IML_GAN_tutorial_jetimages.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Advanced HEPML Tutorial \n",
    "**2nd IML Workshop - CERN - Wed. April 11, 2018**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Today you will learn:\n",
    "* what _jet-images_ are, and how they differ from natural images\n",
    "* to build and train a Wasserstein-GAN\n",
    "* to modify the architecture to improve its stability and functionality\n",
    "\n",
    "Much of today's tutorial is taken from https://github.com/hep-lbdl/adversarial-jets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<p>In this tutorial, we will be using: <img src=\"https://cdn-images-1.medium.com/max/1200/1*jcZLpgh3gppeFFgcpFSP0w.jpeg\" width=\"300\" align=\"top\">\n",
    "\n",
    " > a deep learning framework that puts Python first.\n",
    "\n",
    "<div align=\"center\">- http://pytorch.org/</div>\n",
    "\n",
    "Follow the instructions on the [PyTorch website](http://pytorch.org/) to install on your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Dataset**:\n",
    "\n",
    "We will use the jet-images dataset that our group produced for our LAGAN work [[Comput Softw Big Sci (2017) 1: 4](https://link.springer.com/article/10.1007/s41781-017-0004-6)]:\n",
    "\n",
    "[![DOI](https://zenodo.org/badge/DOI/10.17632/4r4v785rgx.1.svg)](https://doi.org/10.17632/4r4v785rgx.1)\n",
    "<a href=\"https://zenodo.org/record/269622#.WgZFPRNSyRs\"><img src=\"https://github.com/YaleATLAS/CERNDeepLearningTutorial/raw/master/images/zenodo.jpg\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data already exists!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import wget\n",
    "\n",
    "DATAFILE = 'jet-images_Mass60-100_pT250-300_R1.25_Pix25.hdf5'\n",
    "DATAURL = 'https://zenodo.org/record/269622/files/{}'.format(DATAFILE)\n",
    "\n",
    "# download dataset unless it's already present\n",
    "if not os.path.exists(DATAFILE):\n",
    "    print('Downloading {}'.format(DATAURL))\n",
    "    wget.download(DATAURL)\n",
    "else:\n",
    "    print('Data already exists!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Pythia is one of the standard generators used in High Energy Physics to simulate the shower evolution in proton collisions.\n",
    "\n",
    "**Jets** are the observable result of quarks and gluons scattering at high energy. A collimated stream of\n",
    "protons and other hadrons forms in the direction of the initiating quark or gluon. Clusters of such\n",
    "particles are called jets. A **jet image** is a two-dimensional representation of the radiation pattern within\n",
    "a jet: the distribution of the locations and energies of the jet’s constituent particles.\n",
    "\n",
    "In this exercise, we won't model the detector response, but we will simply limit ourselves to discretizing the jet energy depositions over a grid of 25x25 cells with side 0.1 in the eta and phi coordinates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Open dataset using `h5py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'image',\n",
       " u'jet_delta_R',\n",
       " u'jet_eta',\n",
       " u'jet_mass',\n",
       " u'jet_phi',\n",
       " u'jet_pt',\n",
       " u'signal',\n",
       " u'tau_1',\n",
       " u'tau_2',\n",
       " u'tau_21',\n",
       " u'tau_3',\n",
       " u'tau_32']"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = h5py.File(DATAFILE)\n",
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "images = f['image'][:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = f['signal'][:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((10000, 25, 25), (10000,))\n"
     ]
    }
   ],
   "source": [
    "print(images.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Dataset exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import LogNorm, Normalize\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_jet_image(content, vmin=1e-20, vmax=300, title=''):\n",
    "    '''\n",
    "    Function to help you visualize a jet image on a log scale\n",
    "    Args:\n",
    "    -----\n",
    "       content : numpy array of dimensions 25x25, first arg to imshow, content of the image\n",
    "                 e.g.: generated_images.mean(axis=0) --> the average generated image\n",
    "                       real_images.mean(axis=0) --> the average Pythia image\n",
    "                       generated_images[aux_out == 1].mean(axis=0) --> the average generated image labeled as real by the discriminator \n",
    "                       etc...\n",
    "       vmin : (default = 1e-6) float, lower bound of the pixel intensity scale before saturation\n",
    "       vmax : (default = 300) float, upper bound of the pixel intensity scale before saturation\n",
    "       title : (default = '') string, title of the plot, to be displayed on top of the image\n",
    "    '''\n",
    "    fig, ax = plt.subplots(figsize=(7, 6))\n",
    "    extent=[-1.25, 1.25, -1.25, 1.25]\n",
    "    im = ax.imshow(content, interpolation='nearest', norm=LogNorm(vmin=vmin, vmax=vmax), extent=extent)\n",
    "    cbar = plt.colorbar(im, fraction=0.05, pad=0.05)\n",
    "    cbar.set_label(r'Pixel $p_T$ (GeV)', y=0.85)\n",
    "    plt.xlabel(r'[Transformed] Pseudorapidity $(\\eta)$')\n",
    "    plt.ylabel(r'[Transformed] Azimuthal Angle $(\\phi)$')\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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9tMR7UyXT+ZLmUQ3aPW5U/YOBK2z/UNILgc8B72w66PGUJN+zgGslfYtqpPMbgK9JWpdM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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bdc7450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_jet_image(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# plot a bunch of jet images\n",
    "def plot_random_jetimages(images, njets=6):\n",
    "    f, axarr = plt.subplots(1, njets, figsize=(15, 5))\n",
    "    jets = np.random.randint(low=0, high=images.shape[0], size=njets)\n",
    "\n",
    "    for i, sel in enumerate(jets):\n",
    "        def _plot_jet(img, vmin=1e-3, vmax=300):\n",
    "            im = axarr[i].imshow(img,\n",
    "                                    interpolation='nearest',\n",
    "                                    norm=LogNorm(vmin=1e-20, vmax=300)\n",
    "            )\n",
    "            axarr[i].tick_params(axis='both', which='both', bottom=False, top=False,\n",
    "                                 left=False, right=False, labelbottom=False,\n",
    "                                 labelleft=False)\n",
    "            return im\n",
    "        im = _plot_jet(images[sel])\n",
    "\n",
    "    cax = f.add_axes([0.93, 0.3, 0.03, 0.4])\n",
    "    cb = f.colorbar(im, cax=cax)\n",
    "    cb.set_label(r'Pixel $p_T$ (GeV)', y=0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xe7a7n99OXi+pvsjb6RSKZgAAAABIkATfnp1I3b5oXnHKjzucc/WzU4PsyD5h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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef0f350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_jetimages(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tQY8FWVSTwOEH1wTZPq/MGP95b89gztrNpwdZl4p4jVI37u4Rax5aX/b/7+dv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\nAAA04f8BYnLmdpTIehYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195f26d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_jetimages(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "At an individual level, iet images don't yield themselves to a global understanding of the data distribution. A nice way of visualizing jet images is to investigate the mean jet image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gQaoudTdkO0W31VFg33Up/frACR0rk9ts1JlWyANgUXd6bCOL7LcCBQWKFCHo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\n99cgFjMzs7ZQyc+/7SQ9NMD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12598f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n5NuGvJTg+0JEzGpYTczMzNLMBraStAVZ0D0MOGJwq1SdlOB7mqQLgZvJ8vkC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258f0810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J95ctny7pEUnzJJ0wwGZ2AC6PiKOAnQodaI2kZDXqGekKoBOYCPh+r5lZu2vM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\nAPcBY4F1gAfIMiG9bN8l+9iB7EfmncA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1259e5ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_jet_image(np.mean(images, axis=0), title='Average Image')\n",
    "plot_jet_image(np.mean(images[y == 1], axis=0), title=\"Average W' Image\")\n",
    "plot_jet_image(np.mean(images[y == 0], axis=0), title=\"Average QCD Image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Many differences are imperceptible at this level! Let's dive a bit deeper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_diff_jet_image(\n",
    "                    content,\n",
    "                    extr=None,\n",
    "                    title='',\n",
    "                    cmap=matplotlib.cm.PRGn_r):\n",
    "    '''\n",
    "    Function to help you visualize the difference between two sets of jet images on a linear scale\n",
    "    Args:\n",
    "    -----\n",
    "       content : numpy array of dimensions 25x25, first arg to imshow, content of the image\n",
    "                 e.g.: generated_images.mean(axis=0) - real_images.mean(axis=0) --> difference between avg generated and avg Pythia image\n",
    "                       etc...\n",
    "       extr : (default = None) float, magnitude of the upper and lower bounds of the pixel intensity scale before saturation (symmetric around 0)\n",
    "       title : (default = '') string, title of the plot, to be displayed on top of the image\n",
    "       cmap : (default = matplotlib.cm.PRGn_r) matplotlib colormap, ideally white in the middle\n",
    "    '''\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    extent=[-1.25, 1.25, -1.25, 1.25]\n",
    "    if extr == None:\n",
    "        extr = max( abs(content.min()), abs(content.max()))\n",
    "    im = ax.imshow(content,\n",
    "                   interpolation='nearest', norm=Normalize(vmin=-extr, vmax=+extr), extent=extent,\n",
    "                   cmap=cmap)\n",
    "    plt.colorbar(im, fraction=0.05, pad=0.05)\n",
