ex4_GAN_RF_diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff\n",
    "from scipy.spatial.distance import euclidean\n",
    "import pandas as pd\n",
    "import joblib\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import LabelEncoder , StandardScaler , PolynomialFeatures , MinMaxScaler\n",
    "from sklearn.model_selection  import train_test_split , cross_val_score , KFold\n",
    "from sklearn.metrics import mean_squared_error , r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.regularizers import l1_l2\n",
    "import keras.backend as K\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import LeakyReLU, Activation, Input, Dense, Dropout, Concatenate, BatchNormalization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load trained RF BB model\n",
    "BB_clf = joblib.load(\"/home/odedrot/DL_ex_4/trained_rf_80_scaled.joblib\")\n",
    "# probabilities = BB_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.random.seed(42)\n",
    "# tf.random.set_seed(42)\n",
    "feature_dim = 8\n",
    "conf_dim = 1\n",
    "\n",
    "noise_dim = 5\n",
    "batch_size  = 32\n",
    "num_epochs  = 5000\n",
    "optimizer = Adam(lr=0.0005)# , beta_1=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_generator(noise_dim):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=32,input_dim=noise_dim+conf_dim))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dense(units=64))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "#     model.add(Dense(units=128))\n",
    "#     model.add(LeakyReLU(0.2))\n",
    "    model.add(Dense(units=feature_dim, activation='tanh'))\n",
    "#     model.add(LeakyReLU(0.2))\n",
    "    z = Input(shape=(noise_dim, ))\n",
    "    label = Input(shape=(1,))\n",
    "    joined_representation = Concatenate(axis=1)([z, label])\n",
    "    output = model(joined_representation)\n",
    "    return Model([z, label], output)\n",
    "\n",
    "def build_discriminator(feature_dim):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=64,input_dim=feature_dim+conf_dim))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "    model.add(Dense(units=32))\n",
    "    model.add(LeakyReLU(0.2))\n",
    "#     model.add(Dense(units=32))\n",
    "#     model.add(LeakyReLU(0.2))\n",
    "    model.add(Dense(units=1, activation='sigmoid'))\n",
    "    features = Input(shape=feature_dim)\n",
    "    label = Input(shape=(1,))\n",
    "    concatenated = Concatenate(axis=1)([features, label])\n",
    "    prediction = model(concatenated)\n",
    "    return Model([features, label], prediction)\n",
    "\n",
    "disc = build_discriminator(feature_dim)\n",
    "disc.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer=Adam())\n",
    "gen = build_generator(noise_dim)\n",
    "z = Input(shape=(noise_dim,))\n",
    "label = Input(shape=(1,))\n",
    "features = gen([z, label])\n",
    "disc.trainable = False\n",
    "prediction = disc([features, label])\n",
    "cgan = Model([z, label], prediction)\n",
    "cgan.compile(loss='binary_crossentropy', optimizer=Adam())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  D_loss_real 0.5928259491920471  D_loss_fake 0.8046194314956665  G_loss 0.6069357395172119\n",
      "epoch: 1  D_loss_real 0.5688719153404236  D_loss_fake 0.80729079246521  G_loss 0.5948070287704468\n",
      "epoch: 2  D_loss_real 0.5576934814453125  D_loss_fake 0.8389588594436646  G_loss 0.5733901262283325\n",
      "epoch: 3  D_loss_real 0.5367674231529236  D_loss_fake 0.8428053855895996  G_loss 0.5645011067390442\n",
      "epoch: 4  D_loss_real 0.5407103300094604  D_loss_fake 0.85663902759552  G_loss 0.5450419187545776\n",
      "epoch: 5  D_loss_real 0.5266560316085815  D_loss_fake 0.8864667415618896  G_loss 0.5521687269210815\n",
      "epoch: 6  D_loss_real 0.5233132839202881  D_loss_fake 0.8724120855331421  G_loss 0.5361361503601074\n",
      "epoch: 7  D_loss_real 0.5117086172103882  D_loss_fake 0.9035671949386597  G_loss 0.5378137826919556\n",
      "epoch: 8  D_loss_real 0.5127530097961426  D_loss_fake 0.8769310712814331  G_loss 0.5335334539413452\n",
      "epoch: 9  D_loss_real 0.5137735605239868  D_loss_fake 0.8976672291755676  G_loss 0.547885000705719\n",
      "epoch: 10  D_loss_real 0.517431914806366  D_loss_fake 0.9175534248352051  G_loss 0.5197000503540039\n",
      "epoch: 11  D_loss_real 0.5096640586853027  D_loss_fake 0.9396307468414307  G_loss 0.5153495669364929\n",
      "epoch: 12  D_loss_real 0.5127522349357605  D_loss_fake 0.9047045707702637  G_loss 0.4997941255569458\n",
      "epoch: 13  D_loss_real 0.5039786696434021  D_loss_fake 0.9004737138748169  G_loss 0.5095877647399902\n",
      "epoch: 14  D_loss_real 0.5147773623466492  D_loss_fake 0.9221502542495728  G_loss 0.5085407495498657\n",
      "epoch: 15  D_loss_real 0.5133089423179626  D_loss_fake 0.9289309978485107  G_loss 0.5190449357032776\n",
      "epoch: 16  D_loss_real 0.5144668221473694  D_loss_fake 0.906827449798584  G_loss 0.5277100801467896\n",
      "epoch: 17  D_loss_real 0.5213907957077026  D_loss_fake 0.9261458516120911  G_loss 0.5340232849121094\n",
      "epoch: 18  D_loss_real 0.5257349610328674  D_loss_fake 0.899718701839447  G_loss 0.5221532583236694\n",
      "epoch: 19  D_loss_real 0.5345475673675537  D_loss_fake 0.8893771171569824  G_loss 0.5354889035224915\n",
      "epoch: 20  D_loss_real 0.535006046295166  D_loss_fake 0.8862261772155762  G_loss 0.5244427919387817\n",
      "epoch: 21  D_loss_real 0.5418174862861633  D_loss_fake 0.8768261075019836  G_loss 0.5483889579772949\n",
      "epoch: 22  D_loss_real 0.5517990589141846  D_loss_fake 0.8774309754371643  G_loss 0.5400915145874023\n",
      "epoch: 23  D_loss_real 0.5475906133651733  D_loss_fake 0.8543846607208252  G_loss 0.5655572414398193\n",
      "epoch: 24  D_loss_real 0.5599651336669922  D_loss_fake 0.8545429706573486  G_loss 0.5572828650474548\n",
