11_rawdata_convnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bac6499a-0713-4bb2-ad04-c33e4d180432",
   "metadata": {},
   "source": [
    "# Using ConvNet to classify FHR CTG traces as being from infants with abnormal pH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4038cdfb-91c2-4b15-80b6-d410b9a83a7c",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7eb94d0-00ba-4a1f-9427-d8ffea611033",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-03 12:13:23.811460: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-03 12:13:23.882151: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-03 12:13:23.882210: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-03 12:13:23.882263: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-03 12:13:23.896771: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from dataclasses import dataclass\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from statistics import mode\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc53b73",
   "metadata": {},
   "source": [
    "## Set model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f3aa44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set outcome\n",
    "outcome = 'pH'\n",
    "\n",
    "# Threshold to define outcome as abnormal or not\n",
    "outcome_threshold = 7.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53261b32-261e-41f3-9b26-34359f8b3d83",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c697b8ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define file paths\n",
    "@dataclass(frozen=True)\n",
    "class Paths:\n",
    "    '''Singleton object for storing paths to data and database.'''\n",
    "\n",
    "    data = './data_csv'\n",
    "    meta = 'metadata.csv'\n",
    "\n",
    "\n",
    "paths = Paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6d322b2-640b-40ae-98fd-44ffb20ec077",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up dictionary for CSV files\n",
    "csv_files = dict()\n",
    "\n",
    "# Load files into dictionary, but remove metadata\n",
    "filenames = os.listdir(paths.data)\n",
    "filenames.remove(paths.meta)\n",
    "for file in filenames:\n",
    "    # Get filename and file extension\n",
    "    filename, file_extension = os.path.splitext(os.path.join(paths.data, file))\n",
    "    # Load data and save to dict with filename (without path/csv) as index\n",
    "    if file_extension == '.csv':\n",
    "        filename_short = filename.split('/')[-1]\n",
    "        csv_files[filename_short] = pd.read_csv(os.path.join(paths.data, file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2abb3cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1347</th>\n",
       "      <th>2040</th>\n",
       "      <th>1054</th>\n",
       "      <th>1247</th>\n",
       "      <th>1127</th>\n",
       "      <th>1231</th>\n",
       "      <th>1318</th>\n",
       "      <th>1481</th>\n",
       "      <th>1358</th>\n",
       "      <th>1243</th>\n",
       "      <th>...</th>\n",
       "      <th>1421</th>\n",
       "      <th>1236</th>\n",
       "      <th>1256</th>\n",
       "      <th>1435</th>\n",
       "      <th>1404</th>\n",
       "      <th>1118</th>\n",
       "      <th>1018</th>\n",
       "      <th>1447</th>\n",
       "      <th>1152</th>\n",
       "      <th>1229</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>7.14</td>\n",
       "      <td>7.23</td>\n",
       "      <td>7.19</td>\n",
       "      <td>7.32</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.28</td>\n",
       "      <td>7.22</td>\n",
       "      <td>7.29</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.24</td>\n",
       "      <td>...</td>\n",
       "      <td>7.29</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.32</td>\n",
       "      <td>7.16</td>\n",
       "      <td>7.18</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.23</td>\n",
       "      <td>7.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BDecf</th>\n",
       "      <td>8.60</td>\n",
       "      <td>5.27</td>\n",
       "      <td>4.45</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>2.96</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>5.86</td>\n",
       "      <td>2.50</td>\n",
       "      <td>6.52</td>\n",
       "      <td>1.77</td>\n",
       "      <td>...</td>\n",
       "      <td>5.76</td>\n",
       "      <td>5.92</td>\n",
       "      <td>4.73</td>\n",
       "      <td>7.01</td>\n",
       "      <td>7.35</td>\n",
       "      <td>8.76</td>\n",
       "      <td>9.58</td>\n",
       "      <td>7.91</td>\n",
       "      <td>0.16</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pCO2</th>\n",
       "      <td>7.50</td>\n",
       "      <td>6.80</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.90</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.80</td>\n",
       "      <td>6.50</td>\n",
       "      <td>8.40</td>\n",
       "      <td>7.80</td>\n",
       "      <td>...</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.80</td>\n",
       "      <td>5.30</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.10</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.30</td>\n",
       "      <td>7.80</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>-10.80</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>-6.40</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-7.90</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>-8.80</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.80</td>\n",
       "      <td>-7.40</td>\n",
       "      <td>-5.50</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>-8.40</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>-12.10</td>\n",
       "      <td>-9.80</td>\n",
       "      <td>-2.30</td>\n",
       "      <td>-8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apgar1</th>\n",
       "      <td>10.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>...</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 552 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1347  2040  1054  1247  1127  1231  1318  1481  1358   1243  ...  \\\n",
       "parameter                                                                ...   \n",
       "pH          7.14  7.23  7.19  7.32  7.35  7.28  7.22  7.29  7.14   7.24  ...   \n",
       "BDecf       8.60  5.27  4.45 -0.86  2.96 -0.06  5.86  2.50  6.52   1.77  ...   \n",
       "pCO2        7.50  6.80  8.00  6.90  5.30  7.50  6.80  6.50  8.40   7.80  ...   \n",
       "BE        -10.80 -7.00 -6.40 -1.00 -4.10 -1.80 -7.90 -4.10 -8.80  -4.10  ...   \n",
       "Apgar1     10.00  2.00  9.00  9.00  9.00  8.00  5.00  8.00  5.00  10.00  ...   \n",
       "\n",
       "           1421  1236  1256  1435  1404   1118   1018   1447  1152  1229  \n",
       "parameter                                                                 \n",
       "pH         7.29  7.27  7.32  7.16  7.18   7.09   7.09   7.14  7.23  7.13  \n",
       "BDecf      5.76  5.92  4.73  7.01  7.35   8.76   9.58   7.91  0.16  6.21  \n",
       "pCO2       5.50  5.80  5.30  7.70  7.10   8.70   8.30   7.80  8.60  8.80  \n",
       "BE        -6.80 -7.40 -5.50 -8.00 -8.40 -11.70 -12.10  -9.80 -2.30 -8.90  \n",
       "Apgar1     9.00  9.00  9.00  9.00  3.00   8.00   8.00  10.00  8.00  9.00  \n",
       "\n",
       "[5 rows x 552 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load meta data and transform\n",
