GANs for morphology.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Reshape, Dropout, Activation\n",
    "from keras.layers import Input, merge\n",
    "from keras.models import Model\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras import backend as K\n",
    "\n",
    "# Other\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Local\n",
    "\n",
    "import McNeuron\n",
    "import morphology_generator as models\n",
    "import train_one_by_one_morphology as train\n",
    "import batch_utils\n",
    "import data_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "training_data = pickle.load(open(\"/Volumes/Arch/Dropbox/HG-GAN/03-Data/synthetic_Two_segment_model_Different_directions_Parent_distance_n20_parent_id.p\", \"rb\"))\n",
    "#training_data = pickle.load(open(\"/Users/pavanramkumar/Dropbox/HG-GAN/03-Data/train4.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global parameters¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_levels = 1\n",
    "n_nodes = [20]\n",
    "\n",
    "input_dim = 100\n",
    "\n",
    "n_epochs = 5\n",
    "batch_size = 32\n",
    "n_batch_per_epoch = np.floor(training_data['morphology']['n20'].shape[0]/batch_size).astype(int)\n",
    "d_iters = 20\n",
    "lr_discriminator =  0.005\n",
    "lr_generator = 0.0005\n",
    "train_loss = 'wasserstein_loss'\n",
    "\n",
    "rule = 'm'\n",
    "train_one_by_one = False\n",
    "weight_constraint = [-0.05, 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 19, 20)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                    (None, 19, 50)        14200       input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)              (None, 1, 950)        0           lstm_1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "embedding (Dense)                (None, 1, 100)        95100       reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 1, 50)         5050        embedding[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 1, 50)         2550        dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_3 (Dense)                  (None, 1, 1)          51          dense_2[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 116,951\n",
      "Trainable params: 116,951\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "noise_input (InputLayer)         (None, 1, 100)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_4 (Dense)                  (None, 1, 570)        57570       noise_input[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "dense_5 (Dense)                  (None, 1, 570)        325470      dense_4[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_6 (Dense)                  (None, 1, 380)        216980      dense_5[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)              (None, 19, 20)        0           dense_6[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)                (None, 19, 20)        0           reshape_2[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 600,020\n",
      "Trainable params: 600,020\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n",
      "\n",
      "====================\n",
      "Level #0\n",
      "====================\n",
      "\n",
      "    Epoch #0\n",
      "\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -967.821594238\n",
      "\n",
      "    Generator_Loss: -598.791564941\n",
      "2\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1707.41918945\n",
      "\n",
      "    Generator_Loss: 1959.32788086\n",
      "3\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1933.90380859\n",
      "\n",
      "    Generator_Loss: 3052.96264648\n",
      "4\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1526.31030273\n",
      "\n",
      "    Generator_Loss: -349.807556152\n",
      "5\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1722.69042969\n",
      "\n",
      "    Generator_Loss: -34.7394943237\n",
      "6\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1918.12438965\n",
      "\n",
      "    Generator_Loss: -967.039123535\n",
      "7\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1645.14099121\n",
      "\n",
      "    Generator_Loss: -1018.93591309\n",
      "8\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1854.29797363\n",
      "\n",
      "    Generator_Loss: -456.707214355\n",
      "9\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2565.73632812\n",
      "\n",
      "    Generator_Loss: 285.132141113\n",
      "10\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3924.36181641\n",
      "\n",
      "    Generator_Loss: 306.540802002\n",
      "11\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2259.17675781\n",
      "\n",
      "    Generator_Loss: -2471.43603516\n",
      "12\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1316.66455078\n",
      "\n",
      "    Generator_Loss: -1339.39282227\n",
      "13\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3925.7644043\n",
      "\n",
      "    Generator_Loss: 358.579772949\n",
      "14\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1946.88525391\n",
      "\n",
      "    Generator_Loss: 463.483520508\n",
      "15\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1623.52954102\n",
      "\n",
      "    Generator_Loss: -451.703857422\n",
      "16\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1252.53771973\n",
      "\n",
      "    Generator_Loss: -1218.45654297\n",
      "17\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3168.71020508\n",
      "\n",
      "    Generator_Loss: -969.887695312\n",
      "18\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2787.84326172\n",
      "\n",
      "    Generator_Loss: -1239.81286621\n",
      "19\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4092.40209961\n",
      "\n",
      "    Generator_Loss: 270.31060791\n",
      "20\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3846.26806641\n",
      "\n",
      "    Generator_Loss: -1546.2779541\n",
      "21\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4752.11621094\n",
      "\n",
      "    Generator_Loss: -432.53012085\n",
      "22\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3712.97363281\n",
      "\n",
      "    Generator_Loss: -70.369644165\n",
      "23\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3799.32202148\n",
      "\n",
      "    Generator_Loss: -2157.42675781\n",
      "24\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3128.69628906\n",
      "\n",
      "    Generator_Loss: -588.205871582\n",
      "25\n",
      "     Level #0 Epoch #0 Batch #25\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4496.65332031\n",
      "\n",
      "    Generator_Loss: 480.325927734\n",
      "26\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4233.36572266\n",
      "\n",
      "    Generator_Loss: -2917.52270508\n",
      "27\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4021.56640625\n",
      "\n",
      "    Generator_Loss: -896.052246094\n",
      "28\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5446.1015625\n",
      "\n",
      "    Generator_Loss: -4274.1796875\n",
      "29\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5012.55371094\n",
      "\n",
      "    Generator_Loss: 963.571044922\n",
      "30\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4537.92285156\n",
      "\n",
      "    Generator_Loss: -2570.42822266\n",
      "31\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3572.08959961\n",
      "\n",
      "    Generator_Loss: -4481.99707031\n",
      "32\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4583.40332031\n",
      "\n",
      "    Generator_Loss: -1043.99121094\n",
      "33\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2691.2253418\n",
      "\n",
      "    Generator_Loss: -4592.90869141\n",
      "34\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4050.25708008\n",
      "\n",
      "    Generator_Loss: -3923.42236328\n",
      "35\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3791.73364258\n",
      "\n",
      "    Generator_Loss: -4907.62792969\n",
      "36\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4969.0078125\n",
      "\n",
      "    Generator_Loss: 1300.90478516\n",
      "37\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4201.90869141\n",
      "\n",
      "    Generator_Loss: -4069.45703125\n",
      "38\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -606.792419434\n",
      "\n",
      "    Generator_Loss: 8858.18554688\n",
      "39\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1928.75195312\n",
      "\n",
      "    Generator_Loss: 8233.33496094\n",
      "40\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2541.49389648\n",
      "\n",
      "    Generator_Loss: 5992.32958984\n",
      "41\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4176.88671875\n",
      "\n",
      "    Generator_Loss: 7865.19287109\n",
      "42\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2396.06518555\n",
      "\n",
      "    Generator_Loss: 7397.33105469\n",
      "43\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3483.76318359\n",
      "\n",
      "    Generator_Loss: 8488.94628906\n",
      "44\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3493.51367188\n",
      "\n",
      "    Generator_Loss: 8116.08398438\n",
      "45\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4262.38623047\n",
      "\n",
      "    Generator_Loss: 7044.72119141\n",
      "46\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3529.55102539\n",
      "\n",
      "    Generator_Loss: 6655.37011719\n",
      "47\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4170.61474609\n",
      "\n",
      "    Generator_Loss: 7812.07421875\n",
      "48\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3501.26098633\n",
      "\n",
      "    Generator_Loss: 6431.37207031\n",
      "49\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4482.30615234\n",
      "\n",
      "    Generator_Loss: 7573.07714844\n",
      "50\n",
      "     Level #0 Epoch #0 Batch #50\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133ab7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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,
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a386290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3052.70166016\n",
      "\n",
      "    Generator_Loss: -4214.93652344\n",
      "51\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3967.09887695\n",
      "\n",
      "    Generator_Loss: 1865.81140137\n",
      "52\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3815.94287109\n",
      "\n",
      "    Generator_Loss: -4811.34716797\n",
      "53\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4801.12255859\n",
      "\n",
      "    Generator_Loss: -4664.37646484\n",
      "54\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4928.55859375\n",
      "\n",
      "    Generator_Loss: -5265.59033203\n",
      "55\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3656.37255859\n",
      "\n",
      "    Generator_Loss: 9536.13183594\n",
      "56\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4948.10253906\n",
      "\n",
      "    Generator_Loss: 7015.95068359\n",
      "57\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5320.56591797\n",
      "\n",
      "    Generator_Loss: 6060.56494141\n",
      "58\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5437.27783203\n",
      "\n",
      "    Generator_Loss: 7495.49414062\n",
      "59\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2871.97143555\n",
      "\n",
      "    Generator_Loss: -4501.9765625\n",
      "60\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5001.89599609\n",
      "\n",
      "    Generator_Loss: -1963.56994629\n",
      "61\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4224.73535156\n",
      "\n",
      "    Generator_Loss: -2326.64160156\n",
      "62\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4468.15673828\n",
      "\n",
      "    Generator_Loss: 297.103179932\n",
      "63\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2709.78930664\n",
      "\n",
      "    Generator_Loss: 1325.15405273\n",
      "64\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3808.54736328\n",
      "\n",
      "    Generator_Loss: -8056.37255859\n",
      "65\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6213.81445312\n",
      "\n",
      "    Generator_Loss: -1631.66894531\n",
      "66\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5496.10693359\n",
      "\n",
      "    Generator_Loss: -7545.56591797\n",
      "67\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5367.00732422\n",
      "\n",
      "    Generator_Loss: -6422.23388672\n",
      "68\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5534.14648438\n",
      "\n",
      "    Generator_Loss: -1214.80578613\n",
      "69\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5306.92529297\n",
      "\n",
      "    Generator_Loss: 489.946411133\n",
      "70\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4232.97265625\n",
      "\n",
      "    Generator_Loss: -6842.88574219\n",
      "71\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3410.0378418\n",
      "\n",
      "    Generator_Loss: -2075.72729492\n",
      "72\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1335.22851562\n",
      "\n",
      "    Generator_Loss: -6843.86474609\n",
      "73\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -930.603271484\n",
      "\n",
      "    Generator_Loss: 14725.8115234\n",
      "74\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6617.54833984\n",
      "\n",
      "    Generator_Loss: 7128.12597656\n",
      "75\n",
      "     Level #0 Epoch #0 Batch #75\n"
     ]
    },
    {
     "data": {
      "image/png": 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skk9sdzoMmtN39VbUsKUZ1q9k5jVsk2HpP5Lcc93ze3Lt/85mobX27PLPnyV5PNeW9efm\nclW9Nkmq6u4kP93yfDamtfbTtpTknzLPz/80m3X9StSwzLh+JWrYJsPS00neUFX3VtW5JO9O8sUN\njr81VfXyqvqd5eNXJPnTJBf7e+2lLyZ57/Lxe5P8yxbnslHL4vp/3pV5fv6n2WzrV6KGLc22fiVq\n2Fp7w12vtfZ8VX0wyVeSHCR5tLX27U2Nv2V3JXl82c/sMMnnWmtPbHdK61VVn0/yUJLXVNUzSf42\nyceS/HNVvS/JpSR/sb0Zrs9Njv2RJOer6r5cW7r/YZIPbHGKDJp5/UpmVsPmXL8SNexmtDsBAOhw\nB28AgA5hCQCgQ1gCAOgQlgAAOoQlAIAOYQkAoENYAgDoEJYAADr+F1bDfYjGf8kSAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135d4ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T5DPrHQ6D5vRZvRk17NAM61cy8xq2yrD0X0necM3zN+Tq/85mobX2zOGfv0jyWK5O68/N\n5ap6bZJU1euS/HzN41mZ1trP26Ek/5J5vv+n2azrV6KGZcb1K1HDVhmWnkzypqq6u6puT/LeJF9d\n4f7XpqpeWlW/c/j4ZUn+LMlT/bW20leTvP/w8fuT/Nsax7JSh8X1N96Teb7/p9ls61eihh2abf1K\n1LCV9YZrrV2pqg8n+UaSnSSPtNa+u6r9r9ldSR6rquTq7/xLrbUL6x3SclXVl5M8lOQ1VfV0kr9P\n8skk/1pVH0hyKclfrW+Ey3ODY384yV5V3ZurU/c/TvKhNQ6RQTOvX8nMatic61eiht2IdicAAB3u\n4A0A0CEsAQB0CEsAAB3CEgBAh7AEANAhLAEAdAhLAAAdwhIAQMf/AwKjoa65oee4AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115182d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4520.55957031\n",
      "\n",
      "    Generator_Loss: 9116.72558594\n",
      "76\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6571.66357422\n",
      "\n",
      "    Generator_Loss: 6647.03759766\n",
      "77\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7178.015625\n",
      "\n",
      "    Generator_Loss: 6773.55908203\n",
      "78\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5864.77929688\n",
      "\n",
      "    Generator_Loss: 4558.74902344\n",
      "79\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4371.24169922\n",
      "\n",
      "    Generator_Loss: 11469.9472656\n",
      "80\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2305.97094727\n",
      "\n",
      "    Generator_Loss: 15596.5527344\n",
      "81\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2682.28222656\n",
      "\n",
      "    Generator_Loss: 12661.6513672\n",
      "82\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4408.99023438\n",
      "\n",
      "    Generator_Loss: 13648.1191406\n",
      "83\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5181.22460938\n",
      "\n",
      "    Generator_Loss: 12204.2802734\n",
      "84\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5114.24609375\n",
      "\n",
      "    Generator_Loss: 12737.6992188\n",
      "85\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4712.10888672\n",
      "\n",
      "    Generator_Loss: 12343.7011719\n",
      "86\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3106.26025391\n",
      "\n",
      "    Generator_Loss: 6913.90966797\n",
      "87\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5976.72607422\n",
      "\n",
      "    Generator_Loss: -6461.99414062\n",
      "88\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4516.04345703\n",
      "\n",
      "    Generator_Loss: -11238.9755859\n",
      "89\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5889.45068359\n",
      "\n",
      "    Generator_Loss: 1149.67248535\n",
      "90\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6451.30078125\n",
      "\n",
      "    Generator_Loss: 4108.23339844\n",
      "91\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6970.73095703\n",
      "\n",
      "    Generator_Loss: 3121.98681641\n",
      "92\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4312.29003906\n",
      "\n",
      "    Generator_Loss: 5781.65332031\n",
      "93\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7901.17431641\n",
      "\n",
      "    Generator_Loss: -2581.6003418\n",
      "94\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2828.21704102\n",
      "\n",
      "    Generator_Loss: 1526.88500977\n",
      "95\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4199.46386719\n",
      "\n",
      "    Generator_Loss: -3282.33374023\n",
      "96\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4552.60839844\n",
      "\n",
      "    Generator_Loss: -10096.2011719\n",
      "97\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5687.40429688\n",
      "\n",
      "    Generator_Loss: -8829.36328125\n",
      "98\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2416.27612305\n",
      "\n",
      "    Generator_Loss: -5244.08105469\n",
      "99\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5002.41162109\n",
      "\n",
      "    Generator_Loss: 9682.0234375\n",
      "100\n",
      "     Level #0 Epoch #0 Batch #100\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b48f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6yQOttRuSfHv3/ia60L63JHfvPv83t9a+sYJ1MZH6lWReNWzO9StRw15hmUeWbknyk9ba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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7b1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -845.170715332\n",
      "\n",
      "    Generator_Loss: 17454.2910156\n",
      "101\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -2756.9519043\n",
      "\n",
      "    Generator_Loss: 16733.9335938\n",
      "102\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3664.81079102\n",
      "\n",
      "    Generator_Loss: 13349.0\n",
      "103\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3965.38452148\n",
      "\n",
      "    Generator_Loss: 8193.41699219\n",
      "104\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6033.55957031\n",
      "\n",
      "    Generator_Loss: 9821.72167969\n",
      "105\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6042.04931641\n",
      "\n",
      "    Generator_Loss: 8801.96484375\n",
      "106\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6961.49707031\n",
      "\n",
      "    Generator_Loss: 11743.2734375\n",
      "107\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5824.11621094\n",
      "\n",
      "    Generator_Loss: 11498.3056641\n",
      "108\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7711.48925781\n",
      "\n",
      "    Generator_Loss: 11054.3076172\n",
      "109\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6260.00976562\n",
      "\n",
      "    Generator_Loss: 13365.046875\n",
      "110\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8301.13671875\n",
      "\n",
      "    Generator_Loss: 9687.765625\n",
      "111\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5414.53857422\n",
      "\n",
      "    Generator_Loss: 9894.56640625\n",
      "112\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5354.47558594\n",
      "\n",
      "    Generator_Loss: 8115.22900391\n",
      "113\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7045.77148438\n",
      "\n",
      "    Generator_Loss: 8683.57421875\n",
      "114\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6400.98876953\n",
      "\n",
      "    Generator_Loss: 11882.0849609\n",
      "115\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8484.75195312\n",
      "\n",
      "    Generator_Loss: 9329.29003906\n",
      "116\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5728.69580078\n",
      "\n",
      "    Generator_Loss: -7194.27246094\n",
      "117\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5444.94580078\n",
      "\n",
      "    Generator_Loss: -5650.89306641\n",
      "118\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6768.32226562\n",
      "\n",
      "    Generator_Loss: -8530.84667969\n",
      "119\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4714.85498047\n",
      "\n",
      "    Generator_Loss: -5497.6015625\n",
      "120\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4509.04736328\n",
      "\n",
      "    Generator_Loss: 10349.7861328\n",
      "121\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5491.86914062\n",
      "\n",
      "    Generator_Loss: 10726.0615234\n",
      "122\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7930.91015625\n",
      "\n",
      "    Generator_Loss: 12068.7265625\n",
      "123\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7588.01855469\n",
      "\n",
      "    Generator_Loss: 10190.3134766\n",
      "124\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6002.76708984\n",
      "\n",
      "    Generator_Loss: 8186.78173828\n",
      "125\n",
      "     Level #0 Epoch #0 Batch #125\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c47eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4e5910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6302.33300781\n",
      "\n",
      "    Generator_Loss: 6932.5390625\n",
      "126\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6345.16796875\n",
      "\n",
      "    Generator_Loss: 9867.03710938\n",
      "127\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6345.19824219\n",
      "\n",
      "    Generator_Loss: 11088.0722656\n",
      "128\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7369.359375\n",
      "\n",
      "    Generator_Loss: 9889.19042969\n",
      "129\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5408.45166016\n",
      "\n",
      "    Generator_Loss: 11767.1064453\n",
      "130\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7867.62207031\n",
      "\n",
      "    Generator_Loss: 7185.80615234\n",
      "131\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7490.171875\n",
      "\n",
      "    Generator_Loss: 9520.95703125\n",
      "132\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9378.79296875\n",
      "\n",
      "    Generator_Loss: 11401.859375\n",
      "133\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6481.37744141\n",
      "\n",
      "    Generator_Loss: 9763.21386719\n",
      "134\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8994.54296875\n",
      "\n",
      "    Generator_Loss: 8875.97265625\n",
      "135\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9069.75878906\n",
      "\n",
      "    Generator_Loss: 9834.85546875\n",
      "136\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8702.53710938\n",
      "\n",
      "    Generator_Loss: 13059.5107422\n",
      "137\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7584.93164062\n",
      "\n",
      "    Generator_Loss: 11471.5761719\n",
      "138\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5629.88476562\n",
      "\n",
      "    Generator_Loss: 13056.5537109\n",
      "139\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9337.71679688\n",
      "\n",
      "    Generator_Loss: 11401.5097656\n",
      "140\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8723.41796875\n",
      "\n",
      "    Generator_Loss: 11556.7529297\n",
      "141\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7322.47998047\n",
      "\n",
      "    Generator_Loss: 4120.94580078\n",
      "142\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6786.52929688\n",
      "\n",
      "    Generator_Loss: 11253.2460938\n",
      "143\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4455.29443359\n",
      "\n",
      "    Generator_Loss: -3467.16088867\n",
      "144\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7722.66064453\n",
      "\n",
      "    Generator_Loss: 11567.8320312\n",
      "145\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8741.80859375\n",
      "\n",
      "    Generator_Loss: 7860.84863281\n",
      "146\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8479.15332031\n",
      "\n",
      "    Generator_Loss: 12954.1933594\n",
      "147\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8372.26171875\n",
      "\n",
      "    Generator_Loss: 12706.2421875\n",
      "148\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8520.37988281\n",
      "\n",
      "    Generator_Loss: 12518.7089844\n",
      "149\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6641.08349609\n",
      "\n",
      "    Generator_Loss: -7603.21386719\n",
      "150\n",
      "     Level #0 Epoch #0 Batch #150\n"
     ]
    },
    {
     "data": {
      "image/png": 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JN7a4/52pqpdU1R8d335pknckeap/1bn0jSQfOL79gST/ssOxbNVxcf1/78k63/+zbLX1\nK1HDjq22fiVq2EZ7w11vjPFcVX0kybeTHCR5aIzxg23tf8fuTvLIca+tC0m+MsZ4dLdD2qyq+mqS\ntyV5ZVX9LMmnkvxjkq9X1QeTPJ3kvbsb4ebc4NgfTHKxqu7Ntan7nyb58A6HyKSV169kZTVszfUr\nUcNuRLsTAICGb/AGAGgISwAADWEJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQOP/AMdQN0GSyqjW\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103a395d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ST6W5Ltr3P/GVNXrq+r3FpffkOTPkzzVv9dO+m6STywufyLJv26wlrVaNNeXfTTTfP7P\nssn2r0QPW5hs/0r0sLXNhmutXauqzyb5QZK9JA+21n6yrv1v2O1JHqmq5Prf+bdaa49utqTVqqpv\nJ7k7yVuq6pkkf5/kS0n+pao+meRKkr/eXIWrc4Njvy/JrKruyPWl+6eTfHqDJTJo4v0rmVgPm3L/\nSvSwGzHuBACgwzd4AwB0CEsAAB3CEgBAh7AEANAhLAEAdAhLAAAdwhIAQIewBADQ8X+EIW3ZuJhQ\nqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a17e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4111.14404297\n",
      "\n",
      "    Generator_Loss: 509.440124512\n",
      "151\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8712.05566406\n",
      "\n",
      "    Generator_Loss: -3117.1159668\n",
      "152\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5234.60253906\n",
      "\n",
      "    Generator_Loss: -823.056335449\n",
      "153\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6022.40136719\n",
      "\n",
      "    Generator_Loss: -9566.20117188\n",
      "154\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5546.10986328\n",
      "\n",
      "    Generator_Loss: -8015.0390625\n",
      "155\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9306.7265625\n",
      "\n",
      "    Generator_Loss: 11663.8632812\n",
      "156\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -414.717346191\n",
      "\n",
      "    Generator_Loss: 21865.5761719\n",
      "157\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -426.412902832\n",
      "\n",
      "    Generator_Loss: 21180.1972656\n",
      "158\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -431.963531494\n",
      "\n",
      "    Generator_Loss: 21163.8925781\n",
      "159\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -380.592132568\n",
      "\n",
      "    Generator_Loss: 20633.8632812\n",
      "160\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8397.26367188\n",
      "\n",
      "    Generator_Loss: 12471.8183594\n",
      "161\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7628.82421875\n",
      "\n",
      "    Generator_Loss: 11394.8144531\n",
      "162\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8378.10351562\n",
      "\n",
      "    Generator_Loss: 10343.3525391\n",
      "163\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7490.83740234\n",
      "\n",
      "    Generator_Loss: 10615.1835938\n",
      "164\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9779.4375\n",
      "\n",
      "    Generator_Loss: 12065.7675781\n",