    "    plt.xlabel(r'[Transformed] Pseudorapidity $(\\eta)$')\n",
    "    plt.ylabel(r'[Transformed] Azimuthal Angle $(\\phi)$')\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GmEQa+uYhSWuTRrZ+Xm6i+wPpnsodETErrx/NdCetjnUFabihAyVNlLTLkLh/DOwvaVsl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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12554d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -- plot the difference between the mean W' image and the mean QCD image\n",
    "# In this case, the blue pixels are more strongly activated in QCD images than in W' images.\n",
    "# The red pixels are more strongly activated in W' images than QCD images.\n",
    "plot_diff_jet_image(\n",
    "    images[y == 1].mean(axis=0) - images[y == 0].mean(axis=0),\n",
    "    title=\"Difference between average W' image \\n and average QCD image\",\n",
    "    cmap=matplotlib.cm.seismic\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Domain-specific issues with training GANs for jet images\n",
    "\n",
    "Most off the shelf GAN implementations will do ok, but some there are some important distinctions between natural images (the domain on which GAN usage exploded) and scientific images such as jet images. Understanding these distinctions can make your models work *significantly* better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "1) Sparsity\n",
    "\n",
    "We define sparsity (of in this case a matrix $X\\in\\mathbb{R}^{m \\times n}$) to be $$\\mathsf{sparsity}(X) = \\frac{1}{mn}\\sum_{i=1}^{m}\\sum_{j=1}^{m} \\mathbb{I}_{X_{i, j} \\neq 0}$$\n",
    "\n",
    "Another way to think about this is an \"occupancy\" rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only approximately 11% of pixels are non zero.\n"
     ]
    }
   ],
   "source": [
    "print('Only approximately {:.0f}% of pixels are non zero.'.format(\n",
    "    100 * float(np.sum(images != 0))/np.prod(images.shape)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "2) Dynamic range\n",
    "\n",
    "Images from HEP, due to the nature of the interactions that produce them, vary across **many** orders of magniture, whereas natural images only span three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel intensities range from 1.34e-20 to 273.79 GeV.\n"
     ]
    }
   ],
   "source": [
    "print('Pixel intensities range from {:.2e} to {:.2f} GeV.'.format(\n",
    "    images[images !=0].min(), images.max()\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AO4FnJ3ljVb1rqQ8n2QpsBZibm+s6q1bg5SZp7Ziam/Kq6u+AM1u8bxuwDWB+fr66ziVJ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\nJA31/wGrbiZvuhu4ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1270b7450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(images.ravel(), bins=np.logspace(-21, 3, 50))\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1271dffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(images.ravel(), bins=np.linspace(0, 300, 50))\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "3) Pixel representation is physically meaningful. Pixel intensities and positions define jet properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this application, we cut out the region of space very close to the beam axis ($\\vert\\eta\\vert \\gt 1.25$) and the region of $\\vert\\phi\\vert > 1.25$. We divide the remaining space in a grid with pixels of size $[0.1 \\times 0.1]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "grid = 0.5 * (np.linspace(-1.25, 1.25, 26)[:-1] + np.linspace(-1.25, 1.25, 26)[1:])\n",
    "eta = np.tile(grid, (25, 1))\n",
    "phi = np.tile(grid[::-1].reshape(-1, 1), (1, 25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We would like to be able to calculate observables from a generated images, so we define discretized versions of $m$, $p_T$, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def discrete_mass(jet_image):\n",
    "    '''\n",
    "    Calculates the jet mass from a pixelated jet image set\n",
    "    Args:\n",
    "    -----\n",
    "        jet_image: numpy ndarray of dim (n_images, 25, 25)\n",
    "    Returns:\n",
    "    --------\n",
    "        M: float, jet mass\n",
    "    '''\n",
    "    Px = np.sum(jet_image * np.cos(phi), axis=(1, 2))\n",
    "    Py = np.sum(jet_image * np.sin(phi), axis=(1, 2))\n",
    "\n",
    "    Pz = np.sum(jet_image * np.sinh(eta), axis=(1, 2))\n",
    "    E = np.sum(jet_image * np.cosh(eta), axis=(1, 2))\n",
    "\n",
    "    PT2 = np.square(Px) + np.square(Py)\n",
    "    M2 = np.square(E) - (PT2 + np.square(Pz))\n",
    "    M = np.sqrt(M2)\n",
    "    return M\n",
    "\n",
    "def discrete_pt(jet_image):\n",
    "    '''\n",
    "    Calculates the jet transverse momentum from a pixelated jet image set\n",
    "    Args:\n",
    "    -----\n",
    "        jet_image: numpy ndarray of dim (n_images, 25, 25)\n",
    "    Returns:\n",
    "    --------\n",
    "        float, jet transverse momentum\n",
    "    '''\n",
    "    Px = np.sum(jet_image * np.cos(phi), axis=(1, 2))\n",
    "    Py = np.sum(jet_image * np.sin(phi), axis=(1, 2))\n",
    "    return np.sqrt(np.square(Px) + np.square(Py))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11ce35950>"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hlQvJzMxqWZ4xjj9IGgL8EHiI7I6qSysalZmZ1aw8d1V9L328XtIfgIER8VplwzIzs1qV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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125970c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "bins = np.linspace(50, 110, 20)\n",
    "_ = plt.hist(discrete_mass(images[y == 1]), \n",
    "             bins=bins, histtype='step', label=r\"$W' \\rightarrow WZ$\", color='red')\n",
    "\n",
    "_ = plt.hist(discrete_mass(images[y == 0]), \n",
    "             bins=bins, histtype='step', label=r'QCD dijets', color='blue')\n",
    "\n",
    "plt.xlabel(r'Discretized $m$ of Jet Image')\n",
    "plt.ylabel(r'Units normalized to unit area')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1203807d0>"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HiJgWEeMjYvywYcNKDdbMejmp+6/m5npHX7paJ4hZwOQ0PRmYWVF+ZLqbaRfg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f81d590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "bins = np.linspace(230, 310, 20)\n",
    "_ = plt.hist(discrete_pt(images[y == 1]), \n",
    "             bins=bins, histtype='step', label=r\"$W' \\rightarrow WZ$\", color='red')\n",
    "\n",
    "_ = plt.hist(discrete_pt(images[y == 0]), \n",
    "             bins=bins, histtype='step', label=r'QCD dijets', color='blue')\n",
    "\n",
    "plt.xlabel(r'Discretized $m$ of Jet Image')\n",
    "plt.ylabel(r'Units normalized to unit area')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### PyTorch Dataset and DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "PyTorch takes care of lots of data handling for you through the convenient `DataLoader` class. A `DataLoader` allows you to iterate through your dataset and yield batches from it with a few niceties attached. These may not be necessary in this application, and in fact they might be kind of an overkill, but I think everyone should be familiar with them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, you have to create your own custom dataset class that inherits from the abstract PyTorch `Dataset` class. All custom datasets should subclass it, and override `__len__`, that provides the size of the dataset, and `__getitem__`, supporting integer indexing in range from `0` to `len(self)` exclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The beauty of the PyTorch `DataLoader` class is that certain complexities such as batching and multithreaded loading are *abstracted away*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<img src=\"/Users/mp744/Documents/MachineLearning/presentations/images/dataloader.jpg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's remind ourselves of the keys present in the `h5py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'image',\n",
       " u'jet_delta_R',\n",
       " u'jet_eta',\n",
       " u'jet_mass',\n",
       " u'jet_phi',\n",
       " u'jet_pt',\n",
       " u'signal',\n",
       " u'tau_1',\n",
       " u'tau_2',\n",
       " u'tau_21',\n",
       " u'tau_3',\n",
       " u'tau_32']"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class JetSet(Dataset):\n",
    "    \"\"\"\n",
    "    Dataset of jet images.\n",
    "    \"\"\"\n",
    "    def __init__(self, zenodo_link=None, path=None, nb_jets=None):\n",
    "        if not ((zenodo_link is None) ^ (path is None)):\n",
    "            raise ValueError('Exactly *one* of Zenodo link or '\n",
    "                             'filesystem path must be specified')\n",
    "        if zenodo_link is not None:\n",
    "            # download dataset unless it's already present\n",
    "            filename = zenodo_link.split(\"/\")[-1]\n",
    "            if not os.path.exists(filename):\n",
    "                wget.download(zenodo_link)\n",
    "        else:\n",
    "            filename = path\n",
    "            \n",
    "        if nb_jets is not None and not isinstance(nb_jets, int):\n",
    "            raise TypeError('nb_jets must be an int!')\n",
    "        f = h5py.File(filename)\n",
    "        \n",
    "        # load all objects into memory\n",
    "        self.images = f['image'][:nb_jets]\n",
    "        self.jet_dr = f['jet_delta_R'][:nb_jets]\n",
    "        self.jet_eta = f['jet_eta'][:nb_jets]\n",
    "        self.jet_mass = f['jet_mass'][:nb_jets]\n",
    "        self.jet_phi = f['jet_phi'][:nb_jets]\n",
    "        self.jet_pt = f['jet_pt'][:nb_jets]\n",
    "        self.y = f['signal'][:nb_jets]\n",
    "        self.tau_21 = f['tau_21'][:nb_jets]\n",
    "        self.tau_32 = f['tau_32'][:nb_jets]\n",
    "    \n",
    "    def __len__(self):\n",
    "        return self.y.shape[0]\n",
    "    \n",
    "    @staticmethod\n",
    "    def image_transform(image):\n",
    "        return image / 75.0\n",
    "    \n",
    "    @staticmethod\n",
    "    def inverse_image_transform(image):\n",
    "        return image * 75.0\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "#         sample = {\n",
