argmax(y_pred[0], 1).reshape(3600, 1), y_pred[1]], axis=1)\n",
    "true = np.concatenate([np.argmax(y_hours_test, 1).reshape(3600, 1), y_minutes_test], axis=1)\n",
    "y_true = true[:, 0] + true[:, 1] / 60.0\n",
    "y_prediction = prediction[:, 0] + prediction[:, 1] / 60.0\n",
    "\n",
    "def common_sense_multi(y_true, y_pred):\n",
    "    differences = np.abs(y_true - y_pred)\n",
    "    diff = np.minimum(differences, np.subtract(12.0, differences))\n",
    "    diff = np.mean(diff, axis=-1)\n",
    "    return diff\n",
    "\n",
    "cse = common_sense_multi(y_true, y_prediction)\n",
    "print(cse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14cd0f28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}