      "165\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9233.546875\n",
      "\n",
      "    Generator_Loss: 13788.2744141\n",
      "166\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9315.92285156\n",
      "\n",
      "    Generator_Loss: 9731.68945312\n",
      "167\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9658.44921875\n",
      "\n",
      "    Generator_Loss: 10204.7841797\n",
      "168\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7803.36914062\n",
      "\n",
      "    Generator_Loss: 14034.1044922\n",
      "169\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10987.765625\n",
      "\n",
      "    Generator_Loss: 10371.7167969\n",
      "170\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6873.92431641\n",
      "\n",
      "    Generator_Loss: 8968.10644531\n",
      "171\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8957.86035156\n",
      "\n",
      "    Generator_Loss: 11468.9824219\n",
      "172\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9943.11035156\n",
      "\n",
      "    Generator_Loss: 13167.5439453\n",
      "173\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7253.75732422\n",
      "\n",
      "    Generator_Loss: 13868.1484375\n",
      "174\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8699.09667969\n",
      "\n",
      "    Generator_Loss: 10486.2441406\n",
      "175\n",
      "     Level #0 Epoch #0 Batch #175\n"
     ]
    },
    {
     "data": {
      "image/png": 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qelmSDyT5+g63vzdV9fKq+oPj269I8hdJnuvXOpe+nuRDx7c/lORf9jiWnTourv/vfVnn\n63+WrbZ+JWrYsdXWr0QN22pvuJuNMa5V1ceSfDvJhSRPjDF+uKvt79kDSZ467rV1McmXxxiX9zuk\n7aqqryR5Z5JXVdXPk3w6yT8m+VpVfTjJ80nev78Rbs9t9v2xJI9U1dtyY+r+Z0k+uschMmnl9StZ\nWQ1bc/1K1LDb0e4EAKDhE7wBABrCEgBAQ1gCAGgISwAADWEJAKAhLAEANIQlAICGsAQA0Pg/09Kn\nZqHHtt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f641090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Wv2P3JXmyqpLrP/NvjDGe2u2QNquqvpnk4SRvqapfJflikn9O8u2q+mSSF5J8dHcj3Jyb\nbPsjSY6q6r25PnX/yySf3uEQmbTw+pUsrIYtuX4latjNaHcCANBwBW8AgIawBADQEJYAABrCEgBA\nQ1gCAGgISwAADWEJAKAhLAEANP4fhwiC/dTSemMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113845cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9299.71777344\n",
      "\n",
      "    Generator_Loss: 15006.1259766\n",
      "176\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9315.72949219\n",
      "\n",
      "    Generator_Loss: 11066.21875\n",
      "177\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6687.37890625\n",
      "\n",
      "    Generator_Loss: 14825.8632812\n",
      "178\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8435.40136719\n",
      "\n",
      "    Generator_Loss: 10194.0341797\n",
      "179\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9967.62792969\n",
      "\n",
      "    Generator_Loss: 10737.7568359\n",
      "180\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8414.28515625\n",
      "\n",
      "    Generator_Loss: 10667.3847656\n",
      "181\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7453.83837891\n",
      "\n",
      "    Generator_Loss: 11515.765625\n",
      "182\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9498.91992188\n",
      "\n",
      "    Generator_Loss: 11567.390625\n",
      "183\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10925.3398438\n",
      "\n",
      "    Generator_Loss: 13162.3486328\n",
      "184\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10593.3828125\n",
      "\n",
      "    Generator_Loss: 15195.5234375\n",
      "185\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8932.52832031\n",
      "\n",
      "    Generator_Loss: 14443.5263672\n",
      "186\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10018.7988281\n",
      "\n",
      "    Generator_Loss: 12146.6074219\n",
      "187\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8889.80078125\n",
      "\n",
      "    Generator_Loss: 15530.5908203\n",
      "188\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8267.88476562\n",
      "\n",
      "    Generator_Loss: 16310.8535156\n",
      "189\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10487.7158203\n",
      "\n",
      "    Generator_Loss: 11259.2099609\n",
      "190\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -3178.49316406\n",
      "\n",
      "    Generator_Loss: -5843.10839844\n",
      "191\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8285.1328125\n",
      "\n",
      "    Generator_Loss: -2605.76220703\n",
      "192\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8097.86425781\n",
      "\n",
      "    Generator_Loss: -9403.6015625\n",
      "193\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7260.85986328\n",
      "\n",
      "    Generator_Loss: -8879.47167969\n",
      "194\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9799.38183594\n",
      "\n",
      "    Generator_Loss: -11813.3408203\n",
      "195\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6672.49560547\n",
      "\n",
      "    Generator_Loss: 1874.5012207\n",
      "196\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7636.47363281\n",
      "\n",
      "    Generator_Loss: 7701.06103516\n",
      "197\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8541.09277344\n",
      "\n",
      "    Generator_Loss: -7214.12353516\n",
      "198\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9086.13476562\n",
      "\n",
      "    Generator_Loss: -1388.14660645\n",
      "199\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6076.22900391\n",
      "\n",
      "    Generator_Loss: -7133.20166016\n",
      "200\n",
      "     Level #0 Epoch #0 Batch #200\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADXZJREFUeJzt3U+opXd5B/DvMxNcKIUgljEpwpSgRUFIskgLUhxK/0w2\n/oESGShIkeCiZm26adJ2owVdCW6ahCxkpJuUuNFE4cBAFyUh2CwMUTCQNOlE2kC7jJlfF3NKr5M7\nz9z3zPl7388HLnPOmfee87ycy3O/9/e+73lqjBEAAI53ZtcFAADsM2EJAKAhLAEANIQlAICGsAQA\n0BCWAAAad2zqiavKZxLADI0xatc13C79C+bpZv1rr1aWqmrS1xjj4L4ee+yxnddg3+3/pr7mbGr/\nOsQeNoefYfs/333vrByWqupiVb1SVT+vqq+v+jwA26Z/AVOsFJaq6myS7yS5mORTSS5V1SfXWRjA\nJuhfwFSrriw9kOQXY4zXxhjvJvl+ks+vr6zT68KFC7suYWfmvO+J/d8j+teK5v4zPOf9n/O+J0nd\n6jjdsd9U9edJ/myM8fDy/l8k+f0xxiNHtpn8xFXTzgu9du3a1JcANmh5Hs5en+C9L/0r0cNgn3T9\na9Wr4eZ9JieQJFksFlksFrsuYyr9C5jUv1ZdWfqDJI+PMS4u7/91kmtjjG8e2cbKEszMgaws7UX/\nSvQw2Cdd/1r1nKUXkny8qs5X1QeSfCnJs6sWCLBF+hcwyUqH4cYYv66qryX5UZKzSZ4YY/xsrZUB\nbID+BUy10mG4Ez2xw3AwO4dwGO4kHIaD+dnEYTgAgFkQlgAAGhsbpLuKqYcEz549O/k13nvvvcnf\nA3Arq5zSMLWH6V+wG1aWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCAhrAEANAQlgAAGsIS\nAEBDWAIAaGx0NlxVTdp+6tyjbcxiSsxjAjbj2rVrk7bXv2A3rCwBADSEJQCAhrAEANAQlgAAGsIS\nAEBDWAIAaAhLAAANYQkAoCEsAQA0hCUAgIawBADQEJYAABobHaQ71dTBu6uYOrgymT680uBKYBO2\n0b8SPQxuZGUJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCA\nhrAEANDYq0G6+2rq8EqDK4F9Yfgu3D4rSwAADWEJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENY\nAgBoCEsAAA1hCQCgISwBADTMhtsAs5iAQ7aNHqZ/cUisLAEANFZeWaqq15L8d5L3krw7xnhgXUUB\nbJoeBpzU7RyGG0kujDH+a13FAGyRHgacyO0ehqu1VAGwG3oYcEu3E5ZGkh9X1QtV9fC6CgLYEj0M\nOJHbOQz3mTHGW1X120mer6pXxhhX1lUYwIbpYcCJrByWxhhvLf/9VVU9k+SBJFdu2OY3vqfKijec\nJovFIovFYtdlrOQkPQw4vab0r7ox0Jzom6o+mOTsGON/qupDSZ5L8rdjjOeObDOmhqOpn+2xSu1n\nzuznpyWsUpfPKWHfVFXGGHv/V9GtelhVTW8uMze1h+lf7Juuf626snQuyTPLMHRHku8dDUoAe04P\nA05spZWlEz2xlaVJrCxxGhzKytKtWFmazsoSh67rX/uZHAAA9oSwBADQMEh3Txi+CxyyqT1M/+KQ\nWFkCAGgISwAADWEJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADQM\n0j1g2xi+a3AlsAmGh3NIrCwBADSEJQCAhrAEANAQlgAAGsISAEBDWAIAaAhLAAANYQkAoCEsAQA0\nhCUAgIawBADQMBtuZqbOYzKLCdgX5mGyK1aWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCA\nhrAEANAQlgAAGsISAEBDWAIAaAhLAAANg3RpbWNwZWJ4JbAZDz300KTt9S+OY2UJAKAhLAEANIQl\nAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCAhrAEANAwSJe128bwXYMrgZOo\nqknbGx7OcdqVpap6sqquVtXLRx77cFU9X1WvVtVzVXXn5ssEmE4PA9bhVofhnkpy8YbHHk3y/Bjj\nE0l+srwPsI/0MOC2tWFpjHElyTs3PPy5JE8vbz+d5AsbqAvgtulhwDqscoL3uTHG1eXtq0nOrbEe\ngE3Tw4BJbutquDHGSDLWVAvAVulhwEmscjXc1ar66BjjP6rqriRv32zD633o/029KgHYb4vFIovF\nYtdlTHXiHgacXlP6V90YaN63QdX5JD8YY3x6ef8fkvznGOObVfVokjvHGO87QbKqxqYv2bxV7cc5\nc8ZHS+2jqe+Ly273U1VljLFXfxWt0sOqymrTKXHp0qVJ21++fHnya6zye0UP2z9d/7rVRwdcTvIv\nSX6vql6vqr9M8o0kf1JVryb5o+V9gL2jhwHr0B6GG2PcLJL/8QZqAVgrPQxYB8ekAAAawhIAQMNs\nOPbC1JP7zWIC9oV5mKeflSUAgIawBADQEJYAABrCEgBAQ1gCAGgISwAADWEJAKAhLAEANIQlAICG\nsAQA0BCWAAAawhIAQMMgXQ7SNgZXJoZXApthePhhsbIEANAQlgAAGsISAEBDWAIAaAhLAAANYQkA\noCEsAQA0hCUAgIawBADQEJYAABrCEgBAQ1gCAGgYpMtsbGP4rsGVwCYYHr5bVpYAABrCEgBAQ1gC\nAGgISwAADWEJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBomA0HjanzmMxiAvaFeZjrY2UJ\nAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCAhrAEANAwSBfW\naBuDK5P5DK8Etsvw8ONZWQIAaLRhqaqerKqrVfXykccer6o3quql5dfFzZcJMJ0eBqzDrVaWnkpy\nYyMZSb49xrhv+fXDzZQGcNv0MOC2tWFpjHElyTvH/FdtphyA9dHDgHVY9ZylR6rqp1X1RFXdudaK\nADZPDwNObJWr4b6b5O+Wt/8+ybeSfOW4DccYv3G/yh9zcJosFossFotdlzHViXsYcHpN6V91Y6B5\n3wZV55P8YIzx6Yn/N6aGo6mXLN6q9uOcOeMCQPbLKj+T+3rpbVVljLFXfxWt0sOqanpzYS9dunRp\n0vaXL1/eUCWn01z61+S9rKq7jtz9YpKXb7YtwL7Rw4Cp2sNwVXU5yWeTfKSqXk/yWJILVXVvrl9R\n8sskX914lQAr0MOAdWjD0hjjuPXLJzdUC8Ba6WHAOjiBBwCgISwBADQM0oUd28bw3X29+gQ4bHMZ\nHm5lCQCgISwBADSEJQCAhrAEANAQlgAAGsISAEBDWAIAaAhLAAANYQkAoCEsAQA0hCUAgIbZcHCA\nps5jOsRZTMDpdIjzMK0sAQA0hCUAgIawBADQEJYAABrCEgBAQ1gCAGgISwAADWEJAKAhLAEANIQl\nAICGsAQA0BCWAAAaBunCDKwyuPLMmWl/Sz344IOTXwPgJKb2sKn965bPt9ZnAwA4ZYQlAICGsAQA\n0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgsfWwNMbY9ksCAKzMyhIAQENYAgBobHQ23P333/++x958\n883cfffdm3zZ1nE1bdOu93+X5rzvyeHtf1VN2v6ee+7ZUCW7cbNecWjv4zod4r6fP39+0vbd74hD\n3P91ObR9n9q/kuTFF1+8+fNt6hyiqnJyEszQGGN6l9oz+hfM083618bCEgDAaeCcJQCAhrAEANDY\naliqqotV9UpV/byqvr7N1961qnqtqv6tql6qqn/ddT2bVlVPVtXVqnr5yGMfrqrnq+rVqnququ7c\nZY2bcpN9f7yq3li+/y9V1cVd1sh0c+5fybx62Jz7V6KHHWdrYamqzib5TpKLST6V5FJVfXJbr78H\nRpILY4z7xhgP7LqYLXgq19/rox5N8vwY4xNJfrK8fxodt+8jybeX7/99Y4wf7qAuVqR/JZlXD5tz\n/0r0sPfZ5srSA0l+McZ4bYzxbpLvJ/n8Fl9/Hxz8VUInNca4kuSdGx7+XJKnl7efTvKFrRa1JTfZ\n92RG7/8ppH9dN4uf4Tn3r0QPO842w9LvJHn9yP03lo/NxUjy46p6oaoe3nUxO3JujHF1eftqknO7\nLGYHHqmqn1bVE6d5Cf+Umnv/SvSwufevZMY9bJthae6fUfCZMcZ9SR5M8ldV9Ye7LmiXxvXPrJjT\nz8R3k/xuknuTvJXkW7sth4nm9LN6M3rY0gz7VzLzHrbNsPTvST525P7Hcv2vs1kYY7y1/PdXSZ7J\n9WX9ublaVR9Nkqq6K8nbO65na8YYb4+lJP+Yeb7/h2zW/SvRwzLj/pXoYdsMSy8k+XhVna+qDyT5\nUpJnt/j6O1NVH6yq31re/lCSP03ycv9dp9KzSb68vP3lJP+8w1q2atlc/88XM8/3/5DNtn8letjS\nbPtXoodtdDbcUWOMX1fV15L8KMnZJE+MMX62rdffsXNJnlnOqrkjyffGGM/ttqTNqqrLST6b5CNV\n9XqSv0nyjST/VFVfSfJakod2V+HmHLPvjyW5UFX35vrS/S+TfHWHJTLRzPtXMrMeNuf+lehhxzHu\nBACg4RO8AQAawhIAQENYAgBoCEsAAA1hCQCgISwBADSEJQCAhrAEAND4X3cwz8Fp3SwgAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11515f410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c16ee50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6268.90673828\n",
      "\n",
      "    Generator_Loss: -8758.62109375\n",
      "201\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8724.30566406\n",
      "\n",
      "    Generator_Loss: 14158.9482422\n",
      "202\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5168.45654297\n",
      "\n",
      "    Generator_Loss: -8563.91015625\n",
      "203\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6904.87890625\n",
      "\n",
      "    Generator_Loss: -8689.40625\n",
      "204\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -6788.35595703\n",
      "\n",
      "    Generator_Loss: -7007.08886719\n",
      "205\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7737.546875\n",
      "\n",
      "    Generator_Loss: -801.47064209\n",
      "206\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -4205.46679688\n",
      "\n",
      "    Generator_Loss: -10851.7050781\n",
      "207\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -5697.58056641\n",
      "\n",
      "    Generator_Loss: 2218.81616211\n",
      "208\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10388.3720703\n",
      "\n",
      "    Generator_Loss: 11737.8662109\n",
      "209\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10273.4355469\n",
      "\n",
      "    Generator_Loss: 11082.21875\n",
      "210\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10583.9970703\n",
      "\n",
      "    Generator_Loss: 12335.4433594\n",
      "211\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9102.59082031\n",
      "\n",
      "    Generator_Loss: 13639.1875\n",
      "212\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10987.3447266\n",
      "\n",
      "    Generator_Loss: 16257.671875\n",
      "213\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9307.17089844\n",
      "\n",
      "    Generator_Loss: 13823.1953125\n",
      "214\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9082.57714844\n",
      "\n",
      "    Generator_Loss: 16786.3691406\n",
      "215\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10932.7294922\n",
      "\n",
      "    Generator_Loss: 14434.2890625\n",
      "216\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7434.92089844\n",
      "\n",
      "    Generator_Loss: 14612.328125\n",
      "217\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9996.8828125\n",
      "\n",
      "    Generator_Loss: 14157.3691406\n",
      "218\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9652.8203125\n",
      "\n",
      "    Generator_Loss: 11698.1875\n",
      "219\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10273.1005859\n",
      "\n",
      "    Generator_Loss: 14997.1845703\n",
      "220\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10545.1748047\n",
      "\n",
      "    Generator_Loss: 15648.9755859\n",
      "221\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10659.1669922\n",
      "\n",
      "    Generator_Loss: 13280.3388672\n",
      "222\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9866.12792969\n",
      "\n",
      "    Generator_Loss: 14247.359375\n",
      "223\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9955.73632812\n",
      "\n",
      "    Generator_Loss: 14047.1875\n",
      "224\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -7551.13330078\n",
      "\n",
      "    Generator_Loss: 13138.3203125\n",
      "225\n",
      "     Level #0 Epoch #0 Batch #225\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b408fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113781fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11278.4912109\n",
      "\n",
      "    Generator_Loss: 12557.8632812\n",
      "226\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10790.2080078\n",
      "\n",
      "    Generator_Loss: 15782.6181641\n",
      "227\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10420.0839844\n",
      "\n",
      "    Generator_Loss: 13588.6259766\n",
      "228\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9016.81835938\n",
      "\n",
      "    Generator_Loss: 12816.5927734\n",
      "229\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10241.5625\n",
      "\n",
      "    Generator_Loss: 16254.7763672\n",
      "230\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11315.3017578\n",
      "\n",
      "    Generator_Loss: 16990.9101562\n",
      "231\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12118.625\n",
      "\n",
      "    Generator_Loss: 13468.3583984\n",
      "232\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10452.0087891\n",
      "\n",
      "    Generator_Loss: 13288.5917969\n",
      "233\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10586.9072266\n",
      "\n",
      "    Generator_Loss: 12507.7138672\n",
      "234\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11999.0351562\n",
      "\n",
      "    Generator_Loss: 14413.7763672\n",
      "235\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9385.17382812\n",
      "\n",
      "    Generator_Loss: 13283.7988281\n",
      "236\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12440.5957031\n",
      "\n",
      "    Generator_Loss: 14807.5009766\n",
      "237\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9959.83789062\n",
      "\n",
      "    Generator_Loss: 13556.0507812\n",
      "238\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12557.2363281\n",
      "\n",
      "    Generator_Loss: 11715.4824219\n",
      "239\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14068.2939453\n",
      "\n",
      "    Generator_Loss: 11724.7216797\n",
      "240\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11658.8916016\n",
      "\n",
      "    Generator_Loss: 11419.4902344\n",
      "241\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11131.2705078\n",
      "\n",
      "    Generator_Loss: 13321.6679688\n",
      "242\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11522.4023438\n",
      "\n",
      "    Generator_Loss: 13997.1074219\n",
      "243\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12219.5244141\n",
      "\n",
      "    Generator_Loss: 14672.2851562\n",
      "244\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13955.1279297\n",
      "\n",
      "    Generator_Loss: 17265.4238281\n",
      "245\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12971.2128906\n",
      "\n",
      "    Generator_Loss: 12979.8818359\n",
      "246\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10536.3105469\n",
      "\n",
      "    Generator_Loss: 13902.8720703\n",
      "247\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12555.8105469\n",
      "\n",
      "    Generator_Loss: 14741.2373047\n",
      "248\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11909.9169922\n",
      "\n",
      "    Generator_Loss: 14186.4472656\n",
      "249\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9185.11914062\n",
      "\n",
      "    Generator_Loss: 14803.3945312\n",
      "250\n",
      "     Level #0 Epoch #0 Batch #250\n"
     ]
    },
    {
     "data": {
      "image/png": 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1qvpwkm8muZDk8THGD3Z1/D17MMmTx722Lib50hjjqf0Oabuq6stJ3p7kFVX1sySfSPL3\nSb5aVR9I8nyS9+xvhNtzm3N/LMmlqnpzbkzd/zTJh/Y4RCatvH4lK6tha65fiRp2O9qdAAA0fIM3\nAEBDWAIAaAhLAAANYQkAoCEsAQA0hCUAgIawBADQEJYAABr/C5a0YFx0rvgXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c4b2b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XVT2R5FKSN1XVL5J8Ick/JvlGVX0iyfNJPrK9Ea7PTY79wST7VfWe3Ji6/3mST21xiEy0\n8PqVLKyGLbl+JWrYzWh3AgDQcAZvAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAA1hCQCgISwBADT+\nDz43esBhb2ASAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d68d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12566.5751953\n",
      "\n",
      "    Generator_Loss: 16094.8154297\n",
      "251\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11376.3056641\n",
      "\n",
      "    Generator_Loss: 12991.4804688\n",
      "252\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10443.3613281\n",
      "\n",
      "    Generator_Loss: 15747.9140625\n",
      "253\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14413.6640625\n",
      "\n",
      "    Generator_Loss: 12723.9267578\n",
      "254\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11879.7236328\n",
      "\n",
      "    Generator_Loss: 17473.3730469\n",
      "255\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11825.7451172\n",
      "\n",
      "    Generator_Loss: 14826.0390625\n",
      "256\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14263.0791016\n",
      "\n",
      "    Generator_Loss: 12540.5693359\n",
      "257\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12288.0751953\n",
      "\n",
      "    Generator_Loss: 14600.3623047\n",
      "258\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11258.1289062\n",
      "\n",
      "    Generator_Loss: 13822.3974609\n",
      "259\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12666.0566406\n",
      "\n",
      "    Generator_Loss: 14219.6357422\n",
      "260\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10559.6464844\n",
      "\n",
      "    Generator_Loss: 14258.0791016\n",
      "261\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9604.77832031\n",
      "\n",
      "    Generator_Loss: 15793.4267578\n",
      "262\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12252.4248047\n",
      "\n",
      "    Generator_Loss: 15127.6523438\n",
      "263\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10165.7421875\n",
      "\n",
      "    Generator_Loss: 15383.5458984\n",
      "264\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14427.2275391\n",
      "\n",
      "    Generator_Loss: 13586.2246094\n",
      "265\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11597.8652344\n",
      "\n",
      "    Generator_Loss: 17923.1152344\n",
      "266\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11184.9970703\n",
      "\n",
      "    Generator_Loss: 15010.0185547\n",
      "267\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12544.3242188\n",
      "\n",
      "    Generator_Loss: 16266.7724609\n",
      "268\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13906.828125\n",
      "\n",
      "    Generator_Loss: 15513.8525391\n",
      "269\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12110.1933594\n",
      "\n",
      "    Generator_Loss: 14198.8105469\n",
      "270\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12648.6982422\n",
      "\n",
      "    Generator_Loss: 14319.8466797\n",
      "271\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12426.2363281\n",
      "\n",
      "    Generator_Loss: 14047.7617188\n",
      "272\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10657.5849609\n",
      "\n",
      "    Generator_Loss: 15832.0449219\n",
      "273\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13143.3339844\n",
      "\n",
      "    Generator_Loss: 17719.0\n",
      "274\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11832.1523438\n",
      "\n",
      "    Generator_Loss: 16815.9257812\n",
      "275\n",
      "     Level #0 Epoch #0 Batch #275\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3ff690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e72e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9942.60742188\n",
      "\n",
      "    Generator_Loss: 14731.2744141\n",
      "276\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12710.5625\n",
      "\n",
      "    Generator_Loss: 13885.7333984\n",
      "277\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10361.7001953\n",
      "\n",
      "    Generator_Loss: 17021.1738281\n",
      "278\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12019.6923828\n",
      "\n",
      "    Generator_Loss: 16989.8378906\n",
      "279\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10046.1582031\n",
      "\n",
      "    Generator_Loss: 15168.8359375\n",
      "280\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10705.5917969\n",
      "\n",
      "    Generator_Loss: 13984.3808594\n",
      "281\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9806.78808594\n",
      "\n",
      "    Generator_Loss: 12570.0869141\n",
      "282\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9695.64355469\n",
      "\n",
      "    Generator_Loss: 11628.40625\n",
      "283\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12532.7460938\n",
      "\n",
      "    Generator_Loss: 13485.3232422\n",
      "284\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11706.8525391\n",
      "\n",
      "    Generator_Loss: 11674.109375\n",
      "285\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11570.46875\n",
      "\n",
      "    Generator_Loss: 15499.7490234\n",
      "286\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12166.0302734\n",
      "\n",
      "    Generator_Loss: 13541.7783203\n",
      "287\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11472.7011719\n",
      "\n",
      "    Generator_Loss: 14904.8740234\n",
      "288\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11067.3544922\n",
      "\n",
      "    Generator_Loss: 12868.5322266\n",
      "289\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14067.7744141\n",
      "\n",
      "    Generator_Loss: 17754.5644531\n",
      "290\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11292.5224609\n",
      "\n",
      "    Generator_Loss: 13908.0947266\n",
      "291\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15176.4150391\n",
      "\n",
      "    Generator_Loss: 12714.2001953\n",
      "292\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9987.94433594\n",
      "\n",
      "    Generator_Loss: 16080.2626953\n",
      "293\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10811.3505859\n",
      "\n",
      "    Generator_Loss: 16064.9931641\n",
      "294\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9559.15820312\n",
      "\n",
      "    Generator_Loss: 17350.3339844\n",
      "295\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11207.2148438\n",
      "\n",
      "    Generator_Loss: 16797.84375\n",
      "296\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13378.0966797\n",
      "\n",
      "    Generator_Loss: 17504.25\n",
      "297\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11518.5996094\n",
      "\n",
      "    Generator_Loss: 14294.2568359\n",
      "298\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11522.4248047\n",
      "\n",
      "    Generator_Loss: 14850.5\n",
      "299\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11514.0332031\n",
      "\n",
      "    Generator_Loss: 13805.3222656\n",
      "300\n",
      "     Level #0 Epoch #0 Batch #300\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112490250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138d4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14788.8984375\n",
      "\n",
      "    Generator_Loss: 14176.4248047\n",
      "301\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12194.3134766\n",
      "\n",
      "    Generator_Loss: 14811.0166016\n",
      "302\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14281.6767578\n",
      "\n",
      "    Generator_Loss: 16556.9726562\n",
      "303\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12777.4140625\n",
      "\n",
      "    Generator_Loss: 20017.5566406\n",
      "304\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10426.2431641\n",
      "\n",
      "    Generator_Loss: 15964.2216797\n",
      "305\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12458.4091797\n",
      "\n",
      "    Generator_Loss: 16547.3554688\n",
      "306\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12766.4414062\n",
      "\n",
      "    Generator_Loss: 15436.9863281\n",
      "307\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14150.4755859\n",
      "\n",
      "    Generator_Loss: 17153.1699219\n",
      "308\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12763.1689453\n",
      "\n",
      "    Generator_Loss: 16275.2470703\n",
      "309\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10441.6201172\n",
      "\n",
      "    Generator_Loss: 12708.4824219\n",
      "310\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13048.7841797\n",
      "\n",
      "    Generator_Loss: 16261.4707031\n",
      "311\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11372.5478516\n",
      "\n",
      "    Generator_Loss: 13968.2773438\n",
      "312\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9875.19335938\n",
      "\n",
      "    Generator_Loss: 16003.7382812\n",
      "313\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8130.95263672\n",
      "\n",
      "    Generator_Loss: 21746.6230469\n",
      "314\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8186.53759766\n",
      "\n",
      "    Generator_Loss: 20935.0996094\n",
      "315\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11472.9453125\n",
      "\n",
      "    Generator_Loss: 16102.4912109\n",
      "316\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8835.79199219\n",
      "\n",
      "    Generator_Loss: 18338.3984375\n",
      "317\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8801.37597656\n",
      "\n",
      "    Generator_Loss: 21109.1328125\n",
      "318\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10646.2050781\n",
      "\n",
      "    Generator_Loss: 13779.0078125\n",
      "319\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11180.8720703\n",
      "\n",
      "    Generator_Loss: 18828.0605469\n",
      "320\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12138.5322266\n",
      "\n",
      "    Generator_Loss: 18007.0507812\n",
      "321\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12208.3349609\n",
      "\n",
      "    Generator_Loss: 15101.6914062\n",
      "322\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10588.5566406\n",
