{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This notebook implements a one-dimensional GAN which generates the output of a cubic function**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below generates n samples of the cubic function, the domain of the function is chosen to be n random integers in the range -0.5 to 0.5. The function returns a two dimensional vector(inputs in the first column and outputs in the second) and a one dimensional vector of class labels( in this case, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qv/zzgcbq096_v04lckg4gc06280000gn/T/ipykernel_9938/1884976865.py:6: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  dfs = pd.concat([df.Name,dfs],1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.read_csv('/Users/apsys/Downloads/hetfit2/data/dataset.csv')\n",
    "dfs = df.drop(\"Name\",axis=1)\n",
    "dfs = (dfs-dfs.min())/(dfs.max()-dfs.min())\n",
    "dfs = pd.concat([df.Name,dfs],1)\n",
    "def generate_real_samples(n):\n",
    "    '''generate n real samples with class labels'''\n",
    "    x1 = np.random.rand(n) - 0.5 #generate a random number between [-0.5,0.5]\n",
    "    x2 = x1**3        #generate outputs\n",
    "    x1 = x1.reshape(n, 1)\n",
    "    x2 = x2.reshape(n, 1)\n",
    "    X = np.hstack((x1, x2))   #stack layers\n",
    "    y = np.ones((n, 1))     #generate class label\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>Name</th>\n",
       "      <th>U</th>\n",
       "      <th>d</th>\n",
       "      <th>h</th>\n",
       "      <th>j</th>\n",
       "      <th>Isp</th>\n",
       "      <th>nu_t</th>\n",
       "      <th>T</th>\n",
       "      <th>m_a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPT-20 [21]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.006383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPT-25 [22]</td>\n",
       "      <td>0.062885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.104478</td>\n",
       "      <td>0.031915</td>\n",
       "      <td>0.020592</td>\n",
       "      <td>0.099001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HET-100 [23]</td>\n",
       "      <td>0.093711</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.121429</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.238806</td>\n",
       "      <td>0.012766</td>\n",
       "      <td>0.037323</td>\n",
       "      <td>0.496821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KHT-40 [24]</td>\n",
       "      <td>0.103730</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.228571</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.567164</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>0.082368</td>\n",
       "      <td>0.617620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KHT-50 [24]</td>\n",
       "      <td>0.108354</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.385714</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.093617</td>\n",
       "      <td>0.099099</td>\n",
       "      <td>0.454133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HEPS-200</td>\n",
       "      <td>0.109895</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.361111</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.093617</td>\n",
       "      <td>0.093951</td>\n",
       "      <td>0.418710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BHT-200 [2526]</td>\n",
       "      <td>0.113748</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.140299</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.114543</td>\n",
       "      <td>0.500454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KM-32 [27]</td>\n",
       "      <td>0.125308</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.242857</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.283582</td>\n",
       "      <td>0.119149</td>\n",
       "      <td>0.106821</td>\n",
       "      <td>0.367847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SPT-50M [28]</td>\n",
       "      <td>0.148428</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.225532</td>\n",
       "      <td>0.155727</td>\n",
       "      <td>0.226158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SPT-30 [23]</td>\n",
       "      <td>0.158446</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.128571</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.134328</td>\n",
       "      <td>0.114894</td>\n",
       "      <td>0.119691</td>\n",
       "      <td>0.358765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KM-37 [29]</td>\n",
       "      <td>0.177713</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.328358</td>\n",
       "      <td>0.151064</td>\n",
       "      <td>0.187902</td>\n",
       "      <td>0.727520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAM200 [3031]</td>\n",
       "      <td>0.193896</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.522388</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>0.172458</td>\n",
       "      <td>0.679382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SPT-50 [21]</td>\n",
       "      <td>0.203915</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.157447</td>\n",