      "epoch: 25  D_loss_real 0.5687961578369141  D_loss_fake 0.8413466215133667  G_loss 0.5698516964912415\n",
      "epoch: 26  D_loss_real 0.569796085357666  D_loss_fake 0.8358546495437622  G_loss 0.5685927867889404\n",
      "epoch: 27  D_loss_real 0.5749413967132568  D_loss_fake 0.8312526345252991  G_loss 0.5772966742515564\n",
      "epoch: 28  D_loss_real 0.5817426443099976  D_loss_fake 0.8234738111495972  G_loss 0.5813781023025513\n",
      "epoch: 29  D_loss_real 0.5877217054367065  D_loss_fake 0.804848313331604  G_loss 0.5953384637832642\n",
      "epoch: 30  D_loss_real 0.5900031328201294  D_loss_fake 0.8202486038208008  G_loss 0.5915219783782959\n",
      "epoch: 31  D_loss_real 0.5915043354034424  D_loss_fake 0.8100783824920654  G_loss 0.5931798815727234\n",
      "epoch: 32  D_loss_real 0.5971921682357788  D_loss_fake 0.8015270829200745  G_loss 0.5918333530426025\n",
      "epoch: 33  D_loss_real 0.599170446395874  D_loss_fake 0.806046187877655  G_loss 0.594269871711731\n",
      "epoch: 34  D_loss_real 0.5999672412872314  D_loss_fake 0.8018207550048828  G_loss 0.5908603072166443\n",
      "epoch: 35  D_loss_real 0.6004408001899719  D_loss_fake 0.7978560924530029  G_loss 0.6022166013717651\n",
      "epoch: 36  D_loss_real 0.599176287651062  D_loss_fake 0.7952497005462646  G_loss 0.6053533554077148\n",
      "epoch: 37  D_loss_real 0.6057678461074829  D_loss_fake 0.7864421606063843  G_loss 0.6089209318161011\n",
      "epoch: 38  D_loss_real 0.6140096187591553  D_loss_fake 0.7910929918289185  G_loss 0.6127507090568542\n",
      "epoch: 39  D_loss_real 0.6106736660003662  D_loss_fake 0.778854489326477  G_loss 0.6193065643310547\n",
      "epoch: 40  D_loss_real 0.619744598865509  D_loss_fake 0.7766984701156616  G_loss 0.6278383731842041\n",
      "epoch: 41  D_loss_real 0.622217059135437  D_loss_fake 0.7692672610282898  G_loss 0.6265313625335693\n",
      "epoch: 42  D_loss_real 0.6244133710861206  D_loss_fake 0.7618439197540283  G_loss 0.633449912071228\n",
      "epoch: 43  D_loss_real 0.6310617327690125  D_loss_fake 0.7578762769699097  G_loss 0.6361382007598877\n",
      "epoch: 44  D_loss_real 0.6344342231750488  D_loss_fake 0.7528541088104248  G_loss 0.6446245312690735\n",
      "epoch: 45  D_loss_real 0.6396199464797974  D_loss_fake 0.7480016946792603  G_loss 0.6446835994720459\n",
      "epoch: 46  D_loss_real 0.6424283981323242  D_loss_fake 0.7428231239318848  G_loss 0.6489621996879578\n",
      "epoch: 47  D_loss_real 0.6437610387802124  D_loss_fake 0.7400736808776855  G_loss 0.6536121964454651\n",
      "epoch: 48  D_loss_real 0.6470974087715149  D_loss_fake 0.7360810041427612  G_loss 0.6568804979324341\n",
      "epoch: 49  D_loss_real 0.6512753963470459  D_loss_fake 0.7319676876068115  G_loss 0.6590993404388428\n",
      "epoch: 50  D_loss_real 0.6508599519729614  D_loss_fake 0.7278205156326294  G_loss 0.658685564994812\n",
      "epoch: 51  D_loss_real 0.6510138511657715  D_loss_fake 0.7249709367752075  G_loss 0.6631686687469482\n",
      "epoch: 52  D_loss_real 0.6560558080673218  D_loss_fake 0.7246050834655762  G_loss 0.6656603813171387\n",
      "epoch: 53  D_loss_real 0.6573527455329895  D_loss_fake 0.7214391827583313  G_loss 0.6683112978935242\n",
      "epoch: 54  D_loss_real 0.65800940990448  D_loss_fake 0.716850221157074  G_loss 0.6695283651351929\n",
      "epoch: 55  D_loss_real 0.6607564687728882  D_loss_fake 0.71744704246521  G_loss 0.675014317035675\n",
      "epoch: 56  D_loss_real 0.6641740798950195  D_loss_fake 0.7144038677215576  G_loss 0.6820943355560303\n",
      "epoch: 57  D_loss_real 0.6652039289474487  D_loss_fake 0.7071139812469482  G_loss 0.6887995004653931\n",
      "epoch: 58  D_loss_real 0.6666899919509888  D_loss_fake 0.7048420906066895  G_loss 0.687844455242157\n",
      "epoch: 59  D_loss_real 0.6663720607757568  D_loss_fake 0.7076308727264404  G_loss 0.6840937733650208\n",
      "epoch: 60  D_loss_real 0.6709621548652649  D_loss_fake 0.7003644704818726  G_loss 0.6864087581634521\n",
      "epoch: 61  D_loss_real 0.6694108843803406  D_loss_fake 0.7021740674972534  G_loss 0.6897825002670288\n",
      "epoch: 62  D_loss_real 0.6699485182762146  D_loss_fake 0.7002304792404175  G_loss 0.6956806778907776\n",
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      "epoch: 64  D_loss_real 0.6735523343086243  D_loss_fake 0.6992310881614685  G_loss 0.685584306716919\n",
      "epoch: 65  D_loss_real 0.67426997423172  D_loss_fake 0.6955252885818481  G_loss 0.6941342353820801\n",
      "epoch: 66  D_loss_real 0.6743667721748352  D_loss_fake 0.6949305534362793  G_loss 0.6968066692352295\n",
      "epoch: 67  D_loss_real 0.6737635135650635  D_loss_fake 0.6993438601493835  G_loss 0.6916106939315796\n",
      "epoch: 68  D_loss_real 0.6738316416740417  D_loss_fake 0.6914723515510559  G_loss 0.6933631300926208\n",
      "epoch: 69  D_loss_real 0.6732496023178101  D_loss_fake 0.6934397220611572  G_loss 0.7044183015823364\n",
      "epoch: 70  D_loss_real 0.6732248067855835  D_loss_fake 0.6996324062347412  G_loss 0.6922403573989868\n",
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      "epoch: 72  D_loss_real 0.6715542078018188  D_loss_fake 0.6920738816261292  G_loss 0.6933348178863525\n",
      "epoch: 73  D_loss_real 0.6716508865356445  D_loss_fake 0.6931570172309875  G_loss 0.6986353397369385\n",
      "epoch: 74  D_loss_real 0.6716780662536621  D_loss_fake 0.6994112730026245  G_loss 0.7030482888221741\n",
      "epoch: 75  D_loss_real 0.6724253296852112  D_loss_fake 0.6985361576080322  G_loss 0.6973803043365479\n",
      "epoch: 76  D_loss_real 0.6713579297065735  D_loss_fake 0.6789629459381104  G_loss 0.703864336013794\n",
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     ]
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    {
     "name": "stdout",
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     "text": [
      "epoch: 82  D_loss_real 0.6760416030883789  D_loss_fake 0.691119909286499  G_loss 0.7016420364379883\n",