    "metadata = pd.read_csv(os.path.join(paths.data, paths.meta),\n",
    "                       index_col='parameter')\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0548a6d9",
   "metadata": {},
   "source": [
    "## Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "281da6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_fhr(fhr, show_figure=False):\n",
    "    '''\n",
    "    Cleans fetal heart rate (FHR) signal.\n",
    "    Inputs:\n",
    "    fhr - series, the \"FHR\" column from one of the csv files\n",
    "    show_figure - boolean, whether to display the FHR before and after cleaning\n",
    "    Outputs:\n",
    "    fhr - series, clean version of the FHR column\n",
    "    '''\n",
    "    if show_figure:\n",
    "        # Show original trace\n",
    "        fhr.plot(figsize=(15, 5))\n",
    "        plt.show()\n",
    "\n",
    "    # Replace 0 with NaN\n",
    "    fhr.replace(0, np.nan, inplace=True)\n",
    "\n",
    "    # Remove NaN if they occured for more than 15 seconds consecutively\n",
    "    na = fhr.isnull()\n",
    "    fhr = fhr[~(fhr.groupby(na.ne(na.shift()).cumsum().values).transform('size').ge(16) & na)].reset_index(drop=True)\n",
    "\n",
    "    # Set outliers to NaN\n",
    "    fhr[fhr < 50] = np.nan\n",
    "    fhr[fhr > 200] = np.nan\n",
    "\n",
    "    # Replace missing values using linear interpolation\n",
    "    fhr = fhr.interpolate(method='linear')\n",
    "\n",
    "    # Find how each value has changed from the prior value\n",
    "    diff = fhr - fhr.shift()\n",
    "\n",
    "    # Where difference is more than +- 25, set as NaN\n",
    "    fhr[(diff > 25) | (diff < -25)] = np.nan\n",
    "\n",
    "    # Replace missing values using linear interpolation\n",
    "    fhr = fhr.interpolate(method='linear')\n",
    "\n",
    "    # Drop any remaining missing values (e.g. if at start of time series)\n",
    "    fhr = fhr.dropna()\n",
    "\n",
    "    if show_figure:\n",
    "        # Show clean trace\n",
    "        fhr.plot(figsize=(15, 5))\n",
    "        plt.show()\n",
    "\n",
    "    # Keep the last 90 minutes\n",
    "    fhr = fhr.tail(90*60)\n",
    "\n",
    "    return(fhr.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b68fbef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fhr_dict = {}\n",
    "for key, value in csv_files.items():\n",
    "    fhr_dict[key] = clean_fhr(value.FHR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e70320",
   "metadata": {},
   "source": [
    "## View outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b261d69b-ce42-435f-86d4-bda44dec837f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f1ca053e5f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show distribution of outcome\n",
    "plt.hist(metadata.loc[outcome], bins=np.arange(6.8, 7.5, 0.05))\n",
    "plt.xlabel(outcome)\n",
    "plt.ylabel('Number of records')\n",
    "plt.axvline(x=outcome_threshold, color='grey', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f699d0",
   "metadata": {},
   "source": [
    "## Define x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb11d054",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set x as the signals from the dictionary\n",
    "X = list(fhr_dict.values())\n",
    "\n",
    "# Y is a boolean, true/false for each depending on outcome\n",
    "# We set order of metadata according to dictionary keys so they match up\n",
    "y = (metadata[fhr_dict.keys()].loc[outcome] < outcome_threshold).values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3194cb6",
   "metadata": {},
   "source": [
    "## Define training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0929d523-0ecf-41fa-94ef-acd0c686a267",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size = 0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a96773",
   "metadata": {},
   "source": [
    "## Scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a834751-ac59-4286-b83f-e4b6a6d7a451",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_data(X_train, X_test):\n",
    "    \"\"\"Scale data 0-1 based on min and max in training set\"\"\"\n",
    "    \n",
    "    # Initialise a new scaling object for normalising input data\n",
    "    sc = MinMaxScaler()\n",
    "\n",
    "    # Set up the scaler just on the training set\n",
    "    sc.fit(X_train)\n",
    "\n",
    "    # Apply the scaler to the training and test sets\n",
    "    train_sc = sc.transform(X_train)\n",
    "    test_sc = sc.transform(X_test)\n",
    "    \n",
    "    return train_sc, test_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a51856e-1f57-4644-8813-095ddcd61ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale X data\n",
    "X_train_sc, X_test_sc = scale_data(X_train, X_test)\n",
    "\n",
    "X_train_sc = X_train_sc.reshape((X_train_sc.shape[0], X_train_sc.shape[1], 1))\n",
    "X_test_sc = X_test_sc.reshape((X_test_sc.shape[0], X_test_sc.shape[1], 1))\n",
    "\n",
    "n_classes = len(np.unique(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75187823",
   "metadata": {},
   "source": [
    "## Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6060a6ba-7fa5-4962-b94c-594b9a3bdfee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-03 12:13:36.276024: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:36.283334: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:36.283631: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:36.287138: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:36.287529: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:36.287830: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:37.045040: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:37.045309: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:37.045493: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-11-03 12:13:37.045631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2095 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_model(input_shape):\n",
    "    input_layer = keras.layers.Input(input_shape)\n",
    "\n",
    "    conv1 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(input_layer)\n",
    "    conv1 = keras.layers.BatchNormalization()(conv1)\n",
    "    conv1 = keras.layers.ReLU()(conv1)\n",
    "    #conv1 = keras.layers.MaxPooling1D(pool_size=2)(conv1)\n",
    "\n",
    "    conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv1)\n",
    "    conv2 = keras.layers.BatchNormalization()(conv2)\n",
    "    conv2 = keras.layers.ReLU()(conv2)\n",
    "    #conv2 = keras.layers.MaxPooling1D(pool_size=2)(conv2)\n",
    "\n",
    "    conv3 = keras.layers.Conv1D(filters=64, kernel_size=3, padding=\"same\")(conv2)\n",
    "    conv3 = keras.layers.BatchNormalization()(conv3)\n",
    "    conv3 = keras.layers.ReLU()(conv3)\n",
    "    #conv3 = keras.layers.MaxPooling1D(pool_size=2)(conv3)\n",
    "\n",
    "    gap = keras.layers.GlobalAveragePooling1D()(conv3)\n",
    "\n",
    "    output_layer = keras.layers.Dense(n_classes, activation=\"softmax\")(gap)\n",
    "\n",
    "    return keras.models.Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "model = make_model(input_shape=X_train_sc.shape[1:])\n",
    "keras.utils.plot_model(model, show_shapes=True, to_file='outputs/model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41ed751d-897e-4b01-b486-84a170ba7949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-03 12:13:40.772629: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8700\n",