    "#             'images' : self.images[idx],\n",
    "#             'jet_dr' : self.jet_dr[idx],\n",
    "#             'jet_eta' : self.jet_eta[idx],\n",
    "#             'jet_mass' : self.jet_mass[idx],\n",
    "#             'jet_phi' : self.jet_phi[idx],\n",
    "#             'jet_pt' : self.jet_pt[idx],\n",
    "#             'y' : self.y[idx],\n",
    "#             'tau_21' : self.tau_21[idx],\n",
    "#             'tau_32' : self.tau_32[idx]\n",
    "#         }\n",
    "#         return sample\n",
    "        return (self.images[idx], self.y[idx])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dataset = JetSet(DATAURL)#, nb_jets=30000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 100\n",
    "data_loader = torch.utils.data.DataLoader(dataset=dataset,\n",
    "                                          batch_size=BATCH_SIZE, \n",
    "                                          shuffle=True,\n",
    "                                          num_workers=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "n_batches = int(np.ceil(len(dataset)/ BATCH_SIZE)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8726"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_batches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Model Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "`torch.nn` contains all the ingredients to start building your networks. \n",
    "\n",
    "PyTorch is still in beta, so there are no well-defined best practices and lots of ways of doing the same thing, some that are safer and limited in scope, and some that provide you with more flexibility, should you need it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "`torch.nn.Sequential` is a container that allows you to specify Modules to be added to your model in the order they are passed into the constructor. If you are coming from Keras, this is very similar to a `keras.models.Sequential` object. \n",
    "\n",
    "Alternatively, you can also pass layers into the Module as an `OrderedDict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Discriminator\n",
    "D = nn.Sequential(\n",
    "    nn.Linear(25 * 25, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 1),\n",
    "    nn.Sigmoid()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Generator \n",
    "G = nn.Sequential(\n",
    "    nn.Linear(64, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 512),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(512, 25 * 25),\n",
    "    nn.ReLU()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# if you're running on GPU, move models there\n",
    "# if torch.cuda.is_available():\n",
    "#     print 'Will run model on the GPU'\n",
    "#     D.cuda()\n",
    "#     G.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Define your loss function and optimizer by picking from the ones already available in `torch.nn` or defining your own. Note that an optimizer is defined in terms of the network parameters that it's assigned to optimize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0003)\n",
    "g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0003)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Training Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "A `Variable` is a node in a computational graph that allows for automatic differentiation across the graph. A `Variable` wraps a PyTorch `Tensor`, though rumor has it the two concepts will soon be unified. `x.data` gives you access to the `Tensor` wrapped by the `Variable x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "\n",
    "def to_var(x):\n",
    "    # first move to GPU, if necessary\n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda()\n",
    "    return Variable(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "PyTorch allows you to build **dynamic graphs** in a very intuitive and pythonic way. \n",
    "\n",
    "You should write the training loop yourself, looping through the epochs and the batches.\n",
    "\n",
    "Although this adds additional boilerplate and room for error when compared to libraries like Keras, this dynamic construction gives the flexibility required for exotic train loops that do not fit the `model.fit(...)` paradigm, such as GANs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0/10], Step[100/8726], d_loss: 0.8899, g_loss: 2.1014, Mean D(x): 0.68, Mean D(G(z)): 0.23\n",
      "Epoch [0/10], Step[200/8726], d_loss: 0.8140, g_loss: 1.7249, Mean D(x): 0.69, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[300/8726], d_loss: 0.7298, g_loss: 1.6072, Mean D(x): 0.74, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[400/8726], d_loss: 1.0345, g_loss: 1.7660, Mean D(x): 0.68, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[500/8726], d_loss: 0.9914, g_loss: 1.6759, Mean D(x): 0.68, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[600/8726], d_loss: 0.8508, g_loss: 2.0604, Mean D(x): 0.73, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[700/8726], d_loss: 0.8846, g_loss: 1.6677, Mean D(x): 0.73, Mean D(G(z)): 0.29\n",
      "Epoch [0/10], Step[800/8726], d_loss: 0.7938, g_loss: 1.8010, Mean D(x): 0.73, Mean D(G(z)): 0.27\n",
      "Epoch [0/10], Step[900/8726], d_loss: 0.8735, g_loss: 1.9295, Mean D(x): 0.66, Mean D(G(z)): 0.22\n",
      "Epoch [0/10], Step[1000/8726], d_loss: 0.8275, g_loss: 1.8771, Mean D(x): 0.71, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[1100/8726], d_loss: 1.1150, g_loss: 1.7432, Mean D(x): 0.64, Mean D(G(z)): 0.27\n",
      "Epoch [0/10], Step[1200/8726], d_loss: 0.8106, g_loss: 1.8767, Mean D(x): 0.73, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[1300/8726], d_loss: 1.0045, g_loss: 1.3721, Mean D(x): 0.71, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[1400/8726], d_loss: 0.8947, g_loss: 1.7515, Mean D(x): 0.68, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[1500/8726], d_loss: 1.0313, g_loss: 1.6781, Mean D(x): 0.67, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[1600/8726], d_loss: 0.9568, g_loss: 1.6710, Mean D(x): 0.69, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[1700/8726], d_loss: 0.6906, g_loss: 1.9623, Mean D(x): 0.74, Mean D(G(z)): 0.21\n",
      "Epoch [0/10], Step[1800/8726], d_loss: 0.7771, g_loss: 2.2386, Mean D(x): 0.71, Mean D(G(z)): 0.21\n",
      "Epoch [0/10], Step[1900/8726], d_loss: 1.0079, g_loss: 1.8960, Mean D(x): 0.70, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[2000/8726], d_loss: 0.7146, g_loss: 1.7350, Mean D(x): 0.73, Mean D(G(z)): 0.23\n",
      "Epoch [0/10], Step[2100/8726], d_loss: 1.0613, g_loss: 2.0893, Mean D(x): 0.62, Mean D(G(z)): 0.24\n",
      "Epoch [0/10], Step[2200/8726], d_loss: 0.7581, g_loss: 1.3656, Mean D(x): 0.77, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[2300/8726], d_loss: 0.9296, g_loss: 1.4304, Mean D(x): 0.71, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[2400/8726], d_loss: 0.9493, g_loss: 1.7247, Mean D(x): 0.70, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[2500/8726], d_loss: 0.8559, g_loss: 1.5602, Mean D(x): 0.67, Mean D(G(z)): 0.24\n",
      "Epoch [0/10], Step[2600/8726], d_loss: 0.8865, g_loss: 1.4514, Mean D(x): 0.71, Mean D(G(z)): 0.29\n",
      "Epoch [0/10], Step[2700/8726], d_loss: 0.8073, g_loss: 1.6882, Mean D(x): 0.75, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[2800/8726], d_loss: 0.8801, g_loss: 1.5523, Mean D(x): 0.72, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[2900/8726], d_loss: 0.9952, g_loss: 1.6530, Mean D(x): 0.61, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[3000/8726], d_loss: 1.0132, g_loss: 1.6425, Mean D(x): 0.67, Mean D(G(z)): 0.31\n",
      "Epoch [0/10], Step[3100/8726], d_loss: 0.7506, g_loss: 2.0341, Mean D(x): 0.72, Mean D(G(z)): 0.22\n",
      "Epoch [0/10], Step[3200/8726], d_loss: 1.0457, g_loss: 1.6141, Mean D(x): 0.67, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[3300/8726], d_loss: 0.9359, g_loss: 1.5829, Mean D(x): 0.73, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[3400/8726], d_loss: 0.8342, g_loss: 1.5837, Mean D(x): 0.70, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[3500/8726], d_loss: 1.0153, g_loss: 1.3659, Mean D(x): 0.66, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[3600/8726], d_loss: 0.9290, g_loss: 1.5970, Mean D(x): 0.70, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[3700/8726], d_loss: 1.0214, g_loss: 1.5298, Mean D(x): 0.64, Mean D(G(z)): 0.31\n",
      "Epoch [0/10], Step[3800/8726], d_loss: 0.8493, g_loss: 1.6602, Mean D(x): 0.68, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[3900/8726], d_loss: 0.9502, g_loss: 1.4927, Mean D(x): 0.68, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[4000/8726], d_loss: 1.0646, g_loss: 1.2667, Mean D(x): 0.75, Mean D(G(z)): 0.38\n",
      "Epoch [0/10], Step[4100/8726], d_loss: 0.8787, g_loss: 1.7823, Mean D(x): 0.68, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[4200/8726], d_loss: 0.8211, g_loss: 1.7075, Mean D(x): 0.71, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[4300/8726], d_loss: 0.9314, g_loss: 1.5324, Mean D(x): 0.74, Mean D(G(z)): 0.36\n",
      "Epoch [0/10], Step[4400/8726], d_loss: 0.9610, g_loss: 1.5027, Mean D(x): 0.69, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[4500/8726], d_loss: 1.1891, g_loss: 1.7370, Mean D(x): 0.64, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[4600/8726], d_loss: 0.8329, g_loss: 1.4266, Mean D(x): 0.76, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[4700/8726], d_loss: 1.0359, g_loss: 2.0892, Mean D(x): 0.64, Mean D(G(z)): 0.25\n",
      "Epoch [0/10], Step[4800/8726], d_loss: 0.9013, g_loss: 1.6762, Mean D(x): 0.68, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[4900/8726], d_loss: 0.9407, g_loss: 1.8336, Mean D(x): 0.71, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[5000/8726], d_loss: 0.9139, g_loss: 1.6474, Mean D(x): 0.67, Mean D(G(z)): 0.29\n",
      "Epoch [0/10], Step[5100/8726], d_loss: 1.0234, g_loss: 1.6732, Mean D(x): 0.64, Mean D(G(z)): 0.27\n",
