cms_zee_conditional_parametrized.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import GAN.models as models\n",
    "import GAN.cms_datasets as cms\n",
    "import GAN.plotting as plotting\n",
    "import GAN.preprocessing as preprocessing\n",
    "import GAN.base as base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AM_OPTS': {'opt_kwargs': {'decay': 6e-06, 'lr': 0.0002},\n",
       "  'optimizer': 'RMSprop'},\n",
       " 'BATCH_SIZE': 4096,\n",
       " 'C_NAMES': ['Phi', 'ScEta'],\n",
       " 'DM_OPTS': {'opt_kwargs': {'decay': 6e-06, 'lr': 0.0002},\n",
       "  'optimizer': 'RMSprop'},\n",
       " 'D_OPTS': {'kernel_sizes': [256, 256, 256, 256, 256], 'name': 'D_256x5'},\n",
       " 'EPOCHS': 60,\n",
       " 'FEAT_TRANSFORM': 'gaus',\n",
       " 'FRAC_DATA': 10,\n",
       " 'G_OPTS': {'kernel_sizes': [64, 64, 64, 64, 64], 'name': 'G_64x5'},\n",
       " 'LOAD_DATASETS': ['moriond_v9', 'abs(ScEta) < 1.5'],\n",
       " 'PLOT_EVERY': 10,\n",
       " 'X_NAMES': ['EtaWidth']}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import GAN.utils as utils\n",
    "\n",
    "reload(utils)\n",
    "\n",
    "class Parameters(utils.Parameters):\n",
    "    \n",
    "    load_datasets=utils.param([\"moriond_v9\",\"abs(ScEta) < 1.5\"])\n",
    "\n",
    "    c_names = utils.param(['Phi','ScEta'])\n",
    "    x_names = utils.param(['EtaWidth'])\n",
    "#     c_names = utils.param(['Pt','ScEta','Phi','rho'])\n",
    "#     x_names = utils.param(['R9','SigmaIeIe','S4','EtaWidth','PhiWidth','CovarianceIetaIphi',\n",
    "#                'CovarianceIphiIphi', ])\n",
    "    \n",
    "    feat_transform = utils.param('gaus')\n",
    "    \n",
    "    g_opts=utils.param(dict(name=\"G_64x5\",kernel_sizes=[64]*5))\n",
    "    d_opts=utils.param(dict(name=\"D_256x5\",kernel_sizes=[256]*5))\n",
    "    dm_opts=utils.param(dict(optimizer=\"RMSprop\",opt_kwargs=dict(lr=0.0002, decay=6e-6)))\n",
    "    am_opts=utils.param(dict(optimizer=\"RMSprop\",opt_kwargs=dict(lr=0.0002, decay=6e-6)))\n",
    "    \n",
    "    epochs=utils.param(60)\n",
    "    batch_size=utils.param(4096)\n",
    "    plot_every=utils.param(10)\n",
    "    \n",
    "    frac_data=utils.param(10)\n",
    "    \n",
    "class MyApp(utils.MyApp):\n",
    "    classes = utils.List([Parameters])\n",
    "\n",
    "notebook_parameters = Parameters(MyApp()).get_params()\n",
    "\n",
    "globals().update(notebook_parameters)\n",
    "notebook_parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload(cms)\n",
    "\n",
    "data,mc = cms.load_zee(*LOAD_DATASETS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'run', 'rho', 'nvtx', 'mass', 'weight', 'SigMoM', 'Pt',\n",
       "       'ScEta', 'Phi', 'R9', 'S4', 'SigmaIeIe', 'EtaWidth', 'PhiWidth',\n",
       "       'CovarianceIphiIphi', 'SigmaRR', 'ScEnergy', 'CovarianceIetaIphi',\n",
       "       'PhoIso03', 'ChIso03', 'ChIso03worst', 'ScPreshowerEnergy', 'PhoIDMVA',\n",
       "       'SigEOverE', 'run_quantile'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'run', 'rho', 'nvtx', 'mass', 'weight', 'SigMoM', 'Pt',\n",
       "       'ScEta', 'Phi', 'R9', 'S4', 'SigmaIeIe', 'EtaWidth', 'PhiWidth',\n",
       "       'CovarianceIphiIphi', 'SigmaRR', 'ScEnergy', 'CovarianceIetaIphi',\n",
       "       'PhoIso03', 'ChIso03', 'ChIso03worst', 'ScPreshowerEnergy', 'PhoIDMVA',\n",
       "       'SigEOverE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c_names = C_NAMES\n",
    "x_names = X_NAMES\n",
    "\n",
    "data_c = data[c_names]\n",
    "data_x = data[x_names]\n",
    "\n",
    "mc_c = mc[c_names]\n",
    "mc_x = mc[x_names]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['EtaWidth'], dtype='object'),\n",
       " (17520217, 1),\n",
       " Index(['Phi', 'ScEta'], dtype='object'),\n",
       " (17520217, 2))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.columns, data_x.shape, data_c.columns, data_c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['EtaWidth'], dtype='object'), Index(['Phi', 'ScEta'], dtype='object'))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.columns, data_c.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['EtaWidth'], dtype='object'), Index(['Phi', 'ScEta'], dtype='object'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc_x.columns, mc_c.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reload(models)\n",
    "\n",
    "xz_shape = (1,len(x_names))\n",
    "c_shape = (1,len(c_names))\n",
    "gan = models.MyFFGAN( xz_shape, xz_shape, c_shape=c_shape,\n",
    "                     g_opts=G_OPTS,\n",
    "                     d_opts=D_OPTS,\n",
    "                     dm_opts=DM_OPTS,\n",