      "2023-11-03 12:13:42.764871: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f1b7800f160 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-11-03 12:13:42.764911: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3050 Ti Laptop GPU, Compute Capability 8.6\n",
      "2023-11-03 12:13:42.774845: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-11-03 12:13:42.922631: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 9s 183ms/step - loss: 0.6537 - sparse_categorical_accuracy: 0.6465 - val_loss: 0.6718 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 2/500\n",
      " 2/11 [====>.........................] - ETA: 0s - loss: 0.6378 - sparse_categorical_accuracy: 0.7031"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/amy/miniconda3/envs/ctg/lib/python3.10/site-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 1s 75ms/step - loss: 0.6182 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6608 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 3/500\n",
      "11/11 [==============================] - 1s 73ms/step - loss: 0.6159 - sparse_categorical_accuracy: 0.6707 - val_loss: 0.6510 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 4/500\n",
      "11/11 [==============================] - 1s 71ms/step - loss: 0.6074 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6367 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 5/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.6115 - sparse_categorical_accuracy: 0.6707 - val_loss: 0.6274 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 6/500\n",
      "11/11 [==============================] - 1s 69ms/step - loss: 0.5966 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6204 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 7/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.6129 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6176 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 8/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.6077 - sparse_categorical_accuracy: 0.6767 - val_loss: 0.6176 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 9/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.6112 - sparse_categorical_accuracy: 0.6647 - val_loss: 0.6173 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 10/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.6107 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6172 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 11/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6059 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6178 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 12/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6005 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6186 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 13/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6097 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6229 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 14/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6081 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6294 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 15/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6070 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6309 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 16/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6017 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6290 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 17/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6163 - sparse_categorical_accuracy: 0.6707 - val_loss: 0.6305 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 18/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6105 - sparse_categorical_accuracy: 0.6707 - val_loss: 0.6332 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 19/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6025 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6441 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 20/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6039 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6562 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 21/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6092 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6554 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 22/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6016 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6577 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 23/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6072 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6523 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 24/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6094 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6555 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 25/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5989 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6587 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 26/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6020 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6711 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 27/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6042 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6790 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 28/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5998 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6838 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 29/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5918 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6910 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 30/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5987 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6879 - val_sparse_categorical_accuracy: 0.6988 - lr: 0.0010\n",
      "Epoch 31/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5975 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6923 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 32/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5988 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6955 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 33/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5964 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6893 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 34/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.6029 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6878 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 35/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5983 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6894 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 36/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5968 - sparse_categorical_accuracy: 0.6767 - val_loss: 0.6936 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 37/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6013 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6891 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 38/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.6017 - sparse_categorical_accuracy: 0.6767 - val_loss: 0.6762 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 39/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.6042 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6754 