      "epoch: 83  D_loss_real 0.6756587028503418  D_loss_fake 0.6830368041992188  G_loss 0.6942506432533264\n",
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      "epoch: 87  D_loss_real 0.6774968504905701  D_loss_fake 0.6814289093017578  G_loss 0.7098572254180908\n",
      "epoch: 88  D_loss_real 0.6784613132476807  D_loss_fake 0.6886870861053467  G_loss 0.7081850171089172\n",
      "epoch: 89  D_loss_real 0.6767176985740662  D_loss_fake 0.6657319068908691  G_loss 0.7122828960418701\n",
      "epoch: 90  D_loss_real 0.6782577037811279  D_loss_fake 0.6926195621490479  G_loss 0.7014760375022888\n",
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      "epoch: 116  D_loss_real 0.6666538715362549  D_loss_fake 0.6673924326896667  G_loss 0.7482004761695862\n",
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      "epoch: 118  D_loss_real 0.6650947332382202  D_loss_fake 0.6753029227256775  G_loss 0.7464576959609985\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "epoch: 1891  D_loss_real 0.49652788043022156  D_loss_fake 0.594843864440918  G_loss 1.210824966430664\n",
      "epoch: 1892  D_loss_real 0.5537499189376831  D_loss_fake 0.5034140348434448  G_loss 1.7786266803741455\n",
      "epoch: 1893  D_loss_real 0.5487302541732788  D_loss_fake 0.5177404284477234  G_loss 1.3460525274276733\n",
      "epoch: 1894  D_loss_real 0.5726011395454407  D_loss_fake 0.4061059057712555  G_loss 1.5003612041473389\n",
      "epoch: 1895  D_loss_real 0.5061125159263611  D_loss_fake 0.5126619935035706  G_loss 1.6959973573684692\n",
      "epoch: 1896  D_loss_real 0.49843937158584595  D_loss_fake 0.5410069823265076  G_loss 0.8855893611907959\n",
      "epoch: 1897  D_loss_real 0.452157586812973  D_loss_fake 0.5175808668136597  G_loss 1.350620150566101\n",
      "epoch: 1898  D_loss_real 0.5033063888549805  D_loss_fake 0.5282562971115112  G_loss 1.6602425575256348\n",
      "epoch: 1899  D_loss_real 0.4543542265892029  D_loss_fake 0.6405840516090393  G_loss 1.2978546619415283\n",
      "epoch: 1900  D_loss_real 0.4744056463241577  D_loss_fake 0.5537534952163696  G_loss 1.3614604473114014\n",
      "epoch: 1901  D_loss_real 0.5023593902587891  D_loss_fake 0.6180785894393921  G_loss 1.1779776811599731\n",
      "epoch: 1902  D_loss_real 0.41046714782714844  D_loss_fake 0.594763994216919  G_loss 1.5585110187530518\n",
      "epoch: 1903  D_loss_real 0.508131742477417  D_loss_fake 0.613071858882904  G_loss 1.3391332626342773\n",
      "epoch: 1904  D_loss_real 0.5317460298538208  D_loss_fake 0.434983491897583  G_loss 1.1937110424041748\n",
      "epoch: 1905  D_loss_real 0.5143258571624756  D_loss_fake 0.5423188209533691  G_loss 0.9866254329681396\n",
      "epoch: 1906  D_loss_real 0.5198889374732971  D_loss_fake 0.4398167133331299  G_loss 1.28858482837677\n",
      "epoch: 1907  D_loss_real 0.5529472827911377  D_loss_fake 0.5471985936164856  G_loss 1.328305721282959\n",
      "epoch: 1908  D_loss_real 0.6280524730682373  D_loss_fake 0.5921247601509094  G_loss 1.2446894645690918\n",
      "epoch: 1909  D_loss_real 0.513779878616333  D_loss_fake 0.6105104088783264  G_loss 1.0951504707336426\n",
      "epoch: 1910  D_loss_real 0.46572232246398926  D_loss_fake 0.4294135570526123  G_loss 1.1947743892669678\n",
      "epoch: 1911  D_loss_real 0.5041413307189941  D_loss_fake 0.511979341506958  G_loss 1.2774043083190918\n",
      "epoch: 1912  D_loss_real 0.49901580810546875  D_loss_fake 0.5812172889709473  G_loss 1.1957836151123047\n",
      "epoch: 1913  D_loss_real 0.5602348446846008  D_loss_fake 0.595159649848938  G_loss 1.347715139389038\n",
      "epoch: 1914  D_loss_real 0.4747583270072937  D_loss_fake 0.5665498971939087  G_loss 1.2465928792953491\n",
      "epoch: 1915  D_loss_real 0.48115313053131104  D_loss_fake 0.5406660437583923  G_loss 1.320650339126587\n",
      "epoch: 1916  D_loss_real 0.5044418573379517  D_loss_fake 0.5933870077133179  G_loss 1.158881664276123\n",
      "epoch: 1917  D_loss_real 0.46205437183380127  D_loss_fake 0.5333244800567627  G_loss 1.4881715774536133\n",
      "epoch: 1918  D_loss_real 0.5010524988174438  D_loss_fake 0.5487028956413269  G_loss 1.511757731437683\n",
      "epoch: 1919  D_loss_real 0.5058339834213257  D_loss_fake 0.5234644412994385  G_loss 1.2508541345596313\n",
      "epoch: 1920  D_loss_real 0.5158817768096924  D_loss_fake 0.4365497827529907  G_loss 1.3120360374450684\n",
      "epoch: 1921  D_loss_real 0.5154269933700562  D_loss_fake 0.6038732528686523  G_loss 1.4726790189743042\n",
      "epoch: 1922  D_loss_real 0.5174059867858887  D_loss_fake 0.4861457049846649  G_loss 1.4345989227294922\n",
      "epoch: 1923  D_loss_real 0.44257113337516785  D_loss_fake 0.45415565371513367  G_loss 1.6040879487991333\n",
      "epoch: 1924  D_loss_real 0.45852211117744446  D_loss_fake 0.5675395131111145  G_loss 1.417858362197876\n",
      "epoch: 1925  D_loss_real 0.48144322633743286  D_loss_fake 0.5830954313278198  G_loss 1.0709407329559326\n",
      "epoch: 1926  D_loss_real 0.48199164867401123  D_loss_fake 0.5317044258117676  G_loss 1.343165636062622\n",
      "epoch: 1927  D_loss_real 0.4808223247528076  D_loss_fake 0.5780611634254456  G_loss 1.593597650527954\n",
      "epoch: 1928  D_loss_real 0.5027425289154053  D_loss_fake 0.5664792060852051  G_loss 1.1402952671051025\n",
      "epoch: 1929  D_loss_real 0.4495775103569031  D_loss_fake 0.4740477502346039  G_loss 1.4876184463500977\n",
      "epoch: 1930  D_loss_real 0.5100440979003906  D_loss_fake 0.5010285377502441  G_loss 1.6712334156036377\n",
      "epoch: 1931  D_loss_real 0.47938281297683716  D_loss_fake 0.6095315217971802  G_loss 1.6013150215148926\n",
      "epoch: 1932  D_loss_real 0.5246905088424683  D_loss_fake 0.47478818893432617  G_loss 1.2497087717056274\n",
      "epoch: 1933  D_loss_real 0.58406662940979  D_loss_fake 0.544140100479126  G_loss 1.429391622543335\n",