    "                     am_opts=AM_OPTS,\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x2b75ed067a58>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan.get_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x2b74fc2b6588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan.get_discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gan.adversarial_compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "G_64x5_c_input (InputLayer)      (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_input (InputLayer)        (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_all_inputs (Concatenate)  (None, 1, 3)          0           G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_input[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_dense (Dense)         (None, 1, 64)         256         G_64x5_all_inputs[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_activ (PReLU)         (None, 1, 64)         64          G_64x5_up1_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_dense (Dense)         (None, 1, 64)         4160        G_64x5_up1_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_activ (PReLU)         (None, 1, 64)         64          G_64x5_up2_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_dense (Dense)         (None, 1, 64)         4160        G_64x5_up2_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_activ (PReLU)         (None, 1, 64)         64          G_64x5_up3_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_dense (Dense)         (None, 1, 64)         4160        G_64x5_up3_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_activ (PReLU)         (None, 1, 64)         64          G_64x5_up4_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_dense (Dense)         (None, 1, 64)         4160        G_64x5_up4_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_activ (PReLU)         (None, 1, 64)         64          G_64x5_up5_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_output (Dense)            (None, 1, 1)          65          G_64x5_up5_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_add (Add)                 (None, 1, 1)          0           G_64x5_input[0][0]               \n",
      "                                                                   G_64x5_output[0][0]              \n",
      "====================================================================================================\n",
      "Total params: 17,281\n",
      "Trainable params: 17,281\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.get_generator().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "D_256x5_c_input (InputLayer)     (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_input (InputLayer)       (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_all_inputs (Concatenate) (None, 1, 3)          0           D_256x5_c_input[0][0]            \n",
      "                                                                   D_256x5_input[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_dense (Dense)      (None, 1, 256)        1024        D_256x5_all_inputs[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_activ (Activation) (None, 1, 256)        0           D_256x5_down1_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_dense (Dense)      (None, 1, 256)        65792       D_256x5_down1_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_activ (Activation) (None, 1, 256)        0           D_256x5_down2_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_dense (Dense)      (None, 1, 256)        65792       D_256x5_down2_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_activ (Activation) (None, 1, 256)        0           D_256x5_down3_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_dense (Dense)      (None, 1, 256)        65792       D_256x5_down3_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_activ (Activation) (None, 1, 256)        0           D_256x5_down4_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_dense (Dense)      (None, 1, 256)        65792       D_256x5_down4_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_activ (Activation) (None, 1, 256)        0           D_256x5_down5_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_flat (Flatten)           (None, 256)           0           D_256x5_down5_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_output (Dense)           (None, 1)             257         D_256x5_flat[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 264,449\n",