      "\n",
      "    Generator_Loss: 18852.9589844\n",
      "323\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9593.61523438\n",
      "\n",
      "    Generator_Loss: 13512.9150391\n",
      "324\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10867.3847656\n",
      "\n",
      "    Generator_Loss: 18254.8105469\n",
      "325\n",
      "     Level #0 Epoch #0 Batch #325\n"
     ]
    },
    {
     "data": {
      "image/png": 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vw1nm+3+aLbZ+JWrY2mLrV6KGbbU33I3GGFer6pNJvp3kXJInxhg/3NX69+zeJE+te23d\nmeSrY4yn9zuk7aqqryV5f5K3VtUrSf42yWeT/HNVfSzJy0n+Yn8j3J4jtv3xJBer6r25PnX/kySf\n2OMQmWjh9StZWA1bcv1K1LCjaHcCANBwBW8AgIawBADQEJYAABrCEgBAQ1gCAGgISwAADWEJAKAh\nLAEANP4PWrJgtcn9iIkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f2ecd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yh1X1N0m+nWQvyRNjjB9uav9bdn+Sp6oquflv/tUxxtPbLWm9quprSd6f5K1V9WKSv0vy\n2ST/XFUfT/JCkr/YXoXrc5tjfyzJflW9LzeX7n+W5JNbLJGJZt6/kpn1sDn3r0QPux3jTgAAGr7B\nGwCgISwBADSEJQCAhrAEANAQlgAAGsISAEBDWAIAaAhLAACN/wPtgGBspBmBAgAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc85610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11937.5009766\n",
      "\n",
      "    Generator_Loss: 18794.5058594\n",
      "326\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9829.5546875\n",
      "\n",
      "    Generator_Loss: 16217.3818359\n",
      "327\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12317.2314453\n",
      "\n",
      "    Generator_Loss: 13445.9326172\n",
      "328\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13690.1210938\n",
      "\n",
      "    Generator_Loss: 17901.2890625\n",
      "329\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13964.5361328\n",
      "\n",
      "    Generator_Loss: 18134.8242188\n",
      "330\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11329.9404297\n",
      "\n",
      "    Generator_Loss: 17444.5546875\n",
      "331\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8984.68164062\n",
      "\n",
      "    Generator_Loss: 18284.6992188\n",
      "332\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9353.17089844\n",
      "\n",
      "    Generator_Loss: -4235.57617188\n",
      "333\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15076.4580078\n",
      "\n",
      "    Generator_Loss: 14449.5146484\n",
      "334\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14300.1015625\n",
      "\n",
      "    Generator_Loss: 17116.3476562\n",
      "335\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14297.2675781\n",
      "\n",
      "    Generator_Loss: 6542.17871094\n",
      "336\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15641.1455078\n",
      "\n",
      "    Generator_Loss: 15921.1660156\n",
      "337\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12482.9052734\n",
      "\n",
      "    Generator_Loss: 17296.0488281\n",
      "338\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12319.6210938\n",
      "\n",
      "    Generator_Loss: 17157.0019531\n",
      "339\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11188.0996094\n",
      "\n",
      "    Generator_Loss: 18290.3359375\n",
      "340\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10325.2978516\n",
      "\n",
      "    Generator_Loss: 12794.84375\n",
      "341\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16355.9355469\n",
      "\n",
      "    Generator_Loss: 10384.4296875\n",
      "342\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14074.9794922\n",
      "\n",
      "    Generator_Loss: -5959.28369141\n",
      "343\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13164.1621094\n",
      "\n",
      "    Generator_Loss: 15955.2109375\n",
      "344\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14066.4785156\n",
      "\n",
      "    Generator_Loss: 15048.8164062\n",
      "345\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11263.9052734\n",
      "\n",
      "    Generator_Loss: 19290.5234375\n",
      "346\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16085.2480469\n",
      "\n",
      "    Generator_Loss: 17009.1523438\n",
      "347\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12970.8085938\n",
      "\n",
      "    Generator_Loss: 12716.8554688\n",
      "348\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9734.359375\n",
      "\n",
      "    Generator_Loss: 21078.9960938\n",
      "349\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13878.8798828\n",
      "\n",
      "    Generator_Loss: 18548.1425781\n",
      "350\n",
      "     Level #0 Epoch #0 Batch #350\n"
     ]
    },
    {
     "data": {
      "image/png": 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0hCUAgIawBADQEJYAABrCEgBAQ1gCAGgISwAAjf8Hu6EUfIQUg9gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa03f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb53790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12295.4589844\n",
      "\n",
      "    Generator_Loss: 19589.8203125\n",
      "351\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12786.8925781\n",
      "\n",
      "    Generator_Loss: 13743.4511719\n",
      "352\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16367.6396484\n",
      "\n",
      "    Generator_Loss: 19713.0625\n",
      "353\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11125.3486328\n",
      "\n",
      "    Generator_Loss: 18080.9394531\n",
      "354\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11179.7626953\n",
      "\n",
      "    Generator_Loss: 21612.6777344\n",
      "355\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11974.4101562\n",
      "\n",
      "    Generator_Loss: 18668.5390625\n",
      "356\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10997.8691406\n",
      "\n",
      "    Generator_Loss: 19810.9042969\n",
      "357\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13972.9316406\n",
      "\n",
      "    Generator_Loss: 16651.5\n",
      "358\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13781.7412109\n",
      "\n",
      "    Generator_Loss: 19517.3535156\n",
      "359\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11221.5898438\n",
      "\n",
      "    Generator_Loss: 18246.9882812\n",
      "360\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15587.390625\n",
      "\n",
      "    Generator_Loss: 19355.0371094\n",
      "361\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12698.4990234\n",
      "\n",
      "    Generator_Loss: 15428.4980469\n",
      "362\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12380.8144531\n",
      "\n",
      "    Generator_Loss: 20096.7226562\n",
      "363\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13531.4814453\n",
      "\n",
      "    Generator_Loss: 6985.92041016\n",
      "364\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13154.3496094\n",
      "\n",
      "    Generator_Loss: 18724.046875\n",
      "365\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12262.7568359\n",
      "\n",
      "    Generator_Loss: 7272.28613281\n",
      "366\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11506.3984375\n",
      "\n",
      "    Generator_Loss: 5266.89501953\n",
      "367\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14207.0595703\n",
      "\n",
      "    Generator_Loss: 18251.5957031\n",
      "368\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10670.2841797\n",
      "\n",
      "    Generator_Loss: 16592.0722656\n",
      "369\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12662.0859375\n",
      "\n",
      "    Generator_Loss: 16189.8544922\n",
      "370\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -9785.18945312\n",
      "\n",
      "    Generator_Loss: 10724.0234375\n",
      "371\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11667.5898438\n",
      "\n",
      "    Generator_Loss: 17509.9667969\n",
      "372\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16999.140625\n",
      "\n",
      "    Generator_Loss: 16769.5722656\n",
      "373\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14302.0927734\n",
      "\n",
      "    Generator_Loss: 14516.7998047\n",
      "374\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10353.1699219\n",
      "\n",
      "    Generator_Loss: 18724.5761719\n",
      "375\n",
      "     Level #0 Epoch #0 Batch #375\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe9bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ffe3910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15537.8837891\n",
      "\n",
      "    Generator_Loss: 18862.5820312\n",
      "376\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14550.8554688\n",
      "\n",
      "    Generator_Loss: 16163.6357422\n",
      "377\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12363.7900391\n",
      "\n",
      "    Generator_Loss: 20449.71875\n",
      "378\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14745.5947266\n",
      "\n",
      "    Generator_Loss: 12498.4501953\n",
      "379\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12494.7958984\n",
      "\n",
      "    Generator_Loss: 13957.625\n",
      "380\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11522.9179688\n",
      "\n",
      "    Generator_Loss: 16538.8632812\n",
      "381\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14011.2294922\n",
      "\n",
      "    Generator_Loss: 20398.375\n",
      "382\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13863.2011719\n",
      "\n",
      "    Generator_Loss: 18255.6621094\n",
      "383\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14525.1416016\n",
      "\n",
      "    Generator_Loss: 14331.0322266\n",
      "384\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15763.3115234\n",
      "\n",
      "    Generator_Loss: 14168.0029297\n",
      "385\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12963.7841797\n",
      "\n",
      "    Generator_Loss: 19338.2832031\n",
      "386\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11185.4785156\n",
      "\n",
      "    Generator_Loss: 14205.0751953\n",
      "387\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14981.3955078\n",
      "\n",
      "    Generator_Loss: 19882.4257812\n",
      "388\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12826.2441406\n",
      "\n",
      "    Generator_Loss: 20752.9140625\n",
      "389\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16271.6777344\n",
      "\n",
      "    Generator_Loss: 15504.7871094\n",
      "390\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13329.2089844\n",
      "\n",
      "    Generator_Loss: 19684.6445312\n",
      "391\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14194.5869141\n",
      "\n",
      "    Generator_Loss: 16770.0253906\n",
      "392\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14832.8066406\n",
      "\n",
      "    Generator_Loss: 16913.1289062\n",
      "393\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16650.9570312\n",
      "\n",
      "    Generator_Loss: 14686.3671875\n",
      "394\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15124.3886719\n",
      "\n",
      "    Generator_Loss: 15802.5371094\n",
      "395\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12693.2607422\n",
      "\n",
      "    Generator_Loss: 20334.609375\n",
      "396\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13420.8339844\n",
      "\n",
      "    Generator_Loss: 13106.8837891\n",
      "397\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11513.8398438\n",
      "\n",
      "    Generator_Loss: 17762.3027344\n",
      "398\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13340.1386719\n",
      "\n",
      "    Generator_Loss: 15804.7802734\n",
      "399\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14757.3193359\n",
      "\n",
      "    Generator_Loss: 17191.0976562\n",
      "400\n",
      "     Level #0 Epoch #0 Batch #400\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZkIuttavL768mubjJwWzAx6vq+1X1yFmewj+j5l6/EjVs7vUrmXENW2dYmvtnFLyztXZv\nkgeSfLSq/mTTA9qkdv0zK+b0nHg4yR8keUeSZ5N8frPDYdCcnqu3ooYtzbB+JTOvYesMS/+Z5I1H\nbr8x1/86m4XW2rPLf19I8liuT+vPzdWquitJqup1SZ7f8HjWprX2fFtK8s+Z5/HfZbOuX4kalhnX\nr0QNW2dYeirJm6vqnqq6I8n7k3x9jdvfmKp6VVX93vL7C0n+IsnT/bXOpK8n+eDy+w8m+dcNjmWt\nlsX1/7wv8zz+u2y29StRw5ZmW78SNWylveGOaq0dVtXHknwryV6SR1prP1jX9jfsYpLHlr22zif5\nSmvt8c0OabWq6qtJ3pXktVX1TJIHk3w2yb9U1YeSXEny15sb4ercZN8fSnKpqt6R61P3P0vykQ0O\nkUEzr1/JzGrYnOtXoobdjHYnAAAdPsEbAKBDWAIA6BCWAAA6hCUAgA5hCQCgQ1gCAOgQlgAAOoQl\nAICO/wVaQpSJJMEjOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120600f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ia9giw9LYr1Hwpdba3RHx1xHxD5n5Z8ue0DK1y9esGNNr4kcR8ScRcTIi3omI7y93OnQa\n02v1etSwmRHWr4iR17BFhqW3I+IzV9z/TFz+62wUWmvvzP79fUQ8G5eX9cfmXGZ+OiIiM++IiPeW\nPJ+Faa2912Yi4t9inM//zWzU9StCDYsR168INWyRYemliPhsZt6ZmUci4usR8dwCx1+azDyamX80\nu31rRNwXEa/We62l5yLiG7Pb34iIf1/iXBZqVlz/4Gsxzuf/Zjba+hWhhs2Mtn5FqGEL6w3XWtvN\nzG9HxM8jYjMiHm+t/XpR4y/Z8Yh4NjMjLv/Mf9xae365U5qvzHw6Ik5HxKcy882I+KeI+G5EPJOZ\n34yINyLib5c3w/m5xrE/EhGTzDwZl5fufxcR31riFOk08voVMbIaNub6FaGGXYt2JwAABVfwBgAo\nCEsAAAVhCQCgICwBABSEJQCAgrAEAFAQlgAACsISAEDhfwFYseEHnNHGKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12074ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14511.2001953\n",
      "\n",
      "    Generator_Loss: 14893.3271484\n",
      "401\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14571.0283203\n",
      "\n",
      "    Generator_Loss: 17057.4316406\n",
      "402\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13759.8193359\n",
      "\n",
      "    Generator_Loss: 17250.609375\n",
      "403\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14016.0595703\n",
      "\n",
      "    Generator_Loss: 17879.8613281\n",
      "404\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14043.6328125\n",
      "\n",
      "    Generator_Loss: 15737.5957031\n",
      "405\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16498.359375\n",
      "\n",
      "    Generator_Loss: 14854.7197266\n",
      "406\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13394.8896484\n",
      "\n",
      "    Generator_Loss: 18963.7851562\n",
      "407\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12642.9541016\n",
      "\n",
      "    Generator_Loss: 18836.2382812\n",
      "408\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11491.2705078\n",
      "\n",
      "    Generator_Loss: 15399.1982422\n",
      "409\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14902.2568359\n",
      "\n",
      "    Generator_Loss: 18416.5488281\n",
      "410\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17157.625\n",
      "\n",
      "    Generator_Loss: 17963.8417969\n",
      "411\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13959.6689453\n",
      "\n",
      "    Generator_Loss: 17629.9121094\n",
      "412\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15201.7734375\n",
      "\n",
      "    Generator_Loss: 16584.3164062\n",
      "413\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14875.3496094\n",
      "\n",
      "    Generator_Loss: 16615.1347656\n",
      "414\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12854.8320312\n",
      "\n",
      "    Generator_Loss: 18334.2558594\n",
      "415\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15324.2578125\n",
      "\n",
      "    Generator_Loss: 16283.5917969\n",
      "416\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10415.6396484\n",
      "\n",
      "    Generator_Loss: 14930.6044922\n",
      "417\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15928.6230469\n",
      "\n",
      "    Generator_Loss: 14539.1699219\n",
      "418\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12440.4404297\n",
      "\n",
      "    Generator_Loss: 21429.484375\n",
      "419\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15704.4033203\n",
      "\n",
      "    Generator_Loss: 15883.7460938\n",
      "420\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14401.6367188\n",
      "\n",
      "    Generator_Loss: 20629.5097656\n",
      "421\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16723.90625\n",
      "\n",
      "    Generator_Loss: 20233.9589844\n",
      "422\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14454.8339844\n",
      "\n",
      "    Generator_Loss: 19969.171875\n",
      "423\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12702.4902344\n",
      "\n",
      "    Generator_Loss: 15454.5615234\n",
      "424\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14479.7373047\n",
      "\n",
      "    Generator_Loss: 14932.0488281\n",
      "425\n",
      "     Level #0 Epoch #0 Batch #425\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fda7a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADbtJREFUeJzt3T+IpPd5B/Dvo9O5sAkIo3AWwUEpbLDBIKlQAiZoquTS\n+A8EG0PAhREuYtVWGmuTNHHArgxuIgkVRsaNErmxJRsG1AWBcFRY2AYfSEE5mUQhKe+0vxS3Ehdp\n79l9Z3f+7LyfDyw3Mzc383tvli/f/b0z+9QYIwAAHO+ubS8AAGCXKUsAAA1lCQCgoSwBADSUJQCA\nhrIEANC4e10PXFV+JwHM0Bijtr2Gs5JfME93yq+17ixV1Qe+7nR7VWWMsfdfTzzxxNbX4Ngd/7q+\n9smdcupOf7ft/3vfw47fsZ/tq7NyWaqqq1X1WlX9uqq+uerjAGya/AKmWKksVdWlJN9LcjXJp5N8\npao+dZ4LA1gH+QVMterO0sNJfjPGuDbGuJHkh0k+f37L2l+LxWLbS9iaOR974vh3iPxa0dy/h+d8\n/HM+9iSpk87THfuPqv4yyZ+PMR49uv5XSf54jPHYbfcZ757fP63Dw8PJawF2x9F7d3b6Dd7yCzhO\nl1+rfhruVA3r/UVsavgAu225XGa5XG57GVPJL2BSfq26s/QnSQ7GGFePrv9NksMxxrdvu4+fzGBm\nLsjOkvwCPqDLr1Xfs/Rykk9U1f1V9aEkX07y/KoLBNgg+QVMstJpuDHGzar6RpKfJrmU5Mkxxi/P\ndWUAayC/gKlWOg13qge2jQ2zcxFOw52G/IL5WcdpOACAWVCWAAAaaxukmyQ3b96cdP9Lly5Nuv87\n77wz6f4Ap7Xu/EpkGFwUdpYAABrKEgBAQ1kCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAa\nyhIAQENZAgBorHU23F13TetiN27cmHR/s5iAdVl3fiXmYcJFYWcJAKChLAEANJQlAICGsgQA0FCW\nAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBY6yDdqXZxcGVieCVwsqn5lRgeDheF\nnSUAgIayBADQUJYAABrKEgBAQ1kCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAayhIAQGOn\nBulOtYnBlcn04ZUGVwKnYXg4XAx2lgAAGsoSAEBDWQIAaChLAAANZQkAoKEsAQA0lCUAgIayBADQ\nUJYAABrKEgBAQ1kCAGhc6Nlwq9jEPDmzmIB12NV5mIkMY7/ZWQIAaKy8s1RV15L8T5J3ktwYYzx8\nXosCWDcZBpzWWU7DjSSLMcZ/nddiADZIhgGnctbTcHUuqwDYDhkGnOgsZWkk+VlVvVxVj57XggA2\nRIYBp3KW03CfHWO8WVW/n+TFqnptjPHSeS0MYM1kGHAqK5elMcabR3/+rqqeS/Jwkv8XNAcHB+9d\nXiwWWSwWqz4dsIOWy2WWy+W2l7GSkzJMfsF+m5JfNcaY/ARV9eEkl8YY/1tVH0nyQpK/HWO8cNt9\nxiqPvYsODw8n3f/y5cuTn8PvKGEfVFXGGDv/PqCTMmzO+ZXIMOapy69Vd5auJHmuqt59jB/cXpQA\ndpwMA05tpZ2lUz3wjH8y81MZc3VRdpZOMuf8SmQY89Tll9/gDQDQUJYAABqzG6S7iqnDKw2uBHbF\nrg7flV9cJHaWAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIA\naChLAAANg3TXYFcHVyaGVwIn20SGyS8uEjtLAAANZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBA\nQ1kCAGgoSwAADWUJAKChLAEANMyG2xG7Ok/OLCbgNKZmmHmYXCR2lgAAGsoSAEBDWQIAaChLAAAN\nZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBAQ1kCAGgoSwAADYN0L7BNDN81uBJYB8PDuUjsLAEA\nNJQlAICGsgQA0FCWAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGgbpnsIY\nY9L97757+n/rpoY3Th1eefPmzTWtBGAaw8PZlvY7r6qeqqrrVfXqbbd9tKperKpfVdULVXXP+pcJ\nMJ0MA87DSTX96SRX33fb40leHGN8MsnPj64D7CIZBpxZW5bGGC8left9N38uyTNHl59J8oU1rAvg\nzGQYcB5WeYP3lTHG9aPL15NcOcf1AKybDAMmOdOn4catdz5Pe/czwI6QYcBprPJpuOtV9bExxn9U\n1X1J3rrTHQ8ODt67vFgsslgsVng6YFctl8ssl8ttL2OqU2WY/IL9NiW/6qSPxVfV/Ul+PMb4zNH1\nf0zyn2OMb1fV40nuGWN84A2SVTWmfuR+V+3Trw6YapXXsKrWsBIugqrKGGOnvgFWybB9yq+5Ozw8\nnHT/y5cvT36OXc1vpunyqy1LVfVskkeS3Jtb5/a/leRfkvwoyR8muZbkS2OM/z7m3+5N2ChL0yhL\n87VrZWnVDNun/Jo7ZYnTWrksnfFJ9yZslKVplKX52rWytKp9yq+5U5Y4rS6/jDsBAGgoSwAADbPh\nTmHqaaV9mqe2yim1VU5fTD11adsbOI2p8+SmzpJLzJObAztLAAANZQkAoKEsAQA0lCUAgIayBADQ\nUJYAABrKEgBAQ1kCAGgoSwAADWUJAKChLAEANJQlAICGQbprsMrw2X2yyvFPHV5pcCWwDlMH7yab\nGb4rv7bLzhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIAaChLAAAN\nZQkAoGGQLjth6vDKTQyuTAyvBE62ieG78mu77CwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIAaChL\nAAANZQkAoKEsAQA0lCUAgIayBADQMBuOC2kTs5iS6fOYzGICTsM8zIvFzhIAQENZAgBoKEsAAA1l\nCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIAaChLAAANZQkAoGGQLrOxieG7BlcC62B4+HbZ\nWQIAaLRlqaqeqqrrVfXqbbcdVNUbVfXK0dfV9S8TYDoZBpyHk3aWnk7y/iAZSb47xnjw6Osn61ka\nwJnJMODM2rI0xngpydvH/FWtZzkA50eGAedh1fcsPVZVv6iqJ6vqnnNdEcD6yTDg1Fb5NNz3k/zd\n0eW/T/KdJF877o4HBwfvXV4sFlksFis8HbCrlstllsvltpcx1akyTH7BfpuSXzXG6O9QdX+SH48x\nPjPx78ZJjw277vDwcNL9L1++PPk59umjt1WVMcZOneJaJcPkF/tgan4l0zNsLvk1+TRcVd1329Uv\nJnn1TvcF2DUyDJiqPQ1XVc8meSTJvVX1epInkiyq6oHc+kTJb5N8fe2rBFiBDAPOw4mn4VZ+YNvY\n7AGn4abZxdNwq5Bf7AOn4aY519NwAABzoiwBADQM0oXG1OGVmxhcmezX1jewHoaHnx87SwAADWUJ\nAKChLAEANJQlAICGsgQA0FCWAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADTMhoNztIlZTMn0eUwX\ncRYTsHnmYR7PzhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIAaChL\nAAANZQkAoGGQLmzZJobvXsTBlcDum8vwcDtLAAANZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBA\nQ1kCAGhsvCwtl8tNP+VOmfPxz/nYE8e/L+b8Os752JN5H/+cjz1RljZuzsc/52NPHP++mPPrOOdj\nT+Z9/HM+9sRpOACAltlwMAMPPfTQtpcAsLJtZ1iNMdbzwFXreWBgp40xattrOCv5BfN0p/xaW1kC\nANgH3rMEANBQlgAAGhstS1V1tapeq6pfV9U3N/nc21ZV16rq36rqlar6122vZ92q6qmqul5Vr952\n20er6sWq+lVVvVBV92xzjetyh2M/qKo3jl7/V6rq6jbXyHRzzq9kXhk25/xKZNhxNlaWqupSku8l\nuZrk00m+UlWf2tTz74CRZDHGeHCM8fC2F7MBT+fWa327x5O8OMb4ZJKfH13fR8cd+0jy3aPX/8Ex\nxk+2sC5WJL+SzCvD5pxfiQz7gE3uLD2c5DdjjGtjjBtJfpjk8xt8/l1w4T8ldFpjjJeSvP2+mz+X\n5Jmjy88k+cJGF7Uhdzj2ZEav/x6SX7fM4nt4zvmVyLDjbLIs/UGS12+7/sbRbXMxkvysql6uqke3\nvZgtuTLGuH50+XqSK9tczBY8VlW/qKon93kLf0/NPb8SGTb3/EpmnGGbLEtz/x0Fnx1jPJjkL5L8\ndVX96bYXtE3j1u+smNP3xPeT/FGSB5K8meQ7210OE83pe/VOZNiRGeZXMvMM22RZ+vckH7/t+sdz\n66ezWRhjvHn05++SPJdb2/pzc72qPpYkVXVfkre2vJ6NGWO8NY4k+afM8/W/yGadX4kMy4zzK5Fh\nmyxLLyf5RFXdX1UfSvLlJM9v8Pm3pqo+XFW/d3T5I0n+LMmr/b/aS88n+erR5a8m+ectrmWjjsL1\nXV/MPF//i2y2+ZXIsCOzza9Ehm1sNtwY42ZVfSPJT5NcSvLkGOOXm3r+LbuS5LmqSm79n/9gjPHC\ndpe0XlX1bJJHktxbVa8n+VaSf0jyo6r6WpJrSb60vRWuzzHH/kSSRVU9kFtb979N8vUtLpGJZp5f\nycwybM75lciw4xh3AgDQ8Bu8AQAayhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEAND4Pw2W\noeOkJuMNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9f92d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14556.5087891\n",
      "\n",
      "    Generator_Loss: 12841.6005859\n",
      "426\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15596.7050781\n",
      "\n",
      "    Generator_Loss: 13838.9912109\n",
      "427\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14768.3125\n",
      "\n",
      "    Generator_Loss: 17652.3027344\n",
      "428\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10204.296875\n",
      "\n",
      "    Generator_Loss: 19955.9589844\n",
      "429\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13753.8300781\n",
      "\n",
      "    Generator_Loss: 12640.0107422\n",
      "430\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14547.4980469\n",
      "\n",
      "    Generator_Loss: 20830.1210938\n",
      "431\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15373.3710938\n",
      "\n",
      "    Generator_Loss: 15556.7236328\n",
      "432\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16151.1738281\n",
      "\n",
      "    Generator_Loss: 15202.5390625\n",
      "433\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14923.2636719\n",
      "\n",
      "    Generator_Loss: 16600.4570312\n",
      "434\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14066.7285156\n",
      "\n",
      "    Generator_Loss: 15364.8955078\n",
      "435\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17967.7070312\n",
      "\n",
      "    Generator_Loss: 17578.5097656\n",
      "436\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15959.8789062\n",
      "\n",
      "    Generator_Loss: 15244.4707031\n",
      "437\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15374.2685547\n",
      "\n",
      "    Generator_Loss: 18496.6132812\n",
      "438\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13944.2949219\n",
      "\n",
      "    Generator_Loss: 15776.5175781\n",
      "439\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16985.9140625\n",
      "\n",
      "    Generator_Loss: 20475.8613281\n",
      "440\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15422.4179688\n",
      "\n",
      "    Generator_Loss: 18800.4921875\n",
      "441\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16721.3574219\n",
      "\n",
      "    Generator_Loss: 17152.5878906\n",
      "442\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17363.8554688\n",
      "\n",
      "    Generator_Loss: 20556.6660156\n",
      "443\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13723.5341797\n",
      "\n",
      "    Generator_Loss: 16782.8105469\n",
      "444\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17164.0039062\n",
      "\n",
      "    Generator_Loss: 18006.9433594\n",
      "445\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19659.6738281\n",
      "\n",
      "    Generator_Loss: 21938.8339844\n",
      "446\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16907.1132812\n",
      "\n",
      "    Generator_Loss: 20242.2910156\n",
      "447\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14468.8535156\n",
      "\n",
      "    Generator_Loss: 16955.265625\n",
      "448\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13776.3378906\n",
      "\n",
      "    Generator_Loss: 18136.2128906\n",
      "449\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16675.7988281\n",
      "\n",
      "    Generator_Loss: 19361.6582031\n",
      "450\n",
      "     Level #0 Epoch #0 Batch #450\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNIQlAICGsAQA0BCWAAAawhIAQOP/ARor+LbcosMYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a8d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Aatgr9qOf4TsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112879a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13948.3447266\n",
      "\n",
      "    Generator_Loss: 19425.1523438\n",
      "451\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13811.0\n",
      "\n",
      "    Generator_Loss: 13350.5888672\n",
      "452\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16566.7148438\n",
      "\n",
      "    Generator_Loss: 23317.84375\n",
      "453\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13624.7646484\n",
      "\n",
      "    Generator_Loss: 18768.4628906\n",
      "454\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14317.9013672\n",
      "\n",
      "    Generator_Loss: 18128.0546875\n",
      "455\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16795.4648438\n",
      "\n",
      "    Generator_Loss: 25568.6894531\n",
      "456\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17256.1835938\n",
      "\n",
      "    Generator_Loss: 20816.0195312\n",
      "457\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16981.4785156\n",
      "\n",
      "    Generator_Loss: 23445.34375\n",
      "458\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14447.5048828\n",
      "\n",
      "    Generator_Loss: 17659.0332031\n",
      "459\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16527.6445312\n",
      "\n",
      "    Generator_Loss: 19206.0605469\n",
      "460\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17240.6855469\n",
      "\n",
      "    Generator_Loss: 18617.6933594\n",
      "461\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12269.5507812\n",
      "\n",
      "    Generator_Loss: 22796.6152344\n",
      "462\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13418.4716797\n",
      "\n",
      "    Generator_Loss: 21214.3554688\n",
      "463\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18465.15625\n",
      "\n",
      "    Generator_Loss: 17381.1328125\n",
      "464\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16893.6308594\n",
      "\n",
      "    Generator_Loss: 21034.8066406\n",
      "465\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12343.1152344\n",
      "\n",
      "    Generator_Loss: 22104.2558594\n",
      "466\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16188.3271484\n",
      "\n",
      "    Generator_Loss: 18531.1152344\n",
      "467\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14063.75\n",
      "\n",
      "    Generator_Loss: 22774.3066406\n",
      "468\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14554.2128906\n",
      "\n",
      "    Generator_Loss: 22769.2773438\n",
      "469\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14313.5332031\n",
      "\n",
      "    Generator_Loss: 22386.2285156\n",
      "470\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16977.5019531\n",
      "\n",
      "    Generator_Loss: 20480.6230469\n",
      "471\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15770.265625\n",
      "\n",
      "    Generator_Loss: 20856.3007812\n",
      "472\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15545.828125\n",
