iui-group-l-name-zensiert/1-first-project/tdt/NeuralNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1c66d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c3e70d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "delim = ';'\n",
    "\n",
    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
    "\n",
    "Xpickle_file = '../X2.pickle'\n",
    "\n",
    "ypickle_file = '../y2.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "977c5f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "THRESH = 70\n",
    "LEEWAY = 0\n",
    "EPOCH = 50\n",
    "\n",
    "DENSE_COUNT = 2\n",
    "DENSE_NEURONS = 2400\n",
    "\n",
    "DENSE2_COUNT = 3\n",
    "DENSE2_NEURONS = 600\n",
    "\n",
    "AVG_FROM = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15581211",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten(npList):\n",
    "    temp        = npList['Force']\n",
    "    thresh      = THRESH\n",
    "    leeway      = LEEWAY\n",
    "    \n",
    "    temps_over_T = np.where(temp > thresh)[0]\n",
    "    if len(temps_over_T) > 0:\n",
    "        return npList[max(temps_over_T[0]-leeway,0):min(len(npList)-1,temps_over_T[-1]+leeway)]\n",
    "    else:\n",
    "        return npList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7033ae7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "def load_pickles():\n",
    "    _p = open(Xpickle_file, 'rb')\n",
    "    X = pickle.load(_p)\n",
    "    _p.close()\n",
    "        \n",
    "    _p = open(ypickle_file, 'rb')\n",
    "    y = pickle.load(_p)\n",
    "    _p.close()\n",
    "    \n",
    "    return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf52a98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def load_data():\n",
    "    if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
    "        return load_pickles()\n",
    "    data = []\n",
    "    label = []\n",
    "    for user in range(0, user_count):\n",
    "        user_path = base_path + str(user) + '/split_letters_csv/'\n",
    "        for file in os.listdir(user_path):\n",
    "            file_name = user_path + file\n",
    "            letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
    "            data.append(pd.read_csv(file_name, delim))\n",
    "            label.append(letter)\n",
    "    return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str), np.asarray(file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0437ae03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.12 s, sys: 415 ms, total: 5.53 s\n",
      "Wall time: 5.54 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(26179,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "X, y = load_data()\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19e36172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.22 s, sys: 23.5 ms, total: 6.24 s\n",
      "Wall time: 6.24 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "XX = np.array(list(map(shorten, X)), dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cdd9f1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26179.000000\n",
       "mean        47.987394\n",
       "std         35.114351\n",
       "min          2.000000\n",
       "50%         43.000000\n",
       "95%         88.000000\n",
       "96%         94.000000\n",
       "97%        101.000000\n",
       "98%        116.000000\n",
       "99%        150.000000\n",
       "max       1512.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X_len = np.asarray(list(map(len, XX)))\n",
    "l = []\n",
    "sq_xlen = pd.Series(X_len)\n",
    "ptiles = [x*0.01 for x in range(100)]\n",
    "for i in ptiles:\n",
    "    l.append(sq_xlen.quantile(i))\n",
    "plt.plot(l, ptiles)\n",
    "sq_xlen.describe(percentiles=[x*0.01 for x in range(95,100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f1ed5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data):\n",
    "    fig, axs = plt.subplots(4, 3, figsize=(3*3, 3*4))\n",
    "    t = data['Millis']\n",
    "    axs[0][0].plot(t, data['Acc1 X'])\n",
    "    axs[0][1].plot(t, data['Acc1 Y'])\n",
    "    axs[0][2].plot(t, data['Acc1 Z'])\n",
    "    axs[1][0].plot(t, data['Acc2 X'])\n",
    "    axs[1][1].plot(t, data['Acc2 Y'])\n",
    "    axs[1][2].plot(t, data['Acc2 Z'])\n",
    "    axs[2][0].plot(t, data['Gyro X'])\n",
    "    axs[2][1].plot(t, data['Gyro Y'])\n",
    "    axs[2][2].plot(t, data['Gyro Z'])\n",
    "    axs[3][0].plot(t, data['Mag X'])\n",
    "    axs[3][1].plot(t, data['Mag Y'])\n",
    "    axs[3][2].plot(t, data['Mag Z'])\n",
    "\n",
    "    for a in axs:\n",
    "        for b in a:\n",
    "            b.plot(t, data['Force'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8b06a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25918,), (52, 15))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_p = 0.99\n",
    "threshold = int(sq_xlen.quantile(threshold_p))\n",
    "len_mask = np.where(X_len <= threshold)\n",
    "\n",
    "X_filter = XX[len_mask]\n",
    "y_filter = y[len_mask]\n",
    "\n",
    "X_filter.shape, X_filter[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd4c4b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "a = [x.drop(labels='Millis', axis=1) for x in X_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45faadf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_filter = pad_sequences(X_filter, dtype=float, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9e13d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data):\n",