       "      <td>0.175032</td>\n",
       "      <td>0.823797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A-3 [21]</td>\n",
       "      <td>0.209309</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.701493</td>\n",
       "      <td>0.157447</td>\n",
       "      <td>0.181467</td>\n",
       "      <td>0.891916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HEPS-500</td>\n",
       "      <td>0.331073</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.492857</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.261702</td>\n",
       "      <td>0.283140</td>\n",
       "      <td>0.679382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BHT-600 [2632]</td>\n",
       "      <td>0.433570</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.761194</td>\n",
       "      <td>0.459574</td>\n",
       "      <td>0.453024</td>\n",
       "      <td>0.627611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SPT-70 [33]</td>\n",
       "      <td>0.468249</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>0.451064</td>\n",
       "      <td>0.464607</td>\n",
       "      <td>0.684832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SPT-100 [934]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.552239</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.636694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UAH-78AM</td>\n",
       "      <td>0.360358</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.331915</td>\n",
       "      <td>0.335907</td>\n",
       "      <td>0.554950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MaSMi40</td>\n",
       "      <td>0.213933</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.237778</td>\n",
       "      <td>0.180896</td>\n",
       "      <td>0.225532</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>0.237057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MaSMi60</td>\n",
       "      <td>0.499075</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.412222</td>\n",
       "      <td>0.373134</td>\n",
       "      <td>0.451064</td>\n",
       "      <td>0.335907</td>\n",
       "      <td>0.418710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MaSMiDm</td>\n",
       "      <td>0.730271</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.742857</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.432836</td>\n",
       "      <td>0.544681</td>\n",
       "      <td>0.631918</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Music-si</td>\n",
       "      <td>0.067509</td>\n",
       "      <td>0.337500</td>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003861</td>\n",
       "      <td>0.009991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name         U         d         h         j       Isp  \\\n",
       "0      SPT-20 [21]  0.000000  0.000000  0.000000  0.166667  0.761194   \n",
       "1      SPT-25 [22]  0.062885  0.000000  0.071429  0.166667  0.104478   \n",
       "2     HET-100 [23]  0.093711  0.375000  0.121429  0.194444  0.238806   \n",
       "3      KHT-40 [24]  0.103730  0.453125  0.228571  0.388889  0.567164   \n",
       "4      KHT-50 [24]  0.108354  0.218750  0.385714  0.333333  0.552239   \n",
       "5         HEPS-200  0.109895  0.218750  0.392857  0.361111  0.552239   \n",
       "6   BHT-200 [2526]  0.113748  0.218750  0.085714  0.200000  0.140299   \n",
       "7       KM-32 [27]  0.125308  0.218750  0.242857  0.277778  0.283582   \n",
       "8     SPT-50M [28]  0.148428  0.062500  0.342857  0.500000  0.552239   \n",
       "9      SPT-30 [23]  0.158446  0.218750  0.128571  0.222222  0.134328   \n",
       "10      KM-37 [29]  0.177713  0.350000  0.314286  0.388889  0.328358   \n",
       "11   CAM200 [3031]  0.193896  0.296875  0.400000  0.555556  0.522388   \n",
       "12     SPT-50 [21]  0.203915  0.375000  0.342857  0.500000  0.552239   \n",
       "13        A-3 [21]  0.209309  0.375000  0.457143  0.611111  0.701493   \n",
       "14        HEPS-500  0.331073  0.375000  0.492857  0.750000  0.552239   \n",
       "15  BHT-600 [2632]  0.433570  0.375000  0.585714  0.777778  0.761194   \n",
       "16     SPT-70 [33]  0.468249  0.375000  0.585714  0.666667  0.552239   \n",
       "17   SPT-100 [934]  1.000000  0.375000  1.000000  0.722222  0.552239   \n",
       "18        UAH-78AM  0.360358  0.250000  0.900000  1.000000  1.000000   \n",
       "19         MaSMi40  0.213933  0.375000  0.357143  0.237778  0.180896   \n",
       "20         MaSMi60  0.499075  0.218750  0.642857  0.412222  0.373134   \n",
       "21         MaSMiDm  0.730271  1.000000  0.742857  0.472222  0.432836   \n",
       "22        Music-si  0.067509  0.337500  0.042857  0.000000  0.000000   \n",
       "\n",
       "        nu_t         T       m_a  \n",
       "0   0.006383  0.000000  0.000000  \n",
       "1   0.031915  0.020592  0.099001  \n",
       "2   0.012766  0.037323  0.496821  \n",
       "3   0.053191  0.082368  0.617620  \n",
       "4   0.093617  0.099099  0.454133  \n",
       "5   0.093617  0.093951  0.418710  \n",
       "6   0.106383  0.114543  0.500454  \n",