      "epoch: 1934  D_loss_real 0.4783145487308502  D_loss_fake 0.41063737869262695  G_loss 1.068699836730957\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1935  D_loss_real 0.5174923539161682  D_loss_fake 0.5858891010284424  G_loss 1.509732723236084\n",
      "epoch: 1936  D_loss_real 0.4767109751701355  D_loss_fake 0.5195615291595459  G_loss 1.1499961614608765\n",
      "epoch: 1937  D_loss_real 0.5042843818664551  D_loss_fake 0.4707685708999634  G_loss 1.2354481220245361\n",
      "epoch: 1938  D_loss_real 0.6043720245361328  D_loss_fake 0.5807583928108215  G_loss 1.1079789400100708\n",
      "epoch: 1939  D_loss_real 0.48409512639045715  D_loss_fake 0.5088979005813599  G_loss 1.4711028337478638\n",
      "epoch: 1940  D_loss_real 0.48305127024650574  D_loss_fake 0.7494878768920898  G_loss 1.1008710861206055\n",
      "epoch: 1941  D_loss_real 0.48424232006073  D_loss_fake 0.6050961017608643  G_loss 1.0966227054595947\n",
      "epoch: 1942  D_loss_real 0.4398706555366516  D_loss_fake 0.5740880966186523  G_loss 1.1158896684646606\n",
      "epoch: 1943  D_loss_real 0.48409974575042725  D_loss_fake 0.5621180534362793  G_loss 1.2410333156585693\n",
      "epoch: 1944  D_loss_real 0.4987100064754486  D_loss_fake 0.5768593549728394  G_loss 1.2990814447402954\n",
      "epoch: 1945  D_loss_real 0.47107380628585815  D_loss_fake 0.5099973082542419  G_loss 1.2098393440246582\n",
      "epoch: 1946  D_loss_real 0.5192166566848755  D_loss_fake 0.46850788593292236  G_loss 1.3644208908081055\n",
      "epoch: 1947  D_loss_real 0.4897494614124298  D_loss_fake 0.46943390369415283  G_loss 1.5912827253341675\n",
      "epoch: 1948  D_loss_real 0.498057097196579  D_loss_fake 0.6360924243927002  G_loss 1.1190931797027588\n",
      "epoch: 1949  D_loss_real 0.5436828136444092  D_loss_fake 0.4731031060218811  G_loss 1.4520537853240967\n",
      "epoch: 1950  D_loss_real 0.5347484350204468  D_loss_fake 0.5315477252006531  G_loss 1.3404874801635742\n",
      "epoch: 1951  D_loss_real 0.47697749733924866  D_loss_fake 0.5998097062110901  G_loss 1.1752080917358398\n",
      "epoch: 1952  D_loss_real 0.5128869414329529  D_loss_fake 0.5854079723358154  G_loss 1.0087080001831055\n",
      "epoch: 1953  D_loss_real 0.4647444486618042  D_loss_fake 0.5064544677734375  G_loss 1.074212670326233\n",
      "epoch: 1954  D_loss_real 0.4930476248264313  D_loss_fake 0.48458123207092285  G_loss 1.0319055318832397\n",
      "epoch: 1955  D_loss_real 0.5419098734855652  D_loss_fake 0.5641871690750122  G_loss 1.6468942165374756\n",
      "epoch: 1956  D_loss_real 0.4603158235549927  D_loss_fake 0.5008695125579834  G_loss 1.4215093851089478\n",
      "epoch: 1957  D_loss_real 0.5052556991577148  D_loss_fake 0.6670504808425903  G_loss 1.368115782737732\n",
      "epoch: 1958  D_loss_real 0.4703434705734253  D_loss_fake 0.602638840675354  G_loss 1.616288423538208\n",
      "epoch: 1959  D_loss_real 0.5437463521957397  D_loss_fake 0.520100474357605  G_loss 1.47137451171875\n",
      "epoch: 1960  D_loss_real 0.5293035507202148  D_loss_fake 0.525751531124115  G_loss 1.5736631155014038\n",
      "epoch: 1961  D_loss_real 0.5121169090270996  D_loss_fake 0.5071328282356262  G_loss 1.1196209192276\n",
      "epoch: 1962  D_loss_real 0.5237917304039001  D_loss_fake 0.5554548501968384  G_loss 1.4418518543243408\n",
      "epoch: 1963  D_loss_real 0.5653666257858276  D_loss_fake 0.5771312117576599  G_loss 1.3234150409698486\n",
      "epoch: 1964  D_loss_real 0.5435713529586792  D_loss_fake 0.625533938407898  G_loss 1.160412073135376\n",
      "epoch: 1965  D_loss_real 0.5423139333724976  D_loss_fake 0.5312803983688354  G_loss 1.5451059341430664\n",
      "epoch: 1966  D_loss_real 0.5359070301055908  D_loss_fake 0.6106941103935242  G_loss 1.3793418407440186\n",
      "epoch: 1967  D_loss_real 0.5956665873527527  D_loss_fake 0.5184147953987122  G_loss 1.062555193901062\n",
      "epoch: 1968  D_loss_real 0.5524032115936279  D_loss_fake 0.4220503866672516  G_loss 1.4271769523620605\n",
      "epoch: 1969  D_loss_real 0.5214239358901978  D_loss_fake 0.560861349105835  G_loss 1.2265822887420654\n",
      "epoch: 1970  D_loss_real 0.4865676462650299  D_loss_fake 0.7876375913619995  G_loss 1.0301111936569214\n",
      "epoch: 1971  D_loss_real 0.4076949954032898  D_loss_fake 0.6477112770080566  G_loss 1.3398890495300293\n",
      "epoch: 1972  D_loss_real 0.47380751371383667  D_loss_fake 0.6571829319000244  G_loss 1.2289074659347534\n",
      "epoch: 1973  D_loss_real 0.4807898998260498  D_loss_fake 0.5161128044128418  G_loss 1.3041932582855225\n",
      "epoch: 1974  D_loss_real 0.6354918479919434  D_loss_fake 0.5510189533233643  G_loss 1.356247067451477\n",
      "epoch: 1975  D_loss_real 0.5443148612976074  D_loss_fake 0.5535762906074524  G_loss 1.2898881435394287\n",
      "epoch: 1976  D_loss_real 0.47209975123405457  D_loss_fake 0.5848630666732788  G_loss 1.3005980253219604\n",
      "epoch: 1977  D_loss_real 0.49104735255241394  D_loss_fake 0.5495105981826782  G_loss 1.1602009534835815\n",
      "epoch: 1978  D_loss_real 0.5250893831253052  D_loss_fake 0.5343199372291565  G_loss 1.3538087606430054\n",
      "epoch: 1979  D_loss_real 0.5585743188858032  D_loss_fake 0.4070594310760498  G_loss 1.3764004707336426\n",
      "epoch: 1980  D_loss_real 0.5544158220291138  D_loss_fake 0.5474749207496643  G_loss 1.4435566663742065\n",
      "epoch: 1981  D_loss_real 0.46819868683815  D_loss_fake 0.5603952407836914  G_loss 1.337151288986206\n",
      "epoch: 1982  D_loss_real 0.46677759289741516  D_loss_fake 0.588300347328186  G_loss 1.3067958354949951\n",
      "epoch: 1983  D_loss_real 0.5145362615585327  D_loss_fake 0.5701302886009216  G_loss 1.5283693075180054\n",
      "epoch: 1984  D_loss_real 0.5529297590255737  D_loss_fake 0.5739320516586304  G_loss 1.598154067993164\n",
      "epoch: 1985  D_loss_real 0.514988899230957  D_loss_fake 0.5955623388290405  G_loss 1.1770365238189697\n",