      "\n",
      "    Generator_Loss: 22419.1367188\n",
      "473\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16857.40625\n",
      "\n",
      "    Generator_Loss: 20459.203125\n",
      "474\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16076.1328125\n",
      "\n",
      "    Generator_Loss: 19547.6308594\n",
      "475\n",
      "     Level #0 Epoch #0 Batch #475\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120949f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe2c810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18438.1601562\n",
      "\n",
      "    Generator_Loss: 19615.5917969\n",
      "476\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15548.7255859\n",
      "\n",
      "    Generator_Loss: 19817.2695312\n",
      "477\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17179.40625\n",
      "\n",
      "    Generator_Loss: 11008.3359375\n",
      "478\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15069.1064453\n",
      "\n",
      "    Generator_Loss: 12820.3330078\n",
      "479\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20870.6894531\n",
      "\n",
      "    Generator_Loss: 24677.0976562\n",
      "480\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15764.9521484\n",
      "\n",
      "    Generator_Loss: 20855.3378906\n",
      "481\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13069.6464844\n",
      "\n",
      "    Generator_Loss: 19579.9414062\n",
      "482\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13323.3076172\n",
      "\n",
      "    Generator_Loss: 20984.953125\n",
      "483\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13875.6269531\n",
      "\n",
      "    Generator_Loss: 17880.1464844\n",
      "484\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12558.7548828\n",
      "\n",
      "    Generator_Loss: 17440.7363281\n",
      "485\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13988.5214844\n",
      "\n",
      "    Generator_Loss: 22316.0722656\n",
      "486\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15544.0537109\n",
      "\n",
      "    Generator_Loss: 21051.8457031\n",
      "487\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18129.4570312\n",
      "\n",
      "    Generator_Loss: 18291.1328125\n",
      "488\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15364.3789062\n",
      "\n",
      "    Generator_Loss: 17716.7558594\n",
      "489\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17382.2460938\n",
      "\n",
      "    Generator_Loss: 19910.4804688\n",
      "490\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15407.3535156\n",
      "\n",
      "    Generator_Loss: 22300.8242188\n",
      "491\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13726.5556641\n",
      "\n",
      "    Generator_Loss: 11938.2578125\n",
      "492\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13836.9658203\n",
      "\n",
      "    Generator_Loss: 18380.9199219\n",
      "493\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15867.5585938\n",
      "\n",
      "    Generator_Loss: 16849.2050781\n",
      "494\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17959.4296875\n",
      "\n",
      "    Generator_Loss: 19060.6972656\n",
      "495\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20036.9785156\n",
      "\n",
      "    Generator_Loss: 22770.3476562\n",
      "496\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16438.0449219\n",
      "\n",
      "    Generator_Loss: 19488.4726562\n",
      "497\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19913.1582031\n",
      "\n",
      "    Generator_Loss: 19588.9628906\n",
      "498\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15075.9853516\n",
      "\n",
      "    Generator_Loss: 15522.9394531\n",
      "499\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14269.7236328\n",
      "\n",
      "    Generator_Loss: 17269.4746094\n",
      "500\n",
      "     Level #0 Epoch #0 Batch #500\n"
     ]
    },
    {
     "data": {
      "image/png": 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4y3z/D9li61eihq0ttn4lathGe8Pdboxxo6o+leS7SS4meWyM8eNtbX/H7k3yxLrX1qUk\nXxtjPLnbIW1WVX09yfuTvKmqfpHks0n+Mck3q+oTSZ5P8pHdjXBzTtj3R5Ncrar7c2vq/udJPrnD\nITLRwutXsrAatuT6lahhJ9HuBACg4QreAAANYQkAoCEsAQA0hCUAgIawBADQEJYAABrCEgBAQ1gC\nAGj8P4YLcN2tPx/GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff38e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V3Pydf3OM8cRuh7RZVfWtJB9I8paq+mWSLyT5pyTfqapPJnkuyUd3N8LNuc2xP5xkVVXv\nzc2p+18k+fQOh8hEC69fycJq2JLrV6KG3Y52JwAADd/gDQDQEJYAABrCEgBAQ1gCAGgISwAADWEJ\nAKAhLAEANIQlAIDG/wP+9KoWM2gR8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7dd990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17444.0917969\n",
      "\n",
      "    Generator_Loss: 18845.4355469\n",
      "501\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11907.9716797\n",
      "\n",
      "    Generator_Loss: 14087.9023438\n",
      "502\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15342.4179688\n",
      "\n",
      "    Generator_Loss: 22203.8828125\n",
      "503\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11049.4462891\n",
      "\n",
      "    Generator_Loss: 19278.2148438\n",
      "504\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19758.0117188\n",
      "\n",
      "    Generator_Loss: 24179.8378906\n",
      "505\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17139.7402344\n",
      "\n",
      "    Generator_Loss: 21820.7109375\n",
      "506\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14556.9453125\n",
      "\n",
      "    Generator_Loss: 17279.6132812\n",
      "507\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18071.4609375\n",
      "\n",
      "    Generator_Loss: 13028.1083984\n",
      "508\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20490.2910156\n",
      "\n",
      "    Generator_Loss: 20067.5546875\n",
      "509\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15119.921875\n",
      "\n",
      "    Generator_Loss: 17127.5429688\n",
      "510\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13091.078125\n",
      "\n",
      "    Generator_Loss: 24562.4179688\n",
      "511\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17284.2792969\n",
      "\n",
      "    Generator_Loss: 23009.0761719\n",
      "512\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19140.1425781\n",
      "\n",
      "    Generator_Loss: 19276.0332031\n",
      "513\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15963.8164062\n",
      "\n",
      "    Generator_Loss: 19759.7753906\n",
      "514\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -1900.90429688\n",
      "\n",
      "    Generator_Loss: 37513.59375\n",
      "515\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15344.9814453\n",
      "\n",
      "    Generator_Loss: 22083.3710938\n",
      "516\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15422.5263672\n",
      "\n",
      "    Generator_Loss: 9755.62988281\n",
      "517\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16042.9814453\n",
      "\n",
      "    Generator_Loss: 15484.5791016\n",
      "518\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15651.4277344\n",
      "\n",
      "    Generator_Loss: 25452.8867188\n",
      "519\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19157.2089844\n",
      "\n",
      "    Generator_Loss: 19252.2558594\n",
      "520\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16983.9570312\n",
      "\n",
      "    Generator_Loss: 20980.84375\n",
      "521\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17982.8554688\n",
      "\n",
      "    Generator_Loss: 16855.8144531\n",
      "522\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14692.265625\n",
      "\n",
      "    Generator_Loss: 16670.4667969\n",
      "523\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14397.0546875\n",
      "\n",
      "    Generator_Loss: 21684.7929688\n",
      "524\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15431.9042969\n",
      "\n",
      "    Generator_Loss: 17541.1855469\n",
      "525\n",
      "     Level #0 Epoch #0 Batch #525\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2b7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb08b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19659.2792969\n",
      "\n",
      "    Generator_Loss: 20449.6757812\n",
      "526\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17518.7871094\n",
      "\n",
      "    Generator_Loss: 20982.390625\n",
      "527\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20347.1816406\n",
      "\n",
      "    Generator_Loss: 16523.4414062\n",
      "528\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19353.5136719\n",
      "\n",
      "    Generator_Loss: 14938.2568359\n",
      "529\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17659.8261719\n",
      "\n",
      "    Generator_Loss: 14603.7626953\n",
      "530\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16134.0195312\n",
      "\n",
      "    Generator_Loss: 22733.1816406\n",
      "531\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17173.0117188\n",
      "\n",
      "    Generator_Loss: 22979.7832031\n",
      "532\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13222.9287109\n",
      "\n",
      "    Generator_Loss: 21269.9296875\n",
      "533\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15993.0048828\n",
      "\n",
      "    Generator_Loss: 22612.8789062\n",
      "534\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18771.7402344\n",
      "\n",
      "    Generator_Loss: 26232.0566406\n",
      "535\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19590.7695312\n",
      "\n",
      "    Generator_Loss: 19743.9414062\n",
      "536\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16403.2929688\n",
      "\n",
      "    Generator_Loss: 25706.4980469\n",
      "537\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21983.7675781\n",
      "\n",
      "    Generator_Loss: 27575.6640625\n",
      "538\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16916.140625\n",
      "\n",
      "    Generator_Loss: 23121.6308594\n",
      "539\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19211.1347656\n",
      "\n",
      "    Generator_Loss: 22920.4296875\n",
      "540\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19975.3183594\n",
      "\n",
      "    Generator_Loss: 23292.1152344\n",
      "541\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15797.2841797\n",
      "\n",
      "    Generator_Loss: 29259.4628906\n",
      "542\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21322.2597656\n",
      "\n",
      "    Generator_Loss: 24454.9824219\n",
      "543\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18183.5625\n",
      "\n",
      "    Generator_Loss: 26630.3671875\n",
      "544\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14995.59375\n",
      "\n",
      "    Generator_Loss: 13164.3994141\n",
      "545\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14473.3955078\n",
      "\n",
      "    Generator_Loss: 20842.2988281\n",
      "546\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15534.5722656\n",
      "\n",
      "    Generator_Loss: 18686.4335938\n",
      "547\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19786.7714844\n",
      "\n",
      "    Generator_Loss: 23446.6542969\n",
      "548\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13154.7119141\n",
      "\n",
      "    Generator_Loss: 19502.3125\n",
      "549\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15928.8476562\n",
      "\n",
      "    Generator_Loss: 23326.1347656\n",
      "550\n",
      "     Level #0 Epoch #0 Batch #550\n"
     ]
    },
    {
     "data": {
      "image/png": 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fc//1ufq/s0VorT23/vP5JA/n6rT+0lysqtckSVW9NslPdzyerWmt/bStJfmnLPP5P8oW\nXb8SNSwLrl+JGrbNsPRkkjdV1Ruq6rYkH0jyyBb3vzNV9fKq+p317duT/HmSp/prHUuPJPnQ+vaH\nknxth2PZqnVx/V/3ZpnP/1G22PqVqGFri61fiRo2a2+4a7XWLlfVx5J8PclBkgdba+e3tf8dO53k\n4XWvrZNJvtxae2y3Q5pXVX0lybuTvLqqnk3y90k+neSfq+rDSS4k+avdjXA+1zn2+5Kcraq35erU\n/TNJPrrDITJo4fUrWVgNW3L9StSw69HuBACgwzd4AwB0CEsAAB3CEgBAh7AEANAhLAEAdAhLAAAd\nwhIAQIewBADQ8T8M3SbkIWgJeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe9b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SVW9J8vyGx7M2rbXn20KSf8k03//TbNL1K1HDMuH6lahh6wxLTyR5Z1XdXlW3JPlwkm+s\ncfsbU1WvrarfWdx+XZI/S/Jkf62d9I0kH13c/miSf9vgWNZqUVx/7UOZ5vt/mk22fiVq2MJk61ei\nhq2tN1xr7aCqPpnk20n2kjzUWntqXdvfsPNJHqmq5Opr/tXW2qObHdJqVdXXkrwvyZuq6pkkf5/k\nc0n+tao+luRSkr/a3AhX5zr7/kCSWVXdlatT9z9L8okNDpFBE69fycRq2JTrV6KGXY92JwAAHa7g\nDQDQISwBAHQISwAAHcISAECHsAQA0CEsAQB0CEsAAB3CEgBAx/8D0oLTyJjGYugAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c40dc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15564.7880859\n",
      "\n",
      "    Generator_Loss: 20953.9003906\n",
      "551\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18028.5898438\n",
      "\n",
      "    Generator_Loss: 22349.1816406\n",
      "552\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17897.2148438\n",
      "\n",
      "    Generator_Loss: 25235.3925781\n",
      "553\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15148.2246094\n",
      "\n",
      "    Generator_Loss: 26392.2773438\n",
      "554\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17224.4335938\n",
      "\n",
      "    Generator_Loss: 19965.0058594\n",
      "555\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17081.8691406\n",
      "\n",
      "    Generator_Loss: 25414.1152344\n",
      "556\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17174.4042969\n",
      "\n",
      "    Generator_Loss: 21126.890625\n",
      "557\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15489.2285156\n",
      "\n",
      "    Generator_Loss: 15050.1220703\n",
      "558\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18405.0175781\n",
      "\n",
      "    Generator_Loss: 20423.7578125\n",
      "559\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20652.0898438\n",
      "\n",
      "    Generator_Loss: 24912.9589844\n",
      "560\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12647.6611328\n",
      "\n",
      "    Generator_Loss: 21875.2675781\n",
      "561\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17209.5078125\n",
      "\n",
      "    Generator_Loss: 14606.1503906\n",
      "562\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17949.4550781\n",
      "\n",
      "    Generator_Loss: 23889.5839844\n",
      "563\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18160.6289062\n",
      "\n",
      "    Generator_Loss: 22279.6679688\n",
      "564\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18603.96875\n",
      "\n",
      "    Generator_Loss: 24964.7578125\n",
      "565\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16160.0859375\n",
      "\n",
      "    Generator_Loss: 23296.0625\n",
      "566\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18412.5058594\n",
      "\n",
      "    Generator_Loss: 16091.2724609\n",
      "567\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19697.0449219\n",
      "\n",
      "    Generator_Loss: 21548.8046875\n",
      "568\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17348.9511719\n",
      "\n",
      "    Generator_Loss: 19145.1074219\n",
      "569\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19978.6523438\n",
      "\n",
      "    Generator_Loss: 17245.1152344\n",
      "570\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18474.8105469\n",
      "\n",
      "    Generator_Loss: 19896.78125\n",
      "571\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13899.9550781\n",
      "\n",
      "    Generator_Loss: 25973.7617188\n",
      "572\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13937.5283203\n",
      "\n",
      "    Generator_Loss: 25505.9335938\n",
      "573\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16093.4189453\n",
      "\n",
      "    Generator_Loss: 24195.1621094\n",
      "574\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18864.1523438\n",
      "\n",
      "    Generator_Loss: 22341.6582031\n",
      "575\n",
      "     Level #0 Epoch #0 Batch #575\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAICGsgQA0Ph/xHDxcFWPrWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f2ab5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f420210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18180.3710938\n",
      "\n",
      "    Generator_Loss: 27121.3515625\n",
      "576\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17567.5117188\n",
      "\n",
      "    Generator_Loss: 9311.52539062\n",
      "577\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14435.5683594\n",
      "\n",
      "    Generator_Loss: 24373.4511719\n",
      "578\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18733.8417969\n",
      "\n",
      "    Generator_Loss: 20084.6933594\n",
      "579\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19143.4355469\n",
      "\n",
      "    Generator_Loss: 24467.328125\n",
      "580\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17666.8613281\n",
      "\n",
      "    Generator_Loss: 22157.5878906\n",
      "581\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20138.8652344\n",
      "\n",
      "    Generator_Loss: 19917.3125\n",
      "582\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16447.2363281\n",
      "\n",
      "    Generator_Loss: 20184.53125\n",
      "583\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17206.5429688\n",
      "\n",
      "    Generator_Loss: 20716.4003906\n",
      "584\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14839.7978516\n",
      "\n",
      "    Generator_Loss: 16401.9042969\n",
      "585\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17973.6816406\n",
      "\n",
      "    Generator_Loss: 22623.3339844\n",
      "586\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17169.0351562\n",
      "\n",
      "    Generator_Loss: 14141.2089844\n",
      "587\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16908.9179688\n",
      "\n",
      "    Generator_Loss: 15022.1591797\n",
      "588\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16716.4492188\n",
      "\n",
      "    Generator_Loss: 20703.2011719\n",
      "589\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16985.0546875\n",
      "\n",
      "    Generator_Loss: 25245.2148438\n",
      "590\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19700.8320312\n",
      "\n",
      "    Generator_Loss: 23393.0390625\n",
      "591\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17990.515625\n",
      "\n",
      "    Generator_Loss: 27758.4277344\n",
      "592\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19373.3046875\n",
      "\n",
      "    Generator_Loss: 19995.8203125\n",
      "593\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16405.5507812\n",
      "\n",
      "    Generator_Loss: 18504.4589844\n",
      "594\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15812.921875\n",
      "\n",
      "    Generator_Loss: 15181.3984375\n",
      "595\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16126.8691406\n",
      "\n",
      "    Generator_Loss: 18743.5234375\n",
      "596\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15552.8369141\n",
      "\n",
      "    Generator_Loss: 19472.2636719\n",
      "597\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17583.3710938\n",
      "\n",
      "    Generator_Loss: 23022.453125\n",
      "598\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18572.8417969\n",
      "\n",
      "    Generator_Loss: 24026.7539062\n",
      "599\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19357.9199219\n",
      "\n",
      "    Generator_Loss: 22731.4375\n",
      "600\n",
      "     Level #0 Epoch #0 Batch #600\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115182510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DWAIAaAhLAAANYQkAoCEsAQA0/g+PVbLyQD89TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c030f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16383.0585938\n",
      "\n",
      "    Generator_Loss: 20614.5195312\n",
      "601\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18620.1835938\n",
      "\n",
      "    Generator_Loss: 21438.6464844\n",
      "602\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17211.3691406\n",
      "\n",
      "    Generator_Loss: 23137.4140625\n",
      "603\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22053.1699219\n",
      "\n",
      "    Generator_Loss: 22771.5234375\n",
      "604\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20168.4121094\n",
      "\n",
      "    Generator_Loss: 24535.390625\n",
      "605\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14412.4082031\n",
      "\n",
      "    Generator_Loss: 23156.4492188\n",
      "606\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21984.2832031\n",
      "\n",
      "    Generator_Loss: 23044.0957031\n",
      "607\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19572.140625\n",
      "\n",
      "    Generator_Loss: 22871.359375\n",
      "608\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20607.1054688\n",
      "\n",
      "    Generator_Loss: 21051.5097656\n",
      "609\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19346.7617188\n",
      "\n",
      "    Generator_Loss: 22518.8027344\n",
      "610\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18999.0585938\n",
      "\n",
      "    Generator_Loss: 23299.8691406\n",
      "611\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13476.6240234\n",
      "\n",
      "    Generator_Loss: 25748.7207031\n",
      "612\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21151.4804688\n",
      "\n",
      "    Generator_Loss: 23634.4316406\n",
      "613\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18218.6445312\n",
      "\n",
      "    Generator_Loss: 22566.7578125\n",
      "614\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21564.5332031\n",
      "\n",
      "    Generator_Loss: 24377.3730469\n",
      "615\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16175.1083984\n",
      "\n",
      "    Generator_Loss: 12957.3564453\n",
      "616\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21138.3164062\n",
      "\n",
      "    Generator_Loss: 15955.5527344\n",
      "617\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20799.0820312\n",
      "\n",
      "    Generator_Loss: 23800.2578125\n",
      "618\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17998.1386719\n",
      "\n",
      "    Generator_Loss: 26345.7402344\n",
      "619\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18323.2636719\n",
      "\n",
      "    Generator_Loss: 21868.7304688\n",
      "620\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18129.4589844\n",
      "\n",
      "    Generator_Loss: 22347.7070312\n",
      "621\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18566.515625\n",
      "\n",
      "    Generator_Loss: 23237.9941406\n",
      "622\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21516.3300781\n",
      "\n",
      "    Generator_Loss: 10129.8203125\n",
      "623\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18048.4003906\n",
      "\n",
      "    Generator_Loss: 8907.33984375\n",
      "624\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23817.8261719\n",
      "\n",
      "    Generator_Loss: 19578.3203125\n",
      "625\n",
      "     Level #0 Epoch #0 Batch #625\n"
     ]
    },
    {
     "data": {
      "image/png": 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/EjVsm2HpmSRvraoHqupVST6S5Ftb3P/OVNWrq+r3Ti6/JsmfJXm23+pC+laSj51c/liS\nf9rhWLbqpLj+nw9lnc//ebba+pWoYSdWW78SNWyjveFuNsa4VlWfSvK9JAdJHhtj/GRb+9+x+5I8\ncdJr61KSr40xntztkDarqr6e5L1JXl9Vzyf5bJK/T/LNqvp4kueSfHh3I9ycWxz7o0kuV9W7cmPq\n/pdJPrnDITJp5fUrWVkNW3P9StSwW9HuBACg4RO8AQAawhIAQENYAgBoCEsAAA1hCQCgISwBADSE\nJQCAhrAEAND4X+KshZVaECl5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2ad7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SfCfJQZLHxxg/3Nbxd+z+JE9WVXLze/61McYzux3SZlXV15O8L8mbquoXST6X5B+TfLOq\nPp7kxSQf3t0IN+c25/5YklVVvTs3p+5/luSTOxwiEy28fiULq2FLrl+JGnY72p0AADR8gjcAQENY\nAgBoCEsAAA1hCQCgISwBADSEJQCAhrAEANAQlgAAGv8Hsu+spz/aOyoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111597150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21626.234375\n",
      "\n",
      "    Generator_Loss: 23816.375\n",
      "626\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19648.4765625\n",
      "\n",
      "    Generator_Loss: 21294.2050781\n",
      "627\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23789.9335938\n",
      "\n",
      "    Generator_Loss: 28179.859375\n",
      "628\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18815.5957031\n",
      "\n",
      "    Generator_Loss: 8379.63476562\n",
      "629\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13428.2021484\n",
      "\n",
      "    Generator_Loss: 8597.19824219\n",
      "630\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14273.5087891\n",
      "\n",
      "    Generator_Loss: 30144.6855469\n",
      "631\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10876.7460938\n",
      "\n",
      "    Generator_Loss: -3899.28393555\n",
      "632\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13495.2021484\n",
      "\n",
      "    Generator_Loss: -15568.7294922\n",
      "633\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10418.1337891\n",
      "\n",
      "    Generator_Loss: 30590.8164062\n",
      "634\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16735.7304688\n",
      "\n",
      "    Generator_Loss: 11860.8144531\n",
      "635\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16913.4960938\n",
      "\n",
      "    Generator_Loss: 26805.5351562\n",
      "636\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19505.03125\n",
      "\n",
      "    Generator_Loss: 23828.0488281\n",
      "637\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17702.7558594\n",
      "\n",
      "    Generator_Loss: 22192.2382812\n",
      "638\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21249.8984375\n",
      "\n",
      "    Generator_Loss: 24834.2792969\n",
      "639\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20248.8925781\n",
      "\n",
      "    Generator_Loss: 22533.9511719\n",
      "640\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19600.1425781\n",
      "\n",
      "    Generator_Loss: 27214.3886719\n",
      "641\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23790.7011719\n",
      "\n",
      "    Generator_Loss: 27986.2128906\n",
      "642\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20964.9921875\n",
      "\n",
      "    Generator_Loss: 29092.296875\n",
      "643\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15778.5009766\n",
      "\n",
      "    Generator_Loss: 19728.5800781\n",
      "644\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22258.2246094\n",
      "\n",
      "    Generator_Loss: 27151.2910156\n",
      "645\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16389.7675781\n",
      "\n",
      "    Generator_Loss: 24050.6894531\n",
      "646\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20027.6796875\n",
      "\n",
      "    Generator_Loss: 23501.2929688\n",
      "647\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16780.8515625\n",
      "\n",
      "    Generator_Loss: 26152.359375\n",
      "648\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19949.4101562\n",
      "\n",
      "    Generator_Loss: 23209.4179688\n",
      "649\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19081.9199219\n",
      "\n",
      "    Generator_Loss: 27242.1074219\n",
      "650\n",
      "     Level #0 Epoch #0 Batch #650\n"
     ]
    },
    {
     "data": {
      "image/png": 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CEgBAQ1gCAGgISwAADWEJAKDxf8rSEF4xmurtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b40f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J01WV3P0//8oY45ntDmm9quqrSd6X5I1V9VKSv03ymST/XFUfS/Jikr/Y3gjX5z5zfyLJ\nYVW9O3eX7n+S5BNbHCITLbx+JQurYUuuX4kadj/anQAANHyDNwBAQ1gCAGgISwAADWEJAKAhLAEA\nNIQlAICGsAQA0BCWAAAa/wcGY7TfC3eA/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a2e02d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22454.1230469\n",
      "\n",
      "    Generator_Loss: 23339.2050781\n",
      "651\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21750.6933594\n",