       "7   0.119149  0.106821  0.367847  \n",
       "8   0.225532  0.155727  0.226158  \n",
       "9   0.114894  0.119691  0.358765  \n",
       "10  0.151064  0.187902  0.727520  \n",
       "11  0.138298  0.172458  0.679382  \n",
       "12  0.157447  0.175032  0.823797  \n",
       "13  0.157447  0.181467  0.891916  \n",
       "14  0.261702  0.283140  0.679382  \n",
       "15  0.459574  0.453024  0.627611  \n",
       "16  0.451064  0.464607  0.684832  \n",
       "17  1.000000  1.000000  0.636694  \n",
       "18  0.331915  0.335907  0.554950  \n",
       "19  0.225532  0.117117  0.237057  \n",
       "20  0.451064  0.335907  0.418710  \n",
       "21  0.544681  0.631918  1.000000  \n",
       "22  0.000000  0.003861  0.009991  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-1.87734512e-01, -6.61656146e-03],\n",
       "        [ 1.56515103e-01,  3.83414693e-03],\n",
       "        [-9.56463469e-02, -8.74994174e-04],\n",
       "        [ 3.87038474e-01,  5.79778915e-02],\n",
       "        [ 1.38966732e-01,  2.68369114e-03],\n",
       "        [-1.19105367e-01, -1.68963927e-03],\n",
       "        [-8.97560680e-02, -7.23088503e-04],\n",
       "        [ 1.07071289e-02,  1.22749320e-06],\n",
       "        [ 2.05332738e-01,  8.65714309e-03],\n",
       "        [ 3.16184027e-01,  3.16096567e-02]]),\n",
       " array([[1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.],\n",
       "        [1.]]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_real_samples(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below defines the discriminator model, it is one of the two components of a GAN. It's job is to classify whether the numbers generated by the generator model are real or fake( ie: if they are the output to our function or not). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 20)                180       \n",
      "                                                                 \n",
      " leaky_re_lu (LeakyReLU)     (None, 20)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                315       \n",
      "                                                                 \n",
      " leaky_re_lu_1 (LeakyReLU)   (None, 15)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 5)                 80        \n",
      "                                                                 \n",
      " re_lu (ReLU)                (None, 5)                 0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 581\n",
      "Trainable params: 581\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LeakyReLU,ReLU\n",
    "from keras.utils import plot_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def define_discriminator(inputs = 8):\n",
    "    ''' function to return the compiled discriminator model'''\n",
    "    model = Sequential()\n",
    "    model.add(Dense(20, activation = 'relu', kernel_initializer = 'he_uniform', input_dim = inputs))\n",
    "    model.add(LeakyReLU(alpha=0.1))\n",
    "    model.add(Dense(15, activation = 'relu', kernel_initializer = 'he_uniform'))\n",
    "    model.add(LeakyReLU(alpha = 0.1))\n",
    "    model.add(Dense(5, activation = 'relu', kernel_initializer = 'he_uniform'))\n",
    "    model.add(ReLU())\n",
    "    model.add(Dense(1, activation = 'selu'))\n",
    "    model.compile(optimizer = 'adam', loss = 'binary_focal_crossentropy', metrics = ['accuracy'])\n",
    "    return model\n",
    "\n",
    "discriminator_model = define_discriminator()\n",
    "discriminator_model.summary()\n",
    "plot_model(discriminator_model, to_file = 'discriminator_model.png', show_shapes = True, show_layer_names = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below makes up the second component of a GAN. It takes an input point from a latent space and generate a vector with two dimensions(like the X vector returned by the generate_real_samples function)\n",
    "<br>\n",
    "A latent variable is a hidden variable, and the space it belongs to is called the latent space. We can arbitrarily assign a size to our latent space(here it is 5). The points in the latent space are meaningless until the generator model begins learning and starts assigning meaning to the points in the space. After training, the points in the latent space correspond to the generated samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the generator model isn't compiled here, it's because it is fit indirectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_generator(latent_dim, outputs = 8):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(20, activation = 'relu', kernel_initializer= 'he_uniform', input_dim = latent_dim))\n",
    "    model.add(LeakyReLU(alpha = 0.3))\n",
    "    model.add(Dense(15, activation = 'relu', kernel_initializer = 'he_uniform'))\n",
    "    model.add(ReLU())\n",
    "    model.add(Dense(outputs, activation = 'elu'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 20)                340       \n",
      "                                                                 \n",
      " leaky_re_lu_2 (LeakyReLU)   (None, 20)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 15)                315       \n",