      "epoch: 1986  D_loss_real 0.48416805267333984  D_loss_fake 0.4024268388748169  G_loss 1.3865766525268555\n",
      "epoch: 1987  D_loss_real 0.4679257869720459  D_loss_fake 0.495504230260849  G_loss 1.6191928386688232\n",
      "epoch: 1988  D_loss_real 0.5150341391563416  D_loss_fake 0.569589376449585  G_loss 1.2359946966171265\n",
      "epoch: 1989  D_loss_real 0.5224007964134216  D_loss_fake 0.5307248830795288  G_loss 1.3260972499847412\n",
      "epoch: 1990  D_loss_real 0.49177083373069763  D_loss_fake 0.45121318101882935  G_loss 1.5378663539886475\n",
      "epoch: 1991  D_loss_real 0.4865013360977173  D_loss_fake 0.5051587820053101  G_loss 1.3100123405456543\n",
      "epoch: 1992  D_loss_real 0.45523783564567566  D_loss_fake 0.5371360778808594  G_loss 1.3739819526672363\n",
      "epoch: 1993  D_loss_real 0.4704311192035675  D_loss_fake 0.4533580541610718  G_loss 1.390911340713501\n",
      "epoch: 1994  D_loss_real 0.4591069519519806  D_loss_fake 0.6599526405334473  G_loss 1.2212707996368408\n",
      "epoch: 1995  D_loss_real 0.41660332679748535  D_loss_fake 0.4521523416042328  G_loss 1.1576194763183594\n",
      "epoch: 1996  D_loss_real 0.5787258148193359  D_loss_fake 0.5167297124862671  G_loss 1.1287261247634888\n",
      "epoch: 1997  D_loss_real 0.4792516231536865  D_loss_fake 0.5040507912635803  G_loss 1.2743769884109497\n",
      "epoch: 1998  D_loss_real 0.489780068397522  D_loss_fake 0.4879942834377289  G_loss 1.440220832824707\n",
      "epoch: 1999  D_loss_real 0.4389457106590271  D_loss_fake 0.5577148199081421  G_loss 1.2735971212387085\n",
      "epoch: 2000  D_loss_real 0.44884151220321655  D_loss_fake 0.4811195135116577  G_loss 1.4210542440414429\n",
      "epoch: 2001  D_loss_real 0.45839911699295044  D_loss_fake 0.5409082174301147  G_loss 1.2786660194396973\n",
      "epoch: 2002  D_loss_real 0.436288058757782  D_loss_fake 0.6152414679527283  G_loss 1.412233591079712\n",
      "epoch: 2003  D_loss_real 0.506871223449707  D_loss_fake 0.4456973075866699  G_loss 1.1425371170043945\n",
      "epoch: 2004  D_loss_real 0.45165160298347473  D_loss_fake 0.5104823112487793  G_loss 1.3976590633392334\n",
      "epoch: 2005  D_loss_real 0.5012435913085938  D_loss_fake 0.5296224355697632  G_loss 1.1226699352264404\n",
      "epoch: 2006  D_loss_real 0.4369959831237793  D_loss_fake 0.4021945297718048  G_loss 0.8792012929916382\n",
      "epoch: 2007  D_loss_real 0.5061067342758179  D_loss_fake 0.5358743667602539  G_loss 0.9672492742538452\n",
      "epoch: 2008  D_loss_real 0.45024341344833374  D_loss_fake 0.5521068572998047  G_loss 1.1900817155838013\n",
      "epoch: 2009  D_loss_real 0.4823353886604309  D_loss_fake 0.5318883657455444  G_loss 1.487200379371643\n",
      "epoch: 2010  D_loss_real 0.4925491213798523  D_loss_fake 0.5298783183097839  G_loss 1.0727914571762085\n",
      "epoch: 2011  D_loss_real 0.506016731262207  D_loss_fake 0.5220497846603394  G_loss 1.510011911392212\n",
      "epoch: 2012  D_loss_real 0.5091806054115295  D_loss_fake 0.6750898361206055  G_loss 1.419649362564087\n",
      "epoch: 2013  D_loss_real 0.5645723342895508  D_loss_fake 0.6220220923423767  G_loss 1.2669363021850586\n",
      "epoch: 2014  D_loss_real 0.606867253780365  D_loss_fake 0.7383589744567871  G_loss 1.5850805044174194\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2015  D_loss_real 0.531879723072052  D_loss_fake 0.624469518661499  G_loss 1.3910069465637207\n",
      "epoch: 2016  D_loss_real 0.5611039400100708  D_loss_fake 0.6243326663970947  G_loss 1.1199498176574707\n",
      "epoch: 2017  D_loss_real 0.5756457448005676  D_loss_fake 0.565274178981781  G_loss 1.5781681537628174\n",
      "epoch: 2018  D_loss_real 0.6472243070602417  D_loss_fake 0.5560218095779419  G_loss 1.4666157960891724\n",
      "epoch: 2019  D_loss_real 0.4698704481124878  D_loss_fake 0.518941342830658  G_loss 1.1726783514022827\n",
      "epoch: 2020  D_loss_real 0.4818103313446045  D_loss_fake 0.6352443099021912  G_loss 1.1133294105529785\n",
      "epoch: 2021  D_loss_real 0.43719977140426636  D_loss_fake 0.5718557238578796  G_loss 1.1873538494110107\n",
      "epoch: 2022  D_loss_real 0.4799487292766571  D_loss_fake 0.693305253982544  G_loss 1.1787629127502441\n",
      "epoch: 2023  D_loss_real 0.4734206795692444  D_loss_fake 0.6988404989242554  G_loss 1.5140408277511597\n",
      "epoch: 2024  D_loss_real 0.443634033203125  D_loss_fake 0.5843909978866577  G_loss 1.0786874294281006\n",
      "epoch: 2025  D_loss_real 0.48474058508872986  D_loss_fake 0.5847972631454468  G_loss 0.9986202716827393\n",
      "epoch: 2026  D_loss_real 0.49858027696609497  D_loss_fake 0.6167856454849243  G_loss 1.2687604427337646\n",
      "epoch: 2027  D_loss_real 0.5251308083534241  D_loss_fake 0.6327232122421265  G_loss 1.5112006664276123\n",
      "epoch: 2028  D_loss_real 0.5684294700622559  D_loss_fake 0.5321618318557739  G_loss 1.2004092931747437\n",
      "epoch: 2029  D_loss_real 0.5307284593582153  D_loss_fake 0.4814872145652771  G_loss 1.6236724853515625\n",
      "epoch: 2030  D_loss_real 0.5832490921020508  D_loss_fake 0.6075551509857178  G_loss 1.4353387355804443\n",
      "epoch: 2031  D_loss_real 0.5755671262741089  D_loss_fake 0.4216703772544861  G_loss 1.5472687482833862\n",
      "epoch: 2032  D_loss_real 0.5853816270828247  D_loss_fake 0.5047250986099243  G_loss 1.3174651861190796\n",
      "epoch: 2033  D_loss_real 0.5493598580360413  D_loss_fake 0.5209610462188721  G_loss 1.6547785997390747\n",
      "epoch: 2034  D_loss_real 0.5267316699028015  D_loss_fake 0.5169171094894409  G_loss 1.5281660556793213\n",
      "epoch: 2035  D_loss_real 0.48609498143196106  D_loss_fake 0.6275441646575928  G_loss 1.4475934505462646\n",
      "epoch: 2036  D_loss_real 0.5323691368103027  D_loss_fake 0.7009071707725525  G_loss 1.096116304397583\n",