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 40/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5943 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6886 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 41/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5947 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6919 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 42/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5907 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6833 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 43/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5974 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6803 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 44/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.6057 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6774 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 45/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.6019 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6672 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 46/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5983 - sparse_categorical_accuracy: 0.6767 - val_loss: 0.6578 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 47/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5912 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6738 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 48/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5946 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.6626 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 49/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5938 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6420 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 50/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.6013 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6401 - val_sparse_categorical_accuracy: 0.6988 - lr: 5.0000e-04\n",
      "Epoch 51/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5960 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6415 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 52/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5911 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6335 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 53/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5842 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6277 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 54/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5886 - sparse_categorical_accuracy: 0.6798 - val_loss: 0.6233 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 55/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5934 - sparse_categorical_accuracy: 0.6737 - val_loss: 0.6239 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 56/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5849 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6127 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 57/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5815 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6135 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 58/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5857 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6133 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 59/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5854 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6145 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 60/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5888 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6314 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 61/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5834 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6396 - val_sparse_categorical_accuracy: 0.6506 - lr: 2.5000e-04\n",
      "Epoch 62/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5944 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.6008 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 63/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5870 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.6058 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 64/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5846 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6228 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 65/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5884 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6136 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 66/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5859 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.6277 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 67/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5929 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6086 - val_sparse_categorical_accuracy: 0.7229 - lr: 2.5000e-04\n",
      "Epoch 68/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5868 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6200 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 69/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5854 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6226 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 70/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5915 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6153 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 71/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5851 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6464 - val_sparse_categorical_accuracy: 0.6386 - lr: 2.5000e-04\n",
      "Epoch 72/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5828 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6186 - val_sparse_categorical_accuracy: 0.7229 - lr: 2.5000e-04\n",
      "Epoch 73/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5908 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6077 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 74/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5819 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6018 - val_sparse_categorical_accuracy: 0.7349 - lr: 2.5000e-04\n",
      "Epoch 75/500\n",
      "11/11 [==============================] - 1s 67ms/step - loss: 0.5871 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5992 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 76/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5798 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6141 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 77/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5812 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.7863 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 78/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5905 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.7083 - val_sparse_categorical_accuracy: 0.5422 - lr: 2.5000e-04\n",
      "Epoch 79/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5856 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6370 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 80/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5840 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6246 - val_sparse_categorical_accuracy: 0.7229 - lr: 2.5000e-04\n",