      "Trainable params: 264,449\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.get_discriminator().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "G_64x5_c_input (InputLayer)      (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_input (InputLayer)        (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_all_inputs (Concatenate)  (None, 1, 3)          0           G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_input[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_dense (Dense)         (None, 1, 64)         256         G_64x5_all_inputs[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_activ (PReLU)         (None, 1, 64)         64          G_64x5_up1_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_dense (Dense)         (None, 1, 64)         4160        G_64x5_up1_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_activ (PReLU)         (None, 1, 64)         64          G_64x5_up2_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_dense (Dense)         (None, 1, 64)         4160        G_64x5_up2_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_activ (PReLU)         (None, 1, 64)         64          G_64x5_up3_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_dense (Dense)         (None, 1, 64)         4160        G_64x5_up3_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_activ (PReLU)         (None, 1, 64)         64          G_64x5_up4_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_dense (Dense)         (None, 1, 64)         4160        G_64x5_up4_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_activ (PReLU)         (None, 1, 64)         64          G_64x5_up5_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_output (Dense)            (None, 1, 1)          65          G_64x5_up5_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_add (Add)                 (None, 1, 1)          0           G_64x5_input[0][0]               \n",
      "                                                                   G_64x5_output[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "model_2 (Model)                  (None, 1)             264449      G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_add[0][0]                 \n",
      "====================================================================================================\n",
      "Total params: 281,730\n",
      "Trainable params: 281,730\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.am.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "D_256x5_c_input (InputLayer)     (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_input (InputLayer)       (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_all_inputs (Concatenate) (None, 1, 3)          0           D_256x5_c_input[0][0]            \n",
      "                                                                   D_256x5_input[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_dense (Dense)      (None, 1, 256)        1024        D_256x5_all_inputs[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_activ (Activation) (None, 1, 256)        0           D_256x5_down1_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_dense (Dense)      (None, 1, 256)        65792       D_256x5_down1_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_activ (Activation) (None, 1, 256)        0           D_256x5_down2_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_dense (Dense)      (None, 1, 256)        65792       D_256x5_down2_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_activ (Activation) (None, 1, 256)        0           D_256x5_down3_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_dense (Dense)      (None, 1, 256)        65792       D_256x5_down3_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_activ (Activation) (None, 1, 256)        0           D_256x5_down4_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_dense (Dense)      (None, 1, 256)        65792       D_256x5_down4_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_activ (Activation) (None, 1, 256)        0           D_256x5_down5_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_flat (Flatten)           (None, 256)           0           D_256x5_down5_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_output (Dense)           (None, 1)             257         D_256x5_flat[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 264,449\n",
      "Trainable params: 264,449\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.dm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "G_64x5_c_input (InputLayer)      (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_input (InputLayer)        (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_all_inputs (Concatenate)  (None, 1, 3)          0           G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_input[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_c_input (InputLayer)     (None, 1, 2)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_input (InputLayer)       (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_dense (Dense)         (None, 1, 64)         256         G_64x5_all_inputs[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_all_inputs (Concatenate) (None, 1, 3)          0           D_256x5_c_input[0][0]            \n",