      "epoch: 2037  D_loss_real 0.43327367305755615  D_loss_fake 0.5759140253067017  G_loss 1.0847575664520264\n",
      "epoch: 2038  D_loss_real 0.45229899883270264  D_loss_fake 0.5907236337661743  G_loss 1.4558013677597046\n",
      "epoch: 2039  D_loss_real 0.4597121477127075  D_loss_fake 0.6025077700614929  G_loss 1.3012439012527466\n",
      "epoch: 2040  D_loss_real 0.4493260979652405  D_loss_fake 0.5667544603347778  G_loss 1.217714548110962\n",
      "epoch: 2041  D_loss_real 0.49140825867652893  D_loss_fake 0.6042267084121704  G_loss 1.693119764328003\n",
      "epoch: 2042  D_loss_real 0.4736957550048828  D_loss_fake 0.4422908127307892  G_loss 1.432814359664917\n",
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     "output_type": "stream",
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      "epoch: 2921  D_loss_real 0.5217587351799011  D_loss_fake 0.685675859451294  G_loss 1.9071251153945923\n",
      "epoch: 2922  D_loss_real 0.5444812774658203  D_loss_fake 0.6297944784164429  G_loss 1.4623596668243408\n",
      "epoch: 2923  D_loss_real 0.5397865772247314  D_loss_fake 0.5717092752456665  G_loss 1.845163106918335\n",
      "epoch: 2924  D_loss_real 0.515478253364563  D_loss_fake 0.5545181035995483  G_loss 1.756277084350586\n",
      "epoch: 2925  D_loss_real 0.5098835229873657  D_loss_fake 0.6580232977867126  G_loss 1.5401440858840942\n",
      "epoch: 2926  D_loss_real 0.4580157995223999  D_loss_fake 0.6860678195953369  G_loss 1.563974142074585\n",
      "epoch: 2927  D_loss_real 0.4838925302028656  D_loss_fake 0.6310378313064575  G_loss 1.4439949989318848\n",
      "epoch: 2928  D_loss_real 0.49737685918807983  D_loss_fake 0.6788321733474731  G_loss 1.6404404640197754\n",
      "epoch: 2929  D_loss_real 0.5455873012542725  D_loss_fake 0.6214691996574402  G_loss 1.6353118419647217\n",
      "epoch: 2930  D_loss_real 0.48739129304885864  D_loss_fake 0.5063388347625732  G_loss 1.58988618850708\n",
      "epoch: 2931  D_loss_real 0.49574699997901917  D_loss_fake 0.5946736335754395  G_loss 1.7028582096099854\n",
      "epoch: 2932  D_loss_real 0.4823624789714813  D_loss_fake 0.6150293350219727  G_loss 1.5254433155059814\n",
      "epoch: 2933  D_loss_real 0.503706693649292  D_loss_fake 0.6239845752716064  G_loss 1.602370262145996\n",
      "epoch: 2934  D_loss_real 0.4290512204170227  D_loss_fake 0.5205127000808716  G_loss 1.621429204940796\n",
      "epoch: 2935  D_loss_real 0.45421522855758667  D_loss_fake 0.586281418800354  G_loss 1.996447205543518\n",
      "epoch: 2936  D_loss_real 0.4463270902633667  D_loss_fake 0.6694220304489136  G_loss 1.403623104095459\n",
      "epoch: 2937  D_loss_real 0.4901292324066162  D_loss_fake 0.6102675795555115  G_loss 1.407379150390625\n",
      "epoch: 2938  D_loss_real 0.5253567695617676  D_loss_fake 0.550029993057251  G_loss 1.5610840320587158\n",
      "epoch: 2939  D_loss_real 0.5797296762466431  D_loss_fake 0.6444575190544128  G_loss 1.7152632474899292\n",
      "epoch: 2940  D_loss_real 0.44934511184692383  D_loss_fake 0.4732981026172638  G_loss 1.911957859992981\n",
      "epoch: 2941  D_loss_real 0.4789755940437317  D_loss_fake 0.45530614256858826  G_loss 1.9241526126861572\n",
      "epoch: 2942  D_loss_real 0.44767501950263977  D_loss_fake 0.5505229234695435  G_loss 1.4130879640579224\n",
      "epoch: 2943  D_loss_real 0.48477065563201904  D_loss_fake 0.5400659441947937  G_loss 1.5655792951583862\n",
      "epoch: 2944  D_loss_real 0.4561195373535156  D_loss_fake 0.7310943007469177  G_loss 1.2457621097564697\n",
      "epoch: 2945  D_loss_real 0.40312981605529785  D_loss_fake 0.5649490356445312  G_loss 1.7214534282684326\n",
      "epoch: 2946  D_loss_real 0.4629002809524536  D_loss_fake 0.5506134629249573  G_loss 2.1318085193634033\n",
      "epoch: 2947  D_loss_real 0.43748101592063904  D_loss_fake 0.6440771818161011  G_loss 1.4008753299713135\n",
      "epoch: 2948  D_loss_real 0.48808103799819946  D_loss_fake 0.5429323315620422  G_loss 1.4797594547271729\n",
      "epoch: 2949  D_loss_real 0.41092199087142944  D_loss_fake 0.5782004594802856  G_loss 1.7151683568954468\n",
      "epoch: 2950  D_loss_real 0.4766373038291931  D_loss_fake 0.5224378705024719  G_loss 1.4704549312591553\n",
      "epoch: 2951  D_loss_real 0.4806038439273834  D_loss_fake 0.5405431985855103  G_loss 1.008392572402954\n",
      "epoch: 2952  D_loss_real 0.5094394683837891  D_loss_fake 0.696937620639801  G_loss 1.4008640050888062\n",
      "epoch: 2953  D_loss_real 0.47759366035461426  D_loss_fake 0.5344119071960449  G_loss 1.4510223865509033\n",
      "epoch: 2954  D_loss_real 0.42590856552124023  D_loss_fake 0.7122356295585632  G_loss 2.0250892639160156\n",
      "epoch: 2955  D_loss_real 0.4617619812488556  D_loss_fake 0.6078885197639465  G_loss 1.5215452909469604\n",
      "epoch: 2956  D_loss_real 0.44281256198883057  D_loss_fake 0.45046597719192505  G_loss 1.172778606414795\n",
      "epoch: 2957  D_loss_real 0.4946594834327698  D_loss_fake 0.503276526927948  G_loss 2.025383710861206\n",
      "epoch: 2958  D_loss_real 0.41770660877227783  D_loss_fake 0.6517136096954346  G_loss 1.6630204916000366\n",
      "epoch: 2959  D_loss_real 0.617007851600647  D_loss_fake 0.4204851984977722  G_loss 1.8209493160247803\n",
      "epoch: 2960  D_loss_real 0.5281083583831787  D_loss_fake 0.4800572395324707  G_loss 2.0002501010894775\n",
      "epoch: 2961  D_loss_real 0.4946570098400116  D_loss_fake 0.5561553239822388  G_loss 1.701697826385498\n",
      "epoch: 2962  D_loss_real 0.5810164213180542  D_loss_fake 0.6602761745452881  G_loss 1.7867449522018433\n",
      "epoch: 2963  D_loss_real 0.4377076029777527  D_loss_fake 0.49440258741378784  G_loss 1.7591228485107422\n",
      "epoch: 2964  D_loss_real 0.5324333906173706  D_loss_fake 0.49629640579223633  G_loss 1.8103102445602417\n",
      "epoch: 2965  D_loss_real 0.4549946188926697  D_loss_fake 0.6146903038024902  G_loss 1.7545599937438965\n",