      "Epoch [0/10], Step[5200/8726], d_loss: 0.8715, g_loss: 1.7219, Mean D(x): 0.73, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[5300/8726], d_loss: 0.9166, g_loss: 1.6835, Mean D(x): 0.70, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[5400/8726], d_loss: 0.9442, g_loss: 1.5629, Mean D(x): 0.69, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[5500/8726], d_loss: 0.9664, g_loss: 1.5031, Mean D(x): 0.70, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[5600/8726], d_loss: 0.8778, g_loss: 1.3839, Mean D(x): 0.73, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[5700/8726], d_loss: 0.9203, g_loss: 1.7787, Mean D(x): 0.71, Mean D(G(z)): 0.29\n",
      "Epoch [0/10], Step[5800/8726], d_loss: 1.2093, g_loss: 1.4210, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [0/10], Step[5900/8726], d_loss: 0.9546, g_loss: 1.4218, Mean D(x): 0.69, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[6000/8726], d_loss: 1.0819, g_loss: 1.4776, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[6100/8726], d_loss: 0.9602, g_loss: 1.9506, Mean D(x): 0.64, Mean D(G(z)): 0.24\n",
      "Epoch [0/10], Step[6200/8726], d_loss: 1.1592, g_loss: 1.4452, Mean D(x): 0.66, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[6300/8726], d_loss: 0.9205, g_loss: 1.6742, Mean D(x): 0.71, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[6400/8726], d_loss: 0.8745, g_loss: 1.4286, Mean D(x): 0.70, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[6500/8726], d_loss: 1.0025, g_loss: 1.5991, Mean D(x): 0.63, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[6600/8726], d_loss: 0.8814, g_loss: 1.5961, Mean D(x): 0.76, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[6700/8726], d_loss: 0.9610, g_loss: 1.5332, Mean D(x): 0.69, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[6800/8726], d_loss: 0.9960, g_loss: 1.4196, Mean D(x): 0.62, Mean D(G(z)): 0.30\n",
      "Epoch [0/10], Step[6900/8726], d_loss: 0.9518, g_loss: 1.5351, Mean D(x): 0.74, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[7000/8726], d_loss: 0.8009, g_loss: 1.7112, Mean D(x): 0.72, Mean D(G(z)): 0.26\n",
      "Epoch [0/10], Step[7100/8726], d_loss: 1.0283, g_loss: 1.5979, Mean D(x): 0.60, Mean D(G(z)): 0.24\n",
      "Epoch [0/10], Step[7200/8726], d_loss: 0.9362, g_loss: 1.6605, Mean D(x): 0.65, Mean D(G(z)): 0.27\n",
      "Epoch [0/10], Step[7300/8726], d_loss: 0.8035, g_loss: 1.5385, Mean D(x): 0.71, Mean D(G(z)): 0.27\n",
      "Epoch [0/10], Step[7400/8726], d_loss: 0.8905, g_loss: 1.5782, Mean D(x): 0.73, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[7500/8726], d_loss: 0.8416, g_loss: 1.6268, Mean D(x): 0.74, Mean D(G(z)): 0.32\n",
      "Epoch [0/10], Step[7600/8726], d_loss: 0.8894, g_loss: 1.5360, Mean D(x): 0.72, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[7700/8726], d_loss: 0.8488, g_loss: 1.5873, Mean D(x): 0.73, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[7800/8726], d_loss: 1.0740, g_loss: 1.5201, Mean D(x): 0.65, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[7900/8726], d_loss: 1.0136, g_loss: 1.5101, Mean D(x): 0.70, Mean D(G(z)): 0.36\n",
      "Epoch [0/10], Step[8000/8726], d_loss: 0.9045, g_loss: 1.2366, Mean D(x): 0.73, Mean D(G(z)): 0.34\n",
      "Epoch [0/10], Step[8100/8726], d_loss: 0.8101, g_loss: 1.5873, Mean D(x): 0.73, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[8200/8726], d_loss: 0.8980, g_loss: 1.4515, Mean D(x): 0.73, Mean D(G(z)): 0.35\n",
      "Epoch [0/10], Step[8300/8726], d_loss: 0.9055, g_loss: 1.5483, Mean D(x): 0.72, Mean D(G(z)): 0.33\n",
      "Epoch [0/10], Step[8400/8726], d_loss: 0.9632, g_loss: 1.5237, Mean D(x): 0.73, Mean D(G(z)): 0.31\n",
      "Epoch [0/10], Step[8500/8726], d_loss: 0.8290, g_loss: 1.5606, Mean D(x): 0.72, Mean D(G(z)): 0.28\n",
      "Epoch [0/10], Step[8600/8726], d_loss: 0.9251, g_loss: 1.2967, Mean D(x): 0.74, Mean D(G(z)): 0.36\n",
      "Epoch [0/10], Step[8700/8726], d_loss: 0.6823, g_loss: 1.7223, Mean D(x): 0.76, Mean D(G(z)): 0.25\n",
      "Epoch [1/10], Step[100/8726], d_loss: 0.9612, g_loss: 1.5966, Mean D(x): 0.66, Mean D(G(z)): 0.25\n",
      "Epoch [1/10], Step[200/8726], d_loss: 1.1304, g_loss: 1.5712, Mean D(x): 0.60, Mean D(G(z)): 0.29\n",
      "Epoch [1/10], Step[300/8726], d_loss: 0.9426, g_loss: 1.4888, Mean D(x): 0.64, Mean D(G(z)): 0.27\n",
      "Epoch [1/10], Step[400/8726], d_loss: 0.9713, g_loss: 1.4067, Mean D(x): 0.70, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[500/8726], d_loss: 0.9240, g_loss: 1.5048, Mean D(x): 0.64, Mean D(G(z)): 0.25\n",
      "Epoch [1/10], Step[600/8726], d_loss: 0.9931, g_loss: 1.4015, Mean D(x): 0.71, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[700/8726], d_loss: 0.8722, g_loss: 1.5113, Mean D(x): 0.70, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[800/8726], d_loss: 1.1387, g_loss: 1.4090, Mean D(x): 0.69, Mean D(G(z)): 0.40\n",
      "Epoch [1/10], Step[900/8726], d_loss: 0.9294, g_loss: 1.6361, Mean D(x): 0.70, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[1000/8726], d_loss: 0.9493, g_loss: 1.6336, Mean D(x): 0.73, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[1100/8726], d_loss: 0.8320, g_loss: 2.0685, Mean D(x): 0.68, Mean D(G(z)): 0.20\n",
      "Epoch [1/10], Step[1200/8726], d_loss: 0.8312, g_loss: 1.4995, Mean D(x): 0.70, Mean D(G(z)): 0.26\n",
      "Epoch [1/10], Step[1300/8726], d_loss: 0.9067, g_loss: 1.4482, Mean D(x): 0.72, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[1400/8726], d_loss: 0.9468, g_loss: 1.3604, Mean D(x): 0.68, Mean D(G(z)): 0.29\n",
      "Epoch [1/10], Step[1500/8726], d_loss: 0.9849, g_loss: 1.5763, Mean D(x): 0.70, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[1600/8726], d_loss: 1.0182, g_loss: 1.2646, Mean D(x): 0.71, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[1700/8726], d_loss: 0.8401, g_loss: 1.7483, Mean D(x): 0.73, Mean D(G(z)): 0.30\n",
      "Epoch [1/10], Step[1800/8726], d_loss: 0.9494, g_loss: 1.4420, Mean D(x): 0.67, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[1900/8726], d_loss: 0.9794, g_loss: 1.7545, Mean D(x): 0.65, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[2000/8726], d_loss: 0.8798, g_loss: 1.3882, Mean D(x): 0.68, Mean D(G(z)): 0.30\n",
      "Epoch [1/10], Step[2100/8726], d_loss: 0.9857, g_loss: 1.3996, Mean D(x): 0.71, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[2200/8726], d_loss: 0.9455, g_loss: 1.3448, Mean D(x): 0.70, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[2300/8726], d_loss: 0.9835, g_loss: 1.3092, Mean D(x): 0.69, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[2400/8726], d_loss: 0.9702, g_loss: 1.5525, Mean D(x): 0.70, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[2500/8726], d_loss: 0.9329, g_loss: 1.9234, Mean D(x): 0.67, Mean D(G(z)): 0.27\n",
      "Epoch [1/10], Step[2600/8726], d_loss: 0.9568, g_loss: 1.4177, Mean D(x): 0.67, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[2700/8726], d_loss: 0.9141, g_loss: 1.5010, Mean D(x): 0.67, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[2800/8726], d_loss: 1.1205, g_loss: 1.4088, Mean D(x): 0.66, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[2900/8726], d_loss: 0.8950, g_loss: 1.5468, Mean D(x): 0.66, Mean D(G(z)): 0.27\n",
      "Epoch [1/10], Step[3000/8726], d_loss: 0.8486, g_loss: 1.7585, Mean D(x): 0.72, Mean D(G(z)): 0.24\n",
      "Epoch [1/10], Step[3100/8726], d_loss: 1.0911, g_loss: 1.1513, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[3200/8726], d_loss: 1.1858, g_loss: 1.1385, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[3300/8726], d_loss: 1.0092, g_loss: 1.6524, Mean D(x): 0.62, Mean D(G(z)): 0.29\n",
      "Epoch [1/10], Step[3400/8726], d_loss: 1.1181, g_loss: 1.2748, Mean D(x): 0.66, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[3500/8726], d_loss: 0.9920, g_loss: 1.2938, Mean D(x): 0.64, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[3600/8726], d_loss: 1.0336, g_loss: 1.2342, Mean D(x): 0.64, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[3700/8726], d_loss: 1.0376, g_loss: 1.2833, Mean D(x): 0.66, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[3800/8726], d_loss: 1.0407, g_loss: 1.5609, Mean D(x): 0.57, Mean D(G(z)): 0.22\n",
      "Epoch [1/10], Step[3900/8726], d_loss: 0.9839, g_loss: 1.4874, Mean D(x): 0.69, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[4000/8726], d_loss: 1.0198, g_loss: 1.3767, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[4100/8726], d_loss: 0.8637, g_loss: 1.5215, Mean D(x): 0.77, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[4200/8726], d_loss: 0.8870, g_loss: 1.4825, Mean D(x): 0.68, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[4300/8726], d_loss: 0.9144, g_loss: 1.7611, Mean D(x): 0.65, Mean D(G(z)): 0.23\n",
      "Epoch [1/10], Step[4400/8726], d_loss: 1.0380, g_loss: 1.2685, Mean D(x): 0.67, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[4500/8726], d_loss: 1.2327, g_loss: 1.4298, Mean D(x): 0.57, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[4600/8726], d_loss: 1.1403, g_loss: 1.5269, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[4700/8726], d_loss: 1.0398, g_loss: 1.2770, Mean D(x): 0.66, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[4800/8726], d_loss: 1.0455, g_loss: 1.5054, Mean D(x): 0.69, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[4900/8726], d_loss: 0.9357, g_loss: 1.3554, Mean D(x): 0.67, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[5000/8726], d_loss: 0.9632, g_loss: 1.5808, Mean D(x): 0.64, Mean D(G(z)): 0.29\n",
      "Epoch [1/10], Step[5100/8726], d_loss: 1.1138, g_loss: 1.5488, Mean D(x): 0.66, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[5200/8726], d_loss: 0.9714, g_loss: 1.2147, Mean D(x): 0.67, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[5300/8726], d_loss: 1.0648, g_loss: 1.3565, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[5400/8726], d_loss: 0.9883, g_loss: 1.8153, Mean D(x): 0.66, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[5500/8726], d_loss: 1.1566, g_loss: 1.6176, Mean D(x): 0.65, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[5600/8726], d_loss: 0.8959, g_loss: 1.5520, Mean D(x): 0.66, Mean D(G(z)): 0.27\n",
      "Epoch [1/10], Step[5700/8726], d_loss: 0.9265, g_loss: 1.6096, Mean D(x): 0.68, Mean D(G(z)): 0.29\n",