      "                                                                 \n",
      " re_lu_1 (ReLU)              (None, 15)                0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 8)                 128       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 783\n",
      "Trainable params: 783\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_dim = 16\n",
    "generator_model = define_generator(latent_dim)\n",
    "generator_model.summary()\n",
    "plot_model(generator_model, to_file = 'generator_model.png', show_shapes = True, show_layer_names = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function generates_latent_points generates n points in the latent space. The generate_fake_samples function uses the generator model to generate 'fake' samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_latent_points(latent_dim, n):\n",
    "    '''generate points in latent space as input for the generator'''\n",
    "    x_input = np.random.rand(latent_dim*n) #generate points in latent space\n",
    "    x_input = x_input.reshape(n,latent_dim)  #reshape\n",
    "    return x_input\n",
    "\n",
    "def generate_fake_samples(generator, latent_dim, n):\n",
    "    x_input = generate_latent_points(latent_dim, n) #genarate points in latent space\n",
    "    x = generator.predict(x_input) #predict outputs\n",
    "    y = np.zeros((n, 1))\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, the fake samples produced by the generator is garbage because we haven't trained it yet. It is supposed to closely follow our function after training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 688us/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-01 10:21:39.004994: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, _ = generate_fake_samples(generator_model, latent_dim, 100)\n",
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below combines the generator and discriminator models. The layers of the discriminator model are made non-trainable( because we do not want to update it's weights during the training of the generator). Here, the discriminator's only job is to classify real and fake samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_gan(generator, discriminator):\n",
    "    '''define the combined generator and discriminator model'''\n",
    "    discriminator.trainable = False\n",
    "    model = Sequential()\n",
    "    model.add(generator)\n",
    "    model.add(discriminator)\n",
    "    model.compile(optimizer = 'adam', loss = 'binary_crossentropy')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential_1 (Sequential)   (None, 8)                 783       \n",
      "                                                                 \n",
      " sequential (Sequential)     (None, 1)                 581       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,364\n",
      "Trainable params: 783\n",
      "Non-trainable params: 581\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan_model = define_gan(generator_model, discriminator_model)\n",
    "gan_model.summary()\n",
    "plot_model(gan_model, to_file = 'gan_model.png', show_layer_names = True, show_shapes = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want the discriminator model to believe that the samples generated by the generator are real, so we label them as '1'(real). In the ideal case, the discriminator is fooled about half of the times into believing that the samples generated by the generator are real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , 0.16666667, 0.76119403,\n",
       "        0.00638298, 0.        , 0.        ],\n",
       "       [0.06288533, 0.        , 0.07142857, 0.16666667, 0.10447761,\n",
       "        0.03191489, 0.02059202, 0.09900091],\n",
       "       [0.09371147, 0.375     , 0.12142857, 0.19444444, 0.23880597,\n",
       "        0.01276596, 0.03732304, 0.49682107],\n",
       "       [0.10372996, 0.453125  , 0.22857143, 0.38888889, 0.56716418,\n",
       "        0.05319149, 0.08236808, 0.61762035],\n",
       "       [0.10835388, 0.21875   , 0.38571429, 0.33333333, 0.55223881,\n",
       "        0.09361702, 0.0990991 , 0.45413261],\n",
       "       [0.10989519, 0.21875   , 0.39285714, 0.36111111, 0.55223881,\n",
       "        0.09361702, 0.09395109, 0.41871026],\n",
       "       [0.11374846, 0.21875   , 0.08571429, 0.2       , 0.14029851,\n",
       "        0.10638298, 0.11454311, 0.50045413],\n",
       "       [0.12530826, 0.21875   , 0.24285714, 0.27777778, 0.28358209,\n",
       "        0.11914894, 0.10682111, 0.36784741],\n",
       "       [0.14842787, 0.0625    , 0.34285714, 0.5       , 0.55223881,\n",
       "        0.22553191, 0.15572716, 0.22615804],\n",
       "       [0.15844636, 0.21875   , 0.12857143, 0.22222222, 0.13432836,\n",
       "        0.11489362, 0.11969112, 0.35876476],\n",
       "       [0.1777127 , 0.35      , 0.31428571, 0.38888889, 0.32835821,\n",
       "        0.15106383, 0.18790219, 0.72752044],\n",
       "       [0.19389642, 0.296875  , 0.4       , 0.55555556, 0.52238806,\n",
       "        0.13829787, 0.17245817, 0.67938238],\n",