      "Epoch 81/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5940 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6008 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 82/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5810 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6011 - val_sparse_categorical_accuracy: 0.7349 - lr: 2.5000e-04\n",
      "Epoch 83/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5841 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6021 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 84/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5876 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6110 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 85/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5771 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.7108 - val_sparse_categorical_accuracy: 0.5301 - lr: 2.5000e-04\n",
      "Epoch 86/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5702 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.6367 - val_sparse_categorical_accuracy: 0.7108 - lr: 2.5000e-04\n",
      "Epoch 87/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5809 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6555 - val_sparse_categorical_accuracy: 0.5663 - lr: 2.5000e-04\n",
      "Epoch 88/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5811 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5811 - val_sparse_categorical_accuracy: 0.7349 - lr: 2.5000e-04\n",
      "Epoch 89/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5828 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6141 - val_sparse_categorical_accuracy: 0.7470 - lr: 2.5000e-04\n",
      "Epoch 90/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5856 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6060 - val_sparse_categorical_accuracy: 0.6988 - lr: 2.5000e-04\n",
      "Epoch 91/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5769 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6718 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 92/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5929 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6878 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 93/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5920 - sparse_categorical_accuracy: 0.6828 - val_loss: 0.9747 - val_sparse_categorical_accuracy: 0.4458 - lr: 2.5000e-04\n",
      "Epoch 94/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5796 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6842 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 95/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5797 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6572 - val_sparse_categorical_accuracy: 0.6145 - lr: 2.5000e-04\n",
      "Epoch 96/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5849 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.8178 - val_sparse_categorical_accuracy: 0.4458 - lr: 2.5000e-04\n",
      "Epoch 97/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5850 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.9318 - val_sparse_categorical_accuracy: 0.4337 - lr: 2.5000e-04\n",
      "Epoch 98/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5817 - sparse_categorical_accuracy: 0.7009 - val_loss: 1.0039 - val_sparse_categorical_accuracy: 0.4337 - lr: 2.5000e-04\n",
      "Epoch 99/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5818 - sparse_categorical_accuracy: 0.6888 - val_loss: 1.0595 - val_sparse_categorical_accuracy: 0.4337 - lr: 2.5000e-04\n",
      "Epoch 100/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5842 - sparse_categorical_accuracy: 0.7009 - val_loss: 1.0199 - val_sparse_categorical_accuracy: 0.4096 - lr: 2.5000e-04\n",
      "Epoch 101/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5833 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.7366 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 102/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5854 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.9298 - val_sparse_categorical_accuracy: 0.4819 - lr: 2.5000e-04\n",
      "Epoch 103/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5859 - sparse_categorical_accuracy: 0.7100 - val_loss: 1.5211 - val_sparse_categorical_accuracy: 0.3133 - lr: 2.5000e-04\n",
      "Epoch 104/500\n",
      "11/11 [==============================] - 1s 61ms/step - loss: 0.5823 - sparse_categorical_accuracy: 0.7160 - val_loss: 1.1840 - val_sparse_categorical_accuracy: 0.4337 - lr: 2.5000e-04\n",
      "Epoch 105/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5748 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.7190 - val_sparse_categorical_accuracy: 0.5663 - lr: 2.5000e-04\n",
      "Epoch 106/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5815 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6843 - val_sparse_categorical_accuracy: 0.6747 - lr: 2.5000e-04\n",
      "Epoch 107/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5842 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.7045 - val_sparse_categorical_accuracy: 0.5663 - lr: 2.5000e-04\n",
      "Epoch 108/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5835 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.8046 - val_sparse_categorical_accuracy: 0.5181 - lr: 2.5000e-04\n",
      "Epoch 109/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5793 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.7313 - val_sparse_categorical_accuracy: 0.5783 - lr: 1.2500e-04\n",
      "Epoch 110/500\n",
      "11/11 [==============================] - 1s 62ms/step - loss: 0.5816 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6949 - val_sparse_categorical_accuracy: 0.5783 - lr: 1.2500e-04\n",
      "Epoch 111/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5783 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6711 - val_sparse_categorical_accuracy: 0.6867 - lr: 1.2500e-04\n",
      "Epoch 112/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5822 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6722 - val_sparse_categorical_accuracy: 0.6506 - lr: 1.2500e-04\n",
      "Epoch 113/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5823 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.6484 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 114/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5719 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.6065 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 115/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5714 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5859 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 116/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5741 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.5925 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.2500e-04\n",
      "Epoch 117/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5757 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.6002 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 118/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5807 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6153 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 119/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5746 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.6118 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 120/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5705 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6129 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 121/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5752 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5939 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 122/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5755 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.5822 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 123/500\n",