      "                                                                   D_256x5_input[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_activ (PReLU)         (None, 1, 64)         64          G_64x5_up1_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_dense (Dense)      (None, 1, 256)        1024        D_256x5_all_inputs[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_dense (Dense)         (None, 1, 64)         4160        G_64x5_up1_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down1_activ (Activation) (None, 1, 256)        0           D_256x5_down1_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_activ (PReLU)         (None, 1, 64)         64          G_64x5_up2_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_dense (Dense)      (None, 1, 256)        65792       D_256x5_down1_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_dense (Dense)         (None, 1, 64)         4160        G_64x5_up2_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down2_activ (Activation) (None, 1, 256)        0           D_256x5_down2_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_activ (PReLU)         (None, 1, 64)         64          G_64x5_up3_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_dense (Dense)      (None, 1, 256)        65792       D_256x5_down2_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_dense (Dense)         (None, 1, 64)         4160        G_64x5_up3_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down3_activ (Activation) (None, 1, 256)        0           D_256x5_down3_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_activ (PReLU)         (None, 1, 64)         64          G_64x5_up4_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_dense (Dense)      (None, 1, 256)        65792       D_256x5_down3_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_dense (Dense)         (None, 1, 64)         4160        G_64x5_up4_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down4_activ (Activation) (None, 1, 256)        0           D_256x5_down4_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up5_activ (PReLU)         (None, 1, 64)         64          G_64x5_up5_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_dense (Dense)      (None, 1, 256)        65792       D_256x5_down4_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_output (Dense)            (None, 1, 1)          65          G_64x5_up5_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_down5_activ (Activation) (None, 1, 256)        0           D_256x5_down5_dense[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_add (Add)                 (None, 1, 1)          0           G_64x5_input[0][0]               \n",
      "                                                                   G_64x5_output[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_flat (Flatten)           (None, 256)           0           D_256x5_down5_activ[0][0]        \n",
      "____________________________________________________________________________________________________\n",
      "model_2 (Model)                  (None, 1)             264449      G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_add[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "D_256x5_output (Dense)           (None, 1)             257         D_256x5_flat[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 281,730\n",
      "Trainable params: 281,730\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<tf.Tensor 'model_2/D_256x5_output/Sigmoid:0' shape=(?, 1) dtype=float32>,\n",
       "  <tf.Tensor 'D_256x5_output/Sigmoid:0' shape=(?, 1) dtype=float32>],\n",
       " [<tf.Tensor 'G_64x5_c_input:0' shape=(?, 1, 2) dtype=float32>,\n",
       "  <tf.Tensor 'G_64x5_input:0' shape=(?, 1, 1) dtype=float32>,\n",
       "  <tf.Tensor 'D_256x5_c_input:0' shape=(?, 1, 2) dtype=float32>,\n",
       "  <tf.Tensor 'D_256x5_input:0' shape=(?, 1, 1) dtype=float32>])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan.gan.outputs, gan.gan.inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting.plot_hists(data_x.values[:,0],mc_x.values[:,0],bins=100,range=[0,1.2])\n",
    "# plt.show()\n",
    "# plotting.plot_hists(data_x.values[:,1],mc_x.values[:,1],bins=100,range=[0,5e-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reload(preprocessing)\n",
    "\n",