      "\n",
      "    Generator_Loss: 21166.1992188\n",
      "652\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19049.71875\n",
      "\n",
      "    Generator_Loss: 22079.3027344\n",
      "653\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19466.2226562\n",
      "\n",
      "    Generator_Loss: 27156.7226562\n",
      "654\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17596.5957031\n",
      "\n",
      "    Generator_Loss: 25968.421875\n",
      "655\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20390.2773438\n",
      "\n",
      "    Generator_Loss: 29600.5546875\n",
      "656\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19784.2207031\n",
      "\n",
      "    Generator_Loss: 23655.8476562\n",
      "657\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20365.6328125\n",
      "\n",
      "    Generator_Loss: 24180.8242188\n",
      "658\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20712.1191406\n",
      "\n",
      "    Generator_Loss: 24851.3398438\n",
      "659\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17154.6738281\n",
      "\n",
      "    Generator_Loss: 21512.5683594\n",
      "660\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17337.40625\n",
      "\n",
      "    Generator_Loss: 29648.3066406\n",
      "661\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17470.3691406\n",
      "\n",
      "    Generator_Loss: 21684.2460938\n",
      "662\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22364.8632812\n",
      "\n",
      "    Generator_Loss: 23568.7695312\n",
      "663\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19359.1933594\n",
      "\n",
      "    Generator_Loss: 25853.9550781\n",
      "664\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19050.5527344\n",
      "\n",
      "    Generator_Loss: 28904.3007812\n",
      "665\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20470.7207031\n",
      "\n",
      "    Generator_Loss: 27908.3964844\n",
      "666\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19194.0527344\n",
      "\n",
      "    Generator_Loss: 18150.5292969\n",
      "667\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15512.4345703\n",
      "\n",
      "    Generator_Loss: 18370.2128906\n",
      "668\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20250.5429688\n",
      "\n",
      "    Generator_Loss: 20814.9472656\n",
      "669\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16873.0449219\n",
      "\n",
      "    Generator_Loss: 21918.8632812\n",
      "670\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20402.1972656\n",
      "\n",
      "    Generator_Loss: 21730.2519531\n",
      "671\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16918.1503906\n",
      "\n",
      "    Generator_Loss: 26363.59375\n",
      "672\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16624.0996094\n",
      "\n",
      "    Generator_Loss: 25314.8828125\n",
      "673\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21698.4707031\n",
      "\n",
      "    Generator_Loss: 24244.1855469\n",
      "674\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15658.0966797\n",
      "\n",
      "    Generator_Loss: 26414.7792969\n",
      "675\n",
      "     Level #0 Epoch #0 Batch #675\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fceadd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c74b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21931.3671875\n",
      "\n",
      "    Generator_Loss: 33484.4492188\n",
      "676\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21740.3945312\n",
      "\n",
      "    Generator_Loss: 24162.5976562\n",
      "677\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20265.3046875\n",
      "\n",
      "    Generator_Loss: 22304.9863281\n",
      "678\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21704.8125\n",
      "\n",
      "    Generator_Loss: 21313.2695312\n",
      "679\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16205.9306641\n",
      "\n",
      "    Generator_Loss: 22530.5097656\n",
      "680\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22352.4472656\n",
      "\n",
      "    Generator_Loss: 20889.8808594\n",
      "681\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21520.1621094\n",
      "\n",
      "    Generator_Loss: 31061.4941406\n",
      "682\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19539.2148438\n",
      "\n",
      "    Generator_Loss: 20005.765625\n",
      "683\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16692.9921875\n",
      "\n",
      "    Generator_Loss: 22636.9257812\n",
      "684\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22023.0839844\n",
      "\n",
      "    Generator_Loss: 20686.3378906\n",
      "685\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19110.0332031\n",
      "\n",
      "    Generator_Loss: 26048.6074219\n",
      "686\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22807.2519531\n",
      "\n",
      "    Generator_Loss: 22084.4042969\n",
      "687\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22509.1542969\n",
      "\n",
      "    Generator_Loss: 25828.4921875\n",
      "688\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16190.5556641\n",
      "\n",
      "    Generator_Loss: 26102.3105469\n",
      "689\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15719.2402344\n",
      "\n",
      "    Generator_Loss: 24131.6757812\n",
      "690\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19082.2304688\n",
      "\n",
      "    Generator_Loss: 26690.3613281\n",
      "691\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17124.0117188\n",
      "\n",
      "    Generator_Loss: 13650.8427734\n",
      "692\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19588.3203125\n",
      "\n",
      "    Generator_Loss: 22914.5761719\n",
      "693\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18148.1894531\n",
      "\n",
      "    Generator_Loss: 9758.28710938\n",
      "694\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21926.8574219\n",
      "\n",
      "    Generator_Loss: 29220.4238281\n",
      "695\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19638.1972656\n",
      "\n",
      "    Generator_Loss: 18844.09375\n",
      "696\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24388.7070312\n",
      "\n",
      "    Generator_Loss: 19954.6796875\n",
      "697\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19113.9003906\n",
      "\n",
      "    Generator_Loss: 18406.5449219\n",
      "698\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20728.5488281\n",
      "\n",
      "    Generator_Loss: 23450.1757812\n",
      "699\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20644.7695312\n",
      "\n",
      "    Generator_Loss: 24031.609375\n",
      "700\n",
      "     Level #0 Epoch #0 Batch #700\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d57d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1UG4t3f86yZd2OEQmmnn9SmZWw+ZcvxI17CzanQAANHyCNwBAQ1gCAGgISwAADWEJAKAh\nLAEANIQlAICGsAQA0BCWAAAa/wf79cj9OJ2LhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a731710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18147.28125\n",
      "\n",
      "    Generator_Loss: 24736.4921875\n",
      "701\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19055.3886719\n",
      "\n",
      "    Generator_Loss: 28152.9707031\n",
      "702\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20639.5742188\n",
      "\n",
      "    Generator_Loss: 24027.4589844\n",
      "703\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23566.1679688\n",
      "\n",
      "    Generator_Loss: 24926.6035156\n",
      "704\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22433.5058594\n",
      "\n",
      "    Generator_Loss: 18238.7578125\n",
      "705\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20083.890625\n",
      "\n",
      "    Generator_Loss: 19888.4375\n",
      "706\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20712.5351562\n",
      "\n",
      "    Generator_Loss: 24514.078125\n",
      "707\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23042.8105469\n",
      "\n",
      "    Generator_Loss: 25540.9199219\n",
      "708\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22448.8925781\n",
      "\n",
      "    Generator_Loss: 23727.2167969\n",
      "709\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16884.0800781\n",
      "\n",
      "    Generator_Loss: 22170.875\n",
      "710\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21082.9335938\n",
      "\n",
      "    Generator_Loss: 23622.9902344\n",
      "711\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19949.09375\n",
      "\n",
      "    Generator_Loss: 28506.2636719\n",
      "712\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16396.703125\n",
      "\n",
      "    Generator_Loss: 24350.5234375\n",
      "713\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21332.5820312\n",
      "\n",
      "    Generator_Loss: 22288.0292969\n",
      "714\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15774.8398438\n",
      "\n",
      "    Generator_Loss: 29180.9921875\n",
      "715\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20621.6523438\n",
      "\n",
      "    Generator_Loss: 24586.6542969\n",
      "716\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17113.6035156\n",
      "\n",
      "    Generator_Loss: 23437.9824219\n",
      "717\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14030.1025391\n",
      "\n",
      "    Generator_Loss: 28812.0996094\n",
      "718\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20897.3847656\n",
      "\n",
      "    Generator_Loss: 26215.4023438\n",
      "719\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20256.6347656\n",
      "\n",
      "    Generator_Loss: 25492.7949219\n",
      "720\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25479.4921875\n",
      "\n",
      "    Generator_Loss: 25184.9335938\n",
      "721\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15434.1298828\n",
      "\n",
      "    Generator_Loss: 23863.8710938\n",
      "722\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20738.6289062\n",
      "\n",
      "    Generator_Loss: 22845.4042969\n",
      "723\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19656.7832031\n",
      "\n",
      "    Generator_Loss: 24252.8554688\n",
      "724\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18189.2265625\n",
      "\n",
      "    Generator_Loss: 24521.8261719\n",
      "725\n",
      "     Level #0 Epoch #0 Batch #725\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY9wdjBbjYSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113de390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AQ1kCAGj8DsohuO+kqIFwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a04310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21358.0585938\n",
      "\n",
      "    Generator_Loss: 26339.4921875\n",
      "726\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19574.2832031\n",
      "\n",
      "    Generator_Loss: 23329.6230469\n",
      "727\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20412.5019531\n",
      "\n",
      "    Generator_Loss: 23342.9003906\n",
      "728\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19358.3691406\n",
      "\n",
      "    Generator_Loss: 24985.1621094\n",
      "729\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20888.8105469\n",
      "\n",
      "    Generator_Loss: 25518.0039062\n",
      "730\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18289.6660156\n",
      "\n",
      "    Generator_Loss: 15801.5429688\n",
      "731\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22947.7617188\n",
      "\n",
      "    Generator_Loss: 24143.6699219\n",
      "732\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19062.7460938\n",
      "\n",
      "    Generator_Loss: 17417.4726562\n",
      "733\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19728.5722656\n",
      "\n",
      "    Generator_Loss: 10280.1953125\n",
      "734\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24629.890625\n",
      "\n",
      "    Generator_Loss: 19738.6113281\n",
      "735\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17221.1738281\n",
      "\n",
      "    Generator_Loss: 24343.4726562\n",
      "736\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17982.2734375\n",
      "\n",
      "    Generator_Loss: 25936.015625\n",
      "737\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8109.69873047\n",
      "\n",
      "    Generator_Loss: 25181.5058594\n",
      "738\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18269.7285156\n",
      "\n",
      "    Generator_Loss: 11737.9433594\n",
      "739\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21772.9550781\n",
      "\n",
      "    Generator_Loss: 19484.2597656\n",
      "740\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17626.8515625\n",
      "\n",
      "    Generator_Loss: 12152.5351562\n",
      "741\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20376.1796875\n",
      "\n",
      "    Generator_Loss: 29827.7050781\n",
      "742\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17418.4042969\n",
      "\n",
      "    Generator_Loss: 24815.2285156\n",
      "743\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17784.9003906\n",
      "\n",
      "    Generator_Loss: 25232.9765625\n",
      "744\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20990.7109375\n",
      "\n",
      "    Generator_Loss: 20683.2871094\n",
      "745\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22354.2734375\n",
      "\n",
      "    Generator_Loss: 25446.2890625\n",
      "746\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21171.4375\n",
      "\n",
      "    Generator_Loss: 32854.9140625\n",
      "747\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19818.6953125\n",
      "\n",
      "    Generator_Loss: 20649.5898438\n",
      "748\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19391.5859375\n",
      "\n",
      "    Generator_Loss: 21649.8867188\n",
      "749\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20307.3242188\n",
      "\n",
      "    Generator_Loss: 24893.7109375\n",
      "750\n",
      "     Level #0 Epoch #0 Batch #750\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111510cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h6U+SPHXV/V8dPTYXLcn3q+rxqvrkugezJre01i4f3b6c5JZ1DmYNPltVP66qB7b5EP6W\nmnv9StSwudevZMY1bJVhae6fUfCe1todSf4myd9X1V+ue0Dr1K58ZsWcXhP3J/mzJLcneSbJF9c7\nHAbN6bV6PWrYkRnWr2TmNWyVYenpJLdedf/WXPnrbBZaa88c/fubJA/nymH9ublcVW9Okqp6S5Jf\nr3k8K9Na+3U7kuRfM8/n/yybdf1K1LDMuH4latgqw9LjSd5WVbdV1WuSfDTJt1e4/7Wpqpur6o+O\nbr8uyV8leaK/1lb6dpKPH93+eJJ/W+NYVuqouP7BhzLP5/8sm239StSwI7OtX4katrLecK21g6r6\nTJLvJdlJ8kBr7Ser2v+a3ZLk4apKrvzMv95ae2S9Q1quqvpGkruTvKmqnkry+ST/nOShqvpEkieT\nfGR9I1yea8z93iR7VXV7rhy6/2WST61xiAyaef1KZlbD5ly/EjXsWrQ7AQDo8AneAAAdwhIAQIew\nBADQISwBAHQISwAAHcISAECHsAQA0CEsAQB0/C9ZlCgBVF1i+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113923d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23220.8457031\n",
      "\n",
      "    Generator_Loss: 27116.9921875\n",
      "751\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20590.7128906\n",
      "\n",
      "    Generator_Loss: 27756.6308594\n",
      "752\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21671.9316406\n",
      "\n",
      "    Generator_Loss: 26968.9160156\n",
      "753\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24126.3886719\n",
      "\n",
      "    Generator_Loss: 19295.4921875\n",
      "754\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16985.6445312\n",
      "\n",
      "    Generator_Loss: 24263.5175781\n",
      "755\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19380.1484375\n",
      "\n",
      "    Generator_Loss: 30277.4726562\n",
      "756\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22998.6484375\n",
      "\n",
      "    Generator_Loss: 27054.6914062\n",
      "757\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17102.8613281\n",
      "\n",
      "    Generator_Loss: 27983.9882812\n",
      "758\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14774.5771484\n",
      "\n",
      "    Generator_Loss: 24583.9335938\n",
      "759\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15241.2666016\n",
      "\n",
      "    Generator_Loss: 26036.7285156\n",
      "760\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18104.4492188\n",
      "\n",
      "    Generator_Loss: 21547.3203125\n",
      "761\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20849.8125\n",
      "\n",
      "    Generator_Loss: 29229.4492188\n",
      "762\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22347.1855469\n",
      "\n",
      "    Generator_Loss: 27100.1933594\n",
      "763\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19765.9316406\n",
      "\n",
      "    Generator_Loss: 26228.0859375\n",
      "764\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19662.3828125\n",
      "\n",
      "    Generator_Loss: 26918.4277344\n",
      "765\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21620.5097656\n",
      "\n",
      "    Generator_Loss: 22270.2402344\n",
      "766\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16781.6972656\n",
      "\n",
      "    Generator_Loss: 29453.5214844\n",
      "767\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15642.8837891\n",
      "\n",
      "    Generator_Loss: 26965.4863281\n",
      "768\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17450.8027344\n",
      "\n",
      "    Generator_Loss: 25662.0195312\n",
      "769\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17044.4238281\n",
      "\n",
      "    Generator_Loss: 32647.7011719\n",
      "770\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20474.6875\n",
      "\n",
      "    Generator_Loss: 31042.3339844\n",
      "771\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19819.6054688\n",
      "\n",
      "    Generator_Loss: 27950.8515625\n",
      "772\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20736.7695312\n",
      "\n",
      "    Generator_Loss: 21545.8222656\n",
      "773\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18225.4160156\n",
      "\n",
      "    Generator_Loss: 15502.9501953\n",
      "774\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21893.2441406\n",
      "\n",
      "    Generator_Loss: 20667.0722656\n",
      "775\n",
      "     Level #0 Epoch #0 Batch #775\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b386410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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JAEDDFbwBABrCEgBAQ1gCAGgISwAADWEJAKAhLAEANIQlAICGsAQA0Pg/MG7MvGKXp0cA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113636e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13785.7119141\n",
      "\n",
      "    Generator_Loss: 24411.4179688\n",
      "776\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19918.484375\n",
      "\n",
      "    Generator_Loss: 29577.7871094\n",
      "777\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17838.9746094\n",
      "\n",
      "    Generator_Loss: 23461.2148438\n",
      "778\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20408.0136719\n",
      "\n",
      "    Generator_Loss: 24834.9882812\n",
      "779\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22956.2421875\n",
      "\n",
      "    Generator_Loss: 24203.8808594\n",
      "780\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -12265.3212891\n",
      "\n",
      "    Generator_Loss: 23043.8476562\n",
      "781\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15451.1650391\n",
      "\n",
      "    Generator_Loss: 24223.8398438\n",
      "782\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22619.2285156\n",
      "\n",
      "    Generator_Loss: 23624.6816406\n",
      "783\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23499.1289062\n",
      "\n",
      "    Generator_Loss: 27807.5253906\n",
      "784\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22655.96875\n",
      "\n",
      "    Generator_Loss: 26271.1738281\n",
      "785\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20232.5351562\n",
      "\n",
      "    Generator_Loss: 24842.6230469\n",
      "786\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23842.2128906\n",
      "\n",
      "    Generator_Loss: 25616.7285156\n",
      "787\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20581.390625\n",
      "\n",
      "    Generator_Loss: 18282.2519531\n",
      "788\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21693.6835938\n",
      "\n",
      "    Generator_Loss: 18872.2753906\n",
      "789\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19910.5703125\n",
      "\n",
      "    Generator_Loss: 16568.5078125\n",
      "790\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21914.7890625\n",
      "\n",
      "    Generator_Loss: 9599.46777344\n",
      "791\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23091.6894531\n",
      "\n",
      "    Generator_Loss: 25331.8417969\n",
      "792\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19783.1894531\n",
      "\n",
      "    Generator_Loss: 17521.1855469\n",
      "793\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18397.4277344\n",
      "\n",
      "    Generator_Loss: 21570.4902344\n",
      "794\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13806.3417969\n",
      "\n",
      "    Generator_Loss: -21183.4511719\n",
      "795\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18589.8125\n",
      "\n",
      "    Generator_Loss: 12906.8925781\n",
      "796\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24840.53125\n",
      "\n",
      "    Generator_Loss: 21032.0722656\n",
      "797\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18291.1171875\n",
      "\n",
      "    Generator_Loss: 26523.53125\n",
      "798\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17736.7363281\n",
      "\n",
      "    Generator_Loss: 20086.0664062\n",
      "799\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24285.03125\n",
      "\n",
      "    Generator_Loss: 24874.1621094\n",
      "800\n",
      "     Level #0 Epoch #0 Batch #800\n"
     ]
    },
    {
     "data": {
      "image/png": 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M9an7nyb59B6HyEQLr1/JwmrYkutXoobdjHYnAAANn+ANANAQlgAAGsISAEBDWAIAaAhL\nAAANYQkAoCEsAQA0hCUAgMb/AZbT6cIFh8PPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116a1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ihm2tN9wY46iqPpXku0kuJ3l0jPGjba1/x+5N8kRVJbe+518fYzy52yFtVlV9I8n7kry5\nqp5P8g9JvpDk36rq40meS/LXuxvh5pyw7Y8kWVTVu3Nr6v7nST65wyEy0czrVzKzGjbn+pWoYSfR\n7gQAoOEK3gAADWEJAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBo/D/6dJdsnm/k3wAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f13dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15942.8759766\n",
      "\n",
      "    Generator_Loss: 21546.7636719\n",
      "801\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18406.0703125\n",
      "\n",
      "    Generator_Loss: 26769.8222656\n",
      "802\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24344.2109375\n",
      "\n",
      "    Generator_Loss: 20401.1054688\n",
      "803\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20712.6445312\n",
      "\n",
      "    Generator_Loss: 15836.0234375\n",
      "804\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21584.6425781\n",
      "\n",
      "    Generator_Loss: 28040.1542969\n",
      "805\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -11980.4980469\n",
      "\n",
      "    Generator_Loss: 23199.7734375\n",
      "806\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19127.6152344\n",
      "\n",
      "    Generator_Loss: 20244.7578125\n",
      "807\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23279.9160156\n",
      "\n",
      "    Generator_Loss: 29481.9941406\n",
      "808\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15691.7392578\n",
      "\n",
      "    Generator_Loss: 23596.546875\n",
      "809\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24537.3183594\n",
      "\n",
      "    Generator_Loss: 27305.2792969\n",
      "810\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19504.3847656\n",
      "\n",
      "    Generator_Loss: 19746.3535156\n",
      "811\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21306.203125\n",
      "\n",
      "    Generator_Loss: 23962.5625\n",
      "812\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22263.3066406\n",
      "\n",
      "    Generator_Loss: 26087.3476562\n",
      "813\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23906.7871094\n",
      "\n",
      "    Generator_Loss: 25171.9746094\n",
      "814\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15358.5703125\n",
      "\n",
      "    Generator_Loss: 27138.6445312\n",
      "815\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17650.5527344\n",
      "\n",
      "    Generator_Loss: 24690.9140625\n",
      "816\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17651.0664062\n",
      "\n",
      "    Generator_Loss: 23191.40625\n",
      "817\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24931.2304688\n",
      "\n",
      "    Generator_Loss: 17957.3828125\n",
      "818\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16350.9775391\n",
      "\n",
      "    Generator_Loss: 23697.9746094\n",
      "819\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25879.8515625\n",
      "\n",
      "    Generator_Loss: 31656.2246094\n",
      "820\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15785.1054688\n",
      "\n",
      "    Generator_Loss: 30779.1679688\n",
      "821\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20103.6699219\n",
      "\n",
      "    Generator_Loss: 30512.4101562\n",
      "822\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19470.3339844\n",
      "\n",
      "    Generator_Loss: 29821.7617188\n",
      "823\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17324.2011719\n",
      "\n",
      "    Generator_Loss: 25474.5527344\n",
      "824\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27148.4667969\n",
      "\n",
      "    Generator_Loss: 30884.1953125\n",
      "825\n",
      "     Level #0 Epoch #0 Batch #825\n"
     ]
    },
    {
     "data": {
      "image/png": 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A4/8AXVQYS6aH7qUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137d9b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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OkYlmXr+SmdWwOdevRA27Hu1OAAAavsEbAKAhLAEANIQlAICGsAQA0BCWAAAawhIAQENY\nAgBoCEsAAI3fA2jdyhAaU2yAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d35050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21012.2617188\n",
      "\n",
      "    Generator_Loss: 26234.7734375\n",
      "826\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16455.0722656\n",
      "\n",
      "    Generator_Loss: 27439.8242188\n",
      "827\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18755.515625\n",
      "\n",
      "    Generator_Loss: 21755.4667969\n",
      "828\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17804.9667969\n",
      "\n",
      "    Generator_Loss: 24264.3710938\n",
      "829\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17947.3417969\n",
      "\n",
      "    Generator_Loss: 27538.8066406\n",
      "830\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19871.9570312\n",
      "\n",
      "    Generator_Loss: 26171.7246094\n",
      "831\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19704.5605469\n",
      "\n",
      "    Generator_Loss: 25026.2226562\n",
      "832\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17992.3398438\n",
      "\n",
      "    Generator_Loss: 6039.23095703\n",
      "833\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19766.0917969\n",
      "\n",
      "    Generator_Loss: 29146.0859375\n",
      "834\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19495.6875\n",
      "\n",
      "    Generator_Loss: 27587.0429688\n",
      "835\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23752.9140625\n",
      "\n",
      "    Generator_Loss: 32081.3671875\n",
      "836\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17198.578125\n",
      "\n",
      "    Generator_Loss: 31455.3398438\n",
      "837\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21539.671875\n",
      "\n",
