Linear_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-sensor reservoir computing for language learning via two-dimensional memristors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "\n",
    "train_data = pd.read_csv(\"./train_data_01.csv\")\n",
    "test_data = pd.read_csv(\"./test_data_01.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "\n",
    "train_data = shuffle(train_data)\n",
    "test_data = shuffle(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = train_data.iloc[:,1:6], train_data.loc[:,\"LETTER\"]\n",
    "X_test, y_test = test_data.iloc[:,1:6], test_data.loc[:,\"LETTER\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single layer perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(5, activation='softmax' ,input_shape =(5,))\n",
    "])\n",
    "\n",
    "model.compile(optimizer = tf.keras.optimizers.Adam(0.001),\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "32/32 [==============================] - 1s 15ms/step - loss: 47.8660 - accuracy: 0.1902 - val_loss: 35.9372 - val_accuracy: 0.2000\n",
      "Epoch 2/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 33.2156 - accuracy: 0.1183 - val_loss: 26.3643 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 23.3729 - accuracy: 0.0000e+00 - val_loss: 17.1437 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 16.4586 - accuracy: 0.0364 - val_loss: 11.0548 - val_accuracy: 0.1740\n",
      "Epoch 5/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 10.7390 - accuracy: 0.1890 - val_loss: 9.0973 - val_accuracy: 0.2000\n",
      "Epoch 6/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 8.6152 - accuracy: 0.1951 - val_loss: 6.9998 - val_accuracy: 0.2000\n",
      "Epoch 7/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 6.5114 - accuracy: 0.1995 - val_loss: 4.8524 - val_accuracy: 0.2000\n",
      "Epoch 8/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 4.1731 - accuracy: 0.2088 - val_loss: 2.8313 - val_accuracy: 0.2000\n",
      "Epoch 9/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 2.3092 - accuracy: 0.1822 - val_loss: 1.9384 - val_accuracy: 0.0140\n",
      "Epoch 10/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9647 - accuracy: 0.0860 - val_loss: 1.9469 - val_accuracy: 0.0080\n",
      "Epoch 11/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9615 - accuracy: 0.0317 - val_loss: 1.9297 - val_accuracy: 0.0120\n",
      "Epoch 12/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 2.0151 - accuracy: 0.0017 - val_loss: 1.9377 - val_accuracy: 0.1000\n",
      "Epoch 13/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9409 - accuracy: 0.0329 - val_loss: 1.9117 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9766 - accuracy: 0.0031 - val_loss: 1.9493 - val_accuracy: 0.2720\n",
      "Epoch 15/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9282 - accuracy: 0.1640 - val_loss: 1.9173 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9235 - accuracy: 0.0000e+00 - val_loss: 1.9114 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9193 - accuracy: 0.0000e+00 - val_loss: 1.9302 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9554 - accuracy: 0.0020 - val_loss: 1.9085 - val_accuracy: 0.0040\n",
      "Epoch 19/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9232 - accuracy: 5.2138e-04 - val_loss: 1.9036 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9367 - accuracy: 0.0502 - val_loss: 1.9344 - val_accuracy: 0.2000\n",
      "Epoch 21/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9251 - accuracy: 0.1352 - val_loss: 1.9146 - val_accuracy: 0.1920\n",
      "Epoch 22/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9212 - accuracy: 0.0630 - val_loss: 1.9274 - val_accuracy: 0.1880\n",
      "Epoch 23/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9352 - accuracy: 0.0866 - val_loss: 1.9034 - val_accuracy: 0.0920\n",
      "Epoch 24/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9314 - accuracy: 0.0824 - val_loss: 1.9210 - val_accuracy: 0.2100\n",
      "Epoch 25/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9190 - accuracy: 0.1344 - val_loss: 1.9153 - val_accuracy: 0.1900\n",
      "Epoch 26/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9052 - accuracy: 0.0675 - val_loss: 1.9157 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9139 - accuracy: 0.0089 - val_loss: 1.8859 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8818 - accuracy: 0.0978 - val_loss: 1.9062 - val_accuracy: 0.1060\n",
      "Epoch 29/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.9045 - accuracy: 0.0490 - val_loss: 1.8892 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9132 - accuracy: 0.0068 - val_loss: 1.9082 - val_accuracy: 0.0200\n",
      "Epoch 31/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9158 - accuracy: 0.0058 - val_loss: 1.9062 - val_accuracy: 0.0060\n",
      "Epoch 32/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9395 - accuracy: 0.0200 - val_loss: 1.9290 - val_accuracy: 0.3620\n",
      "Epoch 33/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.9559 - accuracy: 0.1252 - val_loss: 1.8844 - val_accuracy: 0.0960\n",
      "Epoch 34/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8886 - accuracy: 0.1358 - val_loss: 1.8977 - val_accuracy: 0.1480\n",
      "Epoch 35/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8797 - accuracy: 0.0925 - val_loss: 1.8963 - val_accuracy: 0.0800\n",
      "Epoch 36/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8912 - accuracy: 0.0248 - val_loss: 1.8674 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8784 - accuracy: 0.0438 - val_loss: 1.8759 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8813 - accuracy: 0.0382 - val_loss: 1.8698 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8908 - accuracy: 0.0235 - val_loss: 1.8721 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.9297 - accuracy: 0.0263 - val_loss: 1.8884 - val_accuracy: 0.2000\n",
      "Epoch 41/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8650 - accuracy: 0.0540 - val_loss: 1.8659 - val_accuracy: 0.0400\n",
      "Epoch 42/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8906 - accuracy: 0.0185 - val_loss: 1.8649 - val_accuracy: 0.0920\n",
      "Epoch 43/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.9061 - accuracy: 0.0247 - val_loss: 1.9731 - val_accuracy: 0.2000\n",
      "Epoch 44/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.9693 - accuracy: 0.1254 - val_loss: 1.9286 - val_accuracy: 0.0820\n",
      "Epoch 45/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.9580 - accuracy: 0.1147 - val_loss: 1.8482 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8567 - accuracy: 0.0089 - val_loss: 1.8710 - val_accuracy: 0.2000\n",
      "Epoch 47/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8935 - accuracy: 0.0716 - val_loss: 1.8735 - val_accuracy: 0.0760\n",
      "Epoch 48/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8477 - accuracy: 0.0161 - val_loss: 1.8522 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8786 - accuracy: 0.0270 - val_loss: 1.8734 - val_accuracy: 0.0420\n",
      "Epoch 50/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8829 - accuracy: 0.0928 - val_loss: 1.8447 - val_accuracy: 0.0860\n",
      "Epoch 51/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8785 - accuracy: 0.0181 - val_loss: 1.8543 - val_accuracy: 0.2000\n",
      "Epoch 52/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8693 - accuracy: 0.0277 - val_loss: 1.8786 - val_accuracy: 0.0020\n",
      "Epoch 53/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8866 - accuracy: 0.0606 - val_loss: 1.8509 - val_accuracy: 0.2080\n",
      "Epoch 54/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8445 - accuracy: 0.1365 - val_loss: 1.8699 - val_accuracy: 0.3160\n",
      "Epoch 55/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8815 - accuracy: 0.0830 - val_loss: 1.8565 - val_accuracy: 0.0100\n",
      "Epoch 56/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8663 - accuracy: 0.0313 - val_loss: 1.8397 - val_accuracy: 0.0480\n",
      "Epoch 57/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8719 - accuracy: 0.0882 - val_loss: 1.9020 - val_accuracy: 0.2120\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8996 - accuracy: 0.1101 - val_loss: 1.8194 - val_accuracy: 0.0280\n",
      "Epoch 59/1000\n",
      "32/32 [==============================] - 0s 5ms/step - loss: 1.8677 - accuracy: 0.0245 - val_loss: 1.8805 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8936 - accuracy: 0.0216 - val_loss: 1.8559 - val_accuracy: 0.1980\n",
      "Epoch 61/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8573 - accuracy: 0.0984 - val_loss: 1.8206 - val_accuracy: 0.1820\n",
      "Epoch 62/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8281 - accuracy: 0.0886 - val_loss: 1.8271 - val_accuracy: 0.2000\n",
      "Epoch 63/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8198 - accuracy: 0.0437 - val_loss: 1.8105 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8694 - accuracy: 0.0679 - val_loss: 1.8261 - val_accuracy: 0.1500\n",
      "Epoch 65/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8472 - accuracy: 0.0614 - val_loss: 1.8179 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8246 - accuracy: 0.0051 - val_loss: 1.8196 - val_accuracy: 0.1500\n",
      "Epoch 67/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8177 - accuracy: 0.0695 - val_loss: 1.8028 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8024 - accuracy: 0.0036 - val_loss: 1.8159 - val_accuracy: 0.2000\n",
      "Epoch 69/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8128 - accuracy: 0.0909 - val_loss: 1.8024 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8398 - accuracy: 0.0501 - val_loss: 1.8086 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8300 - accuracy: 0.0816 - val_loss: 1.8255 - val_accuracy: 0.0880\n",
      "Epoch 72/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8268 - accuracy: 0.1246 - val_loss: 1.8639 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8225 - accuracy: 0.0832 - val_loss: 1.8097 - val_accuracy: 0.1280\n",
      "Epoch 74/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8107 - accuracy: 0.0934 - val_loss: 1.7958 - val_accuracy: 0.0620\n",
      "Epoch 75/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8516 - accuracy: 0.1038 - val_loss: 1.7969 - val_accuracy: 0.0600\n",
      "Epoch 76/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8106 - accuracy: 0.0287 - val_loss: 1.7854 - val_accuracy: 0.0020\n",
      "Epoch 77/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7774 - accuracy: 0.0028 - val_loss: 1.8080 - val_accuracy: 0.1740\n",
      "Epoch 78/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8075 - accuracy: 0.0553 - val_loss: 1.8340 - val_accuracy: 0.1960\n",
      "Epoch 79/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8391 - accuracy: 0.1143 - val_loss: 1.8388 - val_accuracy: 0.2060\n",
      "Epoch 80/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7848 - accuracy: 0.1283 - val_loss: 1.7733 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7632 - accuracy: 0.0673 - val_loss: 1.7672 - val_accuracy: 0.0060\n",
      "Epoch 82/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7604 - accuracy: 0.0510 - val_loss: 1.7868 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.8005 - accuracy: 0.0631 - val_loss: 1.8353 - val_accuracy: 0.1480\n",
      "Epoch 84/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8069 - accuracy: 0.0711 - val_loss: 1.7973 - val_accuracy: 0.2000\n",
      "Epoch 85/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8611 - accuracy: 0.1994 - val_loss: 1.7628 - val_accuracy: 0.1560\n",
      "Epoch 86/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7868 - accuracy: 0.0376 - val_loss: 1.7578 - val_accuracy: 0.0600\n",
      "Epoch 87/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7690 - accuracy: 0.0559 - val_loss: 1.7749 - val_accuracy: 0.1620\n",
      "Epoch 88/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7921 - accuracy: 0.1130 - val_loss: 1.7617 - val_accuracy: 0.0100\n",
      "Epoch 89/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.8056 - accuracy: 0.1094 - val_loss: 1.7778 - val_accuracy: 0.2000\n",
      "Epoch 90/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7925 - accuracy: 0.1471 - val_loss: 1.7606 - val_accuracy: 0.2240\n",
      "Epoch 91/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7750 - accuracy: 0.0204 - val_loss: 1.7813 - val_accuracy: 0.0180\n",
      "Epoch 92/1000\n",
      "32/32 [==============================] - 0s 5ms/step - loss: 1.7849 - accuracy: 0.0815 - val_loss: 1.8019 - val_accuracy: 0.1920\n",
      "Epoch 93/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.8089 - accuracy: 0.0971 - val_loss: 1.7462 - val_accuracy: 0.1880\n",
      "Epoch 94/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7829 - accuracy: 0.2150 - val_loss: 1.7538 - val_accuracy: 0.1140\n",
      "Epoch 95/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7437 - accuracy: 0.1528 - val_loss: 1.7687 - val_accuracy: 0.2400\n",
      "Epoch 96/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7765 - accuracy: 0.1705 - val_loss: 1.7649 - val_accuracy: 0.2120\n",
      "Epoch 97/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7587 - accuracy: 0.1601 - val_loss: 1.7419 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.7521 - accuracy: 0.0156 - val_loss: 1.7366 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7547 - accuracy: 0.0657 - val_loss: 1.7751 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7867 - accuracy: 0.1030 - val_loss: 1.7484 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7801 - accuracy: 0.0221 - val_loss: 1.7481 - val_accuracy: 0.0880\n",
      "Epoch 102/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7071 - accuracy: 0.1116 - val_loss: 1.7995 - val_accuracy: 0.2000\n",
      "Epoch 103/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7951 - accuracy: 0.0923 - val_loss: 1.7486 - val_accuracy: 0.0260\n",
      "Epoch 104/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7705 - accuracy: 0.0479 - val_loss: 1.7501 - val_accuracy: 0.0200\n",
      "Epoch 105/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7832 - accuracy: 0.0260 - val_loss: 1.7635 - val_accuracy: 0.2080\n",
      "Epoch 106/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7224 - accuracy: 0.1879 - val_loss: 1.7589 - val_accuracy: 0.2060\n",
      "Epoch 107/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7341 - accuracy: 0.2089 - val_loss: 1.7283 - val_accuracy: 0.1980\n",
      "Epoch 108/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7578 - accuracy: 0.1386 - val_loss: 1.7198 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7285 - accuracy: 0.0752 - val_loss: 1.7196 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7618 - accuracy: 0.0599 - val_loss: 1.7051 - val_accuracy: 0.0280\n",
      "Epoch 111/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7136 - accuracy: 0.0276 - val_loss: 1.7043 - val_accuracy: 0.1180\n",
      "Epoch 112/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7093 - accuracy: 0.1201 - val_loss: 1.7271 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7496 - accuracy: 0.0218 - val_loss: 1.7121 - val_accuracy: 0.1100\n",
      "Epoch 114/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6969 - accuracy: 0.1541 - val_loss: 1.7143 - val_accuracy: 0.0160\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 115/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7639 - accuracy: 0.0796 - val_loss: 1.7043 - val_accuracy: 0.1500\n",
      "Epoch 116/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7229 - accuracy: 0.1371 - val_loss: 1.6973 - val_accuracy: 0.0120\n",
      "Epoch 117/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7199 - accuracy: 0.0577 - val_loss: 1.7538 - val_accuracy: 0.0540\n",
      "Epoch 118/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7278 - accuracy: 0.2426 - val_loss: 1.7793 - val_accuracy: 0.0080\n",
      "Epoch 119/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7259 - accuracy: 0.0467 - val_loss: 1.7506 - val_accuracy: 0.2980\n",
      "Epoch 120/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7509 - accuracy: 0.0883 - val_loss: 1.7291 - val_accuracy: 0.1880\n",
      "Epoch 121/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7245 - accuracy: 0.1524 - val_loss: 1.6741 - val_accuracy: 0.0340\n",
      "Epoch 122/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6931 - accuracy: 0.1115 - val_loss: 1.7205 - val_accuracy: 0.2520\n",
      "Epoch 123/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7377 - accuracy: 0.1482 - val_loss: 1.7432 - val_accuracy: 0.0120\n",
      "Epoch 124/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7135 - accuracy: 0.0518 - val_loss: 1.6695 - val_accuracy: 0.0080\n",
      "Epoch 125/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7163 - accuracy: 0.0710 - val_loss: 1.6798 - val_accuracy: 0.0380\n",
      "Epoch 126/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7048 - accuracy: 0.0625 - val_loss: 1.6801 - val_accuracy: 0.2000\n",
      "Epoch 127/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.7273 - accuracy: 0.1069 - val_loss: 1.7547 - val_accuracy: 0.2020\n",
      "Epoch 128/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.7215 - accuracy: 0.1806 - val_loss: 1.6921 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6763 - accuracy: 0.0253 - val_loss: 1.6721 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6717 - accuracy: 0.0649 - val_loss: 1.6728 - val_accuracy: 0.0020\n",
      "Epoch 131/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6942 - accuracy: 0.1219 - val_loss: 1.6932 - val_accuracy: 0.3140\n",
      "Epoch 132/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6908 - accuracy: 0.1735 - val_loss: 1.6750 - val_accuracy: 0.0760\n",
      "Epoch 133/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6907 - accuracy: 0.1387 - val_loss: 1.6731 - val_accuracy: 0.0300\n",
      "Epoch 134/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6799 - accuracy: 0.0201 - val_loss: 1.6641 - val_accuracy: 0.1660\n",
      "Epoch 135/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6951 - accuracy: 0.1670 - val_loss: 1.7068 - val_accuracy: 0.2440\n",
      "Epoch 136/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6660 - accuracy: 0.1284 - val_loss: 1.6443 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6574 - accuracy: 0.0942 - val_loss: 1.6453 - val_accuracy: 0.1220\n",
      "Epoch 138/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6453 - accuracy: 0.0212 - val_loss: 1.6523 - val_accuracy: 0.0020\n",
      "Epoch 139/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6777 - accuracy: 0.2103 - val_loss: 1.6569 - val_accuracy: 0.0800\n",
      "Epoch 140/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6758 - accuracy: 0.0678 - val_loss: 1.6448 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6552 - accuracy: 0.1390 - val_loss: 1.6438 - val_accuracy: 0.1760\n",
      "Epoch 142/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6397 - accuracy: 0.1785 - val_loss: 1.6592 - val_accuracy: 0.0300\n",
      "Epoch 143/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6896 - accuracy: 0.0649 - val_loss: 1.6610 - val_accuracy: 0.1380\n",
      "Epoch 144/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6489 - accuracy: 0.1417 - val_loss: 1.6600 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6591 - accuracy: 0.1071 - val_loss: 1.6677 - val_accuracy: 0.1880\n",
      "Epoch 146/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6760 - accuracy: 0.1389 - val_loss: 1.6381 - val_accuracy: 0.1880\n",
      "Epoch 147/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6489 - accuracy: 0.0969 - val_loss: 1.6498 - val_accuracy: 0.3120\n",
      "Epoch 148/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6428 - accuracy: 0.2317 - val_loss: 1.6190 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6252 - accuracy: 0.0190 - val_loss: 1.6492 - val_accuracy: 0.2000\n",
      "Epoch 150/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6378 - accuracy: 0.1540 - val_loss: 1.6215 - val_accuracy: 0.2000\n",
      "Epoch 151/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6432 - accuracy: 0.0786 - val_loss: 1.6119 - val_accuracy: 0.0320\n",
      "Epoch 152/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6500 - accuracy: 0.0768 - val_loss: 1.6388 - val_accuracy: 0.1020\n",
      "Epoch 153/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6346 - accuracy: 0.1214 - val_loss: 1.6444 - val_accuracy: 0.2080\n",
      "Epoch 154/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6567 - accuracy: 0.2188 - val_loss: 1.6902 - val_accuracy: 0.2620\n",
      "Epoch 155/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6282 - accuracy: 0.1273 - val_loss: 1.6151 - val_accuracy: 0.1980\n",
      "Epoch 156/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6062 - accuracy: 0.0972 - val_loss: 1.6334 - val_accuracy: 0.0520\n",
      "Epoch 157/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6281 - accuracy: 0.0539 - val_loss: 1.5967 - val_accuracy: 0.0480\n",
      "Epoch 158/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6243 - accuracy: 0.0631 - val_loss: 1.5999 - val_accuracy: 0.1120\n",
      "Epoch 159/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6153 - accuracy: 0.0517 - val_loss: 1.5901 - val_accuracy: 0.0080\n",
      "Epoch 160/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5830 - accuracy: 0.1503 - val_loss: 1.5930 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5914 - accuracy: 0.0684 - val_loss: 1.6108 - val_accuracy: 0.0020\n",
      "Epoch 162/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6273 - accuracy: 0.0329 - val_loss: 1.6175 - val_accuracy: 0.2840\n",
      "Epoch 163/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6397 - accuracy: 0.1726 - val_loss: 1.6051 - val_accuracy: 0.2980\n",
      "Epoch 164/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5939 - accuracy: 0.2191 - val_loss: 1.5804 - val_accuracy: 0.0760\n",
      "Epoch 165/1000\n",
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      "Epoch 166/1000\n",
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      "Epoch 167/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6046 - accuracy: 0.0560 - val_loss: 1.5896 - val_accuracy: 0.1940\n",
      "Epoch 168/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6286 - accuracy: 0.0758 - val_loss: 1.6270 - val_accuracy: 0.2540\n",
      "Epoch 169/1000\n",
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      "Epoch 170/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6006 - accuracy: 0.2750 - val_loss: 1.5957 - val_accuracy: 0.1980\n",
      "Epoch 171/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6118 - accuracy: 0.1616 - val_loss: 1.5994 - val_accuracy: 0.2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 172/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.6271 - accuracy: 0.1621 - val_loss: 1.5860 - val_accuracy: 0.2900\n",
      "Epoch 173/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5963 - accuracy: 0.2225 - val_loss: 1.5979 - val_accuracy: 0.2040\n",
      "Epoch 174/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5969 - accuracy: 0.1370 - val_loss: 1.5920 - val_accuracy: 0.2120\n",
      "Epoch 175/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6419 - accuracy: 0.0894 - val_loss: 1.5828 - val_accuracy: 0.2000\n",