      "epoch: 2966  D_loss_real 0.4827515482902527  D_loss_fake 0.5393850803375244  G_loss 1.9241660833358765\n",
      "epoch: 2967  D_loss_real 0.5004844069480896  D_loss_fake 0.5347333550453186  G_loss 1.6620821952819824\n",
      "epoch: 2968  D_loss_real 0.4963645339012146  D_loss_fake 0.49618008732795715  G_loss 1.5916905403137207\n",
      "epoch: 2969  D_loss_real 0.515678882598877  D_loss_fake 0.4513474106788635  G_loss 1.6731815338134766\n",
      "epoch: 2970  D_loss_real 0.5208797454833984  D_loss_fake 0.4018024802207947  G_loss 1.8511314392089844\n",
      "epoch: 2971  D_loss_real 0.47795888781547546  D_loss_fake 0.4836527109146118  G_loss 1.902626395225525\n",
      "epoch: 2972  D_loss_real 0.47370147705078125  D_loss_fake 0.37733569741249084  G_loss 1.6344983577728271\n",
      "epoch: 2973  D_loss_real 0.44511300325393677  D_loss_fake 0.5394177436828613  G_loss 1.9874942302703857\n",
      "epoch: 2974  D_loss_real 0.3878597617149353  D_loss_fake 0.5173417329788208  G_loss 1.7123993635177612\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2975  D_loss_real 0.40400755405426025  D_loss_fake 0.3731786608695984  G_loss 1.6320490837097168\n",
      "epoch: 2976  D_loss_real 0.401258647441864  D_loss_fake 0.5262800455093384  G_loss 1.2261039018630981\n",
      "epoch: 2977  D_loss_real 0.392331063747406  D_loss_fake 0.5889626741409302  G_loss 1.7487819194793701\n",
      "epoch: 2978  D_loss_real 0.38945919275283813  D_loss_fake 0.47225743532180786  G_loss 1.6205923557281494\n",
      "epoch: 2979  D_loss_real 0.37192994356155396  D_loss_fake 0.630886971950531  G_loss 2.4039549827575684\n",
      "epoch: 2980  D_loss_real 0.35658571124076843  D_loss_fake 0.6281254887580872  G_loss 0.8721468448638916\n",
      "epoch: 2981  D_loss_real 0.4265906512737274  D_loss_fake 0.5513463020324707  G_loss 1.5195739269256592\n",
      "epoch: 2982  D_loss_real 0.3997824788093567  D_loss_fake 0.42072567343711853  G_loss 1.8604683876037598\n",
      "epoch: 2983  D_loss_real 0.4194831848144531  D_loss_fake 0.5282200574874878  G_loss 1.7497998476028442\n",
      "epoch: 2984  D_loss_real 0.4335852563381195  D_loss_fake 0.5500761270523071  G_loss 1.704024314880371\n",
      "epoch: 2985  D_loss_real 0.42859694361686707  D_loss_fake 0.4182751774787903  G_loss 1.3774476051330566\n",
      "epoch: 2986  D_loss_real 0.4330107569694519  D_loss_fake 0.5344913005828857  G_loss 2.370823860168457\n",
      "epoch: 2987  D_loss_real 0.44457873702049255  D_loss_fake 0.4755430221557617  G_loss 1.5159873962402344\n",
      "epoch: 2988  D_loss_real 0.42742958664894104  D_loss_fake 0.4745016098022461  G_loss 2.058000326156616\n",
      "epoch: 2989  D_loss_real 0.4028976559638977  D_loss_fake 0.4248236417770386  G_loss 2.248849391937256\n",
      "epoch: 2990  D_loss_real 0.43493980169296265  D_loss_fake 0.4140280485153198  G_loss 1.8418912887573242\n",
      "epoch: 2991  D_loss_real 0.38104960322380066  D_loss_fake 0.5408535003662109  G_loss 1.9012327194213867\n",
      "epoch: 2992  D_loss_real 0.3986443281173706  D_loss_fake 0.5034008026123047  G_loss 2.0142662525177\n",
      "epoch: 2993  D_loss_real 0.3545190095901489  D_loss_fake 0.4984303116798401  G_loss 1.5276973247528076\n",
      "epoch: 2994  D_loss_real 0.36532270908355713  D_loss_fake 0.4905606210231781  G_loss 2.247643232345581\n",
      "epoch: 2995  D_loss_real 0.3597036898136139  D_loss_fake 0.613822340965271  G_loss 1.562486171722412\n",
      "epoch: 2996  D_loss_real 0.3762027621269226  D_loss_fake 0.47191232442855835  G_loss 1.6171760559082031\n",
      "epoch: 2997  D_loss_real 0.3814985156059265  D_loss_fake 0.4484841227531433  G_loss 2.20746111869812\n",
      "epoch: 2998  D_loss_real 0.38278037309646606  D_loss_fake 0.40257805585861206  G_loss 1.9406863451004028\n",
      "epoch: 2999  D_loss_real 0.4029075801372528  D_loss_fake 0.5568590760231018  G_loss 2.0946152210235596\n"
     ]
    }
   ],
   "source": [
    "real = np.ones(batch_size)    \n",
    "fake = np.zeros(batch_size)    \n",
    "g_loss_ = []\n",
    "d_loss_ = []\n",
    "d_loss_fake_=[]\n",
    "d_loss_real_=[]\n",
    "d_acc_fake_=[]\n",
    "d_acc_real_=[]\n",
    "for epoch in range(num_epochs):\n",
    "    disc.trainable = True\n",
    "    noise_batch      = np.random.normal(0, 1, (batch_size, noise_dim))    \n",
    "    c_batch      = np.random.uniform(0, 1, (batch_size, conf_dim))\n",
    "    generated_features = gen.predict([noise_batch,c_batch]) # (32, 8)\n",
    "#     y_label = BB_clf.predict(generated_features) # 0,1\n",
    "    y_label = BB_clf.predict_proba(generated_features)[:,0] # [prob for 0, prob for 1]\n",
    "    d_loss_real = disc.train_on_batch([generated_features, y_label],real) # scalar (avg of batch)\n",
    "    # disc gets c lael\n",
    "    noise_batch      = np.random.normal(0, 1, (batch_size, noise_dim))    \n",
    "    c_batch      = np.random.uniform(0, 1, (batch_size, conf_dim))  \n",
    "    generated_features = gen.predict([noise_batch, c_batch])\n",
    "    d_loss_fake = disc.train_on_batch([generated_features,c_batch],fake) # scalar (avg of batch)\n",
    "    \n",
    "    d_loss = 0.5*(d_loss_real[0] + d_loss_fake[0]) # [0]-loss , [1]-acc\n",
    "    \n",
    "    # train gen (freeze disc)\n",
    "    for i in range(2):\n",
    "        disc.trainable = False\n",
    "        noise_batch      = np.random.normal(0, 1, (batch_size, noise_dim))    \n",
    "        c_batch      = np.random.uniform(0, 1, (batch_size, conf_dim))  \n",
    "        g_loss = cgan.train_on_batch([noise_batch, c_batch], real)\n",
    "    \n",
    "    print('epoch: ' + str(epoch) , ' D_loss_real ' + str(d_loss_real[0]) , ' D_loss_fake ' + str(d_loss_fake[0]) , ' G_loss ' + str(g_loss) )\n",
    "\n",
    "    g_loss_.append(g_loss)\n",
    "    d_loss_.append(d_loss)\n",
    "    d_loss_fake_.append(d_loss_fake[0])\n",
    "    d_loss_real_.append(d_loss_real[0])\n",