       "       [0.20391492, 0.375     , 0.34285714, 0.5       , 0.55223881,\n",
       "        0.15744681, 0.17503218, 0.82379655],\n",
       "       [0.20930949, 0.375     , 0.45714286, 0.61111111, 0.70149254,\n",
       "        0.15744681, 0.18146718, 0.89191644],\n",
       "       [0.33107275, 0.375     , 0.49285714, 0.75      , 0.55223881,\n",
       "        0.26170213, 0.28314028, 0.67938238],\n",
       "       [0.43356967, 0.375     , 0.58571429, 0.77777778, 0.76119403,\n",
       "        0.45957447, 0.45302445, 0.62761126],\n",
       "       [0.46824908, 0.375     , 0.58571429, 0.66666667, 0.55223881,\n",
       "        0.45106383, 0.46460746, 0.68483197],\n",
       "       [1.        , 0.375     , 1.        , 0.72222222, 0.55223881,\n",
       "        1.        , 1.        , 0.63669391],\n",
       "       [0.36035758, 0.25      , 0.9       , 1.        , 1.        ,\n",
       "        0.33191489, 0.33590734, 0.55495005],\n",
       "       [0.21393342, 0.375     , 0.35714286, 0.23777778, 0.18089552,\n",
       "        0.22553191, 0.11711712, 0.23705722],\n",
       "       [0.49907522, 0.21875   , 0.64285714, 0.41222222, 0.37313433,\n",
       "        0.45106383, 0.33590734, 0.41871026],\n",
       "       [0.73027127, 1.        , 0.74285714, 0.47222222, 0.43283582,\n",
       "        0.54468085, 0.63191763, 1.        ],\n",
       "       [0.06750925, 0.3375    , 0.04285714, 0.        , 0.        ,\n",
       "        0.        , 0.003861  , 0.00999092]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs.iloc[:,1:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train_gan function simultaneously trains the discriminator and the GAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_gan(g_model,d_model,gan_model,latent_dim, num_epochs = 1000,num_eval = 1000, batch_size = 2):\n",
    "    ''' function to train gan model'''\n",
    "    half_batch = 1\n",
    "  #run epochs\n",
    "    for i in range(num_epochs):\n",
    "        X_real, y_real = dfs.iloc[:,1:].values, np.ones((23, 1)) #generate real examples\n",
    "        d_model.train_on_batch(X_real, y_real)               # train on real data\n",
    "        X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch) #generate fake samples\n",
    "        d_model.train_on_batch(X_fake, y_fake)                #train on fake data\n",
    "        #prepare points in latent space as input for the generator\n",
    "        x_gan = generate_latent_points(latent_dim, batch_size)\n",
    "        y_gan = np.ones((batch_size, 1))    #generate fake labels for gan\n",
    "        gan_model.train_on_batch(x_gan, y_gan)\n",
    "        if (i+1) % num_eval == 0:\n",
    "            summarize_performance(i + 1, g_model, d_model, latent_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function defined below is called every two thousand epochs to summarize the performance of the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_performance(epoch, generator, discriminator, latent_dim, n = 200):\n",
    "    '''evaluate the discriminator and plot real and fake samples'''\n",
    "    x_real, y_real = dfs.iloc[:,1:].values, np.ones((23, 1))\n",
    "    _, acc_real = discriminator.evaluate(x_real, y_real, verbose = 1)\n",
    "    x_fake, y_fake = generate_fake_samples(generator, latent_dim, n)\n",
    "    _, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose = 1)\n",
    "    print('Epoch: ' + str(epoch) + ' Real Acc.: ' + str(acc_real) + ' Fake Acc.: '+ str(acc_fake))\n",
    "    # x_real /= np.max(np.abs(x_real),axis=0)\n",
    "    plt.scatter(x_real[:,0], x_real[:,1], color = 'red')\n",
    "    plt.scatter(x_fake[:,0], x_fake[:,1], color = 'blue',s=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.1332 - accuracy: 0.7826\n",
      "7/7 [==============================] - 0s 396us/step\n",
      "7/7 [==============================] - 0s 615us/step - loss: 0.1625 - accuracy: 0.7300\n",
      "Epoch: 1000 Real Acc.: 0.782608687877655 Fake Acc.: 0.7300000190734863\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_gan(generator_model, discriminator_model, gan_model, latent_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(n):\n",
    "        x_fake, y_fake = generate_fake_samples(generator_model, latent_dim, n)\n",
    "        return x_fake, y_fake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 583us/step\n"
     ]
    }
   ],
   "source": [
    "x,y=predict(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,7][x[:,7]>0][:10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c1d2a110>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,2][x[:,2]>0],x[:,7][x[:,7]>0][:76],c='gray')\n",
    "plt.scatter(dfs.iloc[:,3],dfs.iloc[:,-1],s=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>Name</th>\n",
       "      <th>U</th>\n",
       "      <th>d</th>\n",
       "      <th>h</th>\n",
       "      <th>j</th>\n",
       "      <th>Isp</th>\n",
       "      <th>nu_t</th>\n",
       "      <th>T</th>\n",
       "      <th>m_a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPT-20 [21]</td>\n",