    "    fig, axs = plt.subplots(5, 3, figsize=(3*3, 3*5))\n",
    "    axs[0][0].plot(data[0])\n",
    "    axs[0][1].plot(data[1])\n",
    "    axs[0][2].plot(data[2])\n",
    "    axs[1][0].plot(data[3])\n",
    "    axs[1][1].plot(data[4])\n",
    "    axs[1][2].plot(data[5])\n",
    "    axs[2][0].plot(data[6])\n",
    "    axs[2][1].plot(data[7])\n",
    "    axs[2][2].plot(data[8])\n",
    "    axs[3][0].plot(data[9])\n",
    "    axs[3][1].plot(data[10])\n",
    "    axs[3][2].plot(data[11])\n",
    "    axs[4][0].plot(data[12])\n",
    "    axs[4][1].plot(data[13])\n",
    "\n",
    "#     for a in axs:\n",
    "#         for b in a:\n",
    "#             b.plot(t, data['Force'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0ba25d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20734, 150, 15)\n",
      "(5184, 150, 15)\n",
      "(20734, 52)\n",
      "(5184, 52)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
    "import tensorflow as tf\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "\n",
    "yt_filter = lb.fit_transform(y_filter)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_filter, yt_filter, test_size=0.2, random_state=177013)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "plot_data(X_filter[0].T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533eef6a",
   "metadata": {},
   "source": [
    "fig, axs = plt.subplots(13,2,figsize=(20, 60), sharey=True)\n",
    "data_count = int(len(X_train)/10)\n",
    "for i,j in zip(X_train[:data_count], lb.inverse_transform(y_train)[:data_count]):\n",
    "    num = ord(j) - 64\n",
    "    f = i.T[13]\n",
    "    r = int((num-1)/2)%13\n",
    "    c = (num-1)%2\n",
    "    axs[r][c].title.set_text(f'{j}')\n",
    "    axs[r][c].plot(f)\n",
    "plt.savefig('./all_forces.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7ff0590",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIRST CELL: set these variables to limit GPU usage.\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'          # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3a1053f",
   "metadata": {},
   "outputs": [],
   "source": [
    "accs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edf15e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(BatchNormalization(input_shape=X_filter[0].shape))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "\n",
    "    for i in range(DENSE_COUNT):\n",
    "        model.add(Dense(DENSE_NEURONS, activation='relu'))\n",
    "        \n",
    "    Dropout(0.2)\n",
    "    \n",
    "    for i in range(DENSE2_COUNT):\n",
    "        model.add(Dense(DENSE2_NEURONS, activation='relu'))\n",
    "        \n",
    "    Dropout(0.2)\n",
    "    \n",
    "    model.add(Dense(52, activation='softmax'))\n",
    "\n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(0.0001),\n",
    "        loss=\"categorical_crossentropy\", \n",
    "        metrics=[\"acc\"],\n",
    "    )\n",
    "\n",
    "    return model\n",
    "#     model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d8a548b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "162/162 [==============================] - 2s 5ms/step - loss: 3.1562 - acc: 0.1829 - val_loss: 2.5782 - val_acc: 0.3098\n",
      "Epoch 2/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 2.0657 - acc: 0.4226 - val_loss: 1.9569 - val_acc: 0.4579\n",
      "Epoch 3/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 1.5548 - acc: 0.5398 - val_loss: 1.7179 - val_acc: 0.5149\n",
      "Epoch 4/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 1.2320 - acc: 0.6281 - val_loss: 1.5439 - val_acc: 0.5484\n",
      "Epoch 5/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.9888 - acc: 0.6937 - val_loss: 1.4862 - val_acc: 0.5700\n",
      "Epoch 6/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.7951 - acc: 0.7524 - val_loss: 1.4870 - val_acc: 0.5689\n",
      "Epoch 7/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.6452 - acc: 0.7965 - val_loss: 1.3981 - val_acc: 0.6030\n",
      "Epoch 8/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.5287 - acc: 0.8313 - val_loss: 1.3804 - val_acc: 0.6209\n",
      "Epoch 9/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.4338 - acc: 0.8627 - val_loss: 1.4227 - val_acc: 0.6073\n",
      "Epoch 10/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.3522 - acc: 0.8852 - val_loss: 1.4271 - val_acc: 0.6277\n",
      "Epoch 11/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.3016 - acc: 0.9029 - val_loss: 1.4799 - val_acc: 0.6223\n",
      "Epoch 12/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.2579 - acc: 0.9169 - val_loss: 1.5139 - val_acc: 0.6275\n",
      "Epoch 13/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.2041 - acc: 0.9367 - val_loss: 1.5174 - val_acc: 0.6348\n",
      "Epoch 14/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1761 - acc: 0.9452 - val_loss: 1.5331 - val_acc: 0.6437\n",
      "Epoch 15/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1571 - acc: 0.9501 - val_loss: 1.6265 - val_acc: 0.6404\n",
      "Epoch 16/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1334 - acc: 0.9580 - val_loss: 1.6154 - val_acc: 0.6476\n",
      "Epoch 17/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1110 - acc: 0.9676 - val_loss: 1.7053 - val_acc: 0.6427\n",
      "Epoch 18/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1231 - acc: 0.9628 - val_loss: 1.8015 - val_acc: 0.6117\n",