    "data_x,data_c,mc_x,mc_c,scaler_x,scaler_c = preprocessing.transform(data_x,data_c,mc_x,mc_c,FEAT_TRANSFORM)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17520217, 1, 1), (9616192, 1, 1))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape,mc_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kbxsMfy/JMa21UzIRyD5aVY+c5tpVrbXFrbXFRxxxxKhKAgCYMYbpdN2R5OhJx0cNxnbm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fc94bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bKx/53HRc0xO6OambYPVwkuN2O54/0dbpnC2llNlJjkjy2J4vVGtdlWRVN4W1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fc94bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VpEsAgF02Uqerqs6uqq9V1caqumjI62dW1ReraktVvXib115eVbcPfr18XIUD\nAEyTnYauqto7yRVJfj3J8UleUlXHb3PaN5O8IsnV21z7hCRvS/L0JKcleVtVPX7PywYAmC6jdLpO\nS7KxtXZna+3HSa5Jct7sE1prm1prtyR5ZJtrX5Dk71tr32+t/SDJ3yc5ewx1AwBMlVHmdB2e5K5Z\nzzdnpnM1imHXHj7itQDQjYWXmWvz4u7FqlpZVeurav0999wz6XIAAMZulNB1d5IjZz0/YnBsFCNd\n21pb01pb3lpbvmTJkhHfGgBgeowyvLguyTFVtSwzgemCJKPuufKJJP9t1uT55yd50y5XCQBzbPhy\nNIYXGZ+ddrpaa1uSXJiZAHVbkg+01m6tqour6twkqaoVVbU5yflJ3ldVtw6u/X6St2cmuK1LcvHg\nGADAojLS4qittWuTXLvNsbfOerwuM0OHw669MsmVe1AjAMDUmxcT6QEAFjqhCwCgA3svMq8NWzfH\n31qgl6H/BsX6XewenS4AgA70DJjXht/CDdDHjv8N0uli1+l0AQB0IHQBAHQgdAEAdGBOFwDsomF3\nNbqjkZ3R6QIA6EDoAgDowPAiAOyi4UtJGF7k0el0AQB0IHQBAHQgdAEAdCB0AQB0IHQBAHTg7kXm\njWGLDfobCsBC4Uca88bwW7ABYGEwvAgA0IHQBQDQgdAFANCBOV1MhEnzACw2Ol0AAB3oLTAR7lQE\nYLHR6QIA6ECnCwDmyLD5q5tWnzOBSpgPdLoAADrQ6WLOHfKWl066BACYOJ0uAIAOdLoAYAyGrj8I\nswhdADAGoy+FYyL9YmV4EQCgA6ELAKADoQsAoAOhCwCgA6ELAKADoQsAoAOhCwCgA6ELAKADoQsA\noAMr0jNWQ7fB8LcMAHS6AAB60INgrEbfewwAFhedLgCADoQuAIAOhC4AgA6ELgCADoQuAIAO3L0I\nAB0NW89w0+pzJlAJvel0AQB0oNMFABO26oZV2x87a/tjTLeRQldVnZ3kT5PsneTPWmurt3n9MUn+\ne5KnJbk3yW+11jZV1dIktyX52uDUG1trrxpP6QAwfYYtIn35dduft+qsua+FvnYauqpq7yRXJHle\nks1J1lXV2tbahlmn/W6SH7TW/lNVXZDkj5L81uC1O1prJ4+5bgCAqTJKp+u0JBtba3cmSVVdk+S8\nJLND13lJVg0efzDJ/11VNcY6AWBRMeF+4RkldB2e5K5ZzzcnefqOzmmtbamqHyY5dPDasqr65yT3\nJ3lLa+3VlB7DAAAKA0lEQVQz2/4BVbUyycokOeqoo3bpC6AP8w0AYM/M9d2L305yVGvtlCR/kOTq\nqnrctie11ta01pa31pYvWbJkjksCAOhvlE7X3UmOnPX8iMGxYedsrqp9khyc5N7WWkvycJK01m6q\nqjuSHJtk/Z4WTl+XX/f17Y79+d9t3/p2PywADDdKp2tdkmOqallV7ZfkgiRrtzlnbZKXDx6/OMk/\nttZaVS0ZTMRPVT0xyTFJ7hxP6QAA02OnfYnBHK0Lk3wiM0tGXNlau7WqLk6yvrW2Nsn7k/yPqtqY\n5PuZCWZJcmaSi6vqJ0keSfKq1tr35+ILAYCFZNjSEomJ9NNspMGg1tq1Sa7d5thbZz1+KMn5Q677\nUJIP7WGNzFPD/0EAAIaxDRAAQAdCFwBAB0IXAEAHQhcAQAdCFwBAB5ayZDvD9vvyNwUA9owfpQAw\nJeyDO92ELgCYEsO2ZFt1Vv862D3mdAEAdCB0AQB0YHiR7djeBwDGT6cLAKADnS4AWGDc5Tg/6XQB\nAHSg0wUAC4ylJeYnoWuRG9aCBgDGT+ha5Ib9bwiA6WHrtulhThcAQAdCFwBABxqQADDFLGg9PYSu\nReSQt7x00iUAMCHD5n5tWn3OBCpZvISuBcrESgBmG94RE7p6MqcLAKADvQ8AWKQMOfYldC1QJlYC\nwPwidAHAImWeV19CFwCw1bDt4Vadtf0xdp3QtQBYCgKAcbFZ9txx9yIAQAdCFwBAB0IXAEAH5nQB\nAI9qR3OH77vk6s6VTDeha4oMu6MEAJgOhhcBADrQ6Zqnhm3N8IqzJ1AIADAWQtc8MCxgDTNs7RQA\nYDoIXfOUvRMBYGERuuYBAQuAhWLY6M2m1fZzTISuOTXsL56ABQCLk9A1hwQsABab4T/7dLoSoQsA\n2E1DbwQbkiwMOc4QugCA3TLqiI7u1wyha0ysFg8Ao1uM3S+ha0ysoQUAo1uM3S+hCwCYF4aNGq06\na/tj00ro2g2GEgFg/IaNGq06q38dc0Xo2onha20ZSgQAdo3QtRPW2gKAyVlIQ45CFwAwbw2/UW3V\n9kemIIgt2tA1dEG3YRbtJwQA89OwIHb5dS/d7th9l1zdo5yRLdpIYdgQAOhpr1FOqqqzq+prVbWx\nqi4a8vpjquqvB6//U1UtnfXamwbHv1ZVLxhf6QAA02Onoauq9k5yRZJfT3J8kpdU1fHbnPa7SX7Q\nWvtPSf4kyR8Nrj0+yQVJnpLk7CTvGbwfAMCiMkqn67QkG1trd7bWfpzkmiTnbXPOeUn+YvD4g0l+\nrapqcPya1trDrbVvJNk4eD8AgEVllNB1eJK7Zj3fPDg29JzW2pYkP0xy6IjXAgAsePNiIn1VrUyy\ncvD0gar6Woc/9rAk3+vw5ywWPs/x85mOl89z/Hym4+czHaO69K96fJ5Hj3riKKHr7iRHznp+xODY\nsHM2V9U+SQ5Ocu+I16a1tibJmlGLHoeqWt9aW97zz1zIfJ7j5zMdL5/n+PlMx89nOl7z7fMcZXhx\nXZJjqmpZVe2XmYnxa7c5Z22Slw8evzjJP7bW2uD4BYO7G5clOSbJF8ZTOgDA9Nhpp6u1tqWqLkzy\niSR7J7mytXZrVV2cZH1rbW2S9yf5H1W1Mcn3MxPMMjjvA0k2JNmS5LWttZ/O0dcCADBvjTSnq7V2\nbZJrtzn21lmPH0py/g6uvTTJpXtQ41zpOpy5CPg8x89nOl4+z/HzmY6fz3S85tXnWTOjgAAAzKWR\nVqQHAGDPLOrQVVVvr6pbqurmqvpkVf3ypGuaZlX1jqr66uAz/X+r6pBJ1zTtqur8qrq1qh6pqnlz\nB8602dlWZuyaqrqyqr5bVV+ZdC0LQVUdWVXXV9WGwff76ydd07Srqv2r6gtV9aXBZ/p/TbqmZJEP\nL1bV41pr9w8evy7J8a21V024rKlVVc/PzJ2rW6rqj5KktfZfJ1zWVKuqJyd5JMn7kryxtbZ+wiVN\nncHWY19P8rzMLNC8LslLWmsbJlrYFKuqM5M8kOS/t9ZOmHQ9066qfinJL7XWvlhVByW5KcmL/B3d\nfYNdcQ5orT1QVfsm+WyS17fWbpxkXYu60/WzwDVwQJLFm0DHoLX2ycGOBElyY2bWZWMPtNZua631\nWCx4IRtlKzN2QWvt05m5U50xaK19u7X2xcHjHyW5LXZv2SNtxgODp/sOfk38Z/yiDl1JUlWXVtVd\nSX47yVt3dj4j+9+TfHzSRUBsR8YUqaqlSU5J8k+TrWT6VdXeVXVzku8m+fvW2sQ/0wUfuqrquqr6\nypBf5yVJa+0PW2tHJrkqyYWTrXb+29nnOTjnDzOzLttVk6t0eozymQILX1UdmORDSX5/m5EYdkNr\n7aettZMzM+pyWlVNfCh8Xuy9OJdaa88d8dSrMrMW2dvmsJypt7PPs6pekeQ/J/m1tpgnDO6CXfg7\nyu4ZaTsymKTBvKMPJbmqtfa3k65nIWmt3VdV1yc5O8lEb/5Y8J2uR1NVx8x6el6Sr06qloWgqs5O\n8n8mObe19uCk64GBUbYyg4kZTPp+f5LbWmvvnHQ9C0FVLfnZHfRV9R8ycyPNxH/GL/a7Fz+U5EmZ\nuTvsX5K8qrXmf8C7abAN1GMys9l5ktzobtA9U1W/keTdSZYkuS/Jza21F0y2qulTVf9bksvz/29l\nNh93yZgaVfVXSc5KcliS7yR5W2vt/RMtaopV1bOTfCbJlzPz8yhJ3jzYDYbdUFUnJfmLzHzP75Xk\nA621iydb1SIPXQAAvSzq4UUAgF6ELgCADoQuAIAOhC4AgA6ELgCADoQuYCpV1R9W1a1VdUtV3VxV\nT9/BeWdV1Q8H5/zs13Or6pCqek3vuoHFa8GvSA8sPFX1jMzsfHBqa+3hqjosyX6PcslnWmv/eZv3\nWJrkNUneM1d1Asym0wVMo19K8r3W2sNJ0lr7XmvtW1W1oqo+X1VfqqovVNVBj/Ieq5P8x0Hn6x1V\ndWBV/UNVfbGqvmzvS2DcLI4KTJ3BxsCfTfLYJNcl+esk/ysz23z8VmttXVU9LsmDSZ6d5MNJvjHr\nLX4zyU+TfLS1dsLgPfdJ8tjW2v2DztmNSY6xhygwLoYXganTWnugqp6W5Iwkz8lM6Lo0ybdba+sG\n59yfJDPb2u1wePHnDiX5b1V1Zma2Yjk8yS8m+dc5+0KARUXoAqZSa+2nSW5IckNVfTnJa/fwLX87\nM3tcPq219pOq2pRk/z18T4CtzOkCpk5VPamqjpl16OQktyX5papaMTjnoMGQ4Y78KMnsOV8HJ/nu\nIHA9J8nR464bWNx0uoBpdGCSd1fVIUm2JNmYZGWS/2dw/D8k+fckzx2cf0ZV3Tzr+ktaax+sqs9V\n1VeSfDzJHyX5yKBrtj4z88MAxsZEegCADgwvAgB0IHQBAHQgdAEAdCB0AQB0IHQBAHQgdAEAdCB0\nAQB0IHQBAHTw/wEv823S6ApA0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fc8adb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ix in range(len(x_names)):\n",
