toy_1D_conditional_wgan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "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': {'loss': 'wgan_loss',\n",
       "  'opt_kwargs': {'lr': 0.0001},\n",
       "  'optimizer': 'RMSprop'},\n",
       " 'BATCH_SIZE': 4096,\n",
       " 'DM_OPTS': {'loss': 'wgan_loss',\n",
       "  'opt_kwargs': {'lr': 0.0001},\n",
       "  'optimizer': 'RMSprop'},\n",
       " 'D_OPTS': {'activation': None,\n",
       "  'clip_weights': 0.02,\n",
       "  'kernel_sizes': [256, 256, 256, 256, 256],\n",
       "  'name': 'D_256x5'},\n",
       " 'EPOCHS': 200,\n",
       " 'GAN_TARGETS': 'gan_targets_hinge',\n",
       " 'G_OPTS': {'do_weight_reg': 0.0002,\n",
       "  'kernel_sizes': [32, 64, 128, 256],\n",
       "  'name': 'G_64x5'},\n",
       " 'LOSS': 'wgan_loss',\n",
       " 'PLOT_EVERY': 5,\n",
       " 'SCHEDULE': [0, 0, 1]}"
      ]
     },
     "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",
    "    g_opts=utils.param(dict(name=\"G_64x5\",kernel_sizes=[32,64,128,256],do_weight_reg=1e-2))\n",
    "    d_opts=utils.param(dict(name=\"D_256x5\",kernel_sizes=[256]*5,\n",
    "                        clip_weights=2.e-2,activation=None)) # weight clipping and no actication\n",
    "    dm_opts=utils.param(dict(optimizer=\"RMSprop\",opt_kwargs=dict(lr=0.0001)))#, decay=6e-6)))\n",
    "    am_opts=utils.param(dict(optimizer=\"RMSprop\",opt_kwargs=dict(lr=0.0001)))#, decay=6e-6)))\n",
    "    \n",
    "    epochs=utils.param(200)\n",
    "    batch_size=utils.param(4096)\n",
    "    plot_every=utils.param(5)\n",
    "    \n",
    "    # frac_data=utils.param(10)\n",
    "    \n",
    "    loss = \"wgan_loss\" # use WGAN loss \n",
    "    gan_targets = 'gan_targets_hinge' # hinge targets are 1, -1 instead of 0, 1\n",
    "    schedule = [0]*2+[1] # number of critic iterations per generators iteration\n",
    "    \n",
    "    monitor_dir = utils.param('log')\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",
    "DM_OPTS.update( {\"loss\":LOSS} )\n",
    "AM_OPTS.update( {\"loss\":LOSS} )\n",
    "notebook_parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import GAN.toy_datasets as toys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000000, 1, 1) (2000000, 1, 1) (2000000, 1, 1)\n",
      "(2000000, 1, 1) (2000000, 1, 1) (2000000, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "reload(toys)\n",
    "c_train,c_test,x_train,x_test,z_train,z_test =  toys.three_peaks_conditional_cube(2000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAJCCAYAAADp1TKRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5c759522b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAJCCAYAAAAC4omSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8oUKTMIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5c759de358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hexbin( c_train.ravel(), x_train.ravel() )\n",
    "plt.show()\n",
    "plotting.plot_hists(x_train.ravel(),z_train.ravel())#,range=[-4,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xz_shape = x_train.shape[1:]\n",
    "c_shape = c_train.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xz_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload(models)\n",
    "\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",
    "                     gan_targets=GAN_TARGETS\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x2b5c7d640400>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan.get_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WeightClip 0.02\n",
      "WeightClip 0.02\n",
      "WeightClip 0.02\n",
      "WeightClip 0.02\n",
      "WeightClip 0.02\n",
      "WeightClip 0.02\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<keras.engine.training.Model at 0x2b5c7d3b1e48>,\n",
       " <keras.engine.training.Model at 0x2b5c7d3b1940>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gan.get_discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wgan_loss\n"
     ]
    }
   ],
   "source": [