      "Epoch 176/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5810 - accuracy: 0.1170 - val_loss: 1.5505 - val_accuracy: 0.0460\n",
      "Epoch 177/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5815 - accuracy: 0.0756 - val_loss: 1.7065 - val_accuracy: 0.2020\n",
      "Epoch 178/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6316 - accuracy: 0.0794 - val_loss: 1.5643 - val_accuracy: 0.1880\n",
      "Epoch 179/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5629 - accuracy: 0.0913 - val_loss: 1.5736 - val_accuracy: 0.0800\n",
      "Epoch 180/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.6013 - accuracy: 0.1089 - val_loss: 1.5709 - val_accuracy: 0.3980\n",
      "Epoch 181/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5517 - accuracy: 0.2535 - val_loss: 1.5469 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5773 - accuracy: 0.1293 - val_loss: 1.5584 - val_accuracy: 0.0500\n",
      "Epoch 183/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5794 - accuracy: 0.1604 - val_loss: 1.5421 - val_accuracy: 0.0300\n",
      "Epoch 184/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5492 - accuracy: 0.0363 - val_loss: 1.5355 - val_accuracy: 0.1360\n",
      "Epoch 185/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5769 - accuracy: 0.1147 - val_loss: 1.5359 - val_accuracy: 0.0540\n",
      "Epoch 186/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.5567 - accuracy: 0.0598 - val_loss: 1.5812 - val_accuracy: 0.2440\n",
      "Epoch 187/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5787 - accuracy: 0.1741 - val_loss: 1.5523 - val_accuracy: 0.0220\n",
      "Epoch 188/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5587 - accuracy: 0.0855 - val_loss: 1.5445 - val_accuracy: 0.3180\n",
      "Epoch 189/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5408 - accuracy: 0.2197 - val_loss: 1.5859 - val_accuracy: 0.1160\n",
      "Epoch 190/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5566 - accuracy: 0.1571 - val_loss: 1.5290 - val_accuracy: 0.2000\n",
      "Epoch 191/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5386 - accuracy: 0.0742 - val_loss: 1.5483 - val_accuracy: 0.2500\n",
      "Epoch 192/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5499 - accuracy: 0.1373 - val_loss: 1.5425 - val_accuracy: 0.3080\n",
      "Epoch 193/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5667 - accuracy: 0.1563 - val_loss: 1.5261 - val_accuracy: 0.0760\n",
      "Epoch 194/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5441 - accuracy: 0.0852 - val_loss: 1.5353 - val_accuracy: 0.2940\n",
      "Epoch 195/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5518 - accuracy: 0.1308 - val_loss: 1.5389 - val_accuracy: 0.3160\n",
      "Epoch 196/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5388 - accuracy: 0.0670 - val_loss: 1.5218 - val_accuracy: 0.1980\n",
      "Epoch 197/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5478 - accuracy: 0.0536 - val_loss: 1.5315 - val_accuracy: 0.2140\n",
      "Epoch 198/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5508 - accuracy: 0.1869 - val_loss: 1.5145 - val_accuracy: 0.2260\n",
      "Epoch 199/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5147 - accuracy: 0.1921 - val_loss: 1.5442 - val_accuracy: 0.2320\n",
      "Epoch 200/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5497 - accuracy: 0.1815 - val_loss: 1.5023 - val_accuracy: 0.0000e+00\n",
      "Epoch 201/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5489 - accuracy: 0.1079 - val_loss: 1.5420 - val_accuracy: 0.3500\n",
      "Epoch 202/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5046 - accuracy: 0.3559 - val_loss: 1.5218 - val_accuracy: 0.1400\n",
      "Epoch 203/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5779 - accuracy: 0.2573 - val_loss: 1.4960 - val_accuracy: 0.2200\n",
      "Epoch 204/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5262 - accuracy: 0.2199 - val_loss: 1.4989 - val_accuracy: 0.1920\n",
      "Epoch 205/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5206 - accuracy: 0.1852 - val_loss: 1.4956 - val_accuracy: 0.2160\n",
      "Epoch 206/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5330 - accuracy: 0.0638 - val_loss: 1.4972 - val_accuracy: 0.1900\n",
      "Epoch 207/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5215 - accuracy: 0.1799 - val_loss: 1.4911 - val_accuracy: 0.0100\n",
      "Epoch 208/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5237 - accuracy: 0.2681 - val_loss: 1.5371 - val_accuracy: 0.2940\n",
      "Epoch 209/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5809 - accuracy: 0.1166 - val_loss: 1.4976 - val_accuracy: 0.0640\n",
      "Epoch 210/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5193 - accuracy: 0.1743 - val_loss: 1.5445 - val_accuracy: 0.1420\n",
      "Epoch 211/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5277 - accuracy: 0.1788 - val_loss: 1.4863 - val_accuracy: 0.0080\n",
      "Epoch 212/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4875 - accuracy: 0.1271 - val_loss: 1.5267 - val_accuracy: 0.2080\n",
      "Epoch 213/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5202 - accuracy: 0.1892 - val_loss: 1.4941 - val_accuracy: 0.2500\n",
      "Epoch 214/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4841 - accuracy: 0.2841 - val_loss: 1.4912 - val_accuracy: 0.2000\n",
      "Epoch 215/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5300 - accuracy: 0.1722 - val_loss: 1.5009 - val_accuracy: 0.0320\n",
      "Epoch 216/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4886 - accuracy: 0.0922 - val_loss: 1.5138 - val_accuracy: 0.0700\n",
      "Epoch 217/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5223 - accuracy: 0.1642 - val_loss: 1.5135 - val_accuracy: 0.0240\n",
      "Epoch 218/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5161 - accuracy: 0.1628 - val_loss: 1.4743 - val_accuracy: 0.1360\n",
      "Epoch 219/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5032 - accuracy: 0.2056 - val_loss: 1.4935 - val_accuracy: 0.3180\n",
      "Epoch 220/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4942 - accuracy: 0.2544 - val_loss: 1.4617 - val_accuracy: 0.2760\n",
      "Epoch 221/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4928 - accuracy: 0.1436 - val_loss: 1.4854 - val_accuracy: 0.0460\n",
      "Epoch 222/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5050 - accuracy: 0.1812 - val_loss: 1.4725 - val_accuracy: 0.4260\n",
      "Epoch 223/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4818 - accuracy: 0.3279 - val_loss: 1.4849 - val_accuracy: 0.2160\n",
      "Epoch 224/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5277 - accuracy: 0.1600 - val_loss: 1.4590 - val_accuracy: 0.3140\n",
      "Epoch 225/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4514 - accuracy: 0.2955 - val_loss: 1.5085 - val_accuracy: 0.2220\n",
      "Epoch 226/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5128 - accuracy: 0.2032 - val_loss: 1.4558 - val_accuracy: 0.3920\n",
      "Epoch 227/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4797 - accuracy: 0.2718 - val_loss: 1.4604 - val_accuracy: 0.2800\n",
      "Epoch 228/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4469 - accuracy: 0.2153 - val_loss: 1.4834 - val_accuracy: 0.3980\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 229/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4546 - accuracy: 0.3254 - val_loss: 1.4490 - val_accuracy: 0.0060\n",
      "Epoch 230/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4694 - accuracy: 0.1395 - val_loss: 1.4861 - val_accuracy: 0.2360\n",
      "Epoch 231/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4848 - accuracy: 0.2460 - val_loss: 1.4791 - val_accuracy: 0.1840\n",
      "Epoch 232/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.5206 - accuracy: 0.1068 - val_loss: 1.4681 - val_accuracy: 0.3260\n",
      "Epoch 233/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5168 - accuracy: 0.2879 - val_loss: 1.4510 - val_accuracy: 0.0000e+00\n",
      "Epoch 234/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4793 - accuracy: 0.1705 - val_loss: 1.4680 - val_accuracy: 0.2080\n",
      "Epoch 235/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4865 - accuracy: 0.1783 - val_loss: 1.4466 - val_accuracy: 0.1960\n",
      "Epoch 236/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4504 - accuracy: 0.2028 - val_loss: 1.4424 - val_accuracy: 0.1460\n",
      "Epoch 237/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4928 - accuracy: 0.2548 - val_loss: 1.4358 - val_accuracy: 0.0380\n",
      "Epoch 238/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4443 - accuracy: 0.1260 - val_loss: 1.4477 - val_accuracy: 0.1680\n",
      "Epoch 239/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4487 - accuracy: 0.2593 - val_loss: 1.5295 - val_accuracy: 0.2500\n",
      "Epoch 240/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.5070 - accuracy: 0.2813 - val_loss: 1.4269 - val_accuracy: 0.1920\n",
      "Epoch 241/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4305 - accuracy: 0.1585 - val_loss: 1.4440 - val_accuracy: 0.2000\n",
      "Epoch 242/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4468 - accuracy: 0.1556 - val_loss: 1.4342 - val_accuracy: 0.4380\n",
      "Epoch 243/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4451 - accuracy: 0.3094 - val_loss: 1.4482 - val_accuracy: 0.2360\n",
      "Epoch 244/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4339 - accuracy: 0.1846 - val_loss: 1.4225 - val_accuracy: 0.2060\n",
      "Epoch 245/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4419 - accuracy: 0.2241 - val_loss: 1.4282 - val_accuracy: 0.3660\n",
      "Epoch 246/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4380 - accuracy: 0.2296 - val_loss: 1.4199 - val_accuracy: 0.4620\n",
      "Epoch 247/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4181 - accuracy: 0.3167 - val_loss: 1.4125 - val_accuracy: 0.0160\n",
      "Epoch 248/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4366 - accuracy: 0.2153 - val_loss: 1.4449 - val_accuracy: 0.4000\n",
      "Epoch 249/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.4458 - accuracy: 0.3209 - val_loss: 1.4012 - val_accuracy: 0.2640\n",
      "Epoch 250/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4171 - accuracy: 0.2460 - val_loss: 1.4113 - val_accuracy: 0.0160\n",
      "Epoch 251/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4154 - accuracy: 0.2574 - val_loss: 1.4419 - val_accuracy: 0.0700\n",
      "Epoch 252/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4722 - accuracy: 0.1699 - val_loss: 1.4131 - val_accuracy: 0.2820\n",
      "Epoch 253/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4056 - accuracy: 0.3310 - val_loss: 1.4148 - val_accuracy: 0.3860\n",
      "Epoch 254/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4051 - accuracy: 0.2775 - val_loss: 1.4142 - val_accuracy: 0.3180\n",
      "Epoch 255/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3975 - accuracy: 0.3474 - val_loss: 1.4730 - val_accuracy: 0.2900\n",
      "Epoch 256/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4301 - accuracy: 0.2922 - val_loss: 1.4537 - val_accuracy: 0.3840\n",
      "Epoch 257/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4102 - accuracy: 0.2851 - val_loss: 1.3968 - val_accuracy: 0.3100\n",
      "Epoch 258/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4256 - accuracy: 0.2974 - val_loss: 1.4352 - val_accuracy: 0.4000\n",
      "Epoch 259/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4145 - accuracy: 0.2085 - val_loss: 1.4436 - val_accuracy: 0.2000\n",
      "Epoch 260/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4334 - accuracy: 0.2292 - val_loss: 1.4186 - val_accuracy: 0.1720\n",
      "Epoch 261/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.4180 - accuracy: 0.2422 - val_loss: 1.4189 - val_accuracy: 0.5540\n",
      "Epoch 262/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4262 - accuracy: 0.2897 - val_loss: 1.4538 - val_accuracy: 0.3460\n",
      "Epoch 263/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4363 - accuracy: 0.2086 - val_loss: 1.3731 - val_accuracy: 0.3020\n",
      "Epoch 264/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4191 - accuracy: 0.3748 - val_loss: 1.4136 - val_accuracy: 0.4460\n",
      "Epoch 265/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3836 - accuracy: 0.3731 - val_loss: 1.3939 - val_accuracy: 0.2980\n",
      "Epoch 266/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3981 - accuracy: 0.2867 - val_loss: 1.4083 - val_accuracy: 0.3740\n",
      "Epoch 267/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.3801 - accuracy: 0.3440 - val_loss: 1.3952 - val_accuracy: 0.2020\n",
      "Epoch 268/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3986 - accuracy: 0.1685 - val_loss: 1.3987 - val_accuracy: 0.3920\n",
      "Epoch 269/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3920 - accuracy: 0.3664 - val_loss: 1.3924 - val_accuracy: 0.2660\n",
      "Epoch 270/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3881 - accuracy: 0.2369 - val_loss: 1.3669 - val_accuracy: 0.3200\n",
      "Epoch 271/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3792 - accuracy: 0.2130 - val_loss: 1.3905 - val_accuracy: 0.3860\n",
      "Epoch 272/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4344 - accuracy: 0.2630 - val_loss: 1.3755 - val_accuracy: 0.3660\n",
      "Epoch 273/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3974 - accuracy: 0.3152 - val_loss: 1.4138 - val_accuracy: 0.3500\n",
      "Epoch 274/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4509 - accuracy: 0.3272 - val_loss: 1.4360 - val_accuracy: 0.4020\n",
      "Epoch 275/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.4351 - accuracy: 0.3230 - val_loss: 1.4239 - val_accuracy: 0.3960\n",
      "Epoch 276/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3900 - accuracy: 0.3307 - val_loss: 1.3966 - val_accuracy: 0.3000\n",
      "Epoch 277/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3788 - accuracy: 0.3154 - val_loss: 1.3569 - val_accuracy: 0.3620\n",
      "Epoch 278/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3589 - accuracy: 0.3114 - val_loss: 1.3788 - val_accuracy: 0.2040\n",
      "Epoch 279/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3852 - accuracy: 0.2627 - val_loss: 1.3518 - val_accuracy: 0.4200\n",
      "Epoch 280/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3753 - accuracy: 0.3449 - val_loss: 1.3466 - val_accuracy: 0.3720\n",
      "Epoch 281/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.3389 - accuracy: 0.3158 - val_loss: 1.3504 - val_accuracy: 0.1280\n",
      "Epoch 282/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3541 - accuracy: 0.2312 - val_loss: 1.3393 - val_accuracy: 0.1560\n",
      "Epoch 283/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3636 - accuracy: 0.2829 - val_loss: 1.3557 - val_accuracy: 0.3680\n",
      "Epoch 284/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3971 - accuracy: 0.3685 - val_loss: 1.3529 - val_accuracy: 0.2280\n",
      "Epoch 285/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3875 - accuracy: 0.2998 - val_loss: 1.3563 - val_accuracy: 0.2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 286/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3419 - accuracy: 0.3117 - val_loss: 1.3666 - val_accuracy: 0.3420\n",
      "Epoch 287/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3497 - accuracy: 0.2527 - val_loss: 1.3599 - val_accuracy: 0.3600\n",
      "Epoch 288/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3251 - accuracy: 0.3806 - val_loss: 1.3372 - val_accuracy: 0.2120\n",
      "Epoch 289/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3535 - accuracy: 0.2565 - val_loss: 1.3322 - val_accuracy: 0.2020\n",
      "Epoch 290/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3296 - accuracy: 0.3430 - val_loss: 1.3413 - val_accuracy: 0.2900\n",
      "Epoch 291/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3406 - accuracy: 0.3139 - val_loss: 1.3354 - val_accuracy: 0.3740\n",
      "Epoch 292/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3495 - accuracy: 0.3432 - val_loss: 1.3196 - val_accuracy: 0.1900\n",
      "Epoch 293/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3574 - accuracy: 0.2824 - val_loss: 1.3268 - val_accuracy: 0.4020\n",
      "Epoch 294/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3493 - accuracy: 0.3525 - val_loss: 1.3411 - val_accuracy: 0.3760\n",
      "Epoch 295/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3254 - accuracy: 0.4116 - val_loss: 1.3365 - val_accuracy: 0.2060\n",
      "Epoch 296/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.3338 - accuracy: 0.2921 - val_loss: 1.4221 - val_accuracy: 0.5740\n",
      "Epoch 297/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3663 - accuracy: 0.4728 - val_loss: 1.3411 - val_accuracy: 0.1800\n",
      "Epoch 298/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3406 - accuracy: 0.2497 - val_loss: 1.3248 - val_accuracy: 0.3080\n",
      "Epoch 299/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3519 - accuracy: 0.3670 - val_loss: 1.4037 - val_accuracy: 0.5960\n",
      "Epoch 300/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3626 - accuracy: 0.4134 - val_loss: 1.3219 - val_accuracy: 0.4560\n",
      "Epoch 301/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3351 - accuracy: 0.4215 - val_loss: 1.4125 - val_accuracy: 0.4140\n",
      "Epoch 302/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3443 - accuracy: 0.4049 - val_loss: 1.3304 - val_accuracy: 0.2340\n",
      "Epoch 303/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3448 - accuracy: 0.3135 - val_loss: 1.3180 - val_accuracy: 0.4200\n",
      "Epoch 304/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3254 - accuracy: 0.2922 - val_loss: 1.3141 - val_accuracy: 0.1960\n",
      "Epoch 305/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2986 - accuracy: 0.3774 - val_loss: 1.3102 - val_accuracy: 0.2340\n",
      "Epoch 306/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3459 - accuracy: 0.3090 - val_loss: 1.3472 - val_accuracy: 0.2500\n",
      "Epoch 307/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3359 - accuracy: 0.3249 - val_loss: 1.3518 - val_accuracy: 0.2680\n",
      "Epoch 308/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3132 - accuracy: 0.3088 - val_loss: 1.2962 - val_accuracy: 0.4600\n",
      "Epoch 309/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3206 - accuracy: 0.3836 - val_loss: 1.3059 - val_accuracy: 0.2480\n",
      "Epoch 310/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2992 - accuracy: 0.3727 - val_loss: 1.3063 - val_accuracy: 0.4380\n",
      "Epoch 311/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3052 - accuracy: 0.4000 - val_loss: 1.2929 - val_accuracy: 0.3240\n",
      "Epoch 312/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3052 - accuracy: 0.4455 - val_loss: 1.3113 - val_accuracy: 0.5780\n",
      "Epoch 313/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3183 - accuracy: 0.5164 - val_loss: 1.3420 - val_accuracy: 0.4820\n",
      "Epoch 314/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3548 - accuracy: 0.4129 - val_loss: 1.3255 - val_accuracy: 0.2920\n",
      "Epoch 315/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3582 - accuracy: 0.3797 - val_loss: 1.3434 - val_accuracy: 0.4660\n",
      "Epoch 316/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3184 - accuracy: 0.3532 - val_loss: 1.3144 - val_accuracy: 0.2160\n",
      "Epoch 317/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3283 - accuracy: 0.2832 - val_loss: 1.2779 - val_accuracy: 0.3620\n",
      "Epoch 318/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2873 - accuracy: 0.4306 - val_loss: 1.2942 - val_accuracy: 0.2140\n",
      "Epoch 319/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3118 - accuracy: 0.3153 - val_loss: 1.3109 - val_accuracy: 0.5140\n",
      "Epoch 320/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3332 - accuracy: 0.3918 - val_loss: 1.2674 - val_accuracy: 0.4280\n",
      "Epoch 321/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2829 - accuracy: 0.3957 - val_loss: 1.2789 - val_accuracy: 0.4380\n",
      "Epoch 322/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2915 - accuracy: 0.3365 - val_loss: 1.2833 - val_accuracy: 0.2560\n",
      "Epoch 323/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2786 - accuracy: 0.3577 - val_loss: 1.2981 - val_accuracy: 0.2200\n",
      "Epoch 324/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2905 - accuracy: 0.3912 - val_loss: 1.2839 - val_accuracy: 0.5660\n",
      "Epoch 325/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2880 - accuracy: 0.4144 - val_loss: 1.2665 - val_accuracy: 0.4260\n",
      "Epoch 326/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2898 - accuracy: 0.4345 - val_loss: 1.2864 - val_accuracy: 0.2680\n",
      "Epoch 327/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2672 - accuracy: 0.3625 - val_loss: 1.2991 - val_accuracy: 0.4420\n",
      "Epoch 328/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2974 - accuracy: 0.3397 - val_loss: 1.2702 - val_accuracy: 0.4500\n",
      "Epoch 329/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2750 - accuracy: 0.3595 - val_loss: 1.2692 - val_accuracy: 0.5200\n",
      "Epoch 330/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2876 - accuracy: 0.4427 - val_loss: 1.3418 - val_accuracy: 0.5800\n",
      "Epoch 331/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3418 - accuracy: 0.3477 - val_loss: 1.3293 - val_accuracy: 0.3840\n",
      "Epoch 332/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3162 - accuracy: 0.3951 - val_loss: 1.2797 - val_accuracy: 0.3560\n",
      "Epoch 333/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2677 - accuracy: 0.3853 - val_loss: 1.2670 - val_accuracy: 0.4640\n",
      "Epoch 334/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.2771 - accuracy: 0.3980 - val_loss: 1.2701 - val_accuracy: 0.5780\n",
      "Epoch 335/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.3101 - accuracy: 0.4402 - val_loss: 1.2473 - val_accuracy: 0.6380\n",
      "Epoch 336/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2628 - accuracy: 0.4958 - val_loss: 1.3755 - val_accuracy: 0.4900\n",
      "Epoch 337/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.3600 - accuracy: 0.4153 - val_loss: 1.3082 - val_accuracy: 0.4080\n",
      "Epoch 338/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2783 - accuracy: 0.3094 - val_loss: 1.2633 - val_accuracy: 0.3920\n",
      "Epoch 339/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2757 - accuracy: 0.3945 - val_loss: 1.2975 - val_accuracy: 0.2040\n",
      "Epoch 340/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2734 - accuracy: 0.3198 - val_loss: 1.2553 - val_accuracy: 0.2380\n",
      "Epoch 341/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2774 - accuracy: 0.3297 - val_loss: 1.2603 - val_accuracy: 0.5420\n",
      "Epoch 342/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2977 - accuracy: 0.3825 - val_loss: 1.2858 - val_accuracy: 0.3460\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 343/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2634 - accuracy: 0.3895 - val_loss: 1.2714 - val_accuracy: 0.4000\n",
      "Epoch 344/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.2969 - accuracy: 0.3010 - val_loss: 1.2670 - val_accuracy: 0.6120\n",