      "Epoch [1/10], Step[5800/8726], d_loss: 1.0573, g_loss: 1.1412, Mean D(x): 0.68, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[5900/8726], d_loss: 0.9583, g_loss: 1.3549, Mean D(x): 0.70, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[6000/8726], d_loss: 0.9448, g_loss: 1.1628, Mean D(x): 0.67, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[6100/8726], d_loss: 0.9350, g_loss: 1.3822, Mean D(x): 0.66, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[6200/8726], d_loss: 1.1059, g_loss: 1.3306, Mean D(x): 0.62, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[6300/8726], d_loss: 1.0675, g_loss: 1.3202, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[6400/8726], d_loss: 1.0257, g_loss: 1.2016, Mean D(x): 0.65, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[6500/8726], d_loss: 1.0425, g_loss: 1.2177, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[6600/8726], d_loss: 1.1058, g_loss: 1.4041, Mean D(x): 0.67, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[6700/8726], d_loss: 1.0990, g_loss: 1.6741, Mean D(x): 0.73, Mean D(G(z)): 0.36\n",
      "Epoch [1/10], Step[6800/8726], d_loss: 0.9838, g_loss: 1.4711, Mean D(x): 0.68, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[6900/8726], d_loss: 0.9250, g_loss: 1.4536, Mean D(x): 0.68, Mean D(G(z)): 0.30\n",
      "Epoch [1/10], Step[7000/8726], d_loss: 1.0272, g_loss: 1.3800, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[7100/8726], d_loss: 1.0175, g_loss: 1.4746, Mean D(x): 0.65, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[7200/8726], d_loss: 0.8852, g_loss: 1.2230, Mean D(x): 0.75, Mean D(G(z)): 0.37\n",
      "Epoch [1/10], Step[7300/8726], d_loss: 1.0943, g_loss: 1.7834, Mean D(x): 0.66, Mean D(G(z)): 0.32\n",
      "Epoch [1/10], Step[7400/8726], d_loss: 0.9816, g_loss: 1.4058, Mean D(x): 0.67, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[7500/8726], d_loss: 1.0432, g_loss: 1.3275, Mean D(x): 0.65, Mean D(G(z)): 0.30\n",
      "Epoch [1/10], Step[7600/8726], d_loss: 1.0080, g_loss: 1.3923, Mean D(x): 0.64, Mean D(G(z)): 0.31\n",
      "Epoch [1/10], Step[7700/8726], d_loss: 1.0328, g_loss: 1.4992, Mean D(x): 0.60, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[7800/8726], d_loss: 1.1489, g_loss: 1.3187, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [1/10], Step[7900/8726], d_loss: 0.9871, g_loss: 1.2049, Mean D(x): 0.67, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[8000/8726], d_loss: 0.9094, g_loss: 1.5613, Mean D(x): 0.71, Mean D(G(z)): 0.28\n",
      "Epoch [1/10], Step[8100/8726], d_loss: 1.1214, g_loss: 1.0871, Mean D(x): 0.68, Mean D(G(z)): 0.41\n",
      "Epoch [1/10], Step[8200/8726], d_loss: 1.0021, g_loss: 1.2529, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [1/10], Step[8300/8726], d_loss: 1.1548, g_loss: 1.3634, Mean D(x): 0.58, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[8400/8726], d_loss: 0.9707, g_loss: 1.1881, Mean D(x): 0.70, Mean D(G(z)): 0.35\n",
      "Epoch [1/10], Step[8500/8726], d_loss: 1.0803, g_loss: 1.1776, Mean D(x): 0.60, Mean D(G(z)): 0.33\n",
      "Epoch [1/10], Step[8600/8726], d_loss: 1.1260, g_loss: 1.5895, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [1/10], Step[8700/8726], d_loss: 1.1219, g_loss: 1.1335, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[100/8726], d_loss: 1.0554, g_loss: 1.3900, Mean D(x): 0.63, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[200/8726], d_loss: 1.0558, g_loss: 1.1247, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[300/8726], d_loss: 0.8596, g_loss: 1.3643, Mean D(x): 0.71, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[400/8726], d_loss: 1.1634, g_loss: 1.4186, Mean D(x): 0.52, Mean D(G(z)): 0.27\n",
      "Epoch [2/10], Step[500/8726], d_loss: 1.0397, g_loss: 1.4629, Mean D(x): 0.61, Mean D(G(z)): 0.32\n",
      "Epoch [2/10], Step[600/8726], d_loss: 0.8983, g_loss: 1.2560, Mean D(x): 0.68, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[700/8726], d_loss: 1.0336, g_loss: 1.2793, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[800/8726], d_loss: 1.1515, g_loss: 1.2049, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [2/10], Step[900/8726], d_loss: 1.1820, g_loss: 1.2346, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[1000/8726], d_loss: 1.0217, g_loss: 1.2340, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[1100/8726], d_loss: 1.0273, g_loss: 1.3488, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[1200/8726], d_loss: 1.1543, g_loss: 1.2933, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[1300/8726], d_loss: 1.0932, g_loss: 1.5058, Mean D(x): 0.58, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[1400/8726], d_loss: 1.0826, g_loss: 1.4343, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[1500/8726], d_loss: 1.0309, g_loss: 1.2636, Mean D(x): 0.62, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[1600/8726], d_loss: 0.9658, g_loss: 1.3652, Mean D(x): 0.68, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[1700/8726], d_loss: 1.1501, g_loss: 1.2745, Mean D(x): 0.59, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[1800/8726], d_loss: 1.0177, g_loss: 1.3519, Mean D(x): 0.70, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[1900/8726], d_loss: 1.0942, g_loss: 1.4108, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[2000/8726], d_loss: 1.0657, g_loss: 1.4088, Mean D(x): 0.67, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[2100/8726], d_loss: 0.8859, g_loss: 1.3485, Mean D(x): 0.72, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[2200/8726], d_loss: 1.0608, g_loss: 1.4221, Mean D(x): 0.63, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[2300/8726], d_loss: 1.1217, g_loss: 1.1441, Mean D(x): 0.59, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[2400/8726], d_loss: 1.0554, g_loss: 1.3782, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[2500/8726], d_loss: 1.0591, g_loss: 1.2978, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[2600/8726], d_loss: 1.1222, g_loss: 1.1189, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[2700/8726], d_loss: 1.0016, g_loss: 1.3547, Mean D(x): 0.68, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[2800/8726], d_loss: 1.0630, g_loss: 1.2921, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[2900/8726], d_loss: 1.0392, g_loss: 1.2273, Mean D(x): 0.70, Mean D(G(z)): 0.42\n",
      "Epoch [2/10], Step[3000/8726], d_loss: 1.0394, g_loss: 1.3705, Mean D(x): 0.61, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[3100/8726], d_loss: 1.1974, g_loss: 1.5253, Mean D(x): 0.58, Mean D(G(z)): 0.27\n",
      "Epoch [2/10], Step[3200/8726], d_loss: 1.0590, g_loss: 1.3446, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[3300/8726], d_loss: 1.0264, g_loss: 1.3273, Mean D(x): 0.65, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[3400/8726], d_loss: 1.1987, g_loss: 1.4837, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[3500/8726], d_loss: 0.9259, g_loss: 1.2078, Mean D(x): 0.71, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[3600/8726], d_loss: 1.1352, g_loss: 1.2649, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [2/10], Step[3700/8726], d_loss: 1.0177, g_loss: 1.2321, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[3800/8726], d_loss: 1.0905, g_loss: 1.2723, Mean D(x): 0.59, Mean D(G(z)): 0.30\n",
      "Epoch [2/10], Step[3900/8726], d_loss: 1.0495, g_loss: 1.3006, Mean D(x): 0.62, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[4000/8726], d_loss: 1.0268, g_loss: 1.2577, Mean D(x): 0.70, Mean D(G(z)): 0.39\n",
      "Epoch [2/10], Step[4100/8726], d_loss: 1.0753, g_loss: 1.5075, Mean D(x): 0.63, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[4200/8726], d_loss: 1.2343, g_loss: 1.2822, Mean D(x): 0.56, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[4300/8726], d_loss: 1.0038, g_loss: 1.2509, Mean D(x): 0.65, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[4400/8726], d_loss: 0.9778, g_loss: 1.1968, Mean D(x): 0.66, Mean D(G(z)): 0.32\n",
      "Epoch [2/10], Step[4500/8726], d_loss: 0.7254, g_loss: 1.4776, Mean D(x): 0.75, Mean D(G(z)): 0.28\n",
      "Epoch [2/10], Step[4600/8726], d_loss: 1.1323, g_loss: 1.3868, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[4700/8726], d_loss: 1.0029, g_loss: 1.2322, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[4800/8726], d_loss: 1.0416, g_loss: 1.6666, Mean D(x): 0.62, Mean D(G(z)): 0.32\n",
      "Epoch [2/10], Step[4900/8726], d_loss: 1.0056, g_loss: 1.3067, Mean D(x): 0.71, Mean D(G(z)): 0.39\n",
      "Epoch [2/10], Step[5000/8726], d_loss: 1.1384, g_loss: 1.3030, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[5100/8726], d_loss: 1.0340, g_loss: 1.4176, Mean D(x): 0.60, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[5200/8726], d_loss: 1.1672, g_loss: 1.1346, Mean D(x): 0.63, Mean D(G(z)): 0.42\n",
      "Epoch [2/10], Step[5300/8726], d_loss: 1.0617, g_loss: 1.1511, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[5400/8726], d_loss: 1.0903, g_loss: 1.3324, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[5500/8726], d_loss: 0.9588, g_loss: 1.6621, Mean D(x): 0.64, Mean D(G(z)): 0.28\n",
      "Epoch [2/10], Step[5600/8726], d_loss: 1.0637, g_loss: 1.0345, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[5700/8726], d_loss: 1.1713, g_loss: 1.1631, Mean D(x): 0.65, Mean D(G(z)): 0.41\n",
      "Epoch [2/10], Step[5800/8726], d_loss: 0.9328, g_loss: 1.3725, Mean D(x): 0.72, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[5900/8726], d_loss: 0.9433, g_loss: 1.3528, Mean D(x): 0.66, Mean D(G(z)): 0.31\n",
      "Epoch [2/10], Step[6000/8726], d_loss: 1.1066, g_loss: 1.5481, Mean D(x): 0.63, Mean D(G(z)): 0.32\n",
      "Epoch [2/10], Step[6100/8726], d_loss: 0.9649, g_loss: 1.4173, Mean D(x): 0.59, Mean D(G(z)): 0.26\n",
      "Epoch [2/10], Step[6200/8726], d_loss: 1.1368, g_loss: 1.3916, Mean D(x): 0.60, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[6300/8726], d_loss: 1.0582, g_loss: 1.1759, Mean D(x): 0.65, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[6400/8726], d_loss: 1.3157, g_loss: 1.0672, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [2/10], Step[6500/8726], d_loss: 0.9906, g_loss: 1.4741, Mean D(x): 0.66, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[6600/8726], d_loss: 1.0739, g_loss: 1.2244, Mean D(x): 0.63, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[6700/8726], d_loss: 1.1294, g_loss: 1.2241, Mean D(x): 0.64, Mean D(G(z)): 0.38\n",