    "gan.adversarial_compile(loss=LOSS,schedule=SCHEDULE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_input (InputLayer)        (None, 1, 1)          0                                            \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_all_inputs (Concatenate)  (None, 1, 2)          0           G_64x5_c_input[0][0]             \n",
      "                                                                   G_64x5_input[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_dense (Dense)         (None, 1, 256)        768         G_64x5_all_inputs[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up1_activ (PReLU)         (None, 1, 256)        256         G_64x5_up1_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_dense (Dense)         (None, 1, 128)        32896       G_64x5_up1_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up2_activ (PReLU)         (None, 1, 128)        128         G_64x5_up2_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up3_dense (Dense)         (None, 1, 64)         8256        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, 32)         2080        G_64x5_up3_activ[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_up4_activ (PReLU)         (None, 1, 32)         32          G_64x5_up4_dense[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "G_64x5_output (Dense)            (None, 1, 1)          33          G_64x5_up4_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: 44,513\n",
      "Trainable params: 44,513\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan.get_generator().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# gan.get_discriminator().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# gan.am.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gan.dm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# gan.gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gan.gan.outputs, gan.gan.inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/users/musella/jupyter/GAN/GAN/base.py:179: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  callbacks = [checkpoint,csv,tensorboard,plotter], **kwargs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "calling WeightClip 0.02\n",
      "Epoch 1/201\n",
      "1499136/1500000 [============================>.] - ETA: 0s - loss: 0.1071 - discriminator_loss: 0.0493 - discriminator_model_2_loss: 0.0271 - discriminator_D_256x5_output_loss: -0.0313 - generator_loss: 0.0578 - generator_model_3_loss: -0.0271 - generator_D_256x5_output_loss: 0.0313           [-2.1707997829620935, 2.0175459014303696]\n",
      "[-1.414192372560501, 2.0102448123566781]\n",
      "[-0.95023128986358651, 1.9995040033785139]\n",
      "[-0.91602052166432557, 2.2017407178878763]\n",
      "[-0.91650795491705539, 2.4873297572135913]\n",
      "[-0.92755347325415416, 2.6421164274215698]\n",
      "[-0.92191305341739294, 2.7325156927108756]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8kAAAEyCAYAAAB53+HoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d05dab908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.12146046012639999, 0.19283143132925021]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAACqCAYAAADfliX/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naYJxAAAAAAAAADpNMA4AAAAAAABAp/1/MFwKHX2pNBAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d05f6e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500000/1500000 [==============================] - 68s - loss: 0.1071 - discriminator_loss: 0.0493 - discriminator_model_2_loss: 0.0271 - discriminator_D_256x5_output_loss: -0.0313 - generator_loss: 0.0578 - generator_model_3_loss: -0.0271 - generator_D_256x5_output_loss: 0.0313    \n",
      "Epoch 2/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0919 - discriminator_loss: 0.0329 - discriminator_model_2_loss: 0.1340 - discriminator_D_256x5_output_loss: -0.1471 - generator_loss: 0.0590 - generator_model_3_loss: -0.1340 - generator_D_256x5_output_loss: 0.1471      ETA: 17s - loss: 0.0983 - discriminator_loss: 0.0451 - discriminator_model_2_loss: 0.0611 - discrim\n",