    "    plotting.plot_hists(data_x[:,0,ix],mc_x[:,0,ix],bins=100,range=[-3,3])\n",
    "    plt.xlabel(x_names[ix])\n",
    "    plt.show()\n",
    "\n",
    "for ic in range(len(c_names)):\n",
    "    plotting.plot_hists(data_c[:,0,ic],mc_c[:,0,ic],bins=100,range=[-3,3])\n",
    "    plt.xlabel(c_names[ic])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nmax = min(data_x.shape[0]//FRAC_DATA,mc_x.shape[0])\n",
    "\n",
    "data_x_train,data_x_test,data_c_train,data_c_test = cms.train_test_split(data_x[:nmax],data_c[:nmax])\n",
    "mc_x_train,mc_x_test,mc_c_train,mc_c_test = cms.train_test_split(mc_x[:nmax],mc_c[:nmax])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1752021\n"
     ]
    }
   ],
   "source": [
    "print(nmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/musella/jupyter/GAN/GAN/base.py:132: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  callbacks = [plotter], **kwargs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7742 - generator_loss: 1.3886 - generator_model_2_loss: 0.6945 - generator_D_256x5_output_loss: 0.6942 - discriminator_loss: 1.3855 - discriminator_model_2_loss: 0.6926 - discriminator_D_256x5_output_loss: 0.6929"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74ff782f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74ff072e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 69s - loss: 2.7742 - generator_loss: 1.3886 - generator_model_2_loss: 0.6945 - generator_D_256x5_output_loss: 0.6942 - discriminator_loss: 1.3855 - discriminator_model_2_loss: 0.6926 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 2/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 3/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7733 - generator_loss: 1.3873 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    - ETA: 8s - loss: 2.7733 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_\n",
      "Epoch 4/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7733 - generator_loss: 1.3874 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3859 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 5/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7733 - generator_loss: 1.3873 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931      ETA: 20s - loss: 2.7736 - generator_loss: 1\n",
      "Epoch 6/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7732 - generator_loss: 1.3872 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 7/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    - ETA: 5s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discrim\n",
      "Epoch 8/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7732 - generator_loss: 1.3872 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 9/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 10/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 11/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7516d2af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b751767ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 71s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 12/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7732 - generator_loss: 1.3872 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6935 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931      ETA: 11s - loss: 2.7733 - generator_loss: \n",
      "Epoch 13/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7732 - generator_loss: 1.3871 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6935 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 14/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7732 - generator_loss: 1.3872 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    - ETA: 0s - loss: 2.7732 - generator_loss: 1.3871 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_l\n",
      "Epoch 15/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7733 - generator_loss: 1.3872 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6935 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 16/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7733 - generator_loss: 1.3873 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 17/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7734 - generator_loss: 1.3873 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 18/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6932 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 19/60\n",