      "    Generator_Loss: 23758.0800781\n",
      "838\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21559.5742188\n",
      "\n",
      "    Generator_Loss: 30468.2519531\n",
      "839\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22445.4921875\n",
      "\n",
      "    Generator_Loss: 25165.59375\n",
      "840\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24161.3320312\n",
      "\n",
      "    Generator_Loss: 30868.1640625\n",
      "841\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19896.203125\n",
      "\n",
      "    Generator_Loss: 25852.1640625\n",
      "842\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21406.8183594\n",
      "\n",
      "    Generator_Loss: 30070.1777344\n",
      "843\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18065.875\n",
      "\n",
      "    Generator_Loss: 30573.9316406\n",
      "844\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22912.7421875\n",
      "\n",
      "    Generator_Loss: 27651.1464844\n",
      "845\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15999.6894531\n",
      "\n",
      "    Generator_Loss: 17941.9355469\n",
      "846\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25664.921875\n",
      "\n",
      "    Generator_Loss: 30020.9140625\n",
      "847\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17260.9648438\n",
      "\n",
      "    Generator_Loss: 16869.1113281\n",
      "848\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20139.2871094\n",
      "\n",
      "    Generator_Loss: 27111.359375\n",
      "849\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19719.6679688\n",
      "\n",
      "    Generator_Loss: 26675.2714844\n",
      "850\n",
      "     Level #0 Epoch #0 Batch #850\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADSdJREFUeJzt3U+opXd5B/DvMyMulEKQlDEplpSgoCAkWUwLUjKrdrLx\nDxRFKLiQ4KJmbboxabupBV0JbpqELCTiJm3c6IzCgexKQrCzMKjgQNKkE2lT2mXM/LqYI1ySO8/c\n99zz976fD1zmnDP3nvN7c898872/95z71BgjAAAc79yuFwAAsM+UJQCAhrIEANBQlgAAGsoSAEBD\nWQIAaHxgU3dcVX4nAczQGKN2vYbTkl8wT7fLr73aWaqqSR9jjIP7eOKJJ3a+Bsfu+Df1MWdT8+sQ\nM2wOz2HHP99j76xclqrqclW9WlW/qqpvrHo/ANsmv4ApVipLVXU+yXeTXE7yqSRfrqpPrnNhAJsg\nv4CpVt1Zupjk12OM62OMd5L8IMnn1ress+vSpUu7XsLOzPnYE8e/R+TXiub+HJ7z8c/52JOk7nSe\n7tgvqvqrJH85xnh0ef2vk/zpGOOxI58z+Y6rpr0u9ObNm1MfAtig5etw9voF3vuSX4kMg33S5deq\n74ab9ys5gSTJYrHIYrHY9TKmkl/ApPxadWfpz5I8Oca4vLz+t0lujjG+deRz7CzBzBzIztJe5Fci\nw2CfdPm16muWXkry8aq6r6o+mORLSV5YdYEAWyS/gElWOg03xvhdVX09yU+SnE/y1BjjF2tdGcAG\nyC9gqpVOw53ojp2Gg9k5hNNwJ+E0HMzPJk7DAQDMgrIEANDY2CDdVUw9JXj+/PnJj/Huu+9O/hqA\nO1nlJQ1TM0x+wW7YWQIAaChLAAANZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBAQ1kCAGgoSwAA\nDWUJAKCxV7Phprp58+bkrzFPDtgXUzNMfsFu2FkCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCW\nAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADQOepDuKrYxfNfgSmATDA+H3bCzBADQUJYAABrKEgBA\nQ1kCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAayhIAQENZAgBozG6Q7iqmDq80uBLYF4bv\nwunZWQIAaChLAAANZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBAQ1kCAGgoSwAADWUJAKBhNtwG\nmMUEHLJtZJj84pDYWQIAaKy8s1RV15P8b5J3k7wzxri4rkUBbJoMA07qNKfhRpJLY4z/XtdiALZI\nhgEnctrTcLWWVQDshgwD7ug0ZWkk+WlVvVRVj65rQQBbIsOAEznNabjPjDHerKo/THK1ql4dY7y4\nroUBbJgMA05k5bI0xnhz+edvq+r5JBeTCBqYkcVikcVisetlrESGwbxNya8aY0x+gKr6UJLzY4z/\nq6oPJ7mS5O/GGFeOfM70O56xc+emnxH1e0rYN1WVMcbevw7oThkmv6abmmHyi33T5deqO0sXkjxf\nVb+/j+8fLUoAe06GASe20s7Sie7YT2aT2FniLDiUnaU7kV/T2Vni0HX55Td4AwA0lCUAgIZBunvC\n8F3gkE3NMPnFIbGzBADQUJYAABrKEgBAQ1kCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAa\nyhIAQENZAgBoGKR7wLYxfNfgSmATDA/nkNhZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAA\nGsoSAEBDWQIAaChLAAANZQkAoGE23MxMncdkFhOwL8zDZFfsLAEANJQlAICGsgQA0FCWAAAayhIA\nQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGgbp0trG4MrE8EpgMwwPZx3sLAEANJQl\nAICGsgQA0FCWAAAayhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGgbpsnbbGL5r\ncCWwCYaHc5x2Z6mqnq6qG1V17chtH6mqq1X1y6q6UlV3bX6ZANPJMGAd7nQa7pkkl99z2+NJro4x\nPpHkZ8vrAPtIhgGn1palMcaLSd5+z82fTfLs8vKzST6/gXUBnJoMA9ZhlRd4Xxhj3FhevpHkwhrX\nA7BpMgyY5FTvhhtjjCRjTWsB2CoZBpzEKu+Gu1FVHx1j/GdV3ZPkrXUvCjgMi8Uii8Vi18uYSoYB\nk/Krbv1g1XxC1X1JfjTG+PTy+j8l+a8xxreq6vEkd40x3vcCyary0xondu7ctE1Ob7vdT1WVMUbt\neh1HrZJh8osppuZXIsP2UZdfbVmqqueSPJzk7tw6t//NJP+a5IdJ/jjJ9SRfHGP8zzFfK2w4MWXp\nbNi3srRqhskvplCWzoaVy9IpH1TYcGLK0tmwb2VpVfKLKZSls6HLL+NOAAAayhIAQMNsOPbC1HlM\nZjEB+8I8zLPPzhIAQENZAgBoKEsAAA1lCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBDWQIAaChL\nAAANZQkAoGGQLgdpG4MrE8Mrgc0wPPyw2FkCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAa\nyhIAQENZAgBoKEsAAA1lCQCgoSwBADQM0mU2tjF81+BKYBMMD98tO0sAAA1lCQCgoSwBADSUJQCA\nhrIEANBQlgAAGsoSAEBDWQIAaChLAAANZQkAoKEsAQA0zIaDxtR5TGYxAfvCPMz1sbMEANBQlgAA\nGsoSAEBDWQIAaChLAAANZQkAoKEsAQA0lCUAgIayBADQUJYAABrKEgBAQ1kCAGgYpAtrtI3Blcl8\nhlcC22V4+PHsLAEANNqyVFVPV9WNqrp25LYnq+r1qnpl+XF588sEmE6GAetwp52lZ5K8N0hGku+M\nMR5cfvx4M0sDODUZBpxaW5bGGC8mefuYv6rNLAdgfWQYsA6rvmbpsar6eVU9VVV3rXVFAJsnw4AT\nW+XdcN9L8vfLy/+Q5NtJvrq2FQEHY7FYZLFY7HoZU8kwYFJ+1Rij/4Sq+5L8aIzx6Yl/198xkCQ5\nd276Bu++vvW2qjLG2KtTXKtkmPyCk5lLfk0+yqq658jVLyS5drvPBdg3MgyYqj0NV1XPJXk4yd1V\n9VqSJ5JcqqoHcusdJb9J8rWNrxJgBTIMWIc7noZb+Y5tY8OJzGUb+5DILziZueSX3+ANANBQlgAA\nGgbp7omq6Wcu9nUrk2lWORU+dXil5wqwCXMZHm5nCQCgoSwBADSUJQCAhrIEANBQlgAAGsoSAEBD\nWQIAaChLAAANZQkAoKEsAQA0lCUAgIbZcAdslXlynA1T5zEd4iwm4Gzaxjy5deeXnSUAgIayBADQ\nUJYAABrKEgBAQ1kCAGgoSwAADWUJAKChLAEANJQlAICGsgQA0FCWAAAayhIAQMMgXZiBVQZXnjs3\n7WepRx55ZPJjAJzE1Aybml93vL+13hsAwBmjLAEANJQlAICGsgQA0FCWAAAayhIAQENZAgBoKEsA\nAA1lia1ZLBa7XsJOzf34AQ6VssTWzL0szP34AQ6VsgQA0NjobLiHHnrofbe98cYbuffeezf5sHvt\ndsdfVTtYDYfquH9b6zb1OXn//fdvaCW7cbv/xnPOsDkfezLv4z+0Y1/l/6kvv/zy7e9vjHGa9dz+\njqs2c8fAXhtjHHzzl18wT7fLr42VJQCAs8BrlgAAGsoSAEBjq2Wpqi5X1atV9auq+sY2H3vXqup6\nVf17Vb1SVf+26/VsWlU9XVU3qurakds+UlVXq+qXVXWlqu7a5Ro35TbH/mRVvb78/r9SVZd3uUam\nm3N+JfPKsDnnVyLDjrO1slRV55N8N8nlJJ9K8uWq+uS2Hn8PjCSXxhgPjjEu7noxW/BMbn2vj3o8\nydUxxieS/Gx5/Sw67thHku8sv/8PjjF+vIN1sSL5lWReGTbn/Epk2Ptsc2fpYpJfjzGujzHeSfKD\nJJ/b4uPvg4N/l9BJjTFeTPL2e27+bJJnl5efTfL5rS5qS25z7MmMvv9nkPy6ZRbP4TnnVyLDjrPN\nsvRHSV47cv315W1zMZL8tKpeqqpHd72YHbkwxrixvHwjyYVdLmYHHquqn1fVU2d5C/+Mmnt+JTJs\n7vmVzDjDtlmW5v47Cj4zxngwySNJ/qaq/nzXC9qlcet3VszpOfG9JH+S5IEkbyb59m6Xw0Rzeq7e\njgxbmmF+JTPPsG2Wpf9I8rEj1z+WWz+dzcIY483ln79N8nxubevPzY2q+miSVNU9Sd7a8Xq2Zozx\n1lhK8s+Z5/f/kM06vxIZlhnnVyLDtlmWXkry8aq6r6o+mORLSV7Y4uPvTFV9qKr+YHn5w0n+Ism1\n/qvOpBeSfGV5+StJ/mWHa9mqZbj+3hcyz+//IZttfiUybGm2+ZXIsI3OhjtqjPG7qvp6kp8kOZ/k\nqTHGL7b1+Dt2Icnzy1k1H0jy/THGld0uabOq6rkkDye5u6peS/LNJP+Y5IdV9dUk15N8cXcr3Jxj\njv2JJJeq6oHc2rr/TZKv7XCJTDTz/EpmlmFzzq9Ehh3HuBMAgIbf4A0A0FCWAAAayhIAQENZAgBo\nKEsAAA1lCQCgoSwBADSUJQCAxv8D3i/U8GwxlCYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a751410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12099e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20451.9453125\n",
      "\n",
      "    Generator_Loss: 35141.8554688\n",
      "851\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21020.6699219\n",
      "\n",
      "    Generator_Loss: 25576.0195312\n",
      "852\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16705.9863281\n",
      "\n",
      "    Generator_Loss: 29442.6796875\n",
      "853\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20718.3535156\n",
      "\n",
      "    Generator_Loss: 31741.3183594\n",
      "854\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16467.4824219\n",
      "\n",
      "    Generator_Loss: 23517.9667969\n",
      "855\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19547.9921875\n",
      "\n",
      "    Generator_Loss: 29544.6308594\n",
      "856\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25685.6621094\n",
      "\n",
      "    Generator_Loss: 28027.6367188\n",
      "857\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16118.8994141\n",
      "\n",
      "    Generator_Loss: 30168.2050781\n",
      "858\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -13845.5673828\n",
      "\n",
      "    Generator_Loss: 30346.4902344\n",
      "859\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20290.1640625\n",
      "\n",
      "    Generator_Loss: 34555.7617188\n",
      "860\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21313.8632812\n",
      "\n",
      "    Generator_Loss: 29465.9453125\n",
      "861\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21080.4980469\n",
      "\n",
      "    Generator_Loss: 24896.7675781\n",
      "862\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18966.5625\n",
      "\n",
      "    Generator_Loss: 31033.0097656\n",
      "863\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18528.3105469\n",
      "\n",
      "    Generator_Loss: 31450.7871094\n",
      "864\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18484.4257812\n",
      "\n",
      "    Generator_Loss: 32002.8203125\n",
      "865\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17197.9960938\n",
      "\n",
      "    Generator_Loss: 31000.7246094\n",
      "866\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22287.8125\n",
      "\n",
      "    Generator_Loss: 25748.5039062\n",
      "867\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20655.0507812\n",
      "\n",
      "    Generator_Loss: 27602.2539062\n",
      "868\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25564.0527344\n",
      "\n",
      "    Generator_Loss: 29171.5917969\n",
      "869\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18054.0996094\n",
      "\n",
      "    Generator_Loss: 26479.4023438\n",
      "870\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22391.2871094\n",
      "\n",
      "    Generator_Loss: 30337.7382812\n",
      "871\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19089.0234375\n",
      "\n",
      "    Generator_Loss: 26681.4140625\n",
      "872\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24151.1328125\n",
      "\n",
      "    Generator_Loss: 31870.2167969\n",
      "873\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20680.9746094\n",
      "\n",
      "    Generator_Loss: 28367.7910156\n",
      "874\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16472.5859375\n",
      "\n",
      "    Generator_Loss: 25379.1855469\n",
      "875\n",
      "     Level #0 Epoch #0 Batch #875\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZP8vMRzPz9kWuvWyZeSwz/zszf5qZ/7Xs/cxbZt6VmScy8+hZP9uXmfdl5i8y897MvHyZ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\nAArCEgBA4X8BCCOBYrtuiZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a0d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fdbb490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23115.1582031\n",
      "\n",
      "    Generator_Loss: 13880.3085938\n",
      "876\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14497.34375\n",
      "\n",
      "    Generator_Loss: 29303.9746094\n",
      "877\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20359.2148438\n",
      "\n",
      "    Generator_Loss: 27770.0664062\n",
      "878\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15288.1103516\n",
      "\n",
      "    Generator_Loss: 20217.3476562\n",
      "879\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15809.8173828\n",
      "\n",
      "    Generator_Loss: 13934.4414062\n",
      "880\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20569.9882812\n",
      "\n",
      "    Generator_Loss: 14028.265625\n",
      "881\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18820.7382812\n",
      "\n",
      "    Generator_Loss: 13759.7050781\n",
      "882\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16028.4267578\n",
      "\n",
      "    Generator_Loss: 16982.7734375\n",
      "883\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23800.0390625\n",
      "\n",
      "    Generator_Loss: 26435.3613281\n",
      "884\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17536.6875\n",
      "\n",
      "    Generator_Loss: 23147.6367188\n",
      "885\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17260.5117188\n",
      "\n",
      "    Generator_Loss: 30154.9101562\n",
      "886\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20759.828125\n",
      "\n",
      "    Generator_Loss: 34203.2226562\n",
      "887\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21697.7304688\n",
      "\n",
      "    Generator_Loss: 30167.3085938\n",
      "888\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21272.7285156\n",
      "\n",
      "    Generator_Loss: 28926.5820312\n",
      "889\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20261.8085938\n",
      "\n",
      "    Generator_Loss: 30137.3183594\n",
      "890\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16718.4277344\n",
      "\n",
      "    Generator_Loss: 33078.859375\n",
      "891\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21982.1972656\n",
      "\n",
      "    Generator_Loss: 33235.5234375\n",
      "892\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20012.2246094\n",
      "\n",
      "    Generator_Loss: 26445.2695312\n",
      "893\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21556.0117188\n",
      "\n",
      "    Generator_Loss: 26383.6074219\n",
      "894\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21763.5722656\n",
      "\n",
      "    Generator_Loss: 25636.4511719\n",
      "895\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22181.5957031\n",
      "\n",
      "    Generator_Loss: 23055.0800781\n",
      "896\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22275.0488281\n",
      "\n",
      "    Generator_Loss: 23225.640625\n",
      "897\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27514.1875\n",
      "\n",
      "    Generator_Loss: 28318.6875\n",
      "898\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26033.1796875\n",
      "\n",
      "    Generator_Loss: 25681.9824219\n",
      "899\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22775.7148438\n",
      "\n",
      "    Generator_Loss: 27308.2851562\n",
      "900\n",
      "     Level #0 Epoch #0 Batch #900\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fcbcd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WAAAayhIAQOP/AEAfpzRrQn8qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120489610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19918.9238281\n",
      "\n",
      "    Generator_Loss: 25260.2363281\n",
      "901\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20946.0195312\n",
      "\n",
      "    Generator_Loss: 29672.3378906\n",
      "902\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20267.3789062\n",
      "\n",
      "    Generator_Loss: 23907.671875\n",
      "903\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24998.703125\n",
      "\n",
      "    Generator_Loss: 22906.609375\n",
      "904\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22544.421875\n",
      "\n",
      "    Generator_Loss: 27730.2382812\n",
      "905\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18527.5449219\n",
      "\n",
      "    Generator_Loss: 30214.7402344\n",
      "906\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18601.25\n",
      "\n",
      "    Generator_Loss: 25946.1640625\n",
      "907\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17698.140625\n",
      "\n",
      "    Generator_Loss: 27507.5\n",
      "908\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18203.8300781\n",
      "\n",
      "    Generator_Loss: 28754.9765625\n",
      "909\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15939.390625\n",
      "\n",
      "    Generator_Loss: 28138.6679688\n",
      "910\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22330.0527344\n",
      "\n",
      "    Generator_Loss: 27023.953125\n",
      "911\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20413.109375\n",
      "\n",
      "    Generator_Loss: 35428.0625\n",
      "912\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19561.7148438\n",
      "\n",
      "    Generator_Loss: 30738.1171875\n",
      "913\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21309.6054688\n",
      "\n",
      "    Generator_Loss: 16454.0234375\n",
      "914\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19912.484375\n",
      "\n",
      "    Generator_Loss: 24720.9902344\n",
      "915\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25333.7910156\n",
      "\n",
      "    Generator_Loss: 25015.640625\n",
      "916\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20983.4726562\n",
      "\n",
      "    Generator_Loss: 28822.7011719\n",
      "917\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16073.8193359\n",
      "\n",
      "    Generator_Loss: 28284.9550781\n",
      "918\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20920.875\n",
      "\n",
      "    Generator_Loss: 29949.6660156\n",
      "919\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20659.25\n",
      "\n",
      "    Generator_Loss: 26797.2402344\n",
      "920\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17806.7285156\n",
      "\n",
      "    Generator_Loss: 26265.6113281\n",
      "921\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24067.3632812\n",
      "\n",
      "    Generator_Loss: 30387.1601562\n",
      "922\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22310.3671875\n",
      "\n",
      "    Generator_Loss: 29912.4238281\n",
      "923\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17707.953125\n",
      "\n",
      "    Generator_Loss: 29718.3183594\n",
      "924\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21600.0292969\n",
      "\n",
      "    Generator_Loss: 37138.2773438\n",
      "925\n",
      "     Level #0 Epoch #0 Batch #925\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f17df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120446790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19358.7421875\n",
      "\n",
      "    Generator_Loss: 28982.7734375\n",
      "926\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17725.5800781\n",
      "\n",
      "    Generator_Loss: 28299.9882812\n",
      "927\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18060.8789062\n",
      "\n",
      "    Generator_Loss: 25250.7128906\n",
      "928\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20592.84375\n",
      "\n",
      "    Generator_Loss: 18569.1816406\n",
      "929\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18257.1738281\n",
      "\n",
      "    Generator_Loss: 25475.9511719\n",
      "930\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19065.125\n",
      "\n",
      "    Generator_Loss: 32277.4882812\n",
      "931\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20351.2539062\n",
      "\n",
      "    Generator_Loss: 26690.9160156\n",
      "932\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20219.3613281\n",
      "\n",
      "    Generator_Loss: 30600.3652344\n",
      "933\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19342.0253906\n",
      "\n",
      "    Generator_Loss: 27956.6132812\n",
      "934\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21806.9121094\n",
      "\n",
      "    Generator_Loss: 26818.3085938\n",
      "935\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27875.8144531\n",
      "\n",
      "    Generator_Loss: 26440.1308594\n",
      "936\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20156.3417969\n",
      "\n",
      "    Generator_Loss: 26764.8984375\n",
      "937\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19002.375\n",
      "\n",
      "    Generator_Loss: 25090.234375\n",
      "938\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17101.7714844\n",
      "\n",
      "    Generator_Loss: 25953.0390625\n",
      "939\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17027.2929688\n",
      "\n",
      "    Generator_Loss: 24928.5136719\n",
      "940\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24391.6972656\n",
      "\n",
      "    Generator_Loss: 23097.5878906\n",
      "941\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19218.9511719\n",
      "\n",
      "    Generator_Loss: 19540.7539062\n",
      "942\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19835.7148438\n",
      "\n",
      "    Generator_Loss: 26977.3476562\n",
      "943\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18177.7929688\n",
      "\n",
      "    Generator_Loss: 25238.5527344\n",
      "944\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27133.8476562\n",
      "\n",
      "    Generator_Loss: 24764.8125\n",
      "945\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19348.4804688\n",
      "\n",
      "    Generator_Loss: 31516.8378906\n",
      "946\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19923.2167969\n",
      "\n",
      "    Generator_Loss: 31820.3515625\n",
      "947\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25941.96875\n",
      "\n",
      "    Generator_Loss: 24585.0761719\n",
      "948\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23169.3964844\n",
      "\n",
      "    Generator_Loss: 20546.0429688\n",
      "949\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27944.3515625\n",
      "\n",
      "    Generator_Loss: 31165.5605469\n",
      "950\n",
      "     Level #0 Epoch #0 Batch #950\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c2ed1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f9ac910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17754.2285156\n",
      "\n",
      "    Generator_Loss: 29575.5292969\n",
      "951\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22999.1523438\n",
      "\n",
      "    Generator_Loss: 25507.8496094\n",
      "952\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23586.0820312\n",
      "\n",
      "    Generator_Loss: 26188.9335938\n",
      "953\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24932.2304688\n",
      "\n",
      "    Generator_Loss: 19904.7773438\n",
      "954\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27062.4628906\n",
      "\n",
      "    Generator_Loss: 26029.984375\n",
      "955\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23024.9003906\n",
      "\n",
      "    Generator_Loss: 20916.984375\n",
      "956\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26683.8828125\n",
      "\n",
      "    Generator_Loss: 30261.2734375\n",
      "957\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18241.3398438\n",
      "\n",
      "    Generator_Loss: 27089.2753906\n",
      "958\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18068.1738281\n",
      "\n",
      "    Generator_Loss: 29623.1132812\n",
      "959\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15783.2714844\n",
      "\n",
      "    Generator_Loss: 32277.0820312\n",
      "960\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25029.5390625\n",
      "\n",
      "    Generator_Loss: 21615.5429688\n",
      "961\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26320.5859375\n",
      "\n",
      "    Generator_Loss: 30443.5253906\n",
      "962\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25211.734375\n",
      "\n",
      "    Generator_Loss: 32040.8417969\n",
      "963\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26182.6875\n",
      "\n",
      "    Generator_Loss: 30725.9433594\n",
      "964\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22139.2636719\n",
      "\n",
      "    Generator_Loss: 27117.3613281\n",
      "965\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18384.1269531\n",
      "\n",
      "    Generator_Loss: 29767.6054688\n",
      "966\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18580.6308594\n",
      "\n",
      "    Generator_Loss: 26940.1152344\n",
      "967\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21816.9511719\n",
      "\n",
      "    Generator_Loss: 28210.8203125\n",
      "968\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16470.1621094\n",
      "\n",
      "    Generator_Loss: 30868.3925781\n",
      "969\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -10937.390625\n",
      "\n",
      "    Generator_Loss: 27111.2929688\n",
      "970\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19678.1894531\n",
      "\n",
      "    Generator_Loss: 32865.1523438\n",
      "971\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24962.6933594\n",
      "\n",
      "    Generator_Loss: 34700.8085938\n",
      "972\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17250.0625\n",
      "\n",
      "    Generator_Loss: 44409.9453125\n",
      "973\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21493.5019531\n",
      "\n",
      "    Generator_Loss: 29595.90625\n",
      "974\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19463.3300781\n",
      "\n",
      "    Generator_Loss: 30878.0996094\n",
      "975\n",
      "     Level #0 Epoch #0 Batch #975\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e757f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0e6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20082.7304688\n",
      "\n",
      "    Generator_Loss: 26013.6191406\n",
      "976\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20223.3085938\n",
      "\n",
      "    Generator_Loss: 27723.0449219\n",
      "977\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21343.1132812\n",
      "\n",
      "    Generator_Loss: 28578.6484375\n",
      "978\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21456.1015625\n",
      "\n",
      "    Generator_Loss: 26340.8808594\n",
      "979\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27455.1308594\n",
      "\n",
      "    Generator_Loss: 26410.0957031\n",
      "980\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22336.8984375\n",
      "\n",
      "    Generator_Loss: 35212.4726562\n",
      "981\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19707.3242188\n",
      "\n",
      "    Generator_Loss: 25826.9355469\n",
      "982\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25709.40625\n",
      "\n",
      "    Generator_Loss: 31200.6425781\n",
      "983\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21051.8398438\n",
      "\n",
      "    Generator_Loss: 28002.7226562\n",
      "984\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23042.0878906\n",
      "\n",
      "    Generator_Loss: 28905.7753906\n",
      "985\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21865.9394531\n",
      "\n",
      "    Generator_Loss: 29265.0957031\n",
      "986\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27395.3320312\n",
      "\n",
      "    Generator_Loss: 26055.6035156\n",
      "987\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19250.2519531\n",
      "\n",
      "    Generator_Loss: 30215.5742188\n",
      "988\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28711.9296875\n",
      "\n",
      "    Generator_Loss: 29457.5722656\n",
      "989\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24385.5507812\n",
      "\n",
      "    Generator_Loss: 28293.3007812\n",
      "990\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24927.7558594\n",
      "\n",
      "    Generator_Loss: 32098.625\n",
      "991\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21049.421875\n",
      "\n",
      "    Generator_Loss: 28768.5585938\n",
      "992\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27104.3339844\n",
      "\n",
      "    Generator_Loss: 27944.6816406\n",
      "993\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16196.4824219\n",
      "\n",
      "    Generator_Loss: 28228.2910156\n",
      "994\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19168.4277344\n",
      "\n",
      "    Generator_Loss: 22688.9589844\n",
      "995\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19415.9199219\n",
      "\n",
      "    Generator_Loss: 28886.03125\n",
      "996\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -8524.328125\n",
      "\n",
      "    Generator_Loss: 38501.2734375\n",
      "997\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16986.6953125\n",
      "\n",
      "    Generator_Loss: 26833.6679688\n",
      "998\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17668.6308594\n",
      "\n",
      "    Generator_Loss: 23860.4394531\n",
      "999\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24266.0917969\n",
      "\n",
      "    Generator_Loss: 28118.7832031\n",
      "1000\n",
      "     Level #0 Epoch #0 Batch #1000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noKEsAQA0lCUAgIayBADQUJYAABr/D7II1PAOgRYXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c44cc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9f9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23319.7480469\n",
      "\n",