      "Epoch 3/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0817 - discriminator_loss: 0.0309 - discriminator_model_2_loss: 0.3779 - discriminator_D_256x5_output_loss: -0.3878 - generator_loss: 0.0508 - generator_model_3_loss: -0.3779 - generator_D_256x5_output_loss: 0.3878    - ETA: 7s - loss: 0.0835 - discriminator_loss: 0.0297 - discriminator_model_2_loss: 0.3708 - discriminator_D_\n",
      "Epoch 4/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0734 - discriminator_loss: 0.0289 - discriminator_model_2_loss: 0.3931 - discriminator_D_256x5_output_loss: -0.4008 - generator_loss: 0.0444 - generator_model_3_loss: -0.3931 - generator_D_256x5_output_loss: 0.4008    \n",
      "Epoch 5/201\n",
      "1500000/1500000 [==============================] - 19s - loss: 0.0670 - discriminator_loss: 0.0290 - discriminator_model_2_loss: 0.2754 - discriminator_D_256x5_output_loss: -0.2798 - generator_loss: 0.0379 - generator_model_3_loss: -0.2754 - generator_D_256x5_output_loss: 0.2798    \n",
      "Epoch 6/201\n",
      "1495040/1500000 [============================>.] - ETA: 0s - loss: 0.0618 - discriminator_loss: 0.0274 - discriminator_model_2_loss: 0.2274 - discriminator_D_256x5_output_loss: -0.2309 - generator_loss: 0.0344 - generator_model_3_loss: -0.2274 - generator_D_256x5_output_loss: 0.2309[-2.2633331060409549, 2.0175459014303696]\n",
      "[-1.4161050796508792, 2.0102448123566781]\n",
      "[-0.93014208780302732, 2.0282906174659674]\n",
      "[-0.91602052166432557, 2.0732582211494406]\n",
      "[-0.91650795491705539, 2.0927091002464269]\n",
      "[-0.92755347325415416, 2.2680769681930557]\n",
      "[-0.92191305341739294, 2.573496944727184]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8kAAAEyCAYAAAB53+HoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d05922b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.22567487359046934, 0.15353203564882273]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8AAAACqCAYAAADSiFW4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d058ff908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500000/1500000 [==============================] - 68s - loss: 0.0618 - discriminator_loss: 0.0274 - discriminator_model_2_loss: 0.2272 - discriminator_D_256x5_output_loss: -0.2307 - generator_loss: 0.0344 - generator_model_3_loss: -0.2272 - generator_D_256x5_output_loss: 0.2307    \n",
      "Epoch 7/201\n",
      "1500000/1500000 [==============================] - 19s - loss: 0.0571 - discriminator_loss: 0.0244 - discriminator_model_2_loss: 0.0987 - discriminator_D_256x5_output_loss: -0.1028 - generator_loss: 0.0327 - generator_model_3_loss: -0.0987 - generator_D_256x5_output_loss: 0.1028    \n",
      "Epoch 8/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0535 - discriminator_loss: 0.0225 - discriminator_model_2_loss: 0.0700 - discriminator_D_256x5_output_loss: -0.0743 - generator_loss: 0.0310 - generator_model_3_loss: -0.0700 - generator_D_256x5_output_loss: 0.0743    \n",
      "Epoch 9/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0504 - discriminator_loss: 0.0206 - discriminator_model_2_loss: 0.0469 - discriminator_D_256x5_output_loss: -0.0515 - generator_loss: 0.0298 - generator_model_3_loss: -0.0469 - generator_D_256x5_output_loss: 0.0515    \n",
      "Epoch 10/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0480 - discriminator_loss: 0.0192 - discriminator_model_2_loss: 0.0187 - discriminator_D_256x5_output_loss: -0.0235 - generator_loss: 0.0288 - generator_model_3_loss: -0.0187 - generator_D_256x5_output_loss: 0.0235    \n",
      "Epoch 11/201\n",
      "1499136/1500000 [============================>.] - ETA: 0s - loss: 0.0460 - discriminator_loss: 0.0179 - discriminator_model_2_loss: 0.0011 - discriminator_D_256x5_output_loss: -0.0062 - generator_loss: 0.0282 - generator_model_3_loss: -0.0011 - generator_D_256x5_output_loss: 0.0062        [-2.2086803197860712, 2.0175459014303696]\n",
      "[-1.3325912714004517, 2.0102448123566781]\n",
      "[-0.93014208780302732, 1.9995040033785139]\n",
      "[-0.91602052166432557, 2.0682575464248645]\n",
      "[-0.91650795491705539, 2.1943865060806269]\n",
      "[-0.92755347325415416, 2.3464674711227422]\n",