      "Epoch 345/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2613 - accuracy: 0.5366 - val_loss: 1.2346 - val_accuracy: 0.2780\n",
      "Epoch 346/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2606 - accuracy: 0.3516 - val_loss: 1.2392 - val_accuracy: 0.5940\n",
      "Epoch 347/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2432 - accuracy: 0.5125 - val_loss: 1.2350 - val_accuracy: 0.5140\n",
      "Epoch 348/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2586 - accuracy: 0.4402 - val_loss: 1.2429 - val_accuracy: 0.5740\n",
      "Epoch 349/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2401 - accuracy: 0.5359 - val_loss: 1.2568 - val_accuracy: 0.2500\n",
      "Epoch 350/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2683 - accuracy: 0.3720 - val_loss: 1.2491 - val_accuracy: 0.4240\n",
      "Epoch 351/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2673 - accuracy: 0.4558 - val_loss: 1.2336 - val_accuracy: 0.5740\n",
      "Epoch 352/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.2789 - accuracy: 0.4209 - val_loss: 1.2260 - val_accuracy: 0.6540\n",
      "Epoch 353/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2520 - accuracy: 0.4570 - val_loss: 1.2379 - val_accuracy: 0.4220\n",
      "Epoch 354/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2416 - accuracy: 0.4254 - val_loss: 1.2484 - val_accuracy: 0.3400\n",
      "Epoch 355/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2687 - accuracy: 0.3953 - val_loss: 1.2530 - val_accuracy: 0.3940\n",
      "Epoch 356/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2515 - accuracy: 0.4481 - val_loss: 1.2296 - val_accuracy: 0.5980\n",
      "Epoch 357/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2236 - accuracy: 0.5309 - val_loss: 1.2071 - val_accuracy: 0.4820\n",
      "Epoch 358/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2017 - accuracy: 0.5696 - val_loss: 1.2683 - val_accuracy: 0.3520\n",
      "Epoch 359/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2563 - accuracy: 0.4512 - val_loss: 1.2270 - val_accuracy: 0.4620\n",
      "Epoch 360/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2432 - accuracy: 0.4616 - val_loss: 1.2314 - val_accuracy: 0.4740\n",
      "Epoch 361/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2651 - accuracy: 0.4646 - val_loss: 1.2315 - val_accuracy: 0.4440\n",
      "Epoch 362/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2483 - accuracy: 0.4408 - val_loss: 1.2158 - val_accuracy: 0.5640\n",
      "Epoch 363/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1989 - accuracy: 0.5179 - val_loss: 1.2327 - val_accuracy: 0.5900\n",
      "Epoch 364/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.2252 - accuracy: 0.5396 - val_loss: 1.2067 - val_accuracy: 0.3940\n",
      "Epoch 365/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2250 - accuracy: 0.4121 - val_loss: 1.2036 - val_accuracy: 0.3640\n",
      "Epoch 366/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2041 - accuracy: 0.4482 - val_loss: 1.2549 - val_accuracy: 0.4660\n",
      "Epoch 367/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2476 - accuracy: 0.4464 - val_loss: 1.2220 - val_accuracy: 0.3900\n",
      "Epoch 368/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2592 - accuracy: 0.4371 - val_loss: 1.2037 - val_accuracy: 0.5280\n",
      "Epoch 369/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2279 - accuracy: 0.4518 - val_loss: 1.2225 - val_accuracy: 0.5560\n",
      "Epoch 370/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1809 - accuracy: 0.5171 - val_loss: 1.2131 - val_accuracy: 0.2540\n",
      "Epoch 371/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2242 - accuracy: 0.4347 - val_loss: 1.2148 - val_accuracy: 0.4300\n",
      "Epoch 372/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2321 - accuracy: 0.4432 - val_loss: 1.2326 - val_accuracy: 0.4480\n",
      "Epoch 373/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2537 - accuracy: 0.4758 - val_loss: 1.2375 - val_accuracy: 0.4480\n",
      "Epoch 374/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2128 - accuracy: 0.4672 - val_loss: 1.2670 - val_accuracy: 0.2280\n",
      "Epoch 375/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2189 - accuracy: 0.4081 - val_loss: 1.1845 - val_accuracy: 0.4480\n",
      "Epoch 376/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1993 - accuracy: 0.5049 - val_loss: 1.1907 - val_accuracy: 0.5900\n",
      "Epoch 377/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1965 - accuracy: 0.5480 - val_loss: 1.1999 - val_accuracy: 0.5700\n",
      "Epoch 378/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1955 - accuracy: 0.6087 - val_loss: 1.1739 - val_accuracy: 0.5840\n",
      "Epoch 379/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1807 - accuracy: 0.5816 - val_loss: 1.1798 - val_accuracy: 0.6320\n",
      "Epoch 380/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2019 - accuracy: 0.5951 - val_loss: 1.1790 - val_accuracy: 0.5920\n",
      "Epoch 381/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1923 - accuracy: 0.5251 - val_loss: 1.1932 - val_accuracy: 0.6000\n",
      "Epoch 382/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1943 - accuracy: 0.5397 - val_loss: 1.1699 - val_accuracy: 0.7100\n",
      "Epoch 383/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1871 - accuracy: 0.6347 - val_loss: 1.2216 - val_accuracy: 0.3200\n",
      "Epoch 384/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1755 - accuracy: 0.5121 - val_loss: 1.1757 - val_accuracy: 0.4840\n",
      "Epoch 385/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 1.1768 - accuracy: 0.5029 - val_loss: 1.1928 - val_accuracy: 0.4000\n",
      "Epoch 386/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1805 - accuracy: 0.5439 - val_loss: 1.1646 - val_accuracy: 0.7720\n",
      "Epoch 387/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1815 - accuracy: 0.5828 - val_loss: 1.1569 - val_accuracy: 0.7780\n",
      "Epoch 388/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1532 - accuracy: 0.6301 - val_loss: 1.1606 - val_accuracy: 0.7780\n",
      "Epoch 389/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1914 - accuracy: 0.6151 - val_loss: 1.1546 - val_accuracy: 0.6780\n",
      "Epoch 390/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1529 - accuracy: 0.7042 - val_loss: 1.1710 - val_accuracy: 0.5940\n",
      "Epoch 391/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2071 - accuracy: 0.4313 - val_loss: 1.1501 - val_accuracy: 0.7380\n",
      "Epoch 392/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1667 - accuracy: 0.6231 - val_loss: 1.1650 - val_accuracy: 0.6000\n",
      "Epoch 393/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1741 - accuracy: 0.5769 - val_loss: 1.1674 - val_accuracy: 0.7200\n",
      "Epoch 394/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1414 - accuracy: 0.6796 - val_loss: 1.2304 - val_accuracy: 0.2320\n",
      "Epoch 395/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.2186 - accuracy: 0.3828 - val_loss: 1.1592 - val_accuracy: 0.5000\n",
      "Epoch 396/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1434 - accuracy: 0.6234 - val_loss: 1.1495 - val_accuracy: 0.5960\n",
      "Epoch 397/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1635 - accuracy: 0.5885 - val_loss: 1.1663 - val_accuracy: 0.6240\n",
      "Epoch 398/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1495 - accuracy: 0.6297 - val_loss: 1.1633 - val_accuracy: 0.4860\n",
      "Epoch 399/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1775 - accuracy: 0.5722 - val_loss: 1.1471 - val_accuracy: 0.6600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 400/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1710 - accuracy: 0.5662 - val_loss: 1.1474 - val_accuracy: 0.4800\n",
      "Epoch 401/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1646 - accuracy: 0.5071 - val_loss: 1.1568 - val_accuracy: 0.6440\n",
      "Epoch 402/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1833 - accuracy: 0.5355 - val_loss: 1.2023 - val_accuracy: 0.5980\n",
      "Epoch 403/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1887 - accuracy: 0.5341 - val_loss: 1.1439 - val_accuracy: 0.6280\n",
      "Epoch 404/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1404 - accuracy: 0.6637 - val_loss: 1.1419 - val_accuracy: 0.5580\n",
      "Epoch 405/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1764 - accuracy: 0.6298 - val_loss: 1.1445 - val_accuracy: 0.4980\n",
      "Epoch 406/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1587 - accuracy: 0.5741 - val_loss: 1.1660 - val_accuracy: 0.4520\n",
      "Epoch 407/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1504 - accuracy: 0.5510 - val_loss: 1.1565 - val_accuracy: 0.6340\n",
      "Epoch 408/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1812 - accuracy: 0.5837 - val_loss: 1.1361 - val_accuracy: 0.5360\n",
      "Epoch 409/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1526 - accuracy: 0.5732 - val_loss: 1.1888 - val_accuracy: 0.6000\n",
      "Epoch 410/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1551 - accuracy: 0.6059 - val_loss: 1.1676 - val_accuracy: 0.4060\n",
      "Epoch 411/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1375 - accuracy: 0.5448 - val_loss: 1.1765 - val_accuracy: 0.4180\n",
      "Epoch 412/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1736 - accuracy: 0.5210 - val_loss: 1.1668 - val_accuracy: 0.4220\n",
      "Epoch 413/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1703 - accuracy: 0.5157 - val_loss: 1.1585 - val_accuracy: 0.6100\n",
      "Epoch 414/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1442 - accuracy: 0.5903 - val_loss: 1.1440 - val_accuracy: 0.6360\n",
      "Epoch 415/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1373 - accuracy: 0.5540 - val_loss: 1.1445 - val_accuracy: 0.5800\n",
      "Epoch 416/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1343 - accuracy: 0.6156 - val_loss: 1.1410 - val_accuracy: 0.7220\n",
      "Epoch 417/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1376 - accuracy: 0.6401 - val_loss: 1.1340 - val_accuracy: 0.7180\n",
      "Epoch 418/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1302 - accuracy: 0.5996 - val_loss: 1.1239 - val_accuracy: 0.6560\n",
      "Epoch 419/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1515 - accuracy: 0.5977 - val_loss: 1.1385 - val_accuracy: 0.6100\n",
      "Epoch 420/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1381 - accuracy: 0.6147 - val_loss: 1.1479 - val_accuracy: 0.5860\n",
      "Epoch 421/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1425 - accuracy: 0.5667 - val_loss: 1.1182 - val_accuracy: 0.5560\n",
      "Epoch 422/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1367 - accuracy: 0.5707 - val_loss: 1.1105 - val_accuracy: 0.7260\n",
      "Epoch 423/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1327 - accuracy: 0.6199 - val_loss: 1.1032 - val_accuracy: 0.6680\n",
      "Epoch 424/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1193 - accuracy: 0.6256 - val_loss: 1.1470 - val_accuracy: 0.6000\n",
      "Epoch 425/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1669 - accuracy: 0.6143 - val_loss: 1.1287 - val_accuracy: 0.6120\n",
      "Epoch 426/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0867 - accuracy: 0.6825 - val_loss: 1.1480 - val_accuracy: 0.4240\n",
      "Epoch 427/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1366 - accuracy: 0.4970 - val_loss: 1.1103 - val_accuracy: 0.5980\n",
      "Epoch 428/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1190 - accuracy: 0.6397 - val_loss: 1.1599 - val_accuracy: 0.4140\n",
      "Epoch 429/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1428 - accuracy: 0.5040 - val_loss: 1.0949 - val_accuracy: 0.7600\n",
      "Epoch 430/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0894 - accuracy: 0.6833 - val_loss: 1.1061 - val_accuracy: 0.6080\n",
      "Epoch 431/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1295 - accuracy: 0.6068 - val_loss: 1.1081 - val_accuracy: 0.7260\n",
      "Epoch 432/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1211 - accuracy: 0.6273 - val_loss: 1.1064 - val_accuracy: 0.7640\n",
      "Epoch 433/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1223 - accuracy: 0.6807 - val_loss: 1.0884 - val_accuracy: 0.7360\n",
      "Epoch 434/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1289 - accuracy: 0.6640 - val_loss: 1.0997 - val_accuracy: 0.7340\n",
      "Epoch 435/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1302 - accuracy: 0.5953 - val_loss: 1.1761 - val_accuracy: 0.7260\n",
      "Epoch 436/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1775 - accuracy: 0.6147 - val_loss: 1.1160 - val_accuracy: 0.6000\n",
      "Epoch 437/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0993 - accuracy: 0.6363 - val_loss: 1.1082 - val_accuracy: 0.6080\n",
      "Epoch 438/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0970 - accuracy: 0.7513 - val_loss: 1.0941 - val_accuracy: 0.6500\n",
      "Epoch 439/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0994 - accuracy: 0.6304 - val_loss: 1.1105 - val_accuracy: 0.6660\n",
      "Epoch 440/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0855 - accuracy: 0.6845 - val_loss: 1.0825 - val_accuracy: 0.6080\n",
      "Epoch 441/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1102 - accuracy: 0.5977 - val_loss: 1.1041 - val_accuracy: 0.7400\n",
      "Epoch 442/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0643 - accuracy: 0.6948 - val_loss: 1.0974 - val_accuracy: 0.6000\n",
      "Epoch 443/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0698 - accuracy: 0.6772 - val_loss: 1.0874 - val_accuracy: 0.6520\n",
      "Epoch 444/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1134 - accuracy: 0.6446 - val_loss: 1.0832 - val_accuracy: 0.7580\n",
      "Epoch 445/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1007 - accuracy: 0.6877 - val_loss: 1.0853 - val_accuracy: 0.6600\n",
      "Epoch 446/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1020 - accuracy: 0.6309 - val_loss: 1.0969 - val_accuracy: 0.6000\n",
      "Epoch 447/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0566 - accuracy: 0.6924 - val_loss: 1.1193 - val_accuracy: 0.6000\n",
      "Epoch 448/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1307 - accuracy: 0.5867 - val_loss: 1.1088 - val_accuracy: 0.7480\n",
      "Epoch 449/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1292 - accuracy: 0.6786 - val_loss: 1.0877 - val_accuracy: 0.7740\n",
      "Epoch 450/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.1062 - accuracy: 0.6927 - val_loss: 1.0668 - val_accuracy: 0.7560\n",
      "Epoch 451/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0882 - accuracy: 0.6790 - val_loss: 1.0763 - val_accuracy: 0.7320\n",
      "Epoch 452/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0991 - accuracy: 0.5642 - val_loss: 1.0739 - val_accuracy: 0.7620\n",
      "Epoch 453/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0781 - accuracy: 0.6734 - val_loss: 1.0800 - val_accuracy: 0.7520\n",
      "Epoch 454/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0927 - accuracy: 0.6997 - val_loss: 1.0654 - val_accuracy: 0.7800\n",
      "Epoch 455/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0820 - accuracy: 0.6732 - val_loss: 1.1074 - val_accuracy: 0.5040\n",
      "Epoch 456/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1033 - accuracy: 0.5924 - val_loss: 1.1104 - val_accuracy: 0.6740\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 457/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0995 - accuracy: 0.6916 - val_loss: 1.0583 - val_accuracy: 0.7460\n",
      "Epoch 458/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0765 - accuracy: 0.6573 - val_loss: 1.0788 - val_accuracy: 0.5740\n",
      "Epoch 459/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1059 - accuracy: 0.5502 - val_loss: 1.0864 - val_accuracy: 0.6880\n",
      "Epoch 460/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1400 - accuracy: 0.5884 - val_loss: 1.0584 - val_accuracy: 0.6260\n",
      "Epoch 461/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0471 - accuracy: 0.7570 - val_loss: 1.0674 - val_accuracy: 0.6000\n",
      "Epoch 462/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1034 - accuracy: 0.5865 - val_loss: 1.0943 - val_accuracy: 0.5880\n",
      "Epoch 463/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0464 - accuracy: 0.6639 - val_loss: 1.0621 - val_accuracy: 0.7720\n",
      "Epoch 464/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0736 - accuracy: 0.6402 - val_loss: 1.0923 - val_accuracy: 0.5140\n",
      "Epoch 465/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.1222 - accuracy: 0.4891 - val_loss: 1.0563 - val_accuracy: 0.6520\n",
      "Epoch 466/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0428 - accuracy: 0.6797 - val_loss: 1.0577 - val_accuracy: 0.7800\n",
      "Epoch 467/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0666 - accuracy: 0.7065 - val_loss: 1.0542 - val_accuracy: 0.6320\n",
      "Epoch 468/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0925 - accuracy: 0.6131 - val_loss: 1.0792 - val_accuracy: 0.6520\n",
      "Epoch 469/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0863 - accuracy: 0.6276 - val_loss: 1.0758 - val_accuracy: 0.5680\n",
      "Epoch 470/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0498 - accuracy: 0.6733 - val_loss: 1.0832 - val_accuracy: 0.6580\n",
      "Epoch 471/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0518 - accuracy: 0.7060 - val_loss: 1.0556 - val_accuracy: 0.6000\n",
      "Epoch 472/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0574 - accuracy: 0.6787 - val_loss: 1.0636 - val_accuracy: 0.7600\n",
      "Epoch 473/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0687 - accuracy: 0.7175 - val_loss: 1.0385 - val_accuracy: 0.6540\n",
      "Epoch 474/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0347 - accuracy: 0.7107 - val_loss: 1.0690 - val_accuracy: 0.6000\n",
      "Epoch 475/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0159 - accuracy: 0.6940 - val_loss: 1.0333 - val_accuracy: 0.6560\n",
      "Epoch 476/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0757 - accuracy: 0.6505 - val_loss: 1.0474 - val_accuracy: 0.7620\n",
      "Epoch 477/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0546 - accuracy: 0.6615 - val_loss: 1.0265 - val_accuracy: 0.7540\n",
      "Epoch 478/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0352 - accuracy: 0.7721 - val_loss: 1.0346 - val_accuracy: 0.7820\n",
      "Epoch 479/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0522 - accuracy: 0.6497 - val_loss: 1.0576 - val_accuracy: 0.7360\n",
      "Epoch 480/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0515 - accuracy: 0.7397 - val_loss: 1.0237 - val_accuracy: 0.8000\n",
      "Epoch 481/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0228 - accuracy: 0.7576 - val_loss: 1.0622 - val_accuracy: 0.5440\n",
      "Epoch 482/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0440 - accuracy: 0.6201 - val_loss: 1.0699 - val_accuracy: 0.5520\n",
      "Epoch 483/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0477 - accuracy: 0.6990 - val_loss: 1.0269 - val_accuracy: 0.6160\n",
      "Epoch 484/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0364 - accuracy: 0.7280 - val_loss: 1.0276 - val_accuracy: 0.7600\n",
      "Epoch 485/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0263 - accuracy: 0.7405 - val_loss: 1.0329 - val_accuracy: 0.6780\n",
      "Epoch 486/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0270 - accuracy: 0.7265 - val_loss: 1.0156 - val_accuracy: 0.7940\n",
      "Epoch 487/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0106 - accuracy: 0.7623 - val_loss: 1.0740 - val_accuracy: 0.5620\n",
      "Epoch 488/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0547 - accuracy: 0.6596 - val_loss: 1.0313 - val_accuracy: 0.7820\n",
      "Epoch 489/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0150 - accuracy: 0.8123 - val_loss: 1.0634 - val_accuracy: 0.6000\n",
      "Epoch 490/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0509 - accuracy: 0.6767 - val_loss: 1.0200 - val_accuracy: 0.8000\n",
      "Epoch 491/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0640 - accuracy: 0.6926 - val_loss: 1.0151 - val_accuracy: 0.6120\n",
      "Epoch 492/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0268 - accuracy: 0.6449 - val_loss: 1.0153 - val_accuracy: 0.7160\n",
      "Epoch 493/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0424 - accuracy: 0.7084 - val_loss: 1.0110 - val_accuracy: 0.7540\n",
      "Epoch 494/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0098 - accuracy: 0.7873 - val_loss: 1.0060 - val_accuracy: 0.7980\n",
      "Epoch 495/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9867 - accuracy: 0.8171 - val_loss: 1.0099 - val_accuracy: 0.8820\n",
      "Epoch 496/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0216 - accuracy: 0.7555 - val_loss: 1.0178 - val_accuracy: 0.8000\n",
      "Epoch 497/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0236 - accuracy: 0.7366 - val_loss: 1.0324 - val_accuracy: 0.6280\n",
      "Epoch 498/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0445 - accuracy: 0.7426 - val_loss: 1.0037 - val_accuracy: 0.8900\n",
      "Epoch 499/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0170 - accuracy: 0.7821 - val_loss: 1.0090 - val_accuracy: 0.8040\n",
      "Epoch 500/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9983 - accuracy: 0.7581 - val_loss: 1.0290 - val_accuracy: 0.6480\n",
      "Epoch 501/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0216 - accuracy: 0.6854 - val_loss: 1.0448 - val_accuracy: 0.6000\n",
      "Epoch 502/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0434 - accuracy: 0.6764 - val_loss: 1.0018 - val_accuracy: 0.7900\n",
      "Epoch 503/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0341 - accuracy: 0.7394 - val_loss: 1.0086 - val_accuracy: 0.7940\n",
      "Epoch 504/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0094 - accuracy: 0.7584 - val_loss: 1.0015 - val_accuracy: 0.9400\n",
      "Epoch 505/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0117 - accuracy: 0.8264 - val_loss: 0.9959 - val_accuracy: 0.7900\n",
      "Epoch 506/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0094 - accuracy: 0.7229 - val_loss: 0.9847 - val_accuracy: 0.8360\n",
      "Epoch 507/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9864 - accuracy: 0.8131 - val_loss: 1.0024 - val_accuracy: 0.7880\n",
      "Epoch 508/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0287 - accuracy: 0.6824 - val_loss: 1.0049 - val_accuracy: 0.7380\n",
      "Epoch 509/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.9958 - accuracy: 0.7701 - val_loss: 1.0061 - val_accuracy: 0.6820\n",
      "Epoch 510/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0078 - accuracy: 0.7108 - val_loss: 1.0018 - val_accuracy: 0.7280\n",
      "Epoch 511/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0259 - accuracy: 0.6813 - val_loss: 0.9886 - val_accuracy: 0.7740\n",
      "Epoch 512/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0005 - accuracy: 0.7726 - val_loss: 0.9959 - val_accuracy: 0.7800\n",