      "    Generator_Loss: 30706.546875\n",
      "1001\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21450.3457031\n",
      "\n",
      "    Generator_Loss: 23835.6113281\n",
      "1002\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24371.8671875\n",
      "\n",
      "    Generator_Loss: 29163.7734375\n",
      "1003\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23411.5058594\n",
      "\n",
      "    Generator_Loss: 28645.5566406\n",
      "1004\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25587.3867188\n",
      "\n",
      "    Generator_Loss: 28921.9882812\n",
      "1005\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25557.71875\n",
      "\n",
      "    Generator_Loss: 34717.3789062\n",
      "1006\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18597.2910156\n",
      "\n",
      "    Generator_Loss: 28682.5683594\n",
      "1007\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26780.2597656\n",
      "\n",
      "    Generator_Loss: 29969.6347656\n",
      "1008\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26914.0507812\n",
      "\n",
      "    Generator_Loss: 28775.8183594\n",
      "1009\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22711.4238281\n",
      "\n",
      "    Generator_Loss: 29019.1328125\n",
      "1010\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26210.5253906\n",
      "\n",
      "    Generator_Loss: 25205.5136719\n",
      "1011\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25675.7460938\n",
      "\n",
      "    Generator_Loss: 30146.4179688\n",
      "1012\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15194.7744141\n",
      "\n",
      "    Generator_Loss: 21324.4023438\n",
      "1013\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17611.5761719\n",
      "\n",
      "    Generator_Loss: 27357.8242188\n",
      "1014\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22668.2578125\n",
      "\n",
      "    Generator_Loss: 20150.703125\n",
      "1015\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27881.9667969\n",
      "\n",
      "    Generator_Loss: 32634.4511719\n",
      "1016\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20276.0996094\n",
      "\n",
      "    Generator_Loss: 25788.4726562\n",
      "1017\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28239.3535156\n",
      "\n",
      "    Generator_Loss: 29688.0449219\n",
      "1018\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20607.5019531\n",
      "\n",
      "    Generator_Loss: 32324.0449219\n",
      "1019\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23352.1933594\n",
      "\n",
      "    Generator_Loss: 26308.90625\n",
      "1020\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23014.75\n",
      "\n",
      "    Generator_Loss: 26335.96875\n",
      "1021\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20783.1054688\n",
      "\n",
      "    Generator_Loss: 21687.9765625\n",
      "1022\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21048.5195312\n",
      "\n",
      "    Generator_Loss: 27753.6855469\n",
      "1023\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28397.0351562\n",
      "\n",
      "    Generator_Loss: 26263.1660156\n",
      "1024\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21110.8144531\n",
      "\n",
      "    Generator_Loss: 32397.9511719\n",
      "1025\n",
      "     Level #0 Epoch #0 Batch #1025\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e9bda10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111337b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22413.7734375\n",
      "\n",
      "    Generator_Loss: 33009.2109375\n",
      "1026\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24879.1171875\n",
      "\n",
      "    Generator_Loss: 35234.0898438\n",
      "1027\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24528.4648438\n",
      "\n",
      "    Generator_Loss: 29630.9921875\n",
      "1028\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21156.515625\n",
      "\n",
      "    Generator_Loss: 27439.734375\n",
      "1029\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18424.109375\n",
      "\n",
      "    Generator_Loss: 32794.7304688\n",
      "1030\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21690.5761719\n",
      "\n",
      "    Generator_Loss: 33137.1210938\n",
      "1031\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23000.9394531\n",
      "\n",
      "    Generator_Loss: 34252.703125\n",
      "1032\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19278.1367188\n",
      "\n",
      "    Generator_Loss: 28354.2636719\n",
      "1033\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22473.6191406\n",
      "\n",
      "    Generator_Loss: 32153.7285156\n",
      "1034\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22987.4355469\n",
      "\n",
      "    Generator_Loss: 29696.9179688\n",
      "1035\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19909.4824219\n",
      "\n",
      "    Generator_Loss: 32193.7910156\n",
      "1036\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19532.2578125\n",
      "\n",
      "    Generator_Loss: 37849.8007812\n",
      "1037\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20320.8808594\n",
      "\n",
      "    Generator_Loss: 34038.46875\n",
      "1038\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19039.6386719\n",
      "\n",
      "    Generator_Loss: 30230.6679688\n",
      "1039\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21085.4511719\n",
      "\n",
      "    Generator_Loss: 30160.4589844\n",
      "1040\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26540.765625\n",
      "\n",
      "    Generator_Loss: 29579.3125\n",
      "1041\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27597.4375\n",
      "\n",
      "    Generator_Loss: 34245.90625\n",
      "1042\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22959.7402344\n",
      "\n",
      "    Generator_Loss: 30166.4550781\n",
      "1043\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22176.6601562\n",
      "\n",
      "    Generator_Loss: 29602.1347656\n",
      "1044\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17630.2246094\n",
      "\n",
      "    Generator_Loss: 28951.6523438\n",
      "1045\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21288.921875\n",
      "\n",
      "    Generator_Loss: 29753.4003906\n",
      "1046\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21003.859375\n",
      "\n",
      "    Generator_Loss: 20945.6464844\n",
      "1047\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22202.0449219\n",
      "\n",
      "    Generator_Loss: 34974.2929688\n",
      "1048\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20588.0253906\n",
      "\n",
      "    Generator_Loss: 29363.5546875\n",
      "1049\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16142.1367188\n",
      "\n",
      "    Generator_Loss: 38897.53125\n",
      "1050\n",
      "     Level #0 Epoch #0 Batch #1050\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db1af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db8d810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24155.2089844\n",
      "\n",
      "    Generator_Loss: 23782.9160156\n",
      "1051\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24314.1289062\n",
      "\n",
      "    Generator_Loss: 30770.6210938\n",
      "1052\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24055.2226562\n",
      "\n",
      "    Generator_Loss: 30682.0664062\n",
      "1053\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19497.0\n",
      "\n",
      "    Generator_Loss: 27724.7832031\n",
      "1054\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25838.390625\n",
      "\n",
      "    Generator_Loss: 32448.0722656\n",
      "1055\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21401.4980469\n",
      "\n",
      "    Generator_Loss: 27527.7558594\n",
      "1056\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26751.3554688\n",
      "\n",
      "    Generator_Loss: 25197.9082031\n",
      "1057\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28486.671875\n",
      "\n",
      "    Generator_Loss: 17841.0058594\n",
      "1058\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19786.421875\n",
      "\n",
      "    Generator_Loss: 33696.2265625\n",
      "1059\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23832.0996094\n",
      "\n",
      "    Generator_Loss: 37687.8710938\n",
      "1060\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20850.3613281\n",
      "\n",
      "    Generator_Loss: 29799.5097656\n",
      "1061\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24150.4589844\n",
      "\n",
      "    Generator_Loss: 33004.2460938\n",
      "1062\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26132.2285156\n",
      "\n",
      "    Generator_Loss: 24530.5175781\n",
      "1063\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24240.671875\n",
      "\n",
      "    Generator_Loss: 26270.6542969\n",
      "1064\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24456.0644531\n",
      "\n",
      "    Generator_Loss: 25394.8886719\n",
      "1065\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18367.3515625\n",
      "\n",
      "    Generator_Loss: 26535.0449219\n",
      "1066\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20227.84375\n",
      "\n",
      "    Generator_Loss: 26715.2265625\n",
      "1067\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27735.8261719\n",
      "\n",
      "    Generator_Loss: 33374.3046875\n",
      "1068\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26391.09375\n",
      "\n",
      "    Generator_Loss: 33105.2578125\n",
      "1069\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -14546.7402344\n",
      "\n",
      "    Generator_Loss: 34139.2617188\n",
      "1070\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26904.0859375\n",
      "\n",
      "    Generator_Loss: 31888.609375\n",
      "1071\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18834.7011719\n",
      "\n",
      "    Generator_Loss: 30377.4082031\n",
      "1072\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24808.5800781\n",
      "\n",
      "    Generator_Loss: 39341.9570312\n",
      "1073\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22590.8476562\n",
      "\n",
      "    Generator_Loss: 27145.3125\n",
      "1074\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21770.3339844\n",
      "\n",
      "    Generator_Loss: 27127.4414062\n",
      "1075\n",
      "     Level #0 Epoch #0 Batch #1075\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n02q1SBtiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11169ce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSwAADWUJAKDxf5H8leA3QAAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113864990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27146.6171875\n",
      "\n",
      "    Generator_Loss: 23937.203125\n",
      "1076\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25781.5546875\n",
      "\n",
      "    Generator_Loss: 28494.7871094\n",
      "1077\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20794.3964844\n",
      "\n",
      "    Generator_Loss: 29630.2675781\n",
      "1078\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24962.8476562\n",
      "\n",
      "    Generator_Loss: 29072.9824219\n",
      "1079\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19918.609375\n",
      "\n",
      "    Generator_Loss: 30976.4082031\n",
      "1080\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23463.1074219\n",
      "\n",
      "    Generator_Loss: 30152.4511719\n",
      "1081\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19586.6074219\n",
      "\n",
      "    Generator_Loss: 30170.2949219\n",
      "1082\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24172.7617188\n",
      "\n",
      "    Generator_Loss: 29762.0644531\n",
      "1083\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22894.40625\n",
      "\n",
      "    Generator_Loss: 35268.9101562\n",
      "1084\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25449.75\n",
      "\n",
      "    Generator_Loss: 33596.2851562\n",
      "1085\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25942.6914062\n",
      "\n",
      "    Generator_Loss: 35534.25\n",
      "1086\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20228.3925781\n",
      "\n",
      "    Generator_Loss: 28181.2636719\n",
      "1087\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19093.6230469\n",
      "\n",
      "    Generator_Loss: 25010.765625\n",
      "1088\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27871.4941406\n",
      "\n",
      "    Generator_Loss: 22461.8671875\n",
      "1089\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22134.8242188\n",
      "\n",
      "    Generator_Loss: 32431.34375\n",
      "1090\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24508.7050781\n",
      "\n",
      "    Generator_Loss: 29671.8300781\n",
      "1091\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26278.75\n",
      "\n",
      "    Generator_Loss: 29713.2753906\n",
      "1092\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21319.2109375\n",
      "\n",
      "    Generator_Loss: 35850.0117188\n",
      "1093\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27521.9238281\n",
      "\n",
      "    Generator_Loss: 33605.1640625\n",
      "1094\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25180.8808594\n",
      "\n",
      "    Generator_Loss: 30077.0898438\n",
      "1095\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25299.5898438\n",
      "\n",
      "    Generator_Loss: 24290.0058594\n",
      "1096\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19221.4179688\n",
      "\n",
      "    Generator_Loss: 23741.8183594\n",
      "1097\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23600.390625\n",
      "\n",
      "    Generator_Loss: 27661.2617188\n",
      "1098\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18631.5566406\n",
      "\n",
      "    Generator_Loss: 29605.1699219\n",
      "1099\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22985.4238281\n",
      "\n",
      "    Generator_Loss: 30477.5664062\n",
      "1100\n",
      "     Level #0 Epoch #0 Batch #1100\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9aafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PJflukoMkj48xfrip7W/Zg0meqark9u/8G2OM57Y7pPWqqqeTPJrk7VX18yRfSvIPSb5V\nVZ9O8kqSj29vhOtzl31/LMlhVX0gt6fuf5bks1scIhMtvH4lC6thS65fiRp2N9qdAAA0XMEbAKAh\nLAEANIQlAICGsAQA0BCWAAAawhIAQENYAgBoCEsAAI3/A8b/0wqzPPRnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120660b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27783.4667969\n",
      "\n",
      "    Generator_Loss: 29791.2636719\n",
      "1101\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21166.5566406\n",
      "\n",
      "    Generator_Loss: 35561.2578125\n",
      "1102\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24180.0722656\n",
      "\n",
      "    Generator_Loss: 27758.4765625\n",
      "1103\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23619.7011719\n",
      "\n",
      "    Generator_Loss: 35845.9804688\n",
      "1104\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24734.5390625\n",
      "\n",
      "    Generator_Loss: 33016.3125\n",
      "1105\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21894.2617188\n",
      "\n",
      "    Generator_Loss: 34990.5585938\n",
      "1106\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21354.1152344\n",
      "\n",
      "    Generator_Loss: 26826.3710938\n",
      "1107\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22118.0292969\n",
      "\n",
      "    Generator_Loss: 25087.4140625\n",
      "1108\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25644.875\n",
      "\n",
      "    Generator_Loss: 28995.9824219\n",
      "1109\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -30650.6035156\n",
      "\n",
      "    Generator_Loss: 25904.3847656\n",
      "1110\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24345.1269531\n",
      "\n",
      "    Generator_Loss: 30724.3574219\n",
      "1111\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23428.8867188\n",
      "\n",
      "    Generator_Loss: 28432.1210938\n",
      "1112\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16756.2148438\n",
      "\n",
      "    Generator_Loss: 34108.3164062\n",
      "1113\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26099.234375\n",
      "\n",
      "    Generator_Loss: 27921.6523438\n",
      "1114\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22807.7441406\n",
      "\n",
      "    Generator_Loss: 31615.0273438\n",
      "1115\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23183.8984375\n",
      "\n",
      "    Generator_Loss: 32286.1074219\n",
      "1116\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21138.1875\n",
      "\n",
      "    Generator_Loss: 31850.9746094\n",
      "1117\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20957.8261719\n",
      "\n",
      "    Generator_Loss: 22936.0449219\n",
      "1118\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27110.2519531\n",
      "\n",
      "    Generator_Loss: 34092.5585938\n",
      "1119\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20174.5292969\n",
      "\n",
      "    Generator_Loss: 32094.4472656\n",
      "1120\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -30735.3164062\n",
      "\n",
      "    Generator_Loss: 31973.4472656\n",
      "1121\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25929.9785156\n",
      "\n",
      "    Generator_Loss: 32075.7050781\n",
      "1122\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19421.5664062\n",
      "\n",
      "    Generator_Loss: 30721.2792969\n",
      "1123\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27807.6699219\n",
      "\n",
      "    Generator_Loss: 32180.3652344\n",
      "1124\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25266.5761719\n",
      "\n",
      "    Generator_Loss: 32027.4960938\n",
      "1125\n",
      "     Level #0 Epoch #0 Batch #1125\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109b0cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b272c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21971.5\n",
      "\n",
      "    Generator_Loss: 36625.9335938\n",
      "1126\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26946.9160156\n",
      "\n",
      "    Generator_Loss: 27485.6523438\n",
      "1127\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24940.0351562\n",
      "\n",
      "    Generator_Loss: 34012.9804688\n",
      "1128\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26233.7832031\n",
      "\n",
      "    Generator_Loss: 32441.3789062\n",
      "1129\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -33817.6757812\n",
      "\n",
      "    Generator_Loss: 31334.7910156\n",
      "1130\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24086.7304688\n",
      "\n",
      "    Generator_Loss: 30214.6113281\n",
      "1131\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24285.9375\n",
      "\n",
      "    Generator_Loss: 37168.25\n",
      "1132\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22324.453125\n",
      "\n",
      "    Generator_Loss: 33796.6445312\n",
      "1133\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23265.3476562\n",
      "\n",
      "    Generator_Loss: 28132.0878906\n",
      "1134\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19060.3496094\n",
      "\n",
      "    Generator_Loss: 28910.6367188\n",
      "1135\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19898.2988281\n",
      "\n",
      "    Generator_Loss: 29733.0078125\n",
      "1136\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23166.4140625\n",
      "\n",
      "    Generator_Loss: 29010.2617188\n",
      "1137\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24157.1328125\n",
      "\n",
      "    Generator_Loss: 30287.2148438\n",
      "1138\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22822.46875\n",
      "\n",
      "    Generator_Loss: 30233.2929688\n",
      "1139\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23613.609375\n",
      "\n",
      "    Generator_Loss: 28328.1015625\n",
      "1140\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22340.2871094\n",
      "\n",
      "    Generator_Loss: 23420.1074219\n",
      "1141\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17458.2128906\n",
      "\n",
      "    Generator_Loss: 31224.3300781\n",
      "1142\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23730.8535156\n",
      "\n",
      "    Generator_Loss: 30815.4003906\n",
      "1143\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25146.3847656\n",
      "\n",
      "    Generator_Loss: 29862.4589844\n",
      "1144\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23095.6699219\n",
      "\n",
      "    Generator_Loss: 31112.3984375\n",
      "1145\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23469.5097656\n",
      "\n",
      "    Generator_Loss: 38095.9882812\n",
      "1146\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26687.4941406\n",
      "\n",
      "    Generator_Loss: 28011.8222656\n",
      "1147\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23231.9101562\n",
      "\n",
      "    Generator_Loss: 27999.4414062\n",
      "1148\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24702.5527344\n",
      "\n",
      "    Generator_Loss: 35387.6640625\n",
      "1149\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23311.6640625\n",
      "\n",
      "    Generator_Loss: 32234.4023438\n",
      "1150\n",
      "     Level #0 Epoch #0 Batch #1150\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGv8PRbcJ7kbSiroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5995d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSwAADWUJAKDxe2SyvvhwxQJNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4cde10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24505.3144531\n",
      "\n",
      "    Generator_Loss: 28883.4140625\n",
      "1151\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25990.9589844\n",
      "\n",
      "    Generator_Loss: 28583.4003906\n",
      "1152\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24143.1542969\n",
      "\n",
      "    Generator_Loss: 27878.3671875\n",
      "1153\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21507.9121094\n",
      "\n",
      "    Generator_Loss: 30501.8027344\n",
      "1154\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24158.7617188\n",
      "\n",
      "    Generator_Loss: 27332.0507812\n",
      "1155\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24778.4667969\n",
      "\n",
      "    Generator_Loss: 29549.8164062\n",
      "1156\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27331.578125\n",
      "\n",
      "    Generator_Loss: 31330.7167969\n",
      "1157\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21044.4492188\n",
      "\n",
      "    Generator_Loss: 36045.4648438\n",
      "1158\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20571.3847656\n",
      "\n",
      "    Generator_Loss: 34368.4570312\n",
      "1159\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25537.4238281\n",
      "\n",
      "    Generator_Loss: 35959.4492188\n",
      "1160\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24256.5800781\n",
      "\n",
      "    Generator_Loss: 30498.2734375\n",
      "1161\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27627.1230469\n",
      "\n",
      "    Generator_Loss: 35754.4179688\n",
      "1162\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26122.4296875\n",
      "\n",
      "    Generator_Loss: 30013.484375\n",
      "1163\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23582.9882812\n",
      "\n",
      "    Generator_Loss: 35085.359375\n",
      "1164\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25460.09375\n",
      "\n",
      "    Generator_Loss: 31912.0546875\n",
      "1165\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19181.5371094\n",
      "\n",
      "    Generator_Loss: 31276.234375\n",
      "1166\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24658.7148438\n",
      "\n",
      "    Generator_Loss: 23867.1699219\n",
      "1167\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26995.296875\n",
      "\n",
      "    Generator_Loss: 31041.5839844\n",
      "1168\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22140.5605469\n",
      "\n",
      "    Generator_Loss: 29684.0976562\n",
      "1169\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22367.5605469\n",
      "\n",
      "    Generator_Loss: 34799.328125\n",
      "1170\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26934.9511719\n",
      "\n",
      "    Generator_Loss: 32052.6972656\n",
      "1171\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27619.1894531\n",
      "\n",
      "    Generator_Loss: 32617.8027344\n",
      "1172\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22397.3203125\n",
      "\n",
      "    Generator_Loss: 30681.0136719\n",
      "1173\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29209.6816406\n",
      "\n",
      "    Generator_Loss: 31955.84375\n",
      "1174\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24059.8945312\n",
      "\n",
      "    Generator_Loss: 28989.34375\n",
      "1175\n",
      "     Level #0 Epoch #0 Batch #1175\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njOhx4E4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7c3f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NZQkAoPF/fPSn7IF3dYAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d2ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19197.4179688\n",
      "\n",
      "    Generator_Loss: 32841.0117188\n",
      "1176\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21910.6875\n",
      "\n",
      "    Generator_Loss: 32863.4414062\n",
      "1177\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23693.9882812\n",
      "\n",
      "    Generator_Loss: 33064.65625\n",
      "1178\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23264.328125\n",
      "\n",
      "    Generator_Loss: 32540.3046875\n",
      "1179\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19942.8125\n",
      "\n",
      "    Generator_Loss: 27334.6152344\n",
      "1180\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24599.2226562\n",
      "\n",
      "    Generator_Loss: 33444.046875\n",
      "1181\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26291.2675781\n",
      "\n",
      "    Generator_Loss: 31629.8027344\n",
      "1182\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17608.6660156\n",
      "\n",
      "    Generator_Loss: 33985.171875\n",
      "1183\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26353.9785156\n",
      "\n",
      "    Generator_Loss: 32889.921875\n",
      "1184\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23834.7832031\n",
      "\n",
      "    Generator_Loss: 36218.984375\n",
      "1185\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21571.0234375\n",
      "\n",
      "    Generator_Loss: 31057.2519531\n",
      "1186\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17713.0625\n",
      "\n",
      "    Generator_Loss: 29859.6972656\n",
      "1187\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27633.6640625\n",
      "\n",
      "    Generator_Loss: 26541.8242188\n",
      "1188\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23068.7246094\n",
      "\n",
      "    Generator_Loss: 32819.703125\n",
      "1189\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25476.1640625\n",
      "\n",
      "    Generator_Loss: 32443.1289062\n",
      "1190\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -31675.3007812\n",
      "\n",
      "    Generator_Loss: 27207.3203125\n",
      "1191\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24545.7753906\n",
      "\n",
      "    Generator_Loss: 31013.3417969\n",
      "1192\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28882.3515625\n",
      "\n",
      "    Generator_Loss: 32739.7285156\n",
      "1193\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27235.640625\n",
      "\n",
      "    Generator_Loss: 32835.0\n",
      "1194\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26844.0683594\n",
      "\n",
      "    Generator_Loss: 31436.28125\n",