       "      <td>52.400000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>839.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPT-25 [22]</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>948.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HET-100 [23]</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>1386.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KHT-40 [24]</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>1519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KHT-50 [24]</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>1339.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.508939</td>\n",
       "      <td>-0.354882</td>\n",
       "      <td>0.747163</td>\n",
       "      <td>0.191082</td>\n",
       "      <td>1.318361</td>\n",
       "      <td>0.262004</td>\n",
       "      <td>0.210962</td>\n",
       "      <td>0.839841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.127709</td>\n",
       "      <td>-0.398011</td>\n",
       "      <td>1.063189</td>\n",
       "      <td>1.168259</td>\n",
       "      <td>1.512382</td>\n",
       "      <td>0.259802</td>\n",
       "      <td>0.314027</td>\n",
       "      <td>0.882875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.979954</td>\n",
       "      <td>-0.023562</td>\n",
       "      <td>1.216619</td>\n",
       "      <td>0.557483</td>\n",
       "      <td>1.534266</td>\n",
       "      <td>-0.189749</td>\n",
       "      <td>0.736419</td>\n",
       "      <td>1.130576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027803</td>\n",
       "      <td>-0.496064</td>\n",
       "      <td>1.156737</td>\n",
       "      <td>1.343929</td>\n",
       "      <td>1.987885</td>\n",
       "      <td>0.098937</td>\n",
       "      <td>1.031702</td>\n",
       "      <td>0.915652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.323144</td>\n",
       "      <td>-0.311483</td>\n",
       "      <td>1.114895</td>\n",
       "      <td>1.247261</td>\n",
       "      <td>1.822533</td>\n",
       "      <td>-0.067361</td>\n",
       "      <td>0.989002</td>\n",
       "      <td>1.152484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name           U           d          h         j        Isp  \\\n",
       "0    SPT-20 [21]   52.400000  180.000000  15.000000  5.000000  32.000000   \n",
       "1    SPT-25 [22]  134.000000  180.000000  20.000000  5.000000  10.000000   \n",
       "2   HET-100 [23]  174.000000  300.000000  23.500000  5.500000  14.500000   \n",
       "3    KHT-40 [24]  187.000000  325.000000  31.000000  9.000000  25.500000   \n",
       "4    KHT-50 [24]  193.000000  250.000000  42.000000  8.000000  25.000000   \n",
       "..           ...         ...         ...        ...       ...        ...   \n",
       "95           NaN    0.508939   -0.354882   0.747163  0.191082   1.318361   \n",
       "96           NaN   -0.127709   -0.398011   1.063189  1.168259   1.512382   \n",
       "97           NaN    0.979954   -0.023562   1.216619  0.557483   1.534266   \n",
       "98           NaN    0.027803   -0.496064   1.156737  1.343929   1.987885   \n",
       "99           NaN    0.323144   -0.311483   1.114895  1.247261   1.822533   \n",
       "\n",
       "        nu_t          T          m_a  \n",
       "0   0.470000   3.900000   839.000000  \n",
       "1   0.590000   5.500000   948.000000  \n",
       "2   0.500000   6.800000  1386.000000  \n",
       "3   0.690000  10.300000  1519.000000  \n",
       "4   0.880000  11.600000  1339.000000  \n",
       "..       ...        ...          ...  \n",
       "95  0.262004   0.210962     0.839841  \n",
       "96  0.259802   0.314027     0.882875  \n",
       "97 -0.189749   0.736419     1.130576  \n",
       "98  0.098937   1.031702     0.915652  \n",
       "99 -0.067361   0.989002     1.152484  \n",
       "\n",
       "[123 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df,pd.DataFrame(x,columns=df.columns[1:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apsys/Downloads/hetfit2/utils/data_augmentation.py:32: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  dfs = pd.concat([local.Name,dfs],1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Name         U         d         h         j       Isp      nu_t  \\\n",
      "0   SPT-20 [21]  0.000000  0.000000  0.000000  0.166667  0.761194  0.006383   \n",
      "1   SPT-25 [22]  0.062885  0.000000  0.071429  0.166667  0.104478  0.031915   \n",
      "2  HET-100 [23]  0.093711  0.375000  0.121429  0.194444  0.238806  0.012766   \n",
      "3   KHT-40 [24]  0.103730  0.453125  0.228571  0.388889  0.567164  0.053191   \n",
      "4   KHT-50 [24]  0.108354  0.218750  0.385714  0.333333  0.552239  0.093617   \n",
      "\n",
      "          T       m_a  \n",
      "0  0.000000  0.000000  \n",
      "1  0.020592  0.099001  \n",
      "2  0.037323  0.496821  \n",
      "3  0.082368  0.617620  \n",
      "4  0.099099  0.454133  \n",