      "Epoch 513/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9846 - accuracy: 0.7771 - val_loss: 1.0006 - val_accuracy: 0.6200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 514/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0396 - accuracy: 0.6260 - val_loss: 0.9758 - val_accuracy: 0.8760\n",
      "Epoch 515/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9716 - accuracy: 0.7300 - val_loss: 1.0085 - val_accuracy: 0.7220\n",
      "Epoch 516/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 1.0006 - accuracy: 0.7267 - val_loss: 0.9943 - val_accuracy: 0.7940\n",
      "Epoch 517/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.9921 - accuracy: 0.7103 - val_loss: 0.9831 - val_accuracy: 0.7580\n",
      "Epoch 518/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0003 - accuracy: 0.7399 - val_loss: 0.9727 - val_accuracy: 0.7980\n",
      "Epoch 519/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9625 - accuracy: 0.7620 - val_loss: 0.9925 - val_accuracy: 0.6560\n",
      "Epoch 520/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0020 - accuracy: 0.7571 - val_loss: 0.9954 - val_accuracy: 0.6380\n",
      "Epoch 521/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9966 - accuracy: 0.6634 - val_loss: 0.9669 - val_accuracy: 0.8000\n",
      "Epoch 522/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9756 - accuracy: 0.7851 - val_loss: 0.9737 - val_accuracy: 0.9540\n",
      "Epoch 523/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9786 - accuracy: 0.8454 - val_loss: 0.9715 - val_accuracy: 0.9360\n",
      "Epoch 524/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9779 - accuracy: 0.8355 - val_loss: 0.9938 - val_accuracy: 0.7980\n",
      "Epoch 525/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0017 - accuracy: 0.8040 - val_loss: 0.9906 - val_accuracy: 0.6080\n",
      "Epoch 526/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9648 - accuracy: 0.7566 - val_loss: 0.9767 - val_accuracy: 0.8260\n",
      "Epoch 527/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9970 - accuracy: 0.6838 - val_loss: 0.9844 - val_accuracy: 0.7400\n",
      "Epoch 528/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9890 - accuracy: 0.6947 - val_loss: 1.0002 - val_accuracy: 0.7920\n",
      "Epoch 529/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9961 - accuracy: 0.6992 - val_loss: 0.9949 - val_accuracy: 0.7220\n",
      "Epoch 530/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9925 - accuracy: 0.7330 - val_loss: 0.9776 - val_accuracy: 0.7560\n",
      "Epoch 531/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9898 - accuracy: 0.7204 - val_loss: 0.9733 - val_accuracy: 0.6000\n",
      "Epoch 532/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9652 - accuracy: 0.6832 - val_loss: 0.9733 - val_accuracy: 0.6960\n",
      "Epoch 533/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9713 - accuracy: 0.7085 - val_loss: 0.9513 - val_accuracy: 0.8100\n",
      "Epoch 534/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9355 - accuracy: 0.7875 - val_loss: 0.9779 - val_accuracy: 0.7420\n",
      "Epoch 535/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9557 - accuracy: 0.8450 - val_loss: 0.9571 - val_accuracy: 0.9680\n",
      "Epoch 536/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9834 - accuracy: 0.8547 - val_loss: 0.9512 - val_accuracy: 0.9100\n",
      "Epoch 537/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9993 - accuracy: 0.8047 - val_loss: 0.9645 - val_accuracy: 0.8000\n",
      "Epoch 538/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9540 - accuracy: 0.8555 - val_loss: 0.9594 - val_accuracy: 0.8000\n",
      "Epoch 539/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9618 - accuracy: 0.7745 - val_loss: 0.9476 - val_accuracy: 0.9920\n",
      "Epoch 540/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9776 - accuracy: 0.8057 - val_loss: 0.9835 - val_accuracy: 0.7380\n",
      "Epoch 541/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 1.0043 - accuracy: 0.7085 - val_loss: 0.9595 - val_accuracy: 0.8120\n",
      "Epoch 542/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9594 - accuracy: 0.7587 - val_loss: 0.9632 - val_accuracy: 0.7960\n",
      "Epoch 543/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9557 - accuracy: 0.7634 - val_loss: 0.9712 - val_accuracy: 0.7680\n",
      "Epoch 544/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9607 - accuracy: 0.7872 - val_loss: 0.9725 - val_accuracy: 0.7960\n",
      "Epoch 545/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9672 - accuracy: 0.7977 - val_loss: 0.9486 - val_accuracy: 0.9600\n",
      "Epoch 546/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9712 - accuracy: 0.7281 - val_loss: 0.9631 - val_accuracy: 0.7960\n",
      "Epoch 547/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9794 - accuracy: 0.7958 - val_loss: 0.9386 - val_accuracy: 0.9760\n",
      "Epoch 548/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9584 - accuracy: 0.8089 - val_loss: 0.9458 - val_accuracy: 0.8000\n",
      "Epoch 549/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9745 - accuracy: 0.6656 - val_loss: 0.9653 - val_accuracy: 0.7940\n",
      "Epoch 550/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9885 - accuracy: 0.6725 - val_loss: 0.9678 - val_accuracy: 0.7700\n",
      "Epoch 551/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9618 - accuracy: 0.7899 - val_loss: 0.9354 - val_accuracy: 0.7320\n",
      "Epoch 552/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.9650 - accuracy: 0.7008 - val_loss: 0.9504 - val_accuracy: 0.6160\n",
      "Epoch 553/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.9541 - accuracy: 0.7084 - val_loss: 0.9368 - val_accuracy: 0.8000\n",
      "Epoch 554/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9381 - accuracy: 0.8199 - val_loss: 0.9617 - val_accuracy: 0.7080\n",
      "Epoch 555/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9267 - accuracy: 0.7176 - val_loss: 0.9289 - val_accuracy: 0.8640\n",
      "Epoch 556/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9627 - accuracy: 0.7909 - val_loss: 0.9388 - val_accuracy: 0.6480\n",
      "Epoch 557/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9230 - accuracy: 0.7827 - val_loss: 0.9854 - val_accuracy: 0.6000\n",
      "Epoch 558/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9661 - accuracy: 0.7202 - val_loss: 0.9331 - val_accuracy: 0.8000\n",
      "Epoch 559/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9489 - accuracy: 0.7559 - val_loss: 0.9242 - val_accuracy: 0.8000\n",
      "Epoch 560/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9070 - accuracy: 0.8822 - val_loss: 0.9348 - val_accuracy: 0.6720\n",
      "Epoch 561/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9355 - accuracy: 0.7114 - val_loss: 0.9285 - val_accuracy: 0.7580\n",
      "Epoch 562/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9166 - accuracy: 0.8252 - val_loss: 0.9419 - val_accuracy: 0.8300\n",
      "Epoch 563/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9566 - accuracy: 0.7277 - val_loss: 0.9268 - val_accuracy: 0.9200\n",
      "Epoch 564/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9326 - accuracy: 0.8579 - val_loss: 0.9277 - val_accuracy: 0.8820\n",
      "Epoch 565/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9305 - accuracy: 0.8933 - val_loss: 0.9207 - val_accuracy: 0.7940\n",
      "Epoch 566/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9157 - accuracy: 0.7959 - val_loss: 0.9608 - val_accuracy: 0.7120\n",
      "Epoch 567/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9219 - accuracy: 0.8299 - val_loss: 0.9208 - val_accuracy: 0.9040\n",
      "Epoch 568/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9223 - accuracy: 0.8990 - val_loss: 0.9080 - val_accuracy: 0.9780\n",
      "Epoch 569/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9234 - accuracy: 0.8970 - val_loss: 0.9304 - val_accuracy: 0.7580\n",
      "Epoch 570/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9259 - accuracy: 0.7500 - val_loss: 0.9373 - val_accuracy: 0.6020\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 571/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9293 - accuracy: 0.7291 - val_loss: 0.9038 - val_accuracy: 0.8900\n",
      "Epoch 572/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9053 - accuracy: 0.8322 - val_loss: 0.9262 - val_accuracy: 0.7280\n",
      "Epoch 573/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9367 - accuracy: 0.7208 - val_loss: 0.9352 - val_accuracy: 0.8440\n",
      "Epoch 574/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9384 - accuracy: 0.8451 - val_loss: 0.9162 - val_accuracy: 0.8980\n",
      "Epoch 575/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9106 - accuracy: 0.8818 - val_loss: 0.9816 - val_accuracy: 0.6000\n",
      "Epoch 576/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9676 - accuracy: 0.7206 - val_loss: 0.9063 - val_accuracy: 0.8700\n",
      "Epoch 577/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9082 - accuracy: 0.8650 - val_loss: 0.9134 - val_accuracy: 0.8880\n",
      "Epoch 578/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8988 - accuracy: 0.8803 - val_loss: 0.8975 - val_accuracy: 0.8900\n",
      "Epoch 579/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8937 - accuracy: 0.8840 - val_loss: 0.9049 - val_accuracy: 0.8000\n",
      "Epoch 580/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9095 - accuracy: 0.8452 - val_loss: 0.9084 - val_accuracy: 0.7000\n",
      "Epoch 581/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9497 - accuracy: 0.7329 - val_loss: 0.9219 - val_accuracy: 0.7960\n",
      "Epoch 582/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9551 - accuracy: 0.6871 - val_loss: 0.8959 - val_accuracy: 0.9100\n",
      "Epoch 583/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8905 - accuracy: 0.8276 - val_loss: 0.9351 - val_accuracy: 0.7880\n",
      "Epoch 584/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9239 - accuracy: 0.7886 - val_loss: 0.9081 - val_accuracy: 0.7380\n",
      "Epoch 585/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9085 - accuracy: 0.8156 - val_loss: 0.8983 - val_accuracy: 0.7980\n",
      "Epoch 586/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9097 - accuracy: 0.7699 - val_loss: 0.9015 - val_accuracy: 0.8820\n",
      "Epoch 587/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9065 - accuracy: 0.8480 - val_loss: 0.8999 - val_accuracy: 0.9600\n",
      "Epoch 588/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9011 - accuracy: 0.9008 - val_loss: 0.9210 - val_accuracy: 0.7760\n",
      "Epoch 589/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9133 - accuracy: 0.8262 - val_loss: 0.9329 - val_accuracy: 0.7820\n",
      "Epoch 590/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9279 - accuracy: 0.8436 - val_loss: 0.8974 - val_accuracy: 0.7880\n",
      "Epoch 591/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8800 - accuracy: 0.7553 - val_loss: 0.9160 - val_accuracy: 0.7520\n",
      "Epoch 592/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9136 - accuracy: 0.7903 - val_loss: 0.9023 - val_accuracy: 0.9900\n",
      "Epoch 593/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8949 - accuracy: 0.9116 - val_loss: 0.8975 - val_accuracy: 0.7720\n",
      "Epoch 594/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8884 - accuracy: 0.8567 - val_loss: 0.8865 - val_accuracy: 0.8000\n",
      "Epoch 595/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8618 - accuracy: 0.8557 - val_loss: 0.9095 - val_accuracy: 0.8120\n",
      "Epoch 596/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9127 - accuracy: 0.7513 - val_loss: 0.8761 - val_accuracy: 0.9200\n",
      "Epoch 597/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8973 - accuracy: 0.8050 - val_loss: 0.8905 - val_accuracy: 0.8000\n",
      "Epoch 598/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8958 - accuracy: 0.8819 - val_loss: 0.8778 - val_accuracy: 0.9420\n",
      "Epoch 599/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.9080 - accuracy: 0.8652 - val_loss: 0.8865 - val_accuracy: 0.9220\n",
      "Epoch 600/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8829 - accuracy: 0.8343 - val_loss: 0.8764 - val_accuracy: 0.9400\n",
      "Epoch 601/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9043 - accuracy: 0.9132 - val_loss: 0.8850 - val_accuracy: 0.7980\n",
      "Epoch 602/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8785 - accuracy: 0.7891 - val_loss: 0.9116 - val_accuracy: 0.7620\n",
      "Epoch 603/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9325 - accuracy: 0.7539 - val_loss: 0.8821 - val_accuracy: 0.9900\n",
      "Epoch 604/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8855 - accuracy: 0.8798 - val_loss: 0.8882 - val_accuracy: 0.8780\n",
      "Epoch 605/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8828 - accuracy: 0.7690 - val_loss: 0.8720 - val_accuracy: 0.9920\n",
      "Epoch 606/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8905 - accuracy: 0.8524 - val_loss: 0.9100 - val_accuracy: 0.6220\n",
      "Epoch 607/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8807 - accuracy: 0.7696 - val_loss: 0.8746 - val_accuracy: 0.9960\n",
      "Epoch 608/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8936 - accuracy: 0.9233 - val_loss: 0.8799 - val_accuracy: 0.8940\n",
      "Epoch 609/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8860 - accuracy: 0.8304 - val_loss: 0.8908 - val_accuracy: 0.8280\n",
      "Epoch 610/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.9092 - accuracy: 0.8119 - val_loss: 0.8747 - val_accuracy: 0.7700\n",
      "Epoch 611/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8873 - accuracy: 0.7321 - val_loss: 0.8724 - val_accuracy: 1.0000\n",
      "Epoch 612/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8902 - accuracy: 0.8979 - val_loss: 0.8772 - val_accuracy: 0.9740\n",
      "Epoch 613/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8942 - accuracy: 0.8982 - val_loss: 0.8718 - val_accuracy: 0.8000\n",
      "Epoch 614/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.8821 - accuracy: 0.7861 - val_loss: 0.8695 - val_accuracy: 0.9460\n",
      "Epoch 615/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8704 - accuracy: 0.9018 - val_loss: 0.8787 - val_accuracy: 0.9960\n",
      "Epoch 616/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8836 - accuracy: 0.9182 - val_loss: 0.8539 - val_accuracy: 0.8860\n",
      "Epoch 617/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8863 - accuracy: 0.8556 - val_loss: 0.8639 - val_accuracy: 0.8000\n",
      "Epoch 618/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8540 - accuracy: 0.8092 - val_loss: 0.8841 - val_accuracy: 0.8360\n",
      "Epoch 619/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8840 - accuracy: 0.8444 - val_loss: 0.8618 - val_accuracy: 0.8840\n",
      "Epoch 620/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8763 - accuracy: 0.8834 - val_loss: 0.8691 - val_accuracy: 0.7100\n",
      "Epoch 621/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8788 - accuracy: 0.7482 - val_loss: 0.8547 - val_accuracy: 0.8000\n",
      "Epoch 622/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8526 - accuracy: 0.8699 - val_loss: 0.8601 - val_accuracy: 0.9840\n",
      "Epoch 623/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8698 - accuracy: 0.9155 - val_loss: 0.9185 - val_accuracy: 0.6020\n",
      "Epoch 624/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8835 - accuracy: 0.7574 - val_loss: 0.8540 - val_accuracy: 0.9940\n",
      "Epoch 625/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8876 - accuracy: 0.8366 - val_loss: 0.8665 - val_accuracy: 0.8620\n",
      "Epoch 626/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8803 - accuracy: 0.8410 - val_loss: 0.8530 - val_accuracy: 0.9460\n",
      "Epoch 627/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8635 - accuracy: 0.8770 - val_loss: 0.8572 - val_accuracy: 0.8380\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 628/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8543 - accuracy: 0.9302 - val_loss: 0.8689 - val_accuracy: 0.7980\n",
      "Epoch 629/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8674 - accuracy: 0.8433 - val_loss: 0.8478 - val_accuracy: 0.8000\n",
      "Epoch 630/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8679 - accuracy: 0.8746 - val_loss: 0.8860 - val_accuracy: 0.6000\n",
      "Epoch 631/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8795 - accuracy: 0.7800 - val_loss: 0.8458 - val_accuracy: 0.8900\n",
      "Epoch 632/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8662 - accuracy: 0.8888 - val_loss: 0.8393 - val_accuracy: 0.9720\n",
      "Epoch 633/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8548 - accuracy: 0.8966 - val_loss: 0.8693 - val_accuracy: 0.8000\n",
      "Epoch 634/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8769 - accuracy: 0.8206 - val_loss: 0.8431 - val_accuracy: 0.9380\n",
      "Epoch 635/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8536 - accuracy: 0.7842 - val_loss: 0.8328 - val_accuracy: 1.0000\n",
      "Epoch 636/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8435 - accuracy: 0.9553 - val_loss: 0.8480 - val_accuracy: 0.7940\n",
      "Epoch 637/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8360 - accuracy: 0.8521 - val_loss: 0.8384 - val_accuracy: 0.9740\n",
      "Epoch 638/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8874 - accuracy: 0.8066 - val_loss: 0.8510 - val_accuracy: 0.7920\n",
      "Epoch 639/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8366 - accuracy: 0.8296 - val_loss: 0.8524 - val_accuracy: 0.9620\n",
      "Epoch 640/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8394 - accuracy: 0.9183 - val_loss: 0.8402 - val_accuracy: 0.9960\n",
      "Epoch 641/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8636 - accuracy: 0.9104 - val_loss: 0.8595 - val_accuracy: 0.8000\n",
      "Epoch 642/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8463 - accuracy: 0.8141 - val_loss: 0.8315 - val_accuracy: 0.9980\n",
      "Epoch 643/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8621 - accuracy: 0.8435 - val_loss: 0.8296 - val_accuracy: 1.0000\n",
      "Epoch 644/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8202 - accuracy: 0.9322 - val_loss: 0.8431 - val_accuracy: 0.9180\n",
      "Epoch 645/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8559 - accuracy: 0.8914 - val_loss: 0.8581 - val_accuracy: 0.8780\n",
      "Epoch 646/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8350 - accuracy: 0.9632 - val_loss: 0.8357 - val_accuracy: 0.9220\n",
      "Epoch 647/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8132 - accuracy: 0.9573 - val_loss: 0.8304 - val_accuracy: 0.9680\n",
      "Epoch 648/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8305 - accuracy: 0.8684 - val_loss: 0.8198 - val_accuracy: 0.9980\n",
      "Epoch 649/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8317 - accuracy: 0.9680 - val_loss: 0.8334 - val_accuracy: 0.8000\n",
      "Epoch 650/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8524 - accuracy: 0.8530 - val_loss: 0.8375 - val_accuracy: 0.9780\n",
      "Epoch 651/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8513 - accuracy: 0.9086 - val_loss: 0.8189 - val_accuracy: 0.9880\n",
      "Epoch 652/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8411 - accuracy: 0.9524 - val_loss: 0.8178 - val_accuracy: 0.9140\n",
      "Epoch 653/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8481 - accuracy: 0.9187 - val_loss: 0.8283 - val_accuracy: 0.9980\n",
      "Epoch 654/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8553 - accuracy: 0.8977 - val_loss: 0.8621 - val_accuracy: 0.8000\n",
      "Epoch 655/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8435 - accuracy: 0.8574 - val_loss: 0.8185 - val_accuracy: 0.9800\n",
      "Epoch 656/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8226 - accuracy: 0.9174 - val_loss: 0.8236 - val_accuracy: 0.9980\n",
      "Epoch 657/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8559 - accuracy: 0.8628 - val_loss: 0.8437 - val_accuracy: 0.9360\n",
      "Epoch 658/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8417 - accuracy: 0.9422 - val_loss: 0.8168 - val_accuracy: 0.9700\n",
      "Epoch 659/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8043 - accuracy: 0.9304 - val_loss: 0.8127 - val_accuracy: 0.9440\n",
      "Epoch 660/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8477 - accuracy: 0.8965 - val_loss: 0.8167 - val_accuracy: 1.0000\n",
      "Epoch 661/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8072 - accuracy: 0.8647 - val_loss: 0.8124 - val_accuracy: 0.9920\n",
      "Epoch 662/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8083 - accuracy: 0.8877 - val_loss: 0.8180 - val_accuracy: 0.8340\n",
      "Epoch 663/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8402 - accuracy: 0.8665 - val_loss: 0.8554 - val_accuracy: 0.7540\n",
      "Epoch 664/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8560 - accuracy: 0.7834 - val_loss: 0.8188 - val_accuracy: 1.0000\n",
      "Epoch 665/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.8211 - accuracy: 0.9602 - val_loss: 0.8175 - val_accuracy: 0.8960\n",
      "Epoch 666/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8282 - accuracy: 0.9051 - val_loss: 0.8192 - val_accuracy: 0.9100\n",
      "Epoch 667/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8151 - accuracy: 0.9012 - val_loss: 0.8001 - val_accuracy: 1.0000\n",
      "Epoch 668/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8082 - accuracy: 0.9687 - val_loss: 0.8076 - val_accuracy: 0.9980\n",
      "Epoch 669/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7951 - accuracy: 0.9531 - val_loss: 0.8075 - val_accuracy: 0.9740\n",
      "Epoch 670/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8046 - accuracy: 0.8652 - val_loss: 0.8366 - val_accuracy: 0.9520\n",
      "Epoch 671/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8353 - accuracy: 0.9119 - val_loss: 0.8185 - val_accuracy: 0.8640\n",
      "Epoch 672/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8225 - accuracy: 0.8035 - val_loss: 0.7976 - val_accuracy: 0.9440\n",
      "Epoch 673/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7874 - accuracy: 0.8731 - val_loss: 0.8039 - val_accuracy: 0.9600\n",
      "Epoch 674/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8121 - accuracy: 0.8823 - val_loss: 0.8016 - val_accuracy: 0.9720\n",
      "Epoch 675/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8123 - accuracy: 0.9114 - val_loss: 0.7905 - val_accuracy: 1.0000\n",
      "Epoch 676/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7998 - accuracy: 0.9324 - val_loss: 0.8186 - val_accuracy: 0.9480\n",
      "Epoch 677/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8424 - accuracy: 0.8931 - val_loss: 0.7975 - val_accuracy: 0.8100\n",
      "Epoch 678/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7934 - accuracy: 0.8508 - val_loss: 0.8316 - val_accuracy: 0.9740\n",
      "Epoch 679/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8131 - accuracy: 0.9339 - val_loss: 0.8180 - val_accuracy: 0.6320\n",
      "Epoch 680/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8015 - accuracy: 0.8021 - val_loss: 0.8159 - val_accuracy: 0.9540\n",