      "Epoch [2/10], Step[6800/8726], d_loss: 1.0811, g_loss: 1.2708, Mean D(x): 0.62, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[6900/8726], d_loss: 0.9980, g_loss: 1.3437, Mean D(x): 0.65, Mean D(G(z)): 0.33\n",
      "Epoch [2/10], Step[7000/8726], d_loss: 1.0411, g_loss: 1.3147, Mean D(x): 0.65, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[7100/8726], d_loss: 1.0391, g_loss: 1.3527, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[7200/8726], d_loss: 0.8969, g_loss: 1.1744, Mean D(x): 0.74, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[7300/8726], d_loss: 1.1974, g_loss: 1.2259, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [2/10], Step[7400/8726], d_loss: 1.0386, g_loss: 1.2819, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[7500/8726], d_loss: 1.1389, g_loss: 1.0227, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [2/10], Step[7600/8726], d_loss: 1.0286, g_loss: 1.2367, Mean D(x): 0.65, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[7700/8726], d_loss: 1.1047, g_loss: 1.2668, Mean D(x): 0.65, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[7800/8726], d_loss: 1.0334, g_loss: 1.2618, Mean D(x): 0.68, Mean D(G(z)): 0.37\n",
      "Epoch [2/10], Step[7900/8726], d_loss: 1.2766, g_loss: 1.3648, Mean D(x): 0.63, Mean D(G(z)): 0.41\n",
      "Epoch [2/10], Step[8000/8726], d_loss: 1.0826, g_loss: 1.3109, Mean D(x): 0.60, Mean D(G(z)): 0.30\n",
      "Epoch [2/10], Step[8100/8726], d_loss: 1.1909, g_loss: 1.2324, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [2/10], Step[8200/8726], d_loss: 0.9333, g_loss: 1.4734, Mean D(x): 0.68, Mean D(G(z)): 0.32\n",
      "Epoch [2/10], Step[8300/8726], d_loss: 1.0754, g_loss: 1.1918, Mean D(x): 0.60, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[8400/8726], d_loss: 1.0736, g_loss: 1.4122, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [2/10], Step[8500/8726], d_loss: 1.3098, g_loss: 1.2183, Mean D(x): 0.61, Mean D(G(z)): 0.40\n",
      "Epoch [2/10], Step[8600/8726], d_loss: 1.1529, g_loss: 1.3481, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [2/10], Step[8700/8726], d_loss: 1.0605, g_loss: 1.3315, Mean D(x): 0.65, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[100/8726], d_loss: 1.0340, g_loss: 1.2540, Mean D(x): 0.70, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[200/8726], d_loss: 1.2018, g_loss: 1.3227, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[300/8726], d_loss: 1.1640, g_loss: 1.0644, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[400/8726], d_loss: 1.1435, g_loss: 1.0863, Mean D(x): 0.71, Mean D(G(z)): 0.44\n",
      "Epoch [3/10], Step[500/8726], d_loss: 1.1402, g_loss: 1.3386, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[600/8726], d_loss: 0.9711, g_loss: 1.2826, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[700/8726], d_loss: 1.1964, g_loss: 1.2444, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [3/10], Step[800/8726], d_loss: 1.0621, g_loss: 1.3885, Mean D(x): 0.64, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[900/8726], d_loss: 0.8954, g_loss: 1.4711, Mean D(x): 0.73, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[1000/8726], d_loss: 1.0011, g_loss: 1.2077, Mean D(x): 0.65, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[1100/8726], d_loss: 1.0590, g_loss: 1.2556, Mean D(x): 0.68, Mean D(G(z)): 0.37\n",
      "Epoch [3/10], Step[1200/8726], d_loss: 1.2826, g_loss: 0.9939, Mean D(x): 0.61, Mean D(G(z)): 0.42\n",
      "Epoch [3/10], Step[1300/8726], d_loss: 1.0464, g_loss: 1.0798, Mean D(x): 0.66, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[1400/8726], d_loss: 1.0040, g_loss: 1.0536, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[1500/8726], d_loss: 1.0372, g_loss: 1.3246, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[1600/8726], d_loss: 1.1048, g_loss: 1.2641, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [3/10], Step[1700/8726], d_loss: 1.1084, g_loss: 1.2408, Mean D(x): 0.60, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[1800/8726], d_loss: 0.9054, g_loss: 1.1636, Mean D(x): 0.71, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[1900/8726], d_loss: 1.0382, g_loss: 1.0197, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[2000/8726], d_loss: 0.9696, g_loss: 1.2906, Mean D(x): 0.68, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[2100/8726], d_loss: 1.1944, g_loss: 1.3176, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[2200/8726], d_loss: 1.0443, g_loss: 1.1134, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [3/10], Step[2300/8726], d_loss: 1.0742, g_loss: 1.2448, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [3/10], Step[2400/8726], d_loss: 0.9560, g_loss: 1.3197, Mean D(x): 0.69, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[2500/8726], d_loss: 1.2541, g_loss: 1.1771, Mean D(x): 0.57, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[2600/8726], d_loss: 1.0946, g_loss: 1.2310, Mean D(x): 0.61, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[2700/8726], d_loss: 1.1704, g_loss: 1.1007, Mean D(x): 0.69, Mean D(G(z)): 0.46\n",
      "Epoch [3/10], Step[2800/8726], d_loss: 0.9358, g_loss: 1.0890, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[2900/8726], d_loss: 1.1759, g_loss: 1.1280, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[3000/8726], d_loss: 1.0525, g_loss: 0.9996, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[3100/8726], d_loss: 1.0181, g_loss: 1.0589, Mean D(x): 0.69, Mean D(G(z)): 0.37\n",
      "Epoch [3/10], Step[3200/8726], d_loss: 0.8653, g_loss: 1.4679, Mean D(x): 0.68, Mean D(G(z)): 0.28\n",
      "Epoch [3/10], Step[3300/8726], d_loss: 1.2428, g_loss: 0.9849, Mean D(x): 0.69, Mean D(G(z)): 0.44\n",
      "Epoch [3/10], Step[3400/8726], d_loss: 1.0336, g_loss: 1.1404, Mean D(x): 0.63, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[3500/8726], d_loss: 1.1183, g_loss: 1.2181, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[3600/8726], d_loss: 1.0695, g_loss: 1.2524, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[3700/8726], d_loss: 1.1285, g_loss: 1.1562, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[3800/8726], d_loss: 1.0411, g_loss: 1.1731, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[3900/8726], d_loss: 1.1242, g_loss: 1.0891, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[4000/8726], d_loss: 1.0764, g_loss: 1.2966, Mean D(x): 0.63, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[4100/8726], d_loss: 1.0527, g_loss: 1.3003, Mean D(x): 0.64, Mean D(G(z)): 0.33\n",
      "Epoch [3/10], Step[4200/8726], d_loss: 1.1802, g_loss: 1.1527, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[4300/8726], d_loss: 1.1943, g_loss: 1.1767, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[4400/8726], d_loss: 1.0653, g_loss: 1.3329, Mean D(x): 0.58, Mean D(G(z)): 0.29\n",
      "Epoch [3/10], Step[4500/8726], d_loss: 1.1693, g_loss: 1.3683, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [3/10], Step[4600/8726], d_loss: 0.9350, g_loss: 1.2118, Mean D(x): 0.73, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[4700/8726], d_loss: 0.9460, g_loss: 1.1963, Mean D(x): 0.65, Mean D(G(z)): 0.31\n",
      "Epoch [3/10], Step[4800/8726], d_loss: 1.0137, g_loss: 1.3249, Mean D(x): 0.66, Mean D(G(z)): 0.30\n",
      "Epoch [3/10], Step[4900/8726], d_loss: 1.1061, g_loss: 1.4255, Mean D(x): 0.61, Mean D(G(z)): 0.33\n",
      "Epoch [3/10], Step[5000/8726], d_loss: 1.0826, g_loss: 1.0574, Mean D(x): 0.66, Mean D(G(z)): 0.43\n",
      "Epoch [3/10], Step[5100/8726], d_loss: 1.0246, g_loss: 1.5115, Mean D(x): 0.64, Mean D(G(z)): 0.30\n",
      "Epoch [3/10], Step[5200/8726], d_loss: 1.1107, g_loss: 1.1814, Mean D(x): 0.65, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[5300/8726], d_loss: 1.1939, g_loss: 1.4762, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [3/10], Step[5400/8726], d_loss: 1.1157, g_loss: 1.2582, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[5500/8726], d_loss: 1.0809, g_loss: 1.2518, Mean D(x): 0.63, Mean D(G(z)): 0.31\n",
      "Epoch [3/10], Step[5600/8726], d_loss: 1.2839, g_loss: 0.9622, Mean D(x): 0.64, Mean D(G(z)): 0.47\n",
      "Epoch [3/10], Step[5700/8726], d_loss: 1.0569, g_loss: 1.5258, Mean D(x): 0.61, Mean D(G(z)): 0.30\n",
      "Epoch [3/10], Step[5800/8726], d_loss: 1.0068, g_loss: 1.2124, Mean D(x): 0.68, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[5900/8726], d_loss: 1.2282, g_loss: 1.4225, Mean D(x): 0.59, Mean D(G(z)): 0.31\n",
      "Epoch [3/10], Step[6000/8726], d_loss: 1.0704, g_loss: 0.9651, Mean D(x): 0.70, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[6100/8726], d_loss: 1.0069, g_loss: 1.4186, Mean D(x): 0.69, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[6200/8726], d_loss: 1.1439, g_loss: 1.3087, Mean D(x): 0.62, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[6300/8726], d_loss: 1.0952, g_loss: 1.4626, Mean D(x): 0.59, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[6400/8726], d_loss: 1.1372, g_loss: 1.3905, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [3/10], Step[6500/8726], d_loss: 0.9545, g_loss: 1.1420, Mean D(x): 0.70, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[6600/8726], d_loss: 1.1026, g_loss: 1.3945, Mean D(x): 0.63, Mean D(G(z)): 0.33\n",
      "Epoch [3/10], Step[6700/8726], d_loss: 1.0485, g_loss: 1.4367, Mean D(x): 0.65, Mean D(G(z)): 0.31\n",
      "Epoch [3/10], Step[6800/8726], d_loss: 0.8763, g_loss: 1.1650, Mean D(x): 0.72, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[6900/8726], d_loss: 1.1350, g_loss: 1.2064, Mean D(x): 0.69, Mean D(G(z)): 0.42\n",
      "Epoch [3/10], Step[7000/8726], d_loss: 1.1644, g_loss: 1.1470, Mean D(x): 0.68, Mean D(G(z)): 0.42\n",
      "Epoch [3/10], Step[7100/8726], d_loss: 1.2327, g_loss: 1.0511, Mean D(x): 0.60, Mean D(G(z)): 0.41\n",