      "1195\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20161.1777344\n",
      "\n",
      "    Generator_Loss: 35540.2695312\n",
      "1196\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26923.7832031\n",
      "\n",
      "    Generator_Loss: 31112.40625\n",
      "1197\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19702.4277344\n",
      "\n",
      "    Generator_Loss: 30827.7460938\n",
      "1198\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25999.0410156\n",
      "\n",
      "    Generator_Loss: 36649.3867188\n",
      "1199\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26220.0742188\n",
      "\n",
      "    Generator_Loss: 31526.1289062\n",
      "1200\n",
      "     Level #0 Epoch #0 Batch #1200\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d9c3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dbt9bRJyePv+3ttYeXsC8GEj9iohx1bAx168INext5nlk6baI+GVr7dnW2lZEfD8iPjXH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e61d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29730.9257812\n",
      "\n",
      "    Generator_Loss: 29058.1191406\n",
      "1201\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25165.0742188\n",
      "\n",
      "    Generator_Loss: 33063.9804688\n",
      "1202\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26725.546875\n",
      "\n",
      "    Generator_Loss: 34007.2617188\n",
      "1203\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26668.1386719\n",
      "\n",
      "    Generator_Loss: 32799.5625\n",
      "1204\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24270.6953125\n",
      "\n",
      "    Generator_Loss: 38163.375\n",
      "1205\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15570.1611328\n",
      "\n",
      "    Generator_Loss: 39092.3164062\n",
      "1206\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -17374.0253906\n",
      "\n",
      "    Generator_Loss: 33244.609375\n",
      "1207\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16807.4296875\n",
      "\n",
      "    Generator_Loss: 39023.6601562\n",
      "1208\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -18670.3789062\n",
      "\n",
      "    Generator_Loss: 35719.8203125\n",
      "1209\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22084.4023438\n",
      "\n",
      "    Generator_Loss: 32203.4257812\n",
      "1210\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20557.203125\n",
      "\n",
      "    Generator_Loss: 36221.3203125\n",
      "1211\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25104.2734375\n",
      "\n",
      "    Generator_Loss: 27754.6484375\n",
      "1212\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24209.9765625\n",
      "\n",
      "    Generator_Loss: 27736.5371094\n",
      "1213\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -31585.2246094\n",
      "\n",
      "    Generator_Loss: 32057.7988281\n",
      "1214\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25486.7910156\n",
      "\n",
      "    Generator_Loss: 32979.59375\n",
      "1215\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19419.3945312\n",
      "\n",
      "    Generator_Loss: 23641.4375\n",
      "1216\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21519.234375\n",
      "\n",
      "    Generator_Loss: 28785.7734375\n",
      "1217\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22593.390625\n",
      "\n",
      "    Generator_Loss: 27519.2597656\n",
      "1218\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22845.7695312\n",
      "\n",
      "    Generator_Loss: 30022.6523438\n",
      "1219\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24235.8496094\n",
      "\n",
      "    Generator_Loss: 30944.9257812\n",
      "1220\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26678.3359375\n",
      "\n",
      "    Generator_Loss: 35500.9375\n",
      "1221\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -32270.3066406\n",
      "\n",
      "    Generator_Loss: 33603.6054688\n",
      "1222\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26054.5625\n",
      "\n",
      "    Generator_Loss: 27445.890625\n",
      "1223\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -31589.4765625\n",
      "\n",
      "    Generator_Loss: 32964.9375\n",
      "1224\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -33001.6875\n",
      "\n",
      "    Generator_Loss: 29313.3457031\n",
      "1225\n",
      "     Level #0 Epoch #0 Batch #1225\n"
     ]
    },
    {
     "data": {
      "image/png": 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+6/D2a5L8SZJn+7XOpK8l+dDh7Q8l+ecdjmWrDovr/3l/1nn8T7PV1q9EDTu02vqVqGEb\n7Q13ozHG1ar6WJJvJjmf5LExxve3tf0du5jkicNeNfck+fIY48ndDmmzquorSd6d5P6qej7J3yT5\ndJJ/qqoPJ3kuyZ/vboSbc8S+P5rkUlU9mOtT9z9J8tEdDpFJK69fycpq2JrrV6KGHUW7EwCAhk/w\nBgBoCEsAAA1hCQCgISwBADSEJQCAhrAEANAQlgAAGsISAEDjfwE3IF3fKIiYpAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d978ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+Ick3qupjSV5K8qH9jXB77jP3J5OsqupdubN0/7Mkn9jjEJlo4fUrWVgNW3L9StSw+9Hu\nBACg4QzeAAANYQkAoCEsAQA0hCUAgIawBADQEJYAABrCEgBAQ1gCAGj8H/HjuRCd4N1MAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc89250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27897.953125\n",
      "\n",
      "    Generator_Loss: 32035.5390625\n",
      "1226\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26559.0898438\n",
      "\n",
      "    Generator_Loss: 37600.5585938\n",
      "1227\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -16368.0390625\n",
      "\n",
      "    Generator_Loss: 36861.65625\n",
      "1228\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25777.234375\n",
      "\n",
      "    Generator_Loss: 28075.4765625\n",
      "1229\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19908.5351562\n",
      "\n",
      "    Generator_Loss: 31985.7617188\n",
      "1230\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27958.1894531\n",
      "\n",
      "    Generator_Loss: 31191.7988281\n",
      "1231\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26168.21875\n",
      "\n",
      "    Generator_Loss: 25039.7246094\n",
      "1232\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27418.1015625\n",
      "\n",
      "    Generator_Loss: 26376.7382812\n",
      "1233\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26156.6289062\n",
      "\n",
      "    Generator_Loss: 28602.5996094\n",
      "1234\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23214.6328125\n",
      "\n",
      "    Generator_Loss: 31738.3515625\n",
      "1235\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27525.6054688\n",
      "\n",
      "    Generator_Loss: 31141.8769531\n",
      "1236\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22989.4335938\n",
      "\n",
      "    Generator_Loss: 33213.2226562\n",
      "1237\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24063.1503906\n",
      "\n",
      "    Generator_Loss: 28430.6757812\n",
      "1238\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20030.3378906\n",
      "\n",
      "    Generator_Loss: 21885.53125\n",
      "1239\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22292.5292969\n",
      "\n",
      "    Generator_Loss: 28435.9707031\n",
      "1240\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22029.71875\n",
      "\n",
      "    Generator_Loss: 28516.0390625\n",
      "1241\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25135.390625\n",
      "\n",
      "    Generator_Loss: 35619.1210938\n",
      "1242\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24755.6953125\n",
      "\n",
      "    Generator_Loss: 37179.9570312\n",
      "1243\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26955.8867188\n",
      "\n",
      "    Generator_Loss: 31522.9160156\n",
      "1244\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25089.5351562\n",
      "\n",
      "    Generator_Loss: 35503.6914062\n",
      "1245\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27727.3574219\n",
      "\n",
      "    Generator_Loss: 35866.4453125\n",
      "1246\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23667.9042969\n",
      "\n",
      "    Generator_Loss: 26869.8085938\n",
      "1247\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21234.9609375\n",
      "\n",
      "    Generator_Loss: 24811.2890625\n",
      "1248\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28750.2988281\n",
      "\n",
      "    Generator_Loss: 34305.3515625\n",
      "1249\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25117.84375\n",
      "\n",
      "    Generator_Loss: 34632.515625\n",
      "1250\n",
      "     Level #0 Epoch #0 Batch #1250\n"
     ]
    },
    {
     "data": {
      "image/png": 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HsezUcXH9P+/POt//s2y19StRw46ttn4lathWe8Pdboxxo6o+luSbSS4meXKM8f1drX/P\nHk7y9HGvrUtJvjzGeGa/Q9quqvpKkncneU1VvZjkb5J8Osk/VdWHk7yQ5M/3N8Ltucu2P5HkclW9\nPbem7n+S5KN7HCKTVl6/kpXVsDXXr0QNuxvtTgAAGq7gDQDQEJYAABrCEgBAQ1gCAGgISwAADWEJ\nAKAhLAEANIQlAIDG/wICUlPBxbFxVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11156ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c78b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19511.3925781\n",
      "\n",
      "    Generator_Loss: 23475.1386719\n",
      "1251\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27990.796875\n",
      "\n",
      "    Generator_Loss: 29570.3535156\n",
      "1252\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22346.0800781\n",
      "\n",
      "    Generator_Loss: 32036.9257812\n",
      "1253\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20118.0097656\n",
      "\n",
      "    Generator_Loss: 27098.4257812\n",
      "1254\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27163.8671875\n",
      "\n",
      "    Generator_Loss: 35317.46875\n",
      "1255\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24759.5820312\n",
      "\n",
      "    Generator_Loss: 30313.4375\n",
      "1256\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28198.53125\n",
      "\n",
      "    Generator_Loss: 34195.3125\n",
      "1257\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27938.2832031\n",
      "\n",
      "    Generator_Loss: 29232.5605469\n",
      "1258\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24762.4101562\n",
      "\n",
      "    Generator_Loss: 39683.8242188\n",
      "1259\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22247.8300781\n",
      "\n",
      "    Generator_Loss: 32831.2695312\n",
      "1260\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22120.8945312\n",
      "\n",
      "    Generator_Loss: 22967.6171875\n",
      "1261\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21825.7363281\n",
      "\n",
      "    Generator_Loss: 35086.28125\n",
      "1262\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26409.6269531\n",
      "\n",
      "    Generator_Loss: 26209.4003906\n",
      "1263\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24497.8574219\n",
      "\n",
      "    Generator_Loss: 38610.65625\n",
      "1264\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23390.296875\n",
      "\n",
      "    Generator_Loss: 35671.5351562\n",
      "1265\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15006.4570312\n",
      "\n",
      "    Generator_Loss: 45385.5039062\n",
      "1266\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19579.1523438\n",
      "\n",
      "    Generator_Loss: 30615.9453125\n",
      "1267\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19338.1191406\n",
      "\n",
      "    Generator_Loss: 26587.5214844\n",
      "1268\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23880.328125\n",
      "\n",
      "    Generator_Loss: 29499.1777344\n",
      "1269\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26470.8007812\n",
      "\n",
      "    Generator_Loss: 30128.4707031\n",
      "1270\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24016.0351562\n",
      "\n",
      "    Generator_Loss: 29374.3945312\n",
      "1271\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27120.5683594\n",
      "\n",
      "    Generator_Loss: 37024.9296875\n",
      "1272\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23571.8574219\n",
      "\n",
      "    Generator_Loss: 30886.6113281\n",
      "1273\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24388.046875\n",
      "\n",
      "    Generator_Loss: 32859.984375\n",
      "1274\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26542.4570312\n",
      "\n",
      "    Generator_Loss: 36430.2695312\n",
      "1275\n",
      "     Level #0 Epoch #0 Batch #1275\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120c5f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAayhIAQENZAgBoKEsAAA1lCQCg8X8tX7DvPvoe8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f916910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26288.1796875\n",
      "\n",
      "    Generator_Loss: 33428.8203125\n",
      "1276\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22917.3398438\n",
      "\n",
      "    Generator_Loss: 31123.9785156\n",
      "1277\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22146.8203125\n",
      "\n",
      "    Generator_Loss: 32067.8945312\n",
      "1278\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26121.3984375\n",
      "\n",
      "    Generator_Loss: 33350.1210938\n",
      "1279\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24409.75\n",
      "\n",
      "    Generator_Loss: 32573.8691406\n",
      "1280\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15123.9882812\n",
      "\n",
      "    Generator_Loss: 32942.0234375\n",
      "1281\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26294.9238281\n",
      "\n",
      "    Generator_Loss: 39153.0273438\n",
      "1282\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24764.1132812\n",
      "\n",
      "    Generator_Loss: 32762.390625\n",
      "1283\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23667.2988281\n",
      "\n",
      "    Generator_Loss: 36664.0976562\n",
      "1284\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24840.8984375\n",
      "\n",
      "    Generator_Loss: 29240.8886719\n",
      "1285\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28017.1054688\n",
      "\n",
      "    Generator_Loss: 29277.5800781\n",
      "1286\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28981.9453125\n",
      "\n",
      "    Generator_Loss: 30213.5039062\n",
      "1287\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -30634.0136719\n",
      "\n",
      "    Generator_Loss: 32252.1835938\n",
      "1288\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19894.6464844\n",
      "\n",
      "    Generator_Loss: 34303.5078125\n",
      "1289\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29315.5449219\n",
      "\n",
      "    Generator_Loss: 36143.9179688\n",
      "1290\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -30473.2011719\n",
      "\n",
      "    Generator_Loss: 26906.7460938\n",
      "1291\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19802.5957031\n",
      "\n",
      "    Generator_Loss: 37054.8476562\n",
      "1292\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23203.3828125\n",
      "\n",
      "    Generator_Loss: 33070.3359375\n",
      "1293\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20709.8886719\n",
      "\n",
      "    Generator_Loss: 31174.5898438\n",
      "1294\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28976.5585938\n",
      "\n",
      "    Generator_Loss: 32855.9375\n",
      "1295\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23741.3964844\n",
      "\n",
      "    Generator_Loss: 30185.8164062\n",
      "1296\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26883.4550781\n",
      "\n",
      "    Generator_Loss: 33295.703125\n",
      "1297\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25264.0410156\n",
      "\n",
      "    Generator_Loss: 29461.6972656\n",
      "1298\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28344.5449219\n",
      "\n",
      "    Generator_Loss: 36124.8359375\n",
      "1299\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29065.8027344\n",
      "\n",
      "    Generator_Loss: 31077.3886719\n",
      "1300\n",
      "     Level #0 Epoch #0 Batch #1300\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7wDeSObqVoC10gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130e69d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9d7490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20971.3398438\n",
      "\n",
      "    Generator_Loss: 28852.5\n",
      "1301\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28746.4238281\n",
      "\n",
      "    Generator_Loss: 36195.734375\n",
      "1302\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -33393.0898438\n",
      "\n",
      "    Generator_Loss: 30717.1679688\n",
      "1303\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25396.21875\n",
      "\n",
      "    Generator_Loss: 37060.859375\n",
      "1304\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -22333.3164062\n",
      "\n",
      "    Generator_Loss: 31494.1152344\n",
      "1305\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28629.640625\n",
      "\n",
      "    Generator_Loss: 37465.6289062\n",
      "1306\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29272.4609375\n",
      "\n",
      "    Generator_Loss: 39809.8398438\n",
      "1307\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -30007.1152344\n",
      "\n",
      "    Generator_Loss: 34862.609375\n",
      "1308\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26248.1347656\n",
      "\n",
      "    Generator_Loss: 29151.2519531\n",
      "1309\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24567.9550781\n",
      "\n",
      "    Generator_Loss: 34844.3984375\n",
      "1310\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24699.1894531\n",
      "\n",
      "    Generator_Loss: 35494.296875\n",
      "1311\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15871.9765625\n",
      "\n",
      "    Generator_Loss: 31882.0878906\n",
      "1312\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23298.0253906\n",
      "\n",
      "    Generator_Loss: 41468.7226562\n",
      "1313\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27226.4960938\n",
      "\n",
      "    Generator_Loss: 35869.5703125\n",
      "1314\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29385.4570312\n",
      "\n",
      "    Generator_Loss: 32587.0234375\n",
      "1315\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28023.4960938\n",
      "\n",
      "    Generator_Loss: 37069.2070312\n",
      "1316\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27309.9433594\n",
      "\n",
      "    Generator_Loss: 28547.4003906\n",
      "1317\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27467.0351562\n",
      "\n",
      "    Generator_Loss: 36022.9140625\n",
      "1318\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27967.2480469\n",
      "\n",
      "    Generator_Loss: 38138.1015625\n",
      "1319\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23748.9238281\n",
      "\n",
      "    Generator_Loss: 32655.9765625\n",
      "1320\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29833.4707031\n",
      "\n",
      "    Generator_Loss: 38957.9296875\n",
      "1321\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21463.96875\n",
      "\n",
      "    Generator_Loss: 41261.0546875\n",
      "1322\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21753.6933594\n",
      "\n",
      "    Generator_Loss: 37627.1914062\n",
      "1323\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25494.7246094\n",
      "\n",
      "    Generator_Loss: 38358.5\n",
      "1324\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25401.546875\n",
      "\n",
      "    Generator_Loss: 39544.6171875\n",
      "1325\n",
      "     Level #0 Epoch #0 Batch #1325\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112009d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9c5690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27981.671875\n",
      "\n",
      "    Generator_Loss: 28232.4414062\n",
      "1326\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23042.9375\n",
      "\n",
      "    Generator_Loss: 36932.3515625\n",
      "1327\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24624.0527344\n",
      "\n",
      "    Generator_Loss: 34877.53125\n",
      "1328\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24201.1621094\n",
      "\n",
      "    Generator_Loss: 35868.6523438\n",
      "1329\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -33462.8085938\n",
      "\n",
      "    Generator_Loss: 36121.546875\n",
      "1330\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20862.6796875\n",
      "\n",
      "    Generator_Loss: 35210.4648438\n",
      "1331\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26800.9570312\n",
      "\n",
      "    Generator_Loss: 34977.0\n",
      "1332\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23033.2792969\n",
      "\n",
      "    Generator_Loss: 35612.7539062\n",
      "1333\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27430.8457031\n",
      "\n",
      "    Generator_Loss: 39718.6601562\n",
      "1334\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -15501.2900391\n",
      "\n",
      "    Generator_Loss: 36646.28125\n",
      "1335\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24756.9316406\n",
      "\n",
      "    Generator_Loss: 31974.375\n",
      "1336\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -32330.8300781\n",
      "\n",
      "    Generator_Loss: 32524.5742188\n",
      "1337\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26053.1035156\n",
      "\n",
      "    Generator_Loss: 35382.1367188\n",
      "1338\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24150.9941406\n",
      "\n",
      "    Generator_Loss: 33263.5898438\n",
      "1339\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28245.6933594\n",
      "\n",
      "    Generator_Loss: 36041.1054688\n",
      "1340\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -23324.2421875\n",
      "\n",
      "    Generator_Loss: 36354.140625\n",
      "1341\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24494.828125\n",
      "\n",
      "    Generator_Loss: 37890.1640625\n",
      "1342\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26275.1445312\n",
      "\n",
      "    Generator_Loss: 35609.2421875\n",
      "1343\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28284.4765625\n",
      "\n",
      "    Generator_Loss: 41782.5351562\n",
      "1344\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26082.0878906\n",
      "\n",
      "    Generator_Loss: 35329.8203125\n",
      "1345\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19350.875\n",
      "\n",
      "    Generator_Loss: 33523.8515625\n",
      "1346\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -27056.2207031\n",
      "\n",
      "    Generator_Loss: 34798.703125\n",
      "1347\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -25958.9375\n",
      "\n",
      "    Generator_Loss: 29209.3613281\n",
      "1348\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -26312.7832031\n",
      "\n",
      "    Generator_Loss: 38958.5039062\n",
      "1349\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -29123.1152344\n",
      "\n",
      "    Generator_Loss: 31744.8085938\n",
      "1350\n",
      "     Level #0 Epoch #0 Batch #1350\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121066c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xf6EhquYPxBRnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b22ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24190.0488281\n",
      "\n",
      "    Generator_Loss: 20153.0371094\n",
      "1351\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -28146.2988281\n",
      "\n",
      "    Generator_Loss: 30974.4257812\n",
      "1352\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -21202.7519531\n",
      "\n",
      "    Generator_Loss: 32272.8144531\n",
      "1353\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24186.4609375\n",
      "\n",
      "    Generator_Loss: 31099.4511719\n",
      "1354\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -20945.5117188\n",
      "\n",
      "    Generator_Loss: 33916.4648438\n",
      "1355\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -24588.9375\n",
      "\n",
      "    Generator_Loss: 34127.1601562\n",
      "1356\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19013.2753906\n",
      "\n",
      "    Generator_Loss: 32568.2988281\n",
      "1357\n",
      "    After 20 iterations\n",
      "        Discriminator Loss                     = -19169.0253906\n",
      "\n",
      "    Generator_Loss: 30213.7265625\n",
      "1358\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-b84be581c68b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m                       \u001b[0mtrain_one_by_one\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_one_by_one\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                       \u001b[0mtrain_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                       verbose=True)\n\u001b[0m",
      "\u001b[0;32m/Users/RoozbehFarhoudi/Documents/Repos/McNeuron/train_one_by_one_morphology.pyc\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(training_data, n_levels, n_nodes, input_dim, n_epochs, batch_size, n_batch_per_epoch, d_iters, lr_discriminator, lr_generator, weight_constraint, rule, train_one_by_one, train_loss, verbose)\u001b[0m\n\u001b[1;32m    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m                 \u001b[0mdisc_loss\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m                     \u001b[0md_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX_parent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m                 \u001b[0mlist_d_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisc_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1318\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1321\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    957\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0mt0_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'position_of_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mrval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m    910\u001b[0m             \u001b[0;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m                 \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m                     \u001b[0mcompute_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/RoozbehFarhoudi/anaconda/lib/python2.7/site-packages/theano/tensor/blas.pyc\u001b[0m in \u001b[0;36mperform\u001b[0;34m(self, node, inp, out)\u001b[0m\n\u001b[1;32m   1821\u001b[0m         \u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1822\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1823\u001b[0;31m             \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalar\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1824\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1825\u001b[0m             \u001b[0;31m# The error raised by numpy has no shape information, we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAksAAAENCAYAAAAfVCHuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120708b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BADSUJQCAxv8BgL632zPOP20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138edfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geom_model, cond_geom_model, morph_model, cond_morph_model, disc_model, gan_model = \\\n",
    "    train.train_model(training_data=training_data,\n",
    "                      n_levels=n_levels,\n",
    "                      n_nodes=n_nodes,\n",
    "                      input_dim=input_dim,\n",
    "                      n_epochs=n_epochs,\n",
    "                      batch_size=batch_size,\n",
    "                      n_batch_per_epoch=n_batch_per_epoch,\n",
    "                      d_iters=d_iters,\n",
    "                      lr_discriminator=lr_discriminator,\n",
    "                      lr_generator=lr_generator,\n",
    "                      weight_constraint=weight_constraint,\n",
    "                      rule=rule,\n",
    "                      train_one_by_one=train_one_by_one,\n",
    "                      train_loss=train_loss,\n",
    "                      verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}