      "[-0.92191305341739294, 2.573496944727184]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8kAAAEyCAYAAAB53+HoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d06b98048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.1023152686655521, 0.14244886413216587]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB9AAAACqCAYAAAD1JtRQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d06a869e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500000/1500000 [==============================] - 67s - loss: 0.0460 - discriminator_loss: 0.0179 - discriminator_model_2_loss: 0.0011 - discriminator_D_256x5_output_loss: -0.0062 - generator_loss: 0.0282 - generator_model_3_loss: -0.0011 - generator_D_256x5_output_loss: 0.0062    \n",
      "Epoch 12/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0442 - discriminator_loss: 0.0168 - discriminator_model_2_loss: -0.0225 - discriminator_D_256x5_output_loss: 0.0173 - generator_loss: 0.0274 - generator_model_3_loss: 0.0225 - generator_D_256x5_output_loss: -0.0173    \n",
      "Epoch 13/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0427 - discriminator_loss: 0.0162 - discriminator_model_2_loss: -0.0217 - discriminator_D_256x5_output_loss: 0.0165 - generator_loss: 0.0266 - generator_model_3_loss: 0.0217 - generator_D_256x5_output_loss: -0.0165    \n",
      "Epoch 14/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0415 - discriminator_loss: 0.0157 - discriminator_model_2_loss: -0.0227 - discriminator_D_256x5_output_loss: 0.0177 - generator_loss: 0.0258 - generator_model_3_loss: 0.0227 - generator_D_256x5_output_loss: -0.0177    \n",
      "Epoch 15/201\n",
      "1500000/1500000 [==============================] - 19s - loss: 0.0405 - discriminator_loss: 0.0157 - discriminator_model_2_loss: -0.0339 - discriminator_D_256x5_output_loss: 0.0294 - generator_loss: 0.0248 - generator_model_3_loss: 0.0339 - generator_D_256x5_output_loss: -0.0294    \n",
      "Epoch 16/201\n",
      "1499136/1500000 [============================>.] - ETA: 0s - loss: 0.0397 - discriminator_loss: 0.0158 - discriminator_model_2_loss: -0.0176 - discriminator_D_256x5_output_loss: 0.0136 - generator_loss: 0.0238 - generator_model_3_loss: 0.0176 - generator_D_256x5_output_loss: -0.0136[-2.1707997829620935, 2.0175459014303696]\n",
      "[-1.2633458673954012, 2.0102448123566781]\n",
      "[-0.93014208780302732, 1.9995040033785139]\n",
      "[-0.91602052166432557, 2.0152273994879035]\n",
      "[-0.91650795491705539, 2.0307035326957674]\n",
      "[-0.92755347325415416, 2.2584376811981199]\n",
      "[-0.92191305341739294, 2.573496944727184]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8kAAAEyCAYAAAB53+HoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5d39bb8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.026243185205385093, 0.2353737264871596]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAACqCAYAAADfliX/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQEf7/2JB+QIS0AlVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5caaa3e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500000/1500000 [==============================] - 67s - loss: 0.0397 - discriminator_loss: 0.0158 - discriminator_model_2_loss: -0.0177 - discriminator_D_256x5_output_loss: 0.0137 - generator_loss: 0.0238 - generator_model_3_loss: 0.0177 - generator_D_256x5_output_loss: -0.0137    \n",
      "Epoch 17/201\n",
      "1500000/1500000 [==============================] - 18s - loss: 0.0389 - discriminator_loss: 0.0161 - discriminator_model_2_loss: -0.0140 - discriminator_D_256x5_output_loss: 0.0106 - generator_loss: 0.0228 - generator_model_3_loss: 0.0140 - generator_D_256x5_output_loss: -0.0106    \n",
      "Epoch 18/201\n",