      "11/11 [==============================] - 1s 70ms/step - loss: 0.5660 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5779 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 124/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5718 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5856 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 125/500\n",
      "11/11 [==============================] - 1s 71ms/step - loss: 0.5730 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.5773 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 126/500\n",
      "11/11 [==============================] - 1s 68ms/step - loss: 0.5766 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5754 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 127/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5782 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5783 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 128/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5822 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5882 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 129/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5786 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5874 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 130/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5758 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5961 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 131/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5692 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.6048 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 132/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5779 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6175 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.2500e-04\n",
      "Epoch 133/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5803 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5894 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.2500e-04\n",
      "Epoch 134/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5788 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5986 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 135/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5780 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.6086 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 136/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5752 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6033 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 137/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5745 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6143 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 138/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5688 - sparse_categorical_accuracy: 0.7281 - val_loss: 0.6227 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 139/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5769 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6243 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 140/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5706 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6202 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.2500e-04\n",
      "Epoch 141/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5801 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.6371 - val_sparse_categorical_accuracy: 0.6867 - lr: 1.2500e-04\n",
      "Epoch 142/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5764 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.6450 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 143/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5822 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6549 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.2500e-04\n",
      "Epoch 144/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5704 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.6927 - val_sparse_categorical_accuracy: 0.6024 - lr: 1.2500e-04\n",
      "Epoch 145/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5715 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.7170 - val_sparse_categorical_accuracy: 0.5783 - lr: 1.2500e-04\n",
      "Epoch 146/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5779 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.7184 - val_sparse_categorical_accuracy: 0.5904 - lr: 1.2500e-04\n",
      "Epoch 147/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5842 - sparse_categorical_accuracy: 0.6858 - val_loss: 0.6465 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 148/500\n",
      "11/11 [==============================] - 1s 66ms/step - loss: 0.5711 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.6261 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 149/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5747 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.6084 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 150/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5713 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5943 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 151/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5753 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5847 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 152/500\n",
      "11/11 [==============================] - 1s 69ms/step - loss: 0.5703 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.5693 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 153/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5750 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5735 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 154/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5783 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5766 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 155/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5769 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5742 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 156/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5777 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5721 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 157/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5707 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5770 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 158/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5759 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6150 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 159/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5668 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.6537 - val_sparse_categorical_accuracy: 0.6265 - lr: 1.0000e-04\n",