      "1/1 [==============================] - 0s 39ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-20 20:36:55.213546: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 13ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "1/1 [==============================] - 0s 9ms/step\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [5], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mdata_augmentation\u001b[39;00m \u001b[39mimport\u001b[39;00m dataset\n\u001b[1;32m      2\u001b[0m obj \u001b[39m=\u001b[39m dataset(\u001b[39m1000\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mnew\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m obj\u001b[39m.\u001b[39;49mgenerate()\n",
      "File \u001b[0;32m~/Downloads/hetfit2/utils/data_augmentation.py:36\u001b[0m, in \u001b[0;36mdataset.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     32\u001b[0m     dfs \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([local\u001b[39m.\u001b[39mName,dfs],\u001b[39m1\u001b[39m)\n\u001b[1;32m     34\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvae \u001b[39m=\u001b[39m DCGAN(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlatent_dim,dfs)\n\u001b[0;32m---> 36\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvae\u001b[39m.\u001b[39;49mstart_training()\n\u001b[1;32m     37\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msamples \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvae\u001b[39m.\u001b[39mpredict(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msample_size)\n\u001b[1;32m     38\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mSamples:\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msamples)\n",
      "File \u001b[0;32m~/Downloads/hetfit2/utils/ndgan.py:99\u001b[0m, in \u001b[0;36mDCGAN.start_training\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstart_training\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m---> 99\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_gan(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgenerator_model, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdiscriminator_model, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgan_model, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlatent)\n",
      "File \u001b[0;32m~/Downloads/hetfit2/utils/ndgan.py:89\u001b[0m, in \u001b[0;36mDCGAN.train_gan\u001b[0;34m(self, g_model, d_model, gan_model, latent_dim, num_epochs, num_eval, batch_size)\u001b[0m\n\u001b[1;32m     87\u001b[0m X_real, y_real \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdfs\u001b[39m.\u001b[39miloc[:,\u001b[39m1\u001b[39m:]\u001b[39m.\u001b[39mvalues, np\u001b[39m.\u001b[39mones((\u001b[39m23\u001b[39m, \u001b[39m1\u001b[39m)) \u001b[39m#generate real examples\u001b[39;00m\n\u001b[1;32m     88\u001b[0m d_model\u001b[39m.\u001b[39mtrain_on_batch(X_real, y_real)               \u001b[39m# train on real data\u001b[39;00m\n\u001b[0;32m---> 89\u001b[0m X_fake, y_fake \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgenerate_fake_samples(g_model, latent_dim, half_batch) \u001b[39m#generate fake samples\u001b[39;00m\n\u001b[1;32m     90\u001b[0m d_model\u001b[39m.\u001b[39mtrain_on_batch(X_fake, y_fake)                \u001b[39m#train on fake data\u001b[39;00m\n\u001b[1;32m     91\u001b[0m \u001b[39m#prepare points in latent space as input for the generator\u001b[39;00m\n",
      "File \u001b[0;32m~/Downloads/hetfit2/utils/ndgan.py:56\u001b[0m, in \u001b[0;36mDCGAN.generate_fake_samples\u001b[0;34m(self, generator, latent_dim, n)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgenerate_fake_samples\u001b[39m(\u001b[39mself\u001b[39m,generator, latent_dim, n):\n\u001b[1;32m     55\u001b[0m     x_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgenerate_latent_points(latent_dim, n) \u001b[39m#genarate points in latent space\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m     x \u001b[39m=\u001b[39m generator\u001b[39m.\u001b[39;49mpredict(x_input) \u001b[39m#predict outputs\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     y \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((n, \u001b[39m1\u001b[39m))\n\u001b[1;32m     58\u001b[0m     \u001b[39mreturn\u001b[39;00m x, y\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/keras/engine/training.py:2346\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   2344\u001b[0m callbacks\u001b[39m.\u001b[39mon_predict_begin()\n\u001b[1;32m   2345\u001b[0m batch_outputs \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 2346\u001b[0m \u001b[39mfor\u001b[39;00m _, iterator \u001b[39min\u001b[39;00m data_handler\u001b[39m.\u001b[39menumerate_epochs():  \u001b[39m# Single epoch.\u001b[39;00m\n\u001b[1;32m   2347\u001b[0m     \u001b[39mwith\u001b[39;00m data_handler\u001b[39m.\u001b[39mcatch_stop_iteration():\n\u001b[1;32m   2348\u001b[0m         \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m data_handler\u001b[39m.\u001b[39msteps():\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/keras/engine/data_adapter.py:1304\u001b[0m, in \u001b[0;36mDataHandler.enumerate_epochs\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1302\u001b[0m \u001b[39m\"\"\"Yields `(epoch, tf.data.Iterator)`.