      "1314015/1314015 [==============================] - 18s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 20/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 21/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fed79518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b75179ff5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 75s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 22/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 23/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6937 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 24/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7735 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 25/60\n",
      "1314015/1314015 [==============================] - 15s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6934 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6932 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 26/60\n",
      "1314015/1314015 [==============================] - 15s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6932    \n",
      "Epoch 27/60\n",
      "1314015/1314015 [==============================] - 16s - loss: 2.7735 - generator_loss: 1.3874 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 28/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7737 - generator_loss: 1.3877 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 29/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7735 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930      ETA: 11s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6934 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6932 - d - ETA: 10s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_los\n",
      "Epoch 30/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 31/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931- ETA: 8s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fc94ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b750dea5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 73s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 32/60\n",
      "1314015/1314015 [==============================] - 21s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6939 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6928 - discriminator_D_256x5_output_loss: 0.6932    \n",
      "Epoch 33/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7735 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 34/60\n",
      "1314015/1314015 [==============================] - 17s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 35/60\n",
      "1314015/1314015 [==============================] - 18s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 36/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7735 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 37/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931      ETA\n",
      "Epoch 38/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 39/60\n",
      "1314015/1314015 [==============================] - 22s - loss: 2.7737 - generator_loss: 1.3877 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    - ETA: 7s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6939 - generator_D_256x5_output_loss: 0.6937\n",
      "Epoch 40/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 41/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7737 - generator_loss: 1.3877 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931"
     ]
    },
    {
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cPH4M+GJERHN8X2a+Cvw0ImabP+97vQi+24J1aZZ5M8c3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\nzABwxRVXzF+bnp4eQESSzlWnvYjB/YgljbbiogUXMkiSJJ271rMjnxP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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b751702fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7517558080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 75s - loss: 2.7737 - generator_loss: 1.3877 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 42/60\n",
      "1314015/1314015 [==============================] - 27s - loss: 2.7738 - generator_loss: 1.3878 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6942 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 43/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7738 - generator_loss: 1.3878 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 44/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 45/60\n",
      "1314015/1314015 [==============================] - 20s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6940 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 46/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6935 - generator_D_256x5_output_loss: 0.6941 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6929    \n",