    "    d_acc_fake_.append(d_loss_fake[1])\n",
    "    d_acc_real_.append(d_loss_real[1])\n",
    "    \n",
    "    if epochs > 2000 and d_loss_real[1]<0.6:\n",
    "        break\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "gen.save('/home/odedrot/DL_ex_4/part2_Diabetes_generator_4ok.h5', include_optimizer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Generator and Discriminator Loss - EPOCH {}\".format(epoch))\n",
    "plt.plot(g_loss_,label=\"G\")\n",
    "plt.plot(d_loss_,label=\"D\")\n",
    "plt.plot(d_loss_fake_,label=\"Dfake\")\n",
    "plt.plot(d_loss_real_,label=\"Dreal\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"Generator and Discriminator acc - EPOCH {}\".format(epoch))\n",
    "plt.plot(d_acc_fake_,label=\"Dfake acc\")\n",
    "plt.plot(d_acc_real_,label=\"Dreal acc\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate 1,000 samples from your fully-trained generative model. \n",
    "# Feed the generated samples to the black-box model and provide statistics on the score distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1000\n",
    "noise_batch      = np.random.normal(0, 1, (batch_size, noise_dim))    \n",
    "c_batch      = np.random.uniform(0, 1, (batch_size, conf_dim))\n",
    "c_batch = c_batch[:,0]\n",
    "generated_features = gen.predict([noise_batch,c_batch]) # (32, 8)\n",
    "y_label = BB_clf.predict_proba(generated_features)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KnwMeHnTsONbd7R8AVo/j8e7p96/A7cCVK+F4z1b3uB9v4BLg84v9nset7hVwvH+cmU/tv6rbP3cpj7cz9+Oev2RCVf0v8NwlE55XVc9Ud/SBszj+wax5x45p3ctp0GP2R8AtwJFFjB2FYepeTsMcs5VwvMfNIHX/LvC5qnoMoKqOLGDsvAz349YCj/fsH+zaXiDJFUkeBr4A/N5Cxo7IMHXDTNB/Mck93SUhTpZ5606yFrgC+PhCx47QMHXDGB/vzi8l+WaSf0py0QLHjsIwdcN4H+/XAGcn+XJX37sWMHZeXs/9uHkvmQBQVbcCtyb5VeAvgbcMOnZEhqkb4OKqOpTkXODOJA9X1V2jK/d5g9T9UeBPq+pY8oLu4368P0r/umG8j/e9zFyr5JkkbwX+Edgw4NhRGaZuGO/jfTrwC8ClwMuAryb52oBj5+XM/bgFXTKhe4G8OsnqhY5dYsPUTVUd6m6PALcy81/Ck2GQuieBXUkOAFcCNyW5fMCxozJM3WN9vKvqqap6ptu+HThjJby+56h7rI931+eOqvrvqnoSuAt4/YBj53ey32gY1y9mfot+B7iA429iXHRCn5/m+BuTPw98j5nfsvOOHdO6zwJe0bWfBfw7M1fzHIu6T+j/KY6/oTrWx3uOusf6eAOv7HmdbAIeWyGv79nqHvfj/Vpgb9f3x4AHgNct1fF2WaZTs1wyIckfdPd/HPgt4F1J/g/4H+B3auZfadkutzBM3UnOY2apBmZeUJ+pqjvGqO4FjR33uoFxP95XAn+Y5Cgzr5OtK+T13bfucX99V9VDSe4A7gd+xMxfrHsAYCmOt5cfkKQGueYuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KD/h9WlI6CUFgRqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(c_batch.flatten(), 100)\n",
    "plt.title('c')\n",
    "plt.show()\n",
    "plt.hist(generated_features.flatten(), 100)\n",
    "plt.title('features')\n",
    "plt.show()\n",
    "plt.hist(y_label.flatten(), 100)\n",
    "plt.title('y')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(c_batch.flatten(),'bo', label=\"c\")\n",
    "plt.plot(y_label.flatten(),'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "\n",
    "c_sorted_idx = np.argsort(c_batch, axis=0)\n",
    "sorted_c = c_batch[c_sorted_idx]\n",
    "sorted_y = y_label[c_sorted_idx]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(sorted_c.flatten(),'bo', label=\"c\")\n",
    "plt.plot(sorted_y.flatten(),'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(np.round(sorted_c.flatten()),'bo', label=\"c\")\n",
    "plt.plot(np.round(sorted_y.flatten())+0.1,'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()\n",
    "\n",
    "sorted_y_ = np.copy(sorted_y)\n",
    "y_mean = np.mean(sorted_y)\n",
    "sorted_y_[sorted_y>y_mean] = 1\n",
    "sorted_y_[sorted_y<y_mean] = 0\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('c vs y')\n",
    "plt.plot(np.round(sorted_c.flatten()),'bo', label=\"c\")\n",
    "plt.plot(np.round(sorted_y_.flatten())+0.1,'ro', label=\"y\")\n",
    "plt.xlabel(\"sample\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.legend()\n",
    "ymax = plt.ylim()[1]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.958\n"
     ]
    }
   ],
   "source": [
    "# accuracy\n",
    "from sklearn import metrics\n",
    "c_batch_round = np.round(c_batch)\n",
    "y_label_round = np.round(y_label)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(c_batch_round, y_label_round))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 1 acc  0.9920948616600791\n",
      "class 0 acc  0.9230769230769231\n"
     ]
    }
   ],
   "source": [
    "c_1 = len(c_batch_round[c_batch_round==1])\n",
    "c_0 = len(c_batch_round[c_batch_round==0])\n",
    "y_1 = len(y_label_round[c_batch_round==1][y_label_round[c_batch_round==1]==1])\n",
    "y_0 = len(y_label_round[c_batch_round==0][y_label_round[c_batch_round==0]==0])\n",
    "print('class 1 acc ' , y_1/c_1)\n",
    "print('class 0 acc ' , y_0/c_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EXv2sZ7afBV6f5NvA/wA/X91PyRRkWxM9s10G/HqS48w/bldMy+NWVXck+RTwReBR4H1Vdds0ZOt2vRT4dM3/hTARPbO9E/hAki8xP63xlhr/X1R9s70A+GCSR5g/w2jXSu7DS+klqVFrfRBTkjQkC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ16v8AsLNjoUABpVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw random vectors to check BB dist\n",
    "rand_features  = np.random.uniform(0, 1, (batch_size, 8))\n",
    "y_label_rand = BB_clf.predict_proba(rand_features)\n",
    "plt.hist(y_label_rand.flatten(), 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Are there any conclusions you can draw?\n",
    "# Were you more successful for a specific class of samples than another? \n",
    "# Were you more successful for a specific range of confidence scores?\n",
    "# Did your model suffer from mode collapse or other problems that may have prevented it from generating more effective samples?\n",
    "# If so, in what cases? Elaborate\n",
    "\n",
    "# \n",
    "# 1. RF clf acc=0.75\n",
    "# 2. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "odedenv",
   "language": "python",
   "name": "odedenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
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 "nbformat_minor": 4
}