      "Epoch 681/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8047 - accuracy: 0.8843 - val_loss: 0.8019 - val_accuracy: 0.9940\n",
      "Epoch 682/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8098 - accuracy: 0.9127 - val_loss: 0.8055 - val_accuracy: 0.8000\n",
      "Epoch 683/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8068 - accuracy: 0.8455 - val_loss: 0.8057 - val_accuracy: 0.9560\n",
      "Epoch 684/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7966 - accuracy: 0.9032 - val_loss: 0.8166 - val_accuracy: 0.9280\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 685/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7985 - accuracy: 0.9654 - val_loss: 0.7852 - val_accuracy: 0.9980\n",
      "Epoch 686/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7970 - accuracy: 0.9648 - val_loss: 0.7834 - val_accuracy: 1.0000\n",
      "Epoch 687/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7992 - accuracy: 0.9068 - val_loss: 0.7842 - val_accuracy: 0.8420\n",
      "Epoch 688/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8019 - accuracy: 0.8958 - val_loss: 0.7971 - val_accuracy: 0.9760\n",
      "Epoch 689/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7806 - accuracy: 0.8807 - val_loss: 0.7977 - val_accuracy: 0.7800\n",
      "Epoch 690/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7808 - accuracy: 0.9149 - val_loss: 0.8045 - val_accuracy: 1.0000\n",
      "Epoch 691/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8101 - accuracy: 0.9417 - val_loss: 0.7857 - val_accuracy: 0.9920\n",
      "Epoch 692/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7733 - accuracy: 0.9071 - val_loss: 0.7954 - val_accuracy: 0.7980\n",
      "Epoch 693/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8101 - accuracy: 0.8354 - val_loss: 0.7782 - val_accuracy: 0.9160\n",
      "Epoch 694/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7759 - accuracy: 0.9559 - val_loss: 0.7851 - val_accuracy: 0.9960\n",
      "Epoch 695/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7914 - accuracy: 0.9517 - val_loss: 0.7879 - val_accuracy: 0.9440\n",
      "Epoch 696/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8096 - accuracy: 0.9471 - val_loss: 0.7866 - val_accuracy: 0.8020\n",
      "Epoch 697/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7522 - accuracy: 0.8594 - val_loss: 0.8212 - val_accuracy: 0.8400\n",
      "Epoch 698/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8000 - accuracy: 0.8849 - val_loss: 0.7707 - val_accuracy: 1.0000\n",
      "Epoch 699/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7614 - accuracy: 0.9714 - val_loss: 0.7976 - val_accuracy: 0.8960\n",
      "Epoch 700/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7880 - accuracy: 0.8869 - val_loss: 0.7830 - val_accuracy: 0.9120\n",
      "Epoch 701/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.8123 - accuracy: 0.9067 - val_loss: 0.7972 - val_accuracy: 0.7980\n",
      "Epoch 702/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8019 - accuracy: 0.8367 - val_loss: 0.7710 - val_accuracy: 0.9840\n",
      "Epoch 703/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7598 - accuracy: 0.9744 - val_loss: 0.7811 - val_accuracy: 0.9960\n",
      "Epoch 704/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7945 - accuracy: 0.8694 - val_loss: 0.7697 - val_accuracy: 0.8360\n",
      "Epoch 705/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7970 - accuracy: 0.8959 - val_loss: 0.8510 - val_accuracy: 0.8040\n",
      "Epoch 706/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8275 - accuracy: 0.7604 - val_loss: 0.7797 - val_accuracy: 0.8960\n",
      "Epoch 707/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7770 - accuracy: 0.8576 - val_loss: 0.7870 - val_accuracy: 0.8000\n",
      "Epoch 708/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7766 - accuracy: 0.9127 - val_loss: 0.7696 - val_accuracy: 0.9680\n",
      "Epoch 709/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7704 - accuracy: 0.9848 - val_loss: 0.7653 - val_accuracy: 1.0000\n",
      "Epoch 710/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7480 - accuracy: 0.9067 - val_loss: 0.7977 - val_accuracy: 0.8020\n",
      "Epoch 711/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7873 - accuracy: 0.8778 - val_loss: 0.7578 - val_accuracy: 0.9200\n",
      "Epoch 712/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7726 - accuracy: 0.9448 - val_loss: 0.7752 - val_accuracy: 0.9940\n",
      "Epoch 713/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7691 - accuracy: 0.9376 - val_loss: 0.7626 - val_accuracy: 0.9980\n",
      "Epoch 714/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7593 - accuracy: 0.9747 - val_loss: 0.7704 - val_accuracy: 0.9980\n",
      "Epoch 715/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7632 - accuracy: 0.9641 - val_loss: 0.7600 - val_accuracy: 1.0000\n",
      "Epoch 716/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7910 - accuracy: 0.8003 - val_loss: 0.7731 - val_accuracy: 0.9520\n",
      "Epoch 717/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7759 - accuracy: 0.9521 - val_loss: 0.7611 - val_accuracy: 0.9020\n",
      "Epoch 718/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7670 - accuracy: 0.9495 - val_loss: 0.7731 - val_accuracy: 0.9740\n",
      "Epoch 719/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7679 - accuracy: 0.9660 - val_loss: 0.7600 - val_accuracy: 0.9880\n",
      "Epoch 720/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7505 - accuracy: 0.9531 - val_loss: 0.7596 - val_accuracy: 0.9560\n",
      "Epoch 721/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7794 - accuracy: 0.8579 - val_loss: 0.7948 - val_accuracy: 0.8000\n",
      "Epoch 722/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8168 - accuracy: 0.8591 - val_loss: 0.7513 - val_accuracy: 0.9980\n",
      "Epoch 723/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7790 - accuracy: 0.9887 - val_loss: 0.7626 - val_accuracy: 0.8000\n",
      "Epoch 724/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.7508 - accuracy: 0.8951 - val_loss: 0.7687 - val_accuracy: 0.9100\n",
      "Epoch 725/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7848 - accuracy: 0.8101 - val_loss: 0.7825 - val_accuracy: 0.7520\n",
      "Epoch 726/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7634 - accuracy: 0.9403 - val_loss: 0.7592 - val_accuracy: 0.9480\n",
      "Epoch 727/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.7804 - accuracy: 0.8842 - val_loss: 0.7485 - val_accuracy: 0.9900\n",
      "Epoch 728/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.7288 - accuracy: 0.9053 - val_loss: 0.7630 - val_accuracy: 0.8960\n",
      "Epoch 729/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.7532 - accuracy: 0.9332 - val_loss: 0.7527 - val_accuracy: 0.9560\n",
      "Epoch 730/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.7523 - accuracy: 0.9498 - val_loss: 0.7531 - val_accuracy: 0.8980\n",
      "Epoch 731/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7882 - accuracy: 0.8872 - val_loss: 0.7808 - val_accuracy: 0.8100\n",
      "Epoch 732/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7478 - accuracy: 0.9110 - val_loss: 0.7602 - val_accuracy: 0.8080\n",
      "Epoch 733/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7597 - accuracy: 0.9493 - val_loss: 0.7477 - val_accuracy: 0.9960\n",
      "Epoch 734/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7871 - accuracy: 0.9653 - val_loss: 0.7447 - val_accuracy: 1.0000\n",
      "Epoch 735/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7617 - accuracy: 0.9468 - val_loss: 0.8206 - val_accuracy: 0.8000\n",
      "Epoch 736/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.8002 - accuracy: 0.8633 - val_loss: 0.7528 - val_accuracy: 0.8020\n",
      "Epoch 737/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7473 - accuracy: 0.8638 - val_loss: 0.7388 - val_accuracy: 0.9800\n",
      "Epoch 738/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7498 - accuracy: 0.9778 - val_loss: 0.7354 - val_accuracy: 1.0000\n",
      "Epoch 739/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7359 - accuracy: 0.9515 - val_loss: 0.7499 - val_accuracy: 0.8320\n",
      "Epoch 740/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7435 - accuracy: 0.9512 - val_loss: 0.7744 - val_accuracy: 0.8000\n",
      "Epoch 741/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7361 - accuracy: 0.9389 - val_loss: 0.7335 - val_accuracy: 0.9600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 742/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7285 - accuracy: 0.9813 - val_loss: 0.7292 - val_accuracy: 0.9920\n",
      "Epoch 743/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7438 - accuracy: 0.9545 - val_loss: 0.7531 - val_accuracy: 0.9820\n",
      "Epoch 744/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7484 - accuracy: 0.9530 - val_loss: 0.7432 - val_accuracy: 0.9920\n",
      "Epoch 745/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7359 - accuracy: 0.9633 - val_loss: 0.7269 - val_accuracy: 0.9920\n",
      "Epoch 746/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7364 - accuracy: 0.9760 - val_loss: 0.7272 - val_accuracy: 0.9660\n",
      "Epoch 747/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7193 - accuracy: 0.9478 - val_loss: 0.7306 - val_accuracy: 0.9800\n",
      "Epoch 748/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7224 - accuracy: 0.9168 - val_loss: 0.7318 - val_accuracy: 0.8440\n",
      "Epoch 749/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7381 - accuracy: 0.9267 - val_loss: 0.7401 - val_accuracy: 0.9520\n",
      "Epoch 750/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7522 - accuracy: 0.9044 - val_loss: 0.7446 - val_accuracy: 0.8480\n",
      "Epoch 751/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7169 - accuracy: 0.9579 - val_loss: 0.7373 - val_accuracy: 0.8920\n",
      "Epoch 752/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7618 - accuracy: 0.8768 - val_loss: 0.7336 - val_accuracy: 0.8200\n",
      "Epoch 753/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7269 - accuracy: 0.9561 - val_loss: 0.7269 - val_accuracy: 1.0000\n",
      "Epoch 754/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7072 - accuracy: 0.9491 - val_loss: 0.7446 - val_accuracy: 0.9940\n",
      "Epoch 755/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7157 - accuracy: 0.9975 - val_loss: 0.7240 - val_accuracy: 0.9860\n",
      "Epoch 756/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7401 - accuracy: 0.9368 - val_loss: 0.7854 - val_accuracy: 0.7820\n",
      "Epoch 757/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7627 - accuracy: 0.9154 - val_loss: 0.7554 - val_accuracy: 0.7980\n",
      "Epoch 758/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7811 - accuracy: 0.8217 - val_loss: 0.7183 - val_accuracy: 1.0000\n",
      "Epoch 759/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7529 - accuracy: 0.9587 - val_loss: 0.7175 - val_accuracy: 0.9960\n",
      "Epoch 760/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7295 - accuracy: 0.9153 - val_loss: 0.7323 - val_accuracy: 0.8000\n",
      "Epoch 761/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7531 - accuracy: 0.9271 - val_loss: 0.7171 - val_accuracy: 0.9880\n",
      "Epoch 762/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7342 - accuracy: 0.9258 - val_loss: 0.7130 - val_accuracy: 1.0000\n",
      "Epoch 763/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7328 - accuracy: 0.9649 - val_loss: 0.7256 - val_accuracy: 0.8020\n",
      "Epoch 764/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7357 - accuracy: 0.9512 - val_loss: 0.7145 - val_accuracy: 0.9960\n",
      "Epoch 765/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7196 - accuracy: 0.9959 - val_loss: 0.7094 - val_accuracy: 1.0000\n",
      "Epoch 766/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7273 - accuracy: 0.9732 - val_loss: 0.7207 - val_accuracy: 0.9860\n",
      "Epoch 767/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7097 - accuracy: 0.9432 - val_loss: 0.7163 - val_accuracy: 1.0000\n",
      "Epoch 768/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7224 - accuracy: 0.9850 - val_loss: 0.7417 - val_accuracy: 0.8080\n",
      "Epoch 769/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7557 - accuracy: 0.9332 - val_loss: 0.7281 - val_accuracy: 0.9200\n",
      "Epoch 770/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7095 - accuracy: 0.9325 - val_loss: 0.7361 - val_accuracy: 0.9960\n",
      "Epoch 771/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7008 - accuracy: 0.9125 - val_loss: 0.7062 - val_accuracy: 0.9960\n",
      "Epoch 772/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7060 - accuracy: 0.9623 - val_loss: 0.7354 - val_accuracy: 0.8000\n",
      "Epoch 773/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7326 - accuracy: 0.9222 - val_loss: 0.7403 - val_accuracy: 0.8000\n",
      "Epoch 774/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7504 - accuracy: 0.8607 - val_loss: 0.7228 - val_accuracy: 1.0000\n",
      "Epoch 775/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7238 - accuracy: 0.9313 - val_loss: 0.7133 - val_accuracy: 0.9980\n",
      "Epoch 776/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7265 - accuracy: 0.9923 - val_loss: 0.7068 - val_accuracy: 1.0000\n",
      "Epoch 777/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7022 - accuracy: 0.9629 - val_loss: 0.7019 - val_accuracy: 0.9960\n",
      "Epoch 778/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7509 - accuracy: 0.9435 - val_loss: 0.7015 - val_accuracy: 1.0000\n",
      "Epoch 779/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7038 - accuracy: 0.9431 - val_loss: 0.7448 - val_accuracy: 0.8000\n",
      "Epoch 780/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7397 - accuracy: 0.8690 - val_loss: 0.7216 - val_accuracy: 0.8120\n",
      "Epoch 781/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7348 - accuracy: 0.8597 - val_loss: 0.7194 - val_accuracy: 0.9960\n",
      "Epoch 782/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7439 - accuracy: 0.9273 - val_loss: 0.7507 - val_accuracy: 0.7760\n",
      "Epoch 783/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6938 - accuracy: 0.8964 - val_loss: 0.7225 - val_accuracy: 0.9900\n",
      "Epoch 784/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7123 - accuracy: 0.9981 - val_loss: 0.7001 - val_accuracy: 0.9680\n",
      "Epoch 785/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6905 - accuracy: 0.9318 - val_loss: 0.7092 - val_accuracy: 0.9860\n",
      "Epoch 786/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7142 - accuracy: 0.9169 - val_loss: 0.7035 - val_accuracy: 1.0000\n",
      "Epoch 787/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6812 - accuracy: 0.9284 - val_loss: 0.6897 - val_accuracy: 0.9960\n",
      "Epoch 788/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7052 - accuracy: 0.9468 - val_loss: 0.6939 - val_accuracy: 0.9780\n",
      "Epoch 789/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6871 - accuracy: 0.9423 - val_loss: 0.6951 - val_accuracy: 0.9840\n",
      "Epoch 790/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7010 - accuracy: 0.9826 - val_loss: 0.7079 - val_accuracy: 0.9900\n",
      "Epoch 791/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.7068 - accuracy: 0.9900 - val_loss: 0.6939 - val_accuracy: 0.9980\n",
      "Epoch 792/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6892 - accuracy: 0.9848 - val_loss: 0.7153 - val_accuracy: 0.9980\n",
      "Epoch 793/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6727 - accuracy: 0.9583 - val_loss: 0.7149 - val_accuracy: 0.9780\n",
      "Epoch 794/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7057 - accuracy: 0.9541 - val_loss: 0.6968 - val_accuracy: 1.0000\n",
      "Epoch 795/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7284 - accuracy: 0.8558 - val_loss: 0.6897 - val_accuracy: 0.9980\n",
      "Epoch 796/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7001 - accuracy: 0.9705 - val_loss: 0.6817 - val_accuracy: 1.0000\n",
      "Epoch 797/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7045 - accuracy: 0.9543 - val_loss: 0.6845 - val_accuracy: 1.0000\n",
      "Epoch 798/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7024 - accuracy: 0.9557 - val_loss: 0.6795 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 799/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6963 - accuracy: 0.9973 - val_loss: 0.7052 - val_accuracy: 0.8840\n",
      "Epoch 800/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.7134 - accuracy: 0.8648 - val_loss: 0.6788 - val_accuracy: 1.0000\n",
      "Epoch 801/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6880 - accuracy: 0.9578 - val_loss: 0.6816 - val_accuracy: 1.0000\n",
      "Epoch 802/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.6894 - accuracy: 0.9858 - val_loss: 0.6819 - val_accuracy: 1.0000\n",
      "Epoch 803/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6947 - accuracy: 0.9872 - val_loss: 0.6758 - val_accuracy: 1.0000\n",
      "Epoch 804/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6912 - accuracy: 0.9893 - val_loss: 0.6919 - val_accuracy: 0.9760\n",
      "Epoch 805/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6837 - accuracy: 0.8880 - val_loss: 0.6782 - val_accuracy: 1.0000\n",
      "Epoch 806/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6759 - accuracy: 0.9381 - val_loss: 0.6941 - val_accuracy: 0.8080\n",
      "Epoch 807/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6864 - accuracy: 0.9014 - val_loss: 0.6819 - val_accuracy: 1.0000\n",
      "Epoch 808/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6862 - accuracy: 0.9890 - val_loss: 0.6775 - val_accuracy: 1.0000\n",
      "Epoch 809/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6521 - accuracy: 0.9205 - val_loss: 0.6773 - val_accuracy: 0.9900\n",
      "Epoch 810/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6725 - accuracy: 0.9733 - val_loss: 0.6905 - val_accuracy: 0.9920\n",
      "Epoch 811/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.9370 - val_loss: 0.6801 - val_accuracy: 0.9100\n",
      "Epoch 812/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.9594 - val_loss: 0.6754 - val_accuracy: 1.0000\n",
      "Epoch 813/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6679 - accuracy: 0.9584 - val_loss: 0.6855 - val_accuracy: 0.8200\n",
      "Epoch 814/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6918 - accuracy: 0.8826 - val_loss: 0.7212 - val_accuracy: 0.8000\n",
      "Epoch 815/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6909 - accuracy: 0.8497 - val_loss: 0.6943 - val_accuracy: 0.8000\n",
      "Epoch 816/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6648 - accuracy: 0.9278 - val_loss: 0.6701 - val_accuracy: 0.9760\n",
      "Epoch 817/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6780 - accuracy: 0.9015 - val_loss: 0.6725 - val_accuracy: 1.0000\n",
      "Epoch 818/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6942 - accuracy: 0.9582 - val_loss: 0.6764 - val_accuracy: 0.9980\n",
      "Epoch 819/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6984 - accuracy: 0.9900 - val_loss: 0.7019 - val_accuracy: 0.8000\n",
      "Epoch 820/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6876 - accuracy: 0.8856 - val_loss: 0.6662 - val_accuracy: 0.9920\n",
      "Epoch 821/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6964 - accuracy: 0.9359 - val_loss: 0.6693 - val_accuracy: 0.9900\n",
      "Epoch 822/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6688 - accuracy: 0.9563 - val_loss: 0.6736 - val_accuracy: 0.9980\n",
      "Epoch 823/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6794 - accuracy: 0.9935 - val_loss: 0.6609 - val_accuracy: 1.0000\n",
      "Epoch 824/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6557 - accuracy: 0.9973 - val_loss: 0.6579 - val_accuracy: 1.0000\n",
      "Epoch 825/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6750 - accuracy: 0.9893 - val_loss: 0.6603 - val_accuracy: 1.0000\n",
      "Epoch 826/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6813 - accuracy: 0.9854 - val_loss: 0.6599 - val_accuracy: 0.9960\n",
      "Epoch 827/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6603 - accuracy: 0.9886 - val_loss: 0.6703 - val_accuracy: 0.9940\n",
      "Epoch 828/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6710 - accuracy: 0.9960 - val_loss: 0.6706 - val_accuracy: 0.9800\n",
      "Epoch 829/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6759 - accuracy: 0.9621 - val_loss: 0.6681 - val_accuracy: 0.9960\n",
      "Epoch 830/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6628 - accuracy: 0.8908 - val_loss: 0.6917 - val_accuracy: 0.9080\n",
      "Epoch 831/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6993 - accuracy: 0.9486 - val_loss: 0.6782 - val_accuracy: 0.9560\n",
      "Epoch 832/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6689 - accuracy: 0.9677 - val_loss: 0.6546 - val_accuracy: 1.0000\n",
      "Epoch 833/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6763 - accuracy: 0.9920 - val_loss: 0.6523 - val_accuracy: 1.0000\n",
      "Epoch 834/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6581 - accuracy: 0.9769 - val_loss: 0.6612 - val_accuracy: 1.0000\n",
      "Epoch 835/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6628 - accuracy: 0.9250 - val_loss: 0.6518 - val_accuracy: 1.0000\n",
      "Epoch 836/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6528 - accuracy: 0.9976 - val_loss: 0.6608 - val_accuracy: 0.9820\n",
      "Epoch 837/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6830 - accuracy: 0.9666 - val_loss: 0.6505 - val_accuracy: 1.0000\n",
      "Epoch 838/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6496 - accuracy: 0.9807 - val_loss: 0.6559 - val_accuracy: 0.9980\n",
      "Epoch 839/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6893 - accuracy: 0.8999 - val_loss: 0.6514 - val_accuracy: 1.0000\n",
      "Epoch 840/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6618 - accuracy: 0.9525 - val_loss: 0.6495 - val_accuracy: 0.9920\n",
      "Epoch 841/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6532 - accuracy: 0.9642 - val_loss: 0.6519 - val_accuracy: 1.0000\n",
      "Epoch 842/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6418 - accuracy: 0.9957 - val_loss: 0.6497 - val_accuracy: 1.0000\n",
      "Epoch 843/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6655 - accuracy: 0.9883 - val_loss: 0.6611 - val_accuracy: 1.0000\n",
      "Epoch 844/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6658 - accuracy: 0.9629 - val_loss: 0.6677 - val_accuracy: 0.9560\n",
      "Epoch 845/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6613 - accuracy: 0.9301 - val_loss: 0.6689 - val_accuracy: 0.9680\n",
      "Epoch 846/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6724 - accuracy: 0.9632 - val_loss: 0.6581 - val_accuracy: 0.9760\n",
      "Epoch 847/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6553 - accuracy: 0.9422 - val_loss: 0.6492 - val_accuracy: 1.0000\n",
      "Epoch 848/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6555 - accuracy: 0.9786 - val_loss: 0.6610 - val_accuracy: 0.9700\n",
      "Epoch 849/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6594 - accuracy: 0.9490 - val_loss: 0.6510 - val_accuracy: 0.9780\n",