      "Epoch [3/10], Step[7200/8726], d_loss: 1.1572, g_loss: 1.5141, Mean D(x): 0.58, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[7300/8726], d_loss: 1.1770, g_loss: 1.1324, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[7400/8726], d_loss: 1.1925, g_loss: 1.1865, Mean D(x): 0.56, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[7500/8726], d_loss: 0.9677, g_loss: 1.2776, Mean D(x): 0.68, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[7600/8726], d_loss: 1.1023, g_loss: 1.2010, Mean D(x): 0.64, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[7700/8726], d_loss: 1.0528, g_loss: 1.3242, Mean D(x): 0.61, Mean D(G(z)): 0.32\n",
      "Epoch [3/10], Step[7800/8726], d_loss: 1.1969, g_loss: 1.0869, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[7900/8726], d_loss: 1.1816, g_loss: 1.1401, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[8000/8726], d_loss: 1.0267, g_loss: 1.3332, Mean D(x): 0.59, Mean D(G(z)): 0.31\n",
      "Epoch [3/10], Step[8100/8726], d_loss: 0.9991, g_loss: 1.1608, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [3/10], Step[8200/8726], d_loss: 0.9509, g_loss: 1.1871, Mean D(x): 0.70, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[8300/8726], d_loss: 1.1877, g_loss: 1.4509, Mean D(x): 0.61, Mean D(G(z)): 0.34\n",
      "Epoch [3/10], Step[8400/8726], d_loss: 1.0954, g_loss: 1.0447, Mean D(x): 0.69, Mean D(G(z)): 0.40\n",
      "Epoch [3/10], Step[8500/8726], d_loss: 1.0599, g_loss: 1.1526, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [3/10], Step[8600/8726], d_loss: 1.0956, g_loss: 1.3826, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [3/10], Step[8700/8726], d_loss: 1.1448, g_loss: 1.1888, Mean D(x): 0.65, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[100/8726], d_loss: 0.9499, g_loss: 1.2554, Mean D(x): 0.66, Mean D(G(z)): 0.32\n",
      "Epoch [4/10], Step[200/8726], d_loss: 1.0723, g_loss: 0.9124, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[300/8726], d_loss: 1.0578, g_loss: 1.1644, Mean D(x): 0.59, Mean D(G(z)): 0.32\n",
      "Epoch [4/10], Step[400/8726], d_loss: 1.2743, g_loss: 1.3383, Mean D(x): 0.57, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[500/8726], d_loss: 1.1301, g_loss: 1.4283, Mean D(x): 0.59, Mean D(G(z)): 0.30\n",
      "Epoch [4/10], Step[600/8726], d_loss: 1.1681, g_loss: 0.9940, Mean D(x): 0.63, Mean D(G(z)): 0.42\n",
      "Epoch [4/10], Step[700/8726], d_loss: 1.1878, g_loss: 1.1219, Mean D(x): 0.67, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[800/8726], d_loss: 1.1193, g_loss: 1.1576, Mean D(x): 0.65, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[900/8726], d_loss: 1.0559, g_loss: 1.3542, Mean D(x): 0.72, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[1000/8726], d_loss: 0.9760, g_loss: 1.4894, Mean D(x): 0.65, Mean D(G(z)): 0.31\n",
      "Epoch [4/10], Step[1100/8726], d_loss: 1.2244, g_loss: 1.4156, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[1200/8726], d_loss: 1.1845, g_loss: 1.0393, Mean D(x): 0.57, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[1300/8726], d_loss: 1.0231, g_loss: 1.1768, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[1400/8726], d_loss: 1.0402, g_loss: 1.2282, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[1500/8726], d_loss: 1.0654, g_loss: 1.3209, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [4/10], Step[1600/8726], d_loss: 1.0861, g_loss: 1.2722, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[1700/8726], d_loss: 1.0783, g_loss: 1.2180, Mean D(x): 0.66, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[1800/8726], d_loss: 1.1517, g_loss: 1.1500, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[1900/8726], d_loss: 1.1033, g_loss: 1.1677, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[2000/8726], d_loss: 1.1416, g_loss: 1.1271, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[2100/8726], d_loss: 0.9745, g_loss: 1.4471, Mean D(x): 0.68, Mean D(G(z)): 0.33\n",
      "Epoch [4/10], Step[2200/8726], d_loss: 1.1253, g_loss: 1.2098, Mean D(x): 0.62, Mean D(G(z)): 0.35\n",
      "Epoch [4/10], Step[2300/8726], d_loss: 1.0553, g_loss: 1.1880, Mean D(x): 0.68, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[2400/8726], d_loss: 1.2869, g_loss: 1.1192, Mean D(x): 0.55, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[2500/8726], d_loss: 1.0084, g_loss: 1.1444, Mean D(x): 0.71, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[2600/8726], d_loss: 1.1981, g_loss: 1.3002, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[2700/8726], d_loss: 1.0793, g_loss: 1.1205, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[2800/8726], d_loss: 1.0551, g_loss: 1.0494, Mean D(x): 0.68, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[2900/8726], d_loss: 1.2185, g_loss: 0.9101, Mean D(x): 0.60, Mean D(G(z)): 0.45\n",
      "Epoch [4/10], Step[3000/8726], d_loss: 1.0354, g_loss: 1.1414, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [4/10], Step[3100/8726], d_loss: 1.2215, g_loss: 1.1932, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[3200/8726], d_loss: 1.1374, g_loss: 1.0782, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[3300/8726], d_loss: 1.1368, g_loss: 1.1816, Mean D(x): 0.71, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[3400/8726], d_loss: 1.1196, g_loss: 1.0239, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[3500/8726], d_loss: 1.2202, g_loss: 1.2220, Mean D(x): 0.61, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[3600/8726], d_loss: 1.1220, g_loss: 0.9967, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[3700/8726], d_loss: 1.0702, g_loss: 1.1938, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [4/10], Step[3800/8726], d_loss: 1.1268, g_loss: 1.3370, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[3900/8726], d_loss: 1.1255, g_loss: 1.2033, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[4000/8726], d_loss: 1.0504, g_loss: 1.1746, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [4/10], Step[4100/8726], d_loss: 1.0619, g_loss: 1.1801, Mean D(x): 0.65, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[4200/8726], d_loss: 1.3027, g_loss: 1.3147, Mean D(x): 0.59, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[4300/8726], d_loss: 1.2108, g_loss: 1.0736, Mean D(x): 0.65, Mean D(G(z)): 0.45\n",
      "Epoch [4/10], Step[4400/8726], d_loss: 1.1252, g_loss: 1.0457, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[4500/8726], d_loss: 1.1722, g_loss: 1.0674, Mean D(x): 0.67, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[4600/8726], d_loss: 1.0102, g_loss: 1.1561, Mean D(x): 0.64, Mean D(G(z)): 0.33\n",
      "Epoch [4/10], Step[4700/8726], d_loss: 1.0304, g_loss: 1.1993, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [4/10], Step[4800/8726], d_loss: 0.9953, g_loss: 1.4373, Mean D(x): 0.69, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[4900/8726], d_loss: 1.1773, g_loss: 1.0922, Mean D(x): 0.62, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[5000/8726], d_loss: 0.9680, g_loss: 0.9541, Mean D(x): 0.74, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[5100/8726], d_loss: 0.9986, g_loss: 1.0849, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[5200/8726], d_loss: 1.1384, g_loss: 1.2347, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[5300/8726], d_loss: 1.0261, g_loss: 0.9650, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[5400/8726], d_loss: 1.0544, g_loss: 1.2864, Mean D(x): 0.60, Mean D(G(z)): 0.31\n",
      "Epoch [4/10], Step[5500/8726], d_loss: 1.0799, g_loss: 1.0618, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[5600/8726], d_loss: 1.0768, g_loss: 1.0445, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[5700/8726], d_loss: 1.0418, g_loss: 1.2008, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [4/10], Step[5800/8726], d_loss: 1.1379, g_loss: 1.1836, Mean D(x): 0.60, Mean D(G(z)): 0.35\n",
      "Epoch [4/10], Step[5900/8726], d_loss: 1.0106, g_loss: 1.0903, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[6000/8726], d_loss: 1.0872, g_loss: 1.0278, Mean D(x): 0.65, Mean D(G(z)): 0.42\n",
      "Epoch [4/10], Step[6100/8726], d_loss: 1.2177, g_loss: 1.3072, Mean D(x): 0.57, Mean D(G(z)): 0.39\n",
      "Epoch [4/10], Step[6200/8726], d_loss: 1.0477, g_loss: 1.3570, Mean D(x): 0.60, Mean D(G(z)): 0.27\n",
      "Epoch [4/10], Step[6300/8726], d_loss: 1.1393, g_loss: 0.9795, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[6400/8726], d_loss: 1.0031, g_loss: 1.2787, Mean D(x): 0.64, Mean D(G(z)): 0.30\n",
      "Epoch [4/10], Step[6500/8726], d_loss: 1.0217, g_loss: 1.1188, Mean D(x): 0.65, Mean D(G(z)): 0.33\n",
      "Epoch [4/10], Step[6600/8726], d_loss: 1.1142, g_loss: 1.0992, Mean D(x): 0.57, Mean D(G(z)): 0.32\n",
      "Epoch [4/10], Step[6700/8726], d_loss: 1.0119, g_loss: 1.2245, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [4/10], Step[6800/8726], d_loss: 1.0512, g_loss: 1.2491, Mean D(x): 0.66, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[6900/8726], d_loss: 0.9910, g_loss: 1.2412, Mean D(x): 0.69, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[7000/8726], d_loss: 1.1499, g_loss: 1.1297, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[7100/8726], d_loss: 1.1539, g_loss: 1.2063, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[7200/8726], d_loss: 1.2072, g_loss: 1.2031, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[7300/8726], d_loss: 1.1374, g_loss: 1.1315, Mean D(x): 0.66, Mean D(G(z)): 0.42\n",
      "Epoch [4/10], Step[7400/8726], d_loss: 1.0962, g_loss: 1.1372, Mean D(x): 0.64, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[7500/8726], d_loss: 1.1882, g_loss: 1.1488, Mean D(x): 0.60, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[7600/8726], d_loss: 1.0366, g_loss: 1.1269, Mean D(x): 0.65, Mean D(G(z)): 0.37\n",
      "Epoch [4/10], Step[7700/8726], d_loss: 1.0393, g_loss: 1.2237, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[7800/8726], d_loss: 1.1599, g_loss: 1.1086, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[7900/8726], d_loss: 1.0108, g_loss: 0.9944, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[8000/8726], d_loss: 1.1227, g_loss: 1.0407, Mean D(x): 0.68, Mean D(G(z)): 0.43\n",
      "Epoch [4/10], Step[8100/8726], d_loss: 1.1305, g_loss: 1.1844, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[8200/8726], d_loss: 1.2473, g_loss: 1.3129, Mean D(x): 0.50, Mean D(G(z)): 0.31\n",