      "Epoch 19/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1297 - acc: 0.9598 - val_loss: 1.7488 - val_acc: 0.6360\n",
      "Epoch 20/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1451 - acc: 0.9543 - val_loss: 1.7453 - val_acc: 0.6285\n",
      "Epoch 21/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.1098 - acc: 0.9681 - val_loss: 1.7955 - val_acc: 0.6418\n",
      "Epoch 22/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0817 - acc: 0.9765 - val_loss: 1.7614 - val_acc: 0.6518\n",
      "Epoch 23/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0729 - acc: 0.9790 - val_loss: 1.8825 - val_acc: 0.6337\n",
      "Epoch 24/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0927 - acc: 0.9695 - val_loss: 1.8203 - val_acc: 0.6402\n",
      "Epoch 25/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0796 - acc: 0.9764 - val_loss: 1.9148 - val_acc: 0.6360\n",
      "Epoch 26/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0609 - acc: 0.9815 - val_loss: 1.9998 - val_acc: 0.6337\n",
      "Epoch 27/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0589 - acc: 0.9830 - val_loss: 1.9563 - val_acc: 0.6439\n",
      "Epoch 28/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0562 - acc: 0.9840 - val_loss: 1.9897 - val_acc: 0.6410\n",
      "Epoch 29/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0861 - acc: 0.9730 - val_loss: 2.0437 - val_acc: 0.6329\n",
      "Epoch 30/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0672 - acc: 0.9783 - val_loss: 2.0066 - val_acc: 0.6503\n",
      "Epoch 31/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0713 - acc: 0.9789 - val_loss: 1.9843 - val_acc: 0.6453\n",
      "Epoch 32/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0718 - acc: 0.9777 - val_loss: 1.9756 - val_acc: 0.6424\n",
      "Epoch 33/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0501 - acc: 0.9847 - val_loss: 2.0316 - val_acc: 0.6472\n",
      "Epoch 34/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0588 - acc: 0.9822 - val_loss: 1.9967 - val_acc: 0.6368\n",
      "Epoch 35/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0422 - acc: 0.9885 - val_loss: 2.1204 - val_acc: 0.6522\n",
      "Epoch 36/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0392 - acc: 0.9887 - val_loss: 2.1788 - val_acc: 0.6356\n",
      "Epoch 37/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0835 - acc: 0.9755 - val_loss: 2.1036 - val_acc: 0.6321\n",
      "Epoch 38/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0733 - acc: 0.9771 - val_loss: 2.1187 - val_acc: 0.6443\n",
      "Epoch 39/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0362 - acc: 0.9899 - val_loss: 2.1870 - val_acc: 0.6451\n",
      "Epoch 40/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0526 - acc: 0.9834 - val_loss: 2.0849 - val_acc: 0.6512\n",
      "Epoch 41/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0462 - acc: 0.9865 - val_loss: 2.2498 - val_acc: 0.6366\n",
      "Epoch 42/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0399 - acc: 0.9876 - val_loss: 2.1870 - val_acc: 0.6501\n",
      "Epoch 43/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0492 - acc: 0.9850 - val_loss: 2.0921 - val_acc: 0.6620\n",
      "Epoch 44/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0262 - acc: 0.9932 - val_loss: 2.1486 - val_acc: 0.6586\n",
      "Epoch 45/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0337 - acc: 0.9908 - val_loss: 2.1384 - val_acc: 0.6570\n",
      "Epoch 46/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0399 - acc: 0.9878 - val_loss: 2.1853 - val_acc: 0.6393\n",
      "Epoch 47/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0487 - acc: 0.9860 - val_loss: 2.1394 - val_acc: 0.6607\n",
      "Epoch 48/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0483 - acc: 0.9855 - val_loss: 2.1069 - val_acc: 0.6429\n",
      "Epoch 49/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0494 - acc: 0.9855 - val_loss: 2.2427 - val_acc: 0.6435\n",
      "Epoch 50/50\n",
      "162/162 [==============================] - 1s 4ms/step - loss: 0.0567 - acc: 0.9832 - val_loss: 2.1715 - val_acc: 0.6375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:36<00:00, 36.07s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss, test acc: [2.1715357303619385, 0.6375385522842407]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(AVG_FROM)):\n",
    "    model = build_model()\n",
    "    \n",
    "    model.fit(X_train, y_train, \n",
    "              epochs=EPOCH,\n",
    "              batch_size=128,\n",
    "              shuffle=True,\n",
    "              validation_data=(X_test, y_test),\n",
    "              verbose=1,\n",
    "             )\n",
    "    # Evaluate the model on the test data using `evaluate`\n",
    "#     print(\"Evaluate on test data\")\n",
    "    results = model.evaluate(X_test, y_test, batch_size=128, verbose=0)\n",
    "    print(\"test loss, test acc:\", results)\n",
    "    accs.append((model,results[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de7ce7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6375385522842407"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.delete(accs,0,1).astype('float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8be52141",
   "metadata": {},
   "outputs": [],
   "source": [
    "exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a4c2e8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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