      "Epoch 850/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6518 - accuracy: 1.0000 - val_loss: 0.6420 - val_accuracy: 0.9960\n",
      "Epoch 851/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6216 - accuracy: 1.0000 - val_loss: 0.6482 - val_accuracy: 1.0000\n",
      "Epoch 852/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6396 - accuracy: 0.9841 - val_loss: 0.6436 - val_accuracy: 1.0000\n",
      "Epoch 853/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6496 - accuracy: 0.9680 - val_loss: 0.6438 - val_accuracy: 1.0000\n",
      "Epoch 854/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6669 - accuracy: 0.9906 - val_loss: 0.6351 - val_accuracy: 1.0000\n",
      "Epoch 855/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6489 - accuracy: 1.0000 - val_loss: 0.6440 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 856/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6425 - accuracy: 0.9755 - val_loss: 0.6423 - val_accuracy: 1.0000\n",
      "Epoch 857/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6518 - accuracy: 0.9449 - val_loss: 0.6400 - val_accuracy: 1.0000\n",
      "Epoch 858/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6551 - accuracy: 0.9866 - val_loss: 0.6455 - val_accuracy: 0.9880\n",
      "Epoch 859/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6455 - accuracy: 0.9881 - val_loss: 0.6546 - val_accuracy: 0.9740\n",
      "Epoch 860/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6444 - accuracy: 0.9597 - val_loss: 0.6378 - val_accuracy: 1.0000\n",
      "Epoch 861/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6351 - accuracy: 0.9461 - val_loss: 0.6461 - val_accuracy: 0.9340\n",
      "Epoch 862/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6701 - accuracy: 0.9564 - val_loss: 0.6518 - val_accuracy: 0.9900\n",
      "Epoch 863/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6517 - accuracy: 1.0000 - val_loss: 0.6296 - val_accuracy: 1.0000\n",
      "Epoch 864/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6574 - accuracy: 0.9875 - val_loss: 0.6380 - val_accuracy: 1.0000\n",
      "Epoch 865/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6256 - accuracy: 0.9964 - val_loss: 0.6527 - val_accuracy: 0.9740\n",
      "Epoch 866/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6457 - accuracy: 0.9923 - val_loss: 0.6275 - val_accuracy: 1.0000\n",
      "Epoch 867/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6345 - accuracy: 0.9934 - val_loss: 0.6512 - val_accuracy: 0.8220\n",
      "Epoch 868/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.6154 - accuracy: 0.9558 - val_loss: 0.6287 - val_accuracy: 0.9960\n",
      "Epoch 869/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6394 - accuracy: 0.9962 - val_loss: 0.6329 - val_accuracy: 1.0000\n",
      "Epoch 870/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6422 - accuracy: 0.9956 - val_loss: 0.6278 - val_accuracy: 1.0000\n",
      "Epoch 871/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6271 - accuracy: 0.9945 - val_loss: 0.6392 - val_accuracy: 0.9640\n",
      "Epoch 872/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6545 - accuracy: 0.9570 - val_loss: 0.6466 - val_accuracy: 0.9720\n",
      "Epoch 873/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6712 - accuracy: 0.9890 - val_loss: 0.6638 - val_accuracy: 0.8020\n",
      "Epoch 874/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6595 - accuracy: 0.9136 - val_loss: 0.6512 - val_accuracy: 0.8240\n",
      "Epoch 875/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6231 - accuracy: 0.9094 - val_loss: 0.6364 - val_accuracy: 0.9720\n",
      "Epoch 876/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.9661 - val_loss: 0.6489 - val_accuracy: 0.9560\n",
      "Epoch 877/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6334 - accuracy: 0.9837 - val_loss: 0.6190 - val_accuracy: 1.0000\n",
      "Epoch 878/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6388 - accuracy: 0.9523 - val_loss: 0.6245 - val_accuracy: 1.0000\n",
      "Epoch 879/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6492 - accuracy: 0.9953 - val_loss: 0.6187 - val_accuracy: 1.0000\n",
      "Epoch 880/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6135 - accuracy: 0.9972 - val_loss: 0.6194 - val_accuracy: 1.0000\n",
      "Epoch 881/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6241 - accuracy: 0.9877 - val_loss: 0.6446 - val_accuracy: 1.0000\n",
      "Epoch 882/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6361 - accuracy: 0.9634 - val_loss: 0.6194 - val_accuracy: 1.0000\n",
      "Epoch 883/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6066 - accuracy: 0.9890 - val_loss: 0.6333 - val_accuracy: 0.9980\n",
      "Epoch 884/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6046 - accuracy: 0.9901 - val_loss: 0.6519 - val_accuracy: 0.8020\n",
      "Epoch 885/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6395 - accuracy: 0.8989 - val_loss: 0.6241 - val_accuracy: 1.0000\n",
      "Epoch 886/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6440 - accuracy: 0.9984 - val_loss: 0.6487 - val_accuracy: 0.8500\n",
      "Epoch 887/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6421 - accuracy: 0.9248 - val_loss: 0.6202 - val_accuracy: 0.9940\n",
      "Epoch 888/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6472 - accuracy: 0.9603 - val_loss: 0.6340 - val_accuracy: 0.8540\n",
      "Epoch 889/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6205 - accuracy: 0.9297 - val_loss: 0.6244 - val_accuracy: 0.9940\n",
      "Epoch 890/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6107 - accuracy: 0.9948 - val_loss: 0.6156 - val_accuracy: 0.9960\n",
      "Epoch 891/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6369 - accuracy: 1.0000 - val_loss: 0.6132 - val_accuracy: 1.0000\n",
      "Epoch 892/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6081 - accuracy: 1.0000 - val_loss: 0.6536 - val_accuracy: 0.8000\n",
      "Epoch 893/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6044 - accuracy: 0.8857 - val_loss: 0.6272 - val_accuracy: 0.9980\n",
      "Epoch 894/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6177 - accuracy: 0.9461 - val_loss: 0.6106 - val_accuracy: 1.0000\n",
      "Epoch 895/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6153 - accuracy: 0.9780 - val_loss: 0.6087 - val_accuracy: 1.0000\n",
      "Epoch 896/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6118 - accuracy: 0.9903 - val_loss: 0.6129 - val_accuracy: 1.0000\n",
      "Epoch 897/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6181 - accuracy: 1.0000 - val_loss: 0.6075 - val_accuracy: 1.0000\n",
      "Epoch 898/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6020 - accuracy: 0.9962 - val_loss: 0.6189 - val_accuracy: 0.9940\n",
      "Epoch 899/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6046 - accuracy: 0.9829 - val_loss: 0.6167 - val_accuracy: 0.9980\n",
      "Epoch 900/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6365 - accuracy: 0.9590 - val_loss: 0.6064 - val_accuracy: 1.0000\n",
      "Epoch 901/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6199 - accuracy: 0.9790 - val_loss: 0.6232 - val_accuracy: 0.8980\n",
      "Epoch 902/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6403 - accuracy: 0.9586 - val_loss: 0.6098 - val_accuracy: 1.0000\n",
      "Epoch 903/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6150 - accuracy: 1.0000 - val_loss: 0.6224 - val_accuracy: 0.9880\n",
      "Epoch 904/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6051 - accuracy: 0.9415 - val_loss: 0.6000 - val_accuracy: 1.0000\n",
      "Epoch 905/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6180 - accuracy: 0.9734 - val_loss: 0.6107 - val_accuracy: 0.9980\n",
      "Epoch 906/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6226 - accuracy: 0.9987 - val_loss: 0.6209 - val_accuracy: 0.9980\n",
      "Epoch 907/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6138 - accuracy: 0.9807 - val_loss: 0.6218 - val_accuracy: 0.9980\n",
      "Epoch 908/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6261 - accuracy: 0.9945 - val_loss: 0.5956 - val_accuracy: 1.0000\n",
      "Epoch 909/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5951 - accuracy: 0.9992 - val_loss: 0.5947 - val_accuracy: 1.0000\n",
      "Epoch 910/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6061 - accuracy: 0.9418 - val_loss: 0.6096 - val_accuracy: 1.0000\n",
      "Epoch 911/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6260 - accuracy: 0.9893 - val_loss: 0.6356 - val_accuracy: 0.9400\n",
      "Epoch 912/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6089 - accuracy: 0.9195 - val_loss: 0.6014 - val_accuracy: 0.9960\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 913/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6218 - accuracy: 0.9972 - val_loss: 0.5979 - val_accuracy: 1.0000\n",
      "Epoch 914/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5917 - accuracy: 1.0000 - val_loss: 0.6063 - val_accuracy: 1.0000\n",
      "Epoch 915/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6004 - accuracy: 0.9994 - val_loss: 0.6015 - val_accuracy: 1.0000\n",
      "Epoch 916/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5855 - accuracy: 1.0000 - val_loss: 0.6196 - val_accuracy: 0.9500\n",
      "Epoch 917/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6327 - accuracy: 0.8942 - val_loss: 0.6010 - val_accuracy: 1.0000\n",
      "Epoch 918/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6141 - accuracy: 0.9908 - val_loss: 0.6317 - val_accuracy: 0.9500\n",
      "Epoch 919/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6451 - accuracy: 0.9670 - val_loss: 0.5886 - val_accuracy: 1.0000\n",
      "Epoch 920/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5997 - accuracy: 0.9984 - val_loss: 0.5961 - val_accuracy: 1.0000\n",
      "Epoch 921/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6105 - accuracy: 0.9957 - val_loss: 0.6038 - val_accuracy: 1.0000\n",
      "Epoch 922/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5871 - accuracy: 1.0000 - val_loss: 0.5942 - val_accuracy: 1.0000\n",
      "Epoch 923/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5827 - accuracy: 0.9498 - val_loss: 0.5956 - val_accuracy: 1.0000\n",
      "Epoch 924/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6075 - accuracy: 0.9604 - val_loss: 0.6249 - val_accuracy: 0.8580\n",
      "Epoch 925/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6156 - accuracy: 0.9458 - val_loss: 0.5883 - val_accuracy: 1.0000\n",
      "Epoch 926/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5772 - accuracy: 0.9813 - val_loss: 0.5860 - val_accuracy: 1.0000\n",
      "Epoch 927/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6169 - accuracy: 0.9954 - val_loss: 0.6062 - val_accuracy: 0.9820\n",
      "Epoch 928/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6028 - accuracy: 0.9913 - val_loss: 0.6015 - val_accuracy: 0.9880\n",
      "Epoch 929/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6156 - accuracy: 0.9777 - val_loss: 0.5885 - val_accuracy: 1.0000\n",
      "Epoch 930/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5888 - accuracy: 0.9972 - val_loss: 0.5868 - val_accuracy: 1.0000\n",
      "Epoch 931/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5826 - accuracy: 0.9984 - val_loss: 0.5904 - val_accuracy: 1.0000\n",
      "Epoch 932/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6050 - accuracy: 0.9893 - val_loss: 0.5999 - val_accuracy: 1.0000\n",
      "Epoch 933/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6066 - accuracy: 0.9703 - val_loss: 0.6507 - val_accuracy: 0.8020\n",
      "Epoch 934/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6236 - accuracy: 0.8565 - val_loss: 0.5916 - val_accuracy: 0.9940\n",
      "Epoch 935/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5965 - accuracy: 1.0000 - val_loss: 0.5897 - val_accuracy: 0.9940\n",
      "Epoch 936/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5949 - accuracy: 0.9915 - val_loss: 0.5815 - val_accuracy: 1.0000\n",
      "Epoch 937/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6276 - accuracy: 0.9938 - val_loss: 0.5913 - val_accuracy: 1.0000\n",
      "Epoch 938/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5891 - accuracy: 0.9962 - val_loss: 0.5878 - val_accuracy: 1.0000\n",
      "Epoch 939/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5919 - accuracy: 0.9876 - val_loss: 0.5946 - val_accuracy: 1.0000\n",
      "Epoch 940/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6041 - accuracy: 0.9991 - val_loss: 0.5998 - val_accuracy: 0.8880\n",
      "Epoch 941/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5688 - accuracy: 0.9826 - val_loss: 0.6090 - val_accuracy: 0.9680\n",
      "Epoch 942/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5978 - accuracy: 0.9808 - val_loss: 0.5962 - val_accuracy: 0.9860\n",
      "Epoch 943/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.6359 - accuracy: 0.9342 - val_loss: 0.5962 - val_accuracy: 0.9340\n",
      "Epoch 944/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6143 - accuracy: 0.9522 - val_loss: 0.5833 - val_accuracy: 1.0000\n",
      "Epoch 945/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6003 - accuracy: 0.9842 - val_loss: 0.5921 - val_accuracy: 1.0000\n",
      "Epoch 946/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5826 - accuracy: 0.9991 - val_loss: 0.5982 - val_accuracy: 0.9260\n",
      "Epoch 947/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5536 - accuracy: 0.9941 - val_loss: 0.5720 - val_accuracy: 1.0000\n",
      "Epoch 948/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.5798 - accuracy: 0.9996 - val_loss: 0.5709 - val_accuracy: 1.0000\n",
      "Epoch 949/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5589 - accuracy: 1.0000 - val_loss: 0.5760 - val_accuracy: 1.0000\n",
      "Epoch 950/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5683 - accuracy: 0.9938 - val_loss: 0.5700 - val_accuracy: 1.0000\n",
      "Epoch 951/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5715 - accuracy: 0.9978 - val_loss: 0.5804 - val_accuracy: 0.9980\n",
      "Epoch 952/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.6220 - accuracy: 0.9596 - val_loss: 0.5772 - val_accuracy: 0.9960\n",
      "Epoch 953/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5841 - accuracy: 0.9841 - val_loss: 0.5762 - val_accuracy: 0.9940\n",
      "Epoch 954/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5836 - accuracy: 1.0000 - val_loss: 0.5677 - val_accuracy: 1.0000\n",
      "Epoch 955/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5818 - accuracy: 1.0000 - val_loss: 0.5659 - val_accuracy: 1.0000\n",
      "Epoch 956/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.5580 - accuracy: 1.0000 - val_loss: 0.5712 - val_accuracy: 0.9980\n",
      "Epoch 957/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.5944 - accuracy: 0.9739 - val_loss: 0.6509 - val_accuracy: 0.7880\n",
      "Epoch 958/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.6541 - accuracy: 0.8778 - val_loss: 0.5879 - val_accuracy: 0.9700\n",
      "Epoch 959/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.5754 - accuracy: 0.9744 - val_loss: 0.5700 - val_accuracy: 1.0000\n",
      "Epoch 960/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5690 - accuracy: 0.9961 - val_loss: 0.5680 - val_accuracy: 1.0000\n",
      "Epoch 961/1000\n",
      "32/32 [==============================] - 0s 4ms/step - loss: 0.5611 - accuracy: 1.0000 - val_loss: 0.5666 - val_accuracy: 1.0000\n",
      "Epoch 962/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5745 - accuracy: 1.0000 - val_loss: 0.5947 - val_accuracy: 0.9800\n",
      "Epoch 963/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5857 - accuracy: 1.0000 - val_loss: 0.5707 - val_accuracy: 0.9960\n",
      "Epoch 964/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5795 - accuracy: 0.9944 - val_loss: 0.5624 - val_accuracy: 1.0000\n",
      "Epoch 965/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5499 - accuracy: 0.9998 - val_loss: 0.5662 - val_accuracy: 1.0000\n",
      "Epoch 966/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5667 - accuracy: 0.9925 - val_loss: 0.5881 - val_accuracy: 0.8700\n",
      "Epoch 967/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5846 - accuracy: 0.9284 - val_loss: 0.5625 - val_accuracy: 1.0000\n",
      "Epoch 968/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5675 - accuracy: 1.0000 - val_loss: 0.5615 - val_accuracy: 1.0000\n",
      "Epoch 969/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5508 - accuracy: 0.9638 - val_loss: 0.5565 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 970/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5471 - accuracy: 0.9904 - val_loss: 0.6185 - val_accuracy: 0.8000\n",
      "Epoch 971/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5795 - accuracy: 0.9055 - val_loss: 0.5646 - val_accuracy: 1.0000\n",
      "Epoch 972/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5399 - accuracy: 1.0000 - val_loss: 0.5731 - val_accuracy: 1.0000\n",
      "Epoch 973/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5657 - accuracy: 0.9733 - val_loss: 0.5872 - val_accuracy: 0.9220\n",
      "Epoch 974/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.6171 - accuracy: 0.9248 - val_loss: 0.5838 - val_accuracy: 0.9020\n",
      "Epoch 975/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5729 - accuracy: 0.9697 - val_loss: 0.5729 - val_accuracy: 0.9780\n",
      "Epoch 976/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5372 - accuracy: 0.9756 - val_loss: 0.5540 - val_accuracy: 1.0000\n",
      "Epoch 977/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5633 - accuracy: 0.9834 - val_loss: 0.5564 - val_accuracy: 1.0000\n",
      "Epoch 978/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5703 - accuracy: 0.9675 - val_loss: 0.5582 - val_accuracy: 1.0000\n",
      "Epoch 979/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5623 - accuracy: 0.9801 - val_loss: 0.5711 - val_accuracy: 1.0000\n",
      "Epoch 980/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5640 - accuracy: 1.0000 - val_loss: 0.5638 - val_accuracy: 1.0000\n",
      "Epoch 981/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5914 - accuracy: 1.0000 - val_loss: 0.5478 - val_accuracy: 1.0000\n",
      "Epoch 982/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5722 - accuracy: 0.9984 - val_loss: 0.5519 - val_accuracy: 1.0000\n",
      "Epoch 983/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5557 - accuracy: 1.0000 - val_loss: 0.5532 - val_accuracy: 1.0000\n",
      "Epoch 984/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5498 - accuracy: 1.0000 - val_loss: 0.5541 - val_accuracy: 1.0000\n",
      "Epoch 985/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5606 - accuracy: 0.9916 - val_loss: 0.5656 - val_accuracy: 0.9820\n",
      "Epoch 986/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5637 - accuracy: 1.0000 - val_loss: 0.5514 - val_accuracy: 1.0000\n",
      "Epoch 987/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5740 - accuracy: 0.9523 - val_loss: 0.5468 - val_accuracy: 1.0000\n",
      "Epoch 988/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5582 - accuracy: 0.9863 - val_loss: 0.5757 - val_accuracy: 0.9860\n",
      "Epoch 989/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5709 - accuracy: 0.9902 - val_loss: 0.5670 - val_accuracy: 1.0000\n",
      "Epoch 990/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5687 - accuracy: 0.9974 - val_loss: 0.5452 - val_accuracy: 1.0000\n",
      "Epoch 991/1000\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5768 - accuracy: 0.9716 - val_loss: 0.5497 - val_accuracy: 1.0000\n",
      "Epoch 992/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5496 - accuracy: 1.0000 - val_loss: 0.5504 - val_accuracy: 1.0000\n",
      "Epoch 993/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5562 - accuracy: 1.0000 - val_loss: 0.5610 - val_accuracy: 1.0000\n",
      "Epoch 994/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5929 - accuracy: 0.9713 - val_loss: 0.5481 - val_accuracy: 1.0000\n",
      "Epoch 995/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5430 - accuracy: 0.9985 - val_loss: 0.5728 - val_accuracy: 0.9980\n",
      "Epoch 996/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5492 - accuracy: 1.0000 - val_loss: 0.5488 - val_accuracy: 0.9960\n",
      "Epoch 997/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5579 - accuracy: 0.9992 - val_loss: 0.5761 - val_accuracy: 0.9940\n",
      "Epoch 998/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5625 - accuracy: 0.9831 - val_loss: 0.5463 - val_accuracy: 1.0000\n",
      "Epoch 999/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5426 - accuracy: 1.0000 - val_loss: 0.5646 - val_accuracy: 0.9280\n",
      "Epoch 1000/1000\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.5746 - accuracy: 0.9116 - val_loss: 0.5415 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fba6bedf790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=1000, batch_size=2**4, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 661us/step - loss: 0.5415 - accuracy: 1.0000\n",
      "Test score: 1.00\n"
     ]
    }
   ],
   "source": [
    "print(\"Test score: {:.2f}\".format(model.evaluate(X_test, y_test)[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "pipe = make_pipeline(LogisticRegression(penalty=\"elasticnet\", solver = \"saga\", max_iter=300))\n",
    "\n",
    "param_grid = {'logisticregression__C' : [0.01, 0.1, 1, 10, 100],\n",
    "             'logisticregression__l1_ratio' : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kimyj/.conda/envs/py37_yj/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('logisticregression',\n",
       "                                        LogisticRegression(max_iter=300,\n",
       "                                                           penalty='elasticnet',\n",
       "                                                           solver='saga'))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'logisticregression__C': [0.01, 0.1, 1, 10, 100],\n",
       "                         'logisticregression__l1_ratio': [0, 0.1, 0.2, 0.3, 0.4,\n",
       "                                                          0.5, 0.6, 0.7, 0.8,\n",
       "                                                          0.9, 1.0]})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "grid = GridSearchCV(pipe, param_grid, cv=5, n_jobs=-1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best:\n",
      " Pipeline(steps=[('logisticregression',\n",
      "                 LogisticRegression(C=0.01, l1_ratio=0, max_iter=300,\n",
      "                                    penalty='elasticnet', solver='saga'))])\n"
     ]
    }
   ],
   "source": [
    "print(\"Best:\\n\", grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 1.00\n",
      "Test score: 1.00\n"
     ]
    }
   ],
   "source": [
    "print(\"Train score: {:.2f}\".format(grid.score(X_train, y_train)))\n",
    "print(\"Test score: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.114061</td>\n",
       "      <td>0.12717</td>\n",
       "      <td>0.134688</td>\n",
       "      <td>0.139668</td>\n",
       "      <td>0.120151</td>\n",
       "      <td>0.119518</td>\n",
       "      <td>0.109117</td>\n",