      "Epoch 160/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5707 - sparse_categorical_accuracy: 0.7251 - val_loss: 0.6182 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 161/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5747 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6253 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 162/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5732 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5956 - val_sparse_categorical_accuracy: 0.7711 - lr: 1.0000e-04\n",
      "Epoch 163/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5862 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5817 - val_sparse_categorical_accuracy: 0.7711 - lr: 1.0000e-04\n",
      "Epoch 164/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5787 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5904 - val_sparse_categorical_accuracy: 0.7711 - lr: 1.0000e-04\n",
      "Epoch 165/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5757 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5852 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 166/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5758 - sparse_categorical_accuracy: 0.6888 - val_loss: 0.5962 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 167/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5752 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5791 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 168/500\n",
      "11/11 [==============================] - 1s 69ms/step - loss: 0.5688 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5661 - val_sparse_categorical_accuracy: 0.7711 - lr: 1.0000e-04\n",
      "Epoch 169/500\n",
      "11/11 [==============================] - 1s 69ms/step - loss: 0.5724 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5657 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 170/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5705 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.5689 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 171/500\n",
      "11/11 [==============================] - 1s 69ms/step - loss: 0.5757 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5636 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 172/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5742 - sparse_categorical_accuracy: 0.7251 - val_loss: 0.5763 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 173/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5729 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5823 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 174/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5722 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5790 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 175/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5741 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5820 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 176/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5693 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5647 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 177/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5652 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5667 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 178/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5697 - sparse_categorical_accuracy: 0.7251 - val_loss: 0.5672 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 179/500\n",
      "11/11 [==============================] - 1s 68ms/step - loss: 0.5647 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.5586 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 180/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5691 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5599 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 181/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5676 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5757 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 182/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5616 - sparse_categorical_accuracy: 0.7251 - val_loss: 0.5864 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 183/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5775 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5610 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 184/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5724 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5600 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 185/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5879 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5726 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 186/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5685 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6117 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 187/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5695 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6132 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 188/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5744 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5861 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 189/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5665 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5806 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 190/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5746 - sparse_categorical_accuracy: 0.6949 - val_loss: 0.5836 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 191/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5798 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5967 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 192/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5688 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5994 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 193/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5634 - sparse_categorical_accuracy: 0.7402 - val_loss: 0.5928 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 194/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5699 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5794 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 195/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5687 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5886 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 196/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5696 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.5980 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 197/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5715 - sparse_categorical_accuracy: 0.6979 - val_loss: 0.5964 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 198/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5775 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.5741 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 199/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5664 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5675 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 200/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5682 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5711 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 201/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5707 - sparse_categorical_accuracy: 0.6918 - val_loss: 0.5763 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 202/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5699 - sparse_categorical_accuracy: 0.7281 - val_loss: 0.5768 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 