\"\"\"\u001b[39;00m\n\u001b[1;32m   1303\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_truncate_execution_to_epoch():\n\u001b[0;32m-> 1304\u001b[0m     data_iterator \u001b[39m=\u001b[39m \u001b[39miter\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset)\n\u001b[1;32m   1305\u001b[0m     \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_initial_epoch, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_epochs):\n\u001b[1;32m   1306\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_insufficient_data:  \u001b[39m# Set by `catch_stop_iteration`.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/data/ops/dataset_ops.py:499\u001b[0m, in \u001b[0;36mDatasetV2.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[39mif\u001b[39;00m context\u001b[39m.\u001b[39mexecuting_eagerly() \u001b[39mor\u001b[39;00m ops\u001b[39m.\u001b[39minside_function():\n\u001b[1;32m    498\u001b[0m   \u001b[39mwith\u001b[39;00m ops\u001b[39m.\u001b[39mcolocate_with(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variant_tensor):\n\u001b[0;32m--> 499\u001b[0m     \u001b[39mreturn\u001b[39;00m iterator_ops\u001b[39m.\u001b[39;49mOwnedIterator(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    500\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    501\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`tf.data.Dataset` only supports Python-style \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    502\u001b[0m                      \u001b[39m\"\u001b[39m\u001b[39miteration in eager mode or within tf.function.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/data/ops/iterator_ops.py:703\u001b[0m, in \u001b[0;36mOwnedIterator.__init__\u001b[0;34m(self, dataset, components, element_spec)\u001b[0m\n\u001b[1;32m    699\u001b[0m   \u001b[39mif\u001b[39;00m (components \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m element_spec \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    700\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    701\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mWhen `dataset` is provided, `element_spec` and `components` must \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    702\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mnot be specified.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 703\u001b[0m   \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_iterator(dataset)\n\u001b[1;32m    705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_next_call_count \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/data/ops/iterator_ops.py:742\u001b[0m, in \u001b[0;36mOwnedIterator._create_iterator\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    739\u001b[0m   \u001b[39massert\u001b[39;00m \u001b[39mlen\u001b[39m(fulltype\u001b[39m.\u001b[39margs[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39margs[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39margs) \u001b[39m==\u001b[39m \u001b[39mlen\u001b[39m(\n\u001b[1;32m    740\u001b[0m       \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flat_output_types)\n\u001b[1;32m    741\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterator_resource\u001b[39m.\u001b[39mop\u001b[39m.\u001b[39mexperimental_set_type(fulltype)\n\u001b[0;32m--> 742\u001b[0m gen_dataset_ops\u001b[39m.\u001b[39;49mmake_iterator(ds_variant, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_iterator_resource)\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.10/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3409\u001b[0m, in \u001b[0;36mmake_iterator\u001b[0;34m(dataset, iterator, name)\u001b[0m\n\u001b[1;32m   3407\u001b[0m \u001b[39mif\u001b[39;00m tld\u001b[39m.\u001b[39mis_eager:\n\u001b[1;32m   3408\u001b[0m   \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3409\u001b[0m     _result \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_FastPathExecute(\n\u001b[1;32m   3410\u001b[0m       _ctx, \u001b[39m\"\u001b[39;49m\u001b[39mMakeIterator\u001b[39;49m\u001b[39m\"\u001b[39;49m, name, dataset, iterator)\n\u001b[1;32m   3411\u001b[0m     \u001b[39mreturn\u001b[39;00m _result\n\u001b[1;32m   3412\u001b[0m   \u001b[39mexcept\u001b[39;00m _core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "from data_augmentation import dataset\n",
    "obj = dataset(1000,'new')\n",
    "obj.generate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "obj.vae.gan_model.save_weights('generator_model',save_format='h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.keras.utils.plot_model(\n",
    "    obj.vae.gan_model,\n",
    "    expand_nested=True,\n",
    "    # show_shapes=True,\n",
    "    show_layer_activations=True,\n",
    "    to_file='gan.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "1. <a href = 'https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-1-dimensional-function-from-scratch-in-keras/' >This </a> blog article.\n",
    "2. The GAN paper by Ian Goodfellow: https://arxiv.org/pdf/1406.2661.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Improvements and further insights possible:\n",
    "1. Try deeper layers in discriminator and generator models.\n",
    "2. Try experimenting with different activation functions and learning rates\n",
    "3. Try out more functions!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}