       "      <td>0.10039</td>\n",
       "      <td>0.126077</td>\n",
       "      <td>0.107548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12394</td>\n",
       "      <td>0.115765</td>\n",
       "      <td>0.133173</td>\n",
       "      <td>0.132444</td>\n",
       "      <td>0.124309</td>\n",
       "      <td>0.117002</td>\n",
       "      <td>0.135691</td>\n",
       "      <td>0.13368</td>\n",
       "      <td>0.111038</td>\n",
       "      <td>0.0953297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.00629839</td>\n",
       "      <td>0.0285217</td>\n",
       "      <td>0.00631932</td>\n",
       "      <td>0.0500365</td>\n",
       "      <td>0.0165253</td>\n",
       "      <td>0.0238924</td>\n",
       "      <td>0.0220287</td>\n",
       "      <td>0.0178585</td>\n",
       "      <td>0.0458849</td>\n",
       "      <td>0.0115591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0213808</td>\n",
       "      <td>0.0247112</td>\n",
       "      <td>0.0137031</td>\n",
       "      <td>0.00647662</td>\n",
       "      <td>0.0210253</td>\n",
       "      <td>0.0146541</td>\n",
       "      <td>0.0220283</td>\n",
       "      <td>0.00318526</td>\n",
       "      <td>0.017623</td>\n",
       "      <td>0.019829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.117328</td>\n",
       "      <td>0.153474</td>\n",
       "      <td>0.147664</td>\n",
       "      <td>0.133752</td>\n",
       "      <td>0.126836</td>\n",
       "      <td>0.141874</td>\n",
       "      <td>0.164681</td>\n",
       "      <td>0.121321</td>\n",
       "      <td>0.00295672</td>\n",
       "      <td>0.00313444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00287223</td>\n",
       "      <td>0.00245948</td>\n",
       "      <td>0.00354805</td>\n",
       "      <td>0.00311522</td>\n",
       "      <td>0.00260725</td>\n",
       "      <td>0.00269427</td>\n",
       "      <td>0.00260811</td>\n",
       "      <td>0.00247831</td>\n",
       "      <td>0.00212536</td>\n",
       "      <td>0.00187578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.0403628</td>\n",
       "      <td>0.0377012</td>\n",
       "      <td>0.0403304</td>\n",
       "      <td>0.0552648</td>\n",
       "      <td>0.0400642</td>\n",
       "      <td>0.0349372</td>\n",
       "      <td>0.0140789</td>\n",
       "      <td>0.014501</td>\n",
       "      <td>0.000679502</td>\n",
       "      <td>0.000408506</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000680819</td>\n",
       "      <td>0.00059533</td>\n",
       "      <td>0.00128078</td>\n",
       "      <td>0.000739878</td>\n",
       "      <td>0.00047865</td>\n",
       "      <td>0.000424656</td>\n",
       "      <td>0.000372479</td>\n",
       "      <td>0.000425708</td>\n",
       "      <td>0.000293079</td>\n",
       "      <td>0.000223755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_logisticregression__C</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_logisticregression__l1_ratio</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>{'logisticregression__C': 0.01, 'logisticregre...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "      <td>{'logisticregression__C': 100, 'logisticregres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00489898</td>\n",
       "      <td>0.00748331</td>\n",
       "      <td>0.00979796</td>\n",
       "      <td>0.0172047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   0   \\\n",
       "mean_fit_time                                                                0.114061   \n",
       "std_fit_time                                                               0.00629839   \n",
       "mean_score_time                                                              0.117328   \n",
       "std_score_time                                                              0.0403628   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                  0   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   1   \\\n",
       "mean_fit_time                                                                 0.12717   \n",
       "std_fit_time                                                                0.0285217   \n",
       "mean_score_time                                                              0.153474   \n",
       "std_score_time                                                              0.0377012   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.1   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   2   \\\n",
       "mean_fit_time                                                                0.134688   \n",
       "std_fit_time                                                               0.00631932   \n",
       "mean_score_time                                                              0.147664   \n",
       "std_score_time                                                              0.0403304   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.2   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   3   \\\n",
       "mean_fit_time                                                                0.139668   \n",
       "std_fit_time                                                                0.0500365   \n",
       "mean_score_time                                                              0.133752   \n",
       "std_score_time                                                              0.0552648   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.3   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                0.99   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                0.99   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                 0.996   \n",
       "std_test_score                                                             0.00489898   \n",
       "rank_test_score                                                                    48   \n",
       "\n",
       "                                                                                   4   \\\n",
       "mean_fit_time                                                                0.120151   \n",
       "std_fit_time                                                                0.0165253   \n",
       "mean_score_time                                                              0.126836   \n",
       "std_score_time                                                              0.0400642   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.4   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                0.93   \n",
       "split1_test_score                                                                0.93   \n",
       "split2_test_score                                                                0.94   \n",
       "split3_test_score                                                                0.94   \n",
       "split4_test_score                                                                0.95   \n",
       "mean_test_score                                                                 0.938   \n",
       "std_test_score                                                             0.00748331   \n",
       "rank_test_score                                                                    51   \n",
       "\n",
       "                                                                                   5   \\\n",
       "mean_fit_time                                                                0.119518   \n",
       "std_fit_time                                                                0.0238924   \n",
       "mean_score_time                                                              0.141874   \n",
       "std_score_time                                                              0.0349372   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.5   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                0.97   \n",
       "split1_test_score                                                                0.97   \n",
       "split2_test_score                                                                0.99   \n",
       "split3_test_score                                                                0.97   \n",
       "split4_test_score                                                                0.96   \n",
       "mean_test_score                                                                 0.972   \n",
       "std_test_score                                                             0.00979796   \n",
       "rank_test_score                                                                    49   \n",
       "\n",
       "                                                                                   6   \\\n",
       "mean_fit_time                                                                0.109117   \n",
       "std_fit_time                                                                0.0220287   \n",
       "mean_score_time                                                              0.164681   \n",
       "std_score_time                                                              0.0140789   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.6   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                0.93   \n",
       "split1_test_score                                                                0.96   \n",
       "split2_test_score                                                                0.98   \n",
       "split3_test_score                                                                0.97   \n",
       "split4_test_score                                                                0.95   \n",
       "mean_test_score                                                                 0.958   \n",
       "std_test_score                                                              0.0172047   \n",
       "rank_test_score                                                                    50   \n",
       "\n",
       "                                                                                   7   \\\n",
       "mean_fit_time                                                                 0.10039   \n",
       "std_fit_time                                                                0.0178585   \n",
       "mean_score_time                                                              0.121321   \n",
       "std_score_time                                                               0.014501   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.7   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                 0.6   \n",
       "split1_test_score                                                                 0.6   \n",
       "split2_test_score                                                                 0.6   \n",
       "split3_test_score                                                                 0.6   \n",
       "split4_test_score                                                                 0.6   \n",
       "mean_test_score                                                                   0.6   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                    52   \n",
       "\n",
       "                                                                                   8   \\\n",
       "mean_fit_time                                                                0.126077   \n",
       "std_fit_time                                                                0.0458849   \n",
       "mean_score_time                                                            0.00295672   \n",
       "std_score_time                                                            0.000679502   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.8   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                 0.6   \n",
       "split1_test_score                                                                 0.6   \n",
       "split2_test_score                                                                 0.6   \n",
       "split3_test_score                                                                 0.6   \n",
       "split4_test_score                                                                 0.6   \n",
       "mean_test_score                                                                   0.6   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                    52   \n",
       "\n",
       "                                                                                   9   \\\n",
       "mean_fit_time                                                                0.107548   \n",
       "std_fit_time                                                                0.0115591   \n",
       "mean_score_time                                                            0.00313444   \n",
       "std_score_time                                                            0.000408506   \n",
       "param_logisticregression__C                                                      0.01   \n",
       "param_logisticregression__l1_ratio                                                0.9   \n",
       "params                              {'logisticregression__C': 0.01, 'logisticregre...   \n",
       "split0_test_score                                                                 0.4   \n",
       "split1_test_score                                                                 0.4   \n",
       "split2_test_score                                                                 0.4   \n",
       "split3_test_score                                                                 0.4   \n",
       "split4_test_score                                                                 0.4   \n",
       "mean_test_score                                                                   0.4   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                    54   \n",
       "\n",
       "                                    ...  \\\n",
       "mean_fit_time                       ...   \n",
       "std_fit_time                        ...   \n",
       "mean_score_time                     ...   \n",
       "std_score_time                      ...   \n",
       "param_logisticregression__C         ...   \n",
       "param_logisticregression__l1_ratio  ...   \n",
       "params                              ...   \n",
       "split0_test_score                   ...   \n",
       "split1_test_score                   ...   \n",
       "split2_test_score                   ...   \n",
       "split3_test_score                   ...   \n",
       "split4_test_score                   ...   \n",
       "mean_test_score                     ...   \n",
       "std_test_score                      ...   \n",
       "rank_test_score                     ...   \n",
       "\n",
       "                                                                                   45  \\\n",
       "mean_fit_time                                                                 0.12394   \n",
       "std_fit_time                                                                0.0213808   \n",
       "mean_score_time                                                            0.00287223   \n",
       "std_score_time                                                            0.000680819   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.1   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   46  \\\n",
       "mean_fit_time                                                                0.115765   \n",
       "std_fit_time                                                                0.0247112   \n",
       "mean_score_time                                                            0.00245948   \n",
       "std_score_time                                                             0.00059533   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.2   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   47  \\\n",
       "mean_fit_time                                                                0.133173   \n",
       "std_fit_time                                                                0.0137031   \n",
       "mean_score_time                                                            0.00354805   \n",
       "std_score_time                                                             0.00128078   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.3   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   48  \\\n",
       "mean_fit_time                                                                0.132444   \n",
       "std_fit_time                                                               0.00647662   \n",
       "mean_score_time                                                            0.00311522   \n",
       "std_score_time                                                            0.000739878   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.4   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   49  \\\n",
       "mean_fit_time                                                                0.124309   \n",
       "std_fit_time                                                                0.0210253   \n",
       "mean_score_time                                                            0.00260725   \n",
       "std_score_time                                                             0.00047865   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.5   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   50  \\\n",
       "mean_fit_time                                                                0.117002   \n",
       "std_fit_time                                                                0.0146541   \n",
       "mean_score_time                                                            0.00269427   \n",
       "std_score_time                                                            0.000424656   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.6   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   51  \\\n",
       "mean_fit_time                                                                0.135691   \n",
       "std_fit_time                                                                0.0220283   \n",
       "mean_score_time                                                            0.00260811   \n",
       "std_score_time                                                            0.000372479   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.7   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   52  \\\n",
       "mean_fit_time                                                                 0.13368   \n",
       "std_fit_time                                                               0.00318526   \n",
       "mean_score_time                                                            0.00247831   \n",
       "std_score_time                                                            0.000425708   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.8   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   53  \\\n",
       "mean_fit_time                                                                0.111038   \n",
       "std_fit_time                                                                 0.017623   \n",
       "mean_score_time                                                            0.00212536   \n",
       "std_score_time                                                            0.000293079   \n",
       "param_logisticregression__C                                                       100   \n",
       "param_logisticregression__l1_ratio                                                0.9   \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...   \n",
       "split0_test_score                                                                   1   \n",
       "split1_test_score                                                                   1   \n",
       "split2_test_score                                                                   1   \n",
       "split3_test_score                                                                   1   \n",
       "split4_test_score                                                                   1   \n",
       "mean_test_score                                                                     1   \n",
       "std_test_score                                                                      0   \n",
       "rank_test_score                                                                     1   \n",
       "\n",
       "                                                                                   54  \n",
       "mean_fit_time                                                               0.0953297  \n",
       "std_fit_time                                                                 0.019829  \n",
       "mean_score_time                                                            0.00187578  \n",
       "std_score_time                                                            0.000223755  \n",
       "param_logisticregression__C                                                       100  \n",
       "param_logisticregression__l1_ratio                                                  1  \n",
       "params                              {'logisticregression__C': 100, 'logisticregres...  \n",
       "split0_test_score                                                                   1  \n",
       "split1_test_score                                                                   1  \n",
       "split2_test_score                                                                   1  \n",
       "split3_test_score                                                                   1  \n",
       "split4_test_score                                                                   1  \n",
       "mean_test_score                                                                     1  \n",
       "std_test_score                                                                      0  \n",
       "rank_test_score                                                                     1  \n",
       "\n",
       "[15 rows x 55 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid.cv_results_)\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = [{'kernel' : ['linear'],\n",
    "               'C' : [0.01, 0.1, 1, 10]},\n",
    "              {'kernel' : ['poly'],\n",
    "               'C' : [0.01, 0.1, 1, 10],\n",
    "               'degree' : [2, 3, 4]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid=[{'C': [0.01, 0.1, 1, 10], 'kernel': ['linear']},\n",
       "                         {'C': [0.01, 0.1, 1, 10], 'degree': [2, 3, 4],\n",
       "                          'kernel': ['poly']}])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "grid = GridSearchCV(SVC(), param_grid, cv=5, n_jobs=-1)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best:\n",
      " SVC(C=0.01, kernel='linear')\n"
     ]
    }
   ],
   "source": [
    "print(\"Best:\\n\", grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 1.00\n",
      "Test score: 1.00\n"
     ]
    }
   ],
   "source": [
    "print(\"Train score: {:.2f}\".format(grid.score(X_train, y_train)))\n",
    "print(\"Test score: {:.2f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.013352</td>\n",
       "      <td>0.0102569</td>\n",
       "      <td>0.00683088</td>\n",
       "      <td>0.00525522</td>\n",
       "      <td>0.00571966</td>\n",