      "Epoch 47/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 48/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6928 - discriminator_D_256x5_output_loss: 0.6932    - ETA: 4s - loss: 2.7737 - generator_loss: 1.3877 - generator_model_2_loss: 0.6939 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_m\n",
      "Epoch 49/60\n",
      "1314015/1314015 [==============================] - 25s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 50/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 51/60\n",
      "1310720/1314015 [============================>.] - ETA: 0s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6932"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b74fcc27c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b750dd70eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1314015/1314015 [==============================] - 78s - loss: 2.7734 - generator_loss: 1.3874 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6936 - discriminator_loss: 1.3861 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 52/60\n",
      "1314015/1314015 [==============================] - 19s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 53/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7735 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 54/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6931 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 55/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6931    - ETA: 1s - loss: 2.7737 - generator_loss: 1.3876 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5\n",
      "Epoch 56/60\n",
      "1314015/1314015 [==============================] - 25s - loss: 2.7736 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930      ETA: 14s \n",
      "Epoch 57/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7735 - generator_loss: 1.3875 - generator_model_2_loss: 0.6936 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n",
      "Epoch 58/60\n",
      "1314015/1314015 [==============================] - 24s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6939 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    - ETA: 8s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6938 - generator_D_256x5_output_loss: 0\n",
      "Epoch 59/60\n",
      "1314015/1314015 [==============================] - 23s - loss: 2.7736 - generator_loss: 1.3876 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6929 - discriminator_D_256x5_output_loss: 0.6931    \n",
      "Epoch 60/60\n",
      "1314015/1314015 [==============================] - 21s - loss: 2.7735 - generator_loss: 1.3874 - generator_model_2_loss: 0.6937 - generator_D_256x5_output_loss: 0.6938 - discriminator_loss: 1.3860 - discriminator_model_2_loss: 0.6930 - discriminator_D_256x5_output_loss: 0.6930    \n"
     ]
    }
   ],
   "source": [
    "reload(base)\n",
    "\n",
    "do = dict(\n",
    "    x_train=data_x_train,\n",
    "    z_train=mc_x_train,\n",
    "    c_x_train=data_c_train,\n",
    "    c_z_train=mc_c_train,\n",
    "          \n",
    "    x_test=data_x_test,\n",
    "    z_test=mc_x_test,\n",
    "    c_x_test=data_c_test,\n",
    "    c_z_test=mc_c_test,\n",
    "    \n",
    "    n_epochs=EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    plot_every=PLOT_EVERY,\n",
    ")\n",
    "\n",
    "base.MyGAN.adversarial_fit(gan,**do)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mc_x_morphed = gan.get_generator().predict([mc_c_test,mc_x_test])[1]\n",
    "\n",
    "data_p = gan.get_discriminator().predict([data_c_test,data_x_test])\n",
    "mc_p   = gan.get_discriminator().predict([mc_c_test,mc_x_morphed])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s7zvqG23yjuql3YBBc44F9P7RJun4QtOH2vy+xv7i8Lz3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\nSZIkSeq6DrcTdYLo8LKArp4xsUiSJEmSJGkha3Y+2vb+M7s3lBSJJEm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b7516f75470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b750d927048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reload(plotting)\n",
    "\n",
    "data_quantiles = np.percentile(data_c_test,[0,5,20,40,60,80,95,100])\n",
    "\n",
    "# plotting.plot_summary_cond(data_x_test,data_c_test,mc_x_test,mc_c_test,mc_x_test,data_p,mc_p)\n",
    "# plotting.plot_summary_cond(data_x_test,data_c_test,mc_x_morphed,mc_c_test,mc_x_test,data_p,mc_p)\n",
    "plotting.plot_summary_cond(data_x_test,data_c_test,mc_x_morphed,mc_c_test,mc_x_test,data_p,mc_p,\n",
    "                           do_slices=True,c_bounds=data_quantiles,saveas='gan_test1.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}