203/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5739 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5719 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 204/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5619 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5789 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 205/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5669 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5812 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 206/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5714 - sparse_categorical_accuracy: 0.7009 - val_loss: 0.5806 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 207/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5734 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5898 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 208/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5756 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6095 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 209/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5637 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5941 - val_sparse_categorical_accuracy: 0.6867 - lr: 1.0000e-04\n",
      "Epoch 210/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5752 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5942 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 211/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5655 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.5959 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 212/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5746 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5942 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 213/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5696 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.6079 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 214/500\n",
      "11/11 [==============================] - 1s 65ms/step - loss: 0.5638 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.6143 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 215/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5706 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6126 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 216/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5694 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.5827 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 217/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5658 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6017 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 218/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5783 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5996 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 219/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5619 - sparse_categorical_accuracy: 0.7281 - val_loss: 0.6053 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 220/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5759 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.6103 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 221/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5816 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5942 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 222/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5777 - sparse_categorical_accuracy: 0.7160 - val_loss: 0.6022 - val_sparse_categorical_accuracy: 0.6988 - lr: 1.0000e-04\n",
      "Epoch 223/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5723 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.6254 - val_sparse_categorical_accuracy: 0.7108 - lr: 1.0000e-04\n",
      "Epoch 224/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5701 - sparse_categorical_accuracy: 0.7069 - val_loss: 0.6034 - val_sparse_categorical_accuracy: 0.7349 - lr: 1.0000e-04\n",
      "Epoch 225/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5772 - sparse_categorical_accuracy: 0.7039 - val_loss: 0.5814 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 226/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5681 - sparse_categorical_accuracy: 0.7190 - val_loss: 0.5625 - val_sparse_categorical_accuracy: 0.7229 - lr: 1.0000e-04\n",
      "Epoch 227/500\n",
      "11/11 [==============================] - 1s 63ms/step - loss: 0.5719 - sparse_categorical_accuracy: 0.7221 - val_loss: 0.5660 - val_sparse_categorical_accuracy: 0.7470 - lr: 1.0000e-04\n",
      "Epoch 228/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5653 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5673 - val_sparse_categorical_accuracy: 0.7590 - lr: 1.0000e-04\n",
      "Epoch 229/500\n",
      "11/11 [==============================] - 1s 64ms/step - loss: 0.5697 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.5642 - val_sparse_categorical_accuracy: 0.7711 - lr: 1.0000e-04\n",
      "Epoch 229: early stopping\n"
     ]
    }
   ],
   "source": [
    "epochs = 500\n",
    "batch_size = 32\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        \"outputs/best_model.h5\", save_best_only=True, monitor=\"val_loss\"\n",
    "    ),\n",
    "    keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor=\"val_loss\", factor=0.5, patience=20, min_lr=0.0001\n",
    "    ),\n",
    "    keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1),\n",
    "]\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"sparse_categorical_accuracy\"],\n",
    ")\n",
    "history = model.fit(\n",
    "    X_train_sc,\n",
    "    y_train,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    callbacks=callbacks,\n",
    "    validation_split=0.2,\n",
    "    verbose=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a13f22c",
   "metadata": {},
   "source": [
    "## Evaluate performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71f753fc-7e75-4e9a-86a9-e296cb6594af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 45ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_sc)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56e7cdc0-de25-4c0b-998f-de3fe1c18731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC 0.6975\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print (f'AUC {roc_auc:0.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "790b1c24-c1a5-43cb-9f65-7bf742d317b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "\n",
    "# Plot ROC\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(fpr, tpr, color='orange')\n",
    "ax1.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "ax1.set_xlabel('False Positive Rate')\n",
    "ax1.set_ylabel('True Positive Rate')\n",
    "ax1.set_title('Receiver Operator Characteristic Curve')\n",
    "text = f'ROC AUC: {roc_auc:.3f}'\n",
    "ax1.text(0.64,0.07, text, \n",
    "         bbox=dict(facecolor='white', edgecolor='black'))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d81bcbac-9d55-4eb4-b2d7-f1f9a222fdeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric = \"sparse_categorical_accuracy\"\n",
    "plt.figure()\n",
    "plt.plot(history.history[metric])\n",
    "plt.plot(history.history[\"val_\" + metric])\n",
    "plt.title(\"model \" + metric)\n",
    "plt.ylabel(metric, fontsize=\"large\")\n",
    "plt.xlabel(\"epoch\", fontsize=\"large\")\n",
    "plt.legend([\"train\", \"val\"], loc=\"best\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}