       "      <td>0.00533509</td>\n",
       "      <td>0.00588765</td>\n",
       "      <td>0.00530515</td>\n",
       "      <td>0.00471168</td>\n",
       "      <td>0.00688248</td>\n",
       "      <td>0.0061831</td>\n",
       "      <td>0.00658135</td>\n",
       "      <td>0.00576029</td>\n",
       "      <td>0.00599036</td>\n",
       "      <td>0.00443368</td>\n",
       "      <td>0.00517979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.00161237</td>\n",
       "      <td>0.00183686</td>\n",
       "      <td>0.00173233</td>\n",
       "      <td>0.00151134</td>\n",
       "      <td>0.00170753</td>\n",
       "      <td>0.0014444</td>\n",
       "      <td>0.00199199</td>\n",
       "      <td>0.00238616</td>\n",
       "      <td>0.000593216</td>\n",
       "      <td>0.00155028</td>\n",
       "      <td>0.00169717</td>\n",
       "      <td>0.00260295</td>\n",
       "      <td>0.00172738</td>\n",
       "      <td>0.00210025</td>\n",
       "      <td>0.00169776</td>\n",
       "      <td>0.00201579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.00448561</td>\n",
       "      <td>0.00479803</td>\n",
       "      <td>0.00579619</td>\n",
       "      <td>0.0036036</td>\n",
       "      <td>0.00301218</td>\n",
       "      <td>0.00296521</td>\n",
       "      <td>0.00334873</td>\n",
       "      <td>0.00304995</td>\n",
       "      <td>0.00298033</td>\n",
       "      <td>0.00382204</td>\n",
       "      <td>0.00399189</td>\n",
       "      <td>0.00367999</td>\n",
       "      <td>0.00382276</td>\n",
       "      <td>0.00388131</td>\n",
       "      <td>0.00292091</td>\n",
       "      <td>0.00323243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.000384425</td>\n",
       "      <td>0.000999655</td>\n",
       "      <td>0.00141553</td>\n",
       "      <td>0.00101155</td>\n",
       "      <td>0.00109428</td>\n",
       "      <td>0.000537276</td>\n",
       "      <td>0.000464761</td>\n",
       "      <td>0.000791554</td>\n",
       "      <td>0.000476294</td>\n",
       "      <td>0.000833274</td>\n",
       "      <td>0.000992633</td>\n",
       "      <td>0.00108422</td>\n",
       "      <td>0.00104198</td>\n",
       "      <td>0.00144334</td>\n",
       "      <td>0.00130815</td>\n",
       "      <td>0.00148796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_C</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_kernel</th>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "      <td>poly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_degree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'C': 0.01, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 0.01, 'degree': 2, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 0.01, 'degree': 3, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 0.01, 'degree': 4, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 0.1, 'degree': 2, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 0.1, 'degree': 3, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 0.1, 'degree': 4, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 1, 'degree': 2, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 1, 'degree': 3, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 1, 'degree': 4, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 10, 'degree': 2, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 10, 'degree': 3, 'kernel': 'poly'}</td>\n",
       "      <td>{'C': 10, 'degree': 4, 'kernel': 'poly'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                0   \\\n",
       "mean_fit_time                             0.013352   \n",
       "std_fit_time                            0.00161237   \n",
       "mean_score_time                         0.00448561   \n",
       "std_score_time                         0.000384425   \n",
       "param_C                                       0.01   \n",
       "param_kernel                                linear   \n",
       "param_degree                                   NaN   \n",
       "params             {'C': 0.01, 'kernel': 'linear'}   \n",
       "split0_test_score                                1   \n",
       "split1_test_score                                1   \n",
       "split2_test_score                                1   \n",
       "split3_test_score                                1   \n",
       "split4_test_score                                1   \n",
       "mean_test_score                                  1   \n",
       "std_test_score                                   0   \n",
       "rank_test_score                                  1   \n",
       "\n",
       "                                               1   \\\n",
       "mean_fit_time                           0.0102569   \n",
       "std_fit_time                           0.00183686   \n",
       "mean_score_time                        0.00479803   \n",
       "std_score_time                        0.000999655   \n",
       "param_C                                       0.1   \n",
       "param_kernel                               linear   \n",
       "param_degree                                  NaN   \n",
       "params             {'C': 0.1, 'kernel': 'linear'}   \n",
       "split0_test_score                               1   \n",
       "split1_test_score                               1   \n",
       "split2_test_score                               1   \n",
       "split3_test_score                               1   \n",
       "split4_test_score                               1   \n",
       "mean_test_score                                 1   \n",
       "std_test_score                                  0   \n",
       "rank_test_score                                 1   \n",
       "\n",
       "                                             2   \\\n",
       "mean_fit_time                        0.00683088   \n",
       "std_fit_time                         0.00173233   \n",
       "mean_score_time                      0.00579619   \n",
       "std_score_time                       0.00141553   \n",
       "param_C                                       1   \n",
       "param_kernel                             linear   \n",
       "param_degree                                NaN   \n",
       "params             {'C': 1, 'kernel': 'linear'}   \n",
       "split0_test_score                             1   \n",
       "split1_test_score                             1   \n",
       "split2_test_score                             1   \n",
       "split3_test_score                             1   \n",
       "split4_test_score                             1   \n",
       "mean_test_score                               1   \n",
       "std_test_score                                0   \n",
       "rank_test_score                               1   \n",
       "\n",
       "                                              3   \\\n",
       "mean_fit_time                         0.00525522   \n",
       "std_fit_time                          0.00151134   \n",
       "mean_score_time                        0.0036036   \n",
       "std_score_time                        0.00101155   \n",
       "param_C                                       10   \n",
       "param_kernel                              linear   \n",
       "param_degree                                 NaN   \n",
       "params             {'C': 10, 'kernel': 'linear'}   \n",
       "split0_test_score                              1   \n",
       "split1_test_score                              1   \n",
       "split2_test_score                              1   \n",
       "split3_test_score                              1   \n",
       "split4_test_score                              1   \n",
       "mean_test_score                                1   \n",
       "std_test_score                                 0   \n",
       "rank_test_score                                1   \n",
       "\n",
       "                                                           4   \\\n",
       "mean_fit_time                                      0.00571966   \n",
       "std_fit_time                                       0.00170753   \n",
       "mean_score_time                                    0.00301218   \n",
       "std_score_time                                     0.00109428   \n",
       "param_C                                                  0.01   \n",
       "param_kernel                                             poly   \n",
       "param_degree                                                2   \n",
       "params             {'C': 0.01, 'degree': 2, 'kernel': 'poly'}   \n",
       "split0_test_score                                           1   \n",
       "split1_test_score                                           1   \n",
       "split2_test_score                                           1   \n",
       "split3_test_score                                           1   \n",
       "split4_test_score                                           1   \n",
       "mean_test_score                                             1   \n",
       "std_test_score                                              0   \n",
       "rank_test_score                                             1   \n",
       "\n",
       "                                                           5   \\\n",
       "mean_fit_time                                      0.00533509   \n",
       "std_fit_time                                        0.0014444   \n",
       "mean_score_time                                    0.00296521   \n",
       "std_score_time                                    0.000537276   \n",
       "param_C                                                  0.01   \n",
       "param_kernel                                             poly   \n",
       "param_degree                                                3   \n",
       "params             {'C': 0.01, 'degree': 3, 'kernel': 'poly'}   \n",
       "split0_test_score                                           1   \n",
       "split1_test_score                                           1   \n",
       "split2_test_score                                           1   \n",
       "split3_test_score                                           1   \n",
       "split4_test_score                                           1   \n",
       "mean_test_score                                             1   \n",
       "std_test_score                                              0   \n",
       "rank_test_score                                             1   \n",
       "\n",
       "                                                           6   \\\n",
       "mean_fit_time                                      0.00588765   \n",
       "std_fit_time                                       0.00199199   \n",
       "mean_score_time                                    0.00334873   \n",
       "std_score_time                                    0.000464761   \n",
       "param_C                                                  0.01   \n",
       "param_kernel                                             poly   \n",
       "param_degree                                                4   \n",
       "params             {'C': 0.01, 'degree': 4, 'kernel': 'poly'}   \n",
       "split0_test_score                                           1   \n",
       "split1_test_score                                           1   \n",
       "split2_test_score                                           1   \n",
       "split3_test_score                                           1   \n",
       "split4_test_score                                           1   \n",
       "mean_test_score                                             1   \n",
       "std_test_score                                              0   \n",
       "rank_test_score                                             1   \n",
       "\n",
       "                                                          7   \\\n",
       "mean_fit_time                                     0.00530515   \n",
       "std_fit_time                                      0.00238616   \n",
       "mean_score_time                                   0.00304995   \n",
       "std_score_time                                   0.000791554   \n",
       "param_C                                                  0.1   \n",
       "param_kernel                                            poly   \n",
       "param_degree                                               2   \n",
       "params             {'C': 0.1, 'degree': 2, 'kernel': 'poly'}   \n",
       "split0_test_score                                          1   \n",
       "split1_test_score                                          1   \n",
       "split2_test_score                                          1   \n",
       "split3_test_score                                          1   \n",
       "split4_test_score                                          1   \n",
       "mean_test_score                                            1   \n",
       "std_test_score                                             0   \n",
       "rank_test_score                                            1   \n",
       "\n",
       "                                                          8   \\\n",
       "mean_fit_time                                     0.00471168   \n",
       "std_fit_time                                     0.000593216   \n",
       "mean_score_time                                   0.00298033   \n",
       "std_score_time                                   0.000476294   \n",
       "param_C                                                  0.1   \n",
       "param_kernel                                            poly   \n",
       "param_degree                                               3   \n",
       "params             {'C': 0.1, 'degree': 3, 'kernel': 'poly'}   \n",
       "split0_test_score                                          1   \n",
       "split1_test_score                                          1   \n",
       "split2_test_score                                          1   \n",
       "split3_test_score                                          1   \n",
       "split4_test_score                                          1   \n",
       "mean_test_score                                            1   \n",
       "std_test_score                                             0   \n",
       "rank_test_score                                            1   \n",
       "\n",
       "                                                          9   \\\n",
       "mean_fit_time                                     0.00688248   \n",
       "std_fit_time                                      0.00155028   \n",
       "mean_score_time                                   0.00382204   \n",
       "std_score_time                                   0.000833274   \n",
       "param_C                                                  0.1   \n",
       "param_kernel                                            poly   \n",
       "param_degree                                               4   \n",
       "params             {'C': 0.1, 'degree': 4, 'kernel': 'poly'}   \n",
       "split0_test_score                                          1   \n",
       "split1_test_score                                          1   \n",
       "split2_test_score                                          1   \n",
       "split3_test_score                                          1   \n",
       "split4_test_score                                          1   \n",
       "mean_test_score                                            1   \n",
       "std_test_score                                             0   \n",
       "rank_test_score                                            1   \n",
       "\n",
       "                                                        10  \\\n",
       "mean_fit_time                                    0.0061831   \n",
       "std_fit_time                                    0.00169717   \n",
       "mean_score_time                                 0.00399189   \n",
       "std_score_time                                 0.000992633   \n",
       "param_C                                                  1   \n",
       "param_kernel                                          poly   \n",
       "param_degree                                             2   \n",
       "params             {'C': 1, 'degree': 2, 'kernel': 'poly'}   \n",
       "split0_test_score                                        1   \n",
       "split1_test_score                                        1   \n",
       "split2_test_score                                        1   \n",
       "split3_test_score                                        1   \n",
       "split4_test_score                                        1   \n",
       "mean_test_score                                          1   \n",
       "std_test_score                                           0   \n",
       "rank_test_score                                          1   \n",
       "\n",
       "                                                        11  \\\n",
       "mean_fit_time                                   0.00658135   \n",
       "std_fit_time                                    0.00260295   \n",
       "mean_score_time                                 0.00367999   \n",
       "std_score_time                                  0.00108422   \n",
       "param_C                                                  1   \n",
       "param_kernel                                          poly   \n",
       "param_degree                                             3   \n",
       "params             {'C': 1, 'degree': 3, 'kernel': 'poly'}   \n",
       "split0_test_score                                        1   \n",
       "split1_test_score                                        1   \n",
       "split2_test_score                                        1   \n",
       "split3_test_score                                        1   \n",
       "split4_test_score                                        1   \n",
       "mean_test_score                                          1   \n",
       "std_test_score                                           0   \n",
       "rank_test_score                                          1   \n",
       "\n",
       "                                                        12  \\\n",
       "mean_fit_time                                   0.00576029   \n",
       "std_fit_time                                    0.00172738   \n",
       "mean_score_time                                 0.00382276   \n",
       "std_score_time                                  0.00104198   \n",
       "param_C                                                  1   \n",
       "param_kernel                                          poly   \n",
       "param_degree                                             4   \n",
       "params             {'C': 1, 'degree': 4, 'kernel': 'poly'}   \n",
       "split0_test_score                                        1   \n",
       "split1_test_score                                        1   \n",
       "split2_test_score                                        1   \n",
       "split3_test_score                                        1   \n",
       "split4_test_score                                        1   \n",
       "mean_test_score                                          1   \n",
       "std_test_score                                           0   \n",
       "rank_test_score                                          1   \n",
       "\n",
       "                                                         13  \\\n",
       "mean_fit_time                                    0.00599036   \n",
       "std_fit_time                                     0.00210025   \n",
       "mean_score_time                                  0.00388131   \n",
       "std_score_time                                   0.00144334   \n",
       "param_C                                                  10   \n",
       "param_kernel                                           poly   \n",
       "param_degree                                              2   \n",
       "params             {'C': 10, 'degree': 2, 'kernel': 'poly'}   \n",
       "split0_test_score                                         1   \n",
       "split1_test_score                                         1   \n",
       "split2_test_score                                         1   \n",
       "split3_test_score                                         1   \n",
       "split4_test_score                                         1   \n",
       "mean_test_score                                           1   \n",
       "std_test_score                                            0   \n",
       "rank_test_score                                           1   \n",
       "\n",
       "                                                         14  \\\n",
       "mean_fit_time                                    0.00443368   \n",
       "std_fit_time                                     0.00169776   \n",
       "mean_score_time                                  0.00292091   \n",
       "std_score_time                                   0.00130815   \n",
       "param_C                                                  10   \n",
       "param_kernel                                           poly   \n",
       "param_degree                                              3   \n",
       "params             {'C': 10, 'degree': 3, 'kernel': 'poly'}   \n",
       "split0_test_score                                         1   \n",
       "split1_test_score                                         1   \n",
       "split2_test_score                                         1   \n",
       "split3_test_score                                         1   \n",
       "split4_test_score                                         1   \n",
       "mean_test_score                                           1   \n",
       "std_test_score                                            0   \n",
       "rank_test_score                                           1   \n",
       "\n",
       "                                                         15  \n",
       "mean_fit_time                                    0.00517979  \n",
       "std_fit_time                                     0.00201579  \n",
       "mean_score_time                                  0.00323243  \n",
       "std_score_time                                   0.00148796  \n",
       "param_C                                                  10  \n",
       "param_kernel                                           poly  \n",
       "param_degree                                              4  \n",
       "params             {'C': 10, 'degree': 4, 'kernel': 'poly'}  \n",
       "split0_test_score                                         1  \n",
       "split1_test_score                                         1  \n",
       "split2_test_score                                         1  \n",
       "split3_test_score                                         1  \n",
       "split4_test_score                                         1  \n",
       "mean_test_score                                           1  \n",
       "std_test_score                                            0  \n",
       "rank_test_score                                           1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid.cv_results_)\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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