      " 688128/1500000 [============>.................] - ETA: 10s - loss: 0.0384 - discriminator_loss: 0.0164 - discriminator_model_2_loss: -0.0044 - discriminator_D_256x5_output_loss: 0.0015 - generator_loss: 0.0221 - generator_model_3_loss: 0.0044 - generator_D_256x5_output_loss: -0.0015"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-2f4294fcbe9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m )\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMyGAN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madversarial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/jupyter/GAN/GAN/base.py\u001b[0m in \u001b[0;36madversarial_fit\u001b[0;34m(self, x_train, z_train, c_x_train, c_z_train, w_x_train, w_z_train, x_test, z_test, c_x_test, c_z_test, w_x_test, w_z_test, batch_size, n_epochs, plot_every, monitor_dir, checkpoint_every, **kwargs)\u001b[0m\n\u001b[1;32m    177\u001b[0m         self.model.fit( train_x, train_y,  sample_weight=train_w,\n\u001b[1;32m    178\u001b[0m                         \u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m                         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtensorboard\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplotter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m         )\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/my-env/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1596\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1598\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m     def evaluate(self, x, y,\n",
      "\u001b[0;32m~/my-env/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/my-env/lib/python3.5/site-packages/keras_adversarial-0.0.3-py3.5.egg/keras_adversarial/adversarial_optimizers.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(_inputs)\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/my-env/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2271\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2272\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2273\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2274\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/apps/dom/UES/jenkins/6.0.UP04/gpu/easybuild/software/TensorFlow/1.2.1-CrayGNU-17.08-cuda-8.0-python3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/apps/dom/UES/jenkins/6.0.UP04/gpu/easybuild/software/TensorFlow/1.2.1-CrayGNU-17.08-cuda-8.0-python3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/apps/dom/UES/jenkins/6.0.UP04/gpu/easybuild/software/TensorFlow/1.2.1-CrayGNU-17.08-cuda-8.0-python3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/apps/dom/UES/jenkins/6.0.UP04/gpu/easybuild/software/TensorFlow/1.2.1-CrayGNU-17.08-cuda-8.0-python3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/apps/dom/UES/jenkins/6.0.UP04/gpu/easybuild/software/TensorFlow/1.2.1-CrayGNU-17.08-cuda-8.0-python3/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "reload(base)\n",
    "\n",
    "initial_epoch = 0\n",
    "if hasattr(gan.model,\"history\"):\n",
    "    initial_epoch = gan.model.history.epoch[-1]\n",
    "\n",
    "do = dict(\n",
    "    x_train=x_train,\n",
    "    z_train=z_train,\n",
    "    c_x_train=c_train,\n",
    "    c_z_train=c_train,\n",
    "          \n",
    "    x_test=x_test,\n",
    "    z_test=z_test,\n",
    "    c_x_test=c_test,\n",
    "    c_z_test=c_test,\n",
    "    \n",
    "    n_epochs=EPOCHS + initial_epoch +1,\n",
    "    initial_epoch=initial_epoch,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    plot_every=PLOT_EVERY,\n",
    "    \n",
    "     monitor_dir=MONITOR_DIR\n",
    ")\n",
    "\n",
    "base.MyGAN.adversarial_fit(gan,**do)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_morphed = gan.get_generator().predict([c_test,z_test])[1]\n",
    "\n",
    "x_p = gan.get_discriminator()[1].predict([c_test,x_test])\n",
    "z_p   = gan.get_discriminator()[1].predict([c_test,x_morphed])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "reload(plotting)\n",
    "\n",
    "quantiles = np.percentile(c_test,[0,5,20,40,60,80,95,100])\n",
    "\n",
    "plotting.plot_summary_cond(x_test,c_test,x_morphed,c_test,z_test,x_p,z_p,\n",
    "                           do_slices=False,c_bounds=quantiles)\n",
    "plotting.plot_summary_cond(x_test,c_test,x_morphed,c_test,z_test,x_p,z_p,\n",
    "                           do_slices=True,c_bounds=quantiles)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "gan.model.train_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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
}