      "Epoch [4/10], Step[8300/8726], d_loss: 1.1484, g_loss: 1.1943, Mean D(x): 0.65, Mean D(G(z)): 0.41\n",
      "Epoch [4/10], Step[8400/8726], d_loss: 0.9393, g_loss: 1.1075, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [4/10], Step[8500/8726], d_loss: 1.2498, g_loss: 1.0705, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [4/10], Step[8600/8726], d_loss: 1.1494, g_loss: 1.2022, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [4/10], Step[8700/8726], d_loss: 1.1569, g_loss: 1.2061, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[100/8726], d_loss: 1.0661, g_loss: 1.3365, Mean D(x): 0.66, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[200/8726], d_loss: 1.2191, g_loss: 0.9281, Mean D(x): 0.62, Mean D(G(z)): 0.43\n",
      "Epoch [5/10], Step[300/8726], d_loss: 1.1307, g_loss: 1.2981, Mean D(x): 0.55, Mean D(G(z)): 0.31\n",
      "Epoch [5/10], Step[400/8726], d_loss: 1.0833, g_loss: 1.0559, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[500/8726], d_loss: 1.0871, g_loss: 1.0308, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[600/8726], d_loss: 1.1778, g_loss: 1.0511, Mean D(x): 0.63, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[700/8726], d_loss: 1.1291, g_loss: 1.2946, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[800/8726], d_loss: 1.2410, g_loss: 0.9461, Mean D(x): 0.65, Mean D(G(z)): 0.46\n",
      "Epoch [5/10], Step[900/8726], d_loss: 1.1530, g_loss: 1.1393, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [5/10], Step[1000/8726], d_loss: 1.2105, g_loss: 1.1151, Mean D(x): 0.54, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[1100/8726], d_loss: 1.1811, g_loss: 1.3465, Mean D(x): 0.58, Mean D(G(z)): 0.34\n",
      "Epoch [5/10], Step[1200/8726], d_loss: 1.0492, g_loss: 1.1322, Mean D(x): 0.67, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[1300/8726], d_loss: 1.0972, g_loss: 1.2285, Mean D(x): 0.66, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[1400/8726], d_loss: 1.1395, g_loss: 1.0450, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[1500/8726], d_loss: 1.0968, g_loss: 1.1084, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[1600/8726], d_loss: 1.1388, g_loss: 0.9980, Mean D(x): 0.60, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[1700/8726], d_loss: 1.2452, g_loss: 1.0745, Mean D(x): 0.58, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[1800/8726], d_loss: 1.0769, g_loss: 1.1956, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[1900/8726], d_loss: 1.1915, g_loss: 1.1776, Mean D(x): 0.59, Mean D(G(z)): 0.41\n",
      "Epoch [5/10], Step[2000/8726], d_loss: 1.0634, g_loss: 1.2568, Mean D(x): 0.61, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[2100/8726], d_loss: 1.2497, g_loss: 1.2030, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[2200/8726], d_loss: 1.1781, g_loss: 1.0477, Mean D(x): 0.58, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[2300/8726], d_loss: 1.0777, g_loss: 1.0343, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[2400/8726], d_loss: 1.1305, g_loss: 1.2604, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[2500/8726], d_loss: 1.0330, g_loss: 1.4380, Mean D(x): 0.63, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[2600/8726], d_loss: 1.0316, g_loss: 1.3515, Mean D(x): 0.63, Mean D(G(z)): 0.31\n",
      "Epoch [5/10], Step[2700/8726], d_loss: 0.9928, g_loss: 1.2588, Mean D(x): 0.64, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[2800/8726], d_loss: 1.2232, g_loss: 1.0666, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[2900/8726], d_loss: 1.0259, g_loss: 1.1029, Mean D(x): 0.63, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[3000/8726], d_loss: 1.2944, g_loss: 1.0552, Mean D(x): 0.55, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[3100/8726], d_loss: 1.1198, g_loss: 1.1724, Mean D(x): 0.64, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[3200/8726], d_loss: 1.0775, g_loss: 1.1201, Mean D(x): 0.60, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[3300/8726], d_loss: 1.0788, g_loss: 0.9038, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[3400/8726], d_loss: 1.0206, g_loss: 1.2023, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [5/10], Step[3500/8726], d_loss: 1.0923, g_loss: 1.2726, Mean D(x): 0.68, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[3600/8726], d_loss: 1.0941, g_loss: 1.1194, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[3700/8726], d_loss: 1.0543, g_loss: 1.4584, Mean D(x): 0.70, Mean D(G(z)): 0.36\n",
      "Epoch [5/10], Step[3800/8726], d_loss: 1.0640, g_loss: 1.1836, Mean D(x): 0.61, Mean D(G(z)): 0.32\n",
      "Epoch [5/10], Step[3900/8726], d_loss: 1.1081, g_loss: 1.2505, Mean D(x): 0.59, Mean D(G(z)): 0.34\n",
      "Epoch [5/10], Step[4000/8726], d_loss: 1.1118, g_loss: 0.9521, Mean D(x): 0.70, Mean D(G(z)): 0.43\n",
      "Epoch [5/10], Step[4100/8726], d_loss: 1.0291, g_loss: 1.6351, Mean D(x): 0.58, Mean D(G(z)): 0.28\n",
      "Epoch [5/10], Step[4200/8726], d_loss: 1.1619, g_loss: 1.1658, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[4300/8726], d_loss: 1.1273, g_loss: 1.4980, Mean D(x): 0.59, Mean D(G(z)): 0.30\n",
      "Epoch [5/10], Step[4400/8726], d_loss: 0.9731, g_loss: 1.4471, Mean D(x): 0.67, Mean D(G(z)): 0.31\n",
      "Epoch [5/10], Step[4500/8726], d_loss: 1.2058, g_loss: 1.1074, Mean D(x): 0.61, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[4600/8726], d_loss: 1.0819, g_loss: 1.2172, Mean D(x): 0.66, Mean D(G(z)): 0.41\n",
      "Epoch [5/10], Step[4700/8726], d_loss: 1.3150, g_loss: 1.1102, Mean D(x): 0.60, Mean D(G(z)): 0.43\n",
      "Epoch [5/10], Step[4800/8726], d_loss: 0.9487, g_loss: 1.3072, Mean D(x): 0.68, Mean D(G(z)): 0.34\n",
      "Epoch [5/10], Step[4900/8726], d_loss: 1.1423, g_loss: 1.1110, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[5000/8726], d_loss: 1.0909, g_loss: 1.0659, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[5100/8726], d_loss: 1.1554, g_loss: 1.0534, Mean D(x): 0.62, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[5200/8726], d_loss: 1.2097, g_loss: 1.0055, Mean D(x): 0.60, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[5300/8726], d_loss: 1.1967, g_loss: 1.1710, Mean D(x): 0.58, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[5400/8726], d_loss: 1.0233, g_loss: 1.5120, Mean D(x): 0.57, Mean D(G(z)): 0.26\n",
      "Epoch [5/10], Step[5500/8726], d_loss: 1.0946, g_loss: 1.0449, Mean D(x): 0.67, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[5600/8726], d_loss: 1.1580, g_loss: 1.1725, Mean D(x): 0.64, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[5700/8726], d_loss: 1.2377, g_loss: 1.1221, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[5800/8726], d_loss: 1.1089, g_loss: 1.1540, Mean D(x): 0.60, Mean D(G(z)): 0.35\n",
      "Epoch [5/10], Step[5900/8726], d_loss: 1.1513, g_loss: 1.1689, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[6000/8726], d_loss: 1.1065, g_loss: 1.0845, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[6100/8726], d_loss: 1.2306, g_loss: 1.1966, Mean D(x): 0.56, Mean D(G(z)): 0.35\n",
      "Epoch [5/10], Step[6200/8726], d_loss: 1.1490, g_loss: 1.1946, Mean D(x): 0.55, Mean D(G(z)): 0.30\n",
      "Epoch [5/10], Step[6300/8726], d_loss: 1.1490, g_loss: 1.0400, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[6400/8726], d_loss: 1.1794, g_loss: 1.2059, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[6500/8726], d_loss: 1.1887, g_loss: 1.0754, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [5/10], Step[6600/8726], d_loss: 1.0051, g_loss: 1.2571, Mean D(x): 0.62, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[6700/8726], d_loss: 1.2662, g_loss: 1.2937, Mean D(x): 0.54, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[6800/8726], d_loss: 1.0943, g_loss: 1.2313, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[6900/8726], d_loss: 0.8593, g_loss: 1.3093, Mean D(x): 0.70, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[7000/8726], d_loss: 1.1324, g_loss: 1.2294, Mean D(x): 0.69, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[7100/8726], d_loss: 1.0392, g_loss: 1.1446, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[7200/8726], d_loss: 1.0992, g_loss: 1.1748, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [5/10], Step[7300/8726], d_loss: 1.0588, g_loss: 1.1434, Mean D(x): 0.66, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[7400/8726], d_loss: 1.1018, g_loss: 1.3652, Mean D(x): 0.71, Mean D(G(z)): 0.42\n",
      "Epoch [5/10], Step[7500/8726], d_loss: 1.0842, g_loss: 1.1718, Mean D(x): 0.62, Mean D(G(z)): 0.33\n",
      "Epoch [5/10], Step[7600/8726], d_loss: 1.1840, g_loss: 1.0497, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[7700/8726], d_loss: 1.0432, g_loss: 1.1692, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [5/10], Step[7800/8726], d_loss: 1.3082, g_loss: 1.1640, Mean D(x): 0.60, Mean D(G(z)): 0.43\n",
      "Epoch [5/10], Step[7900/8726], d_loss: 1.1951, g_loss: 1.2096, Mean D(x): 0.61, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[8000/8726], d_loss: 1.1301, g_loss: 1.0929, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [5/10], Step[8100/8726], d_loss: 0.9825, g_loss: 1.1876, Mean D(x): 0.66, Mean D(G(z)): 0.32\n",
      "Epoch [5/10], Step[8200/8726], d_loss: 1.0632, g_loss: 1.0869, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [5/10], Step[8300/8726], d_loss: 0.9229, g_loss: 1.3609, Mean D(x): 0.67, Mean D(G(z)): 0.32\n",
      "Epoch [5/10], Step[8400/8726], d_loss: 1.1108, g_loss: 1.2451, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [5/10], Step[8500/8726], d_loss: 1.2015, g_loss: 1.2259, Mean D(x): 0.65, Mean D(G(z)): 0.43\n",
      "Epoch [5/10], Step[8600/8726], d_loss: 0.9285, g_loss: 1.1555, Mean D(x): 0.66, Mean D(G(z)): 0.31\n",
      "Epoch [5/10], Step[8700/8726], d_loss: 1.2457, g_loss: 1.1386, Mean D(x): 0.64, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[100/8726], d_loss: 1.0111, g_loss: 1.2588, Mean D(x): 0.63, Mean D(G(z)): 0.32\n",
      "Epoch [6/10], Step[200/8726], d_loss: 1.1951, g_loss: 1.0658, Mean D(x): 0.64, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[300/8726], d_loss: 0.9889, g_loss: 1.0937, Mean D(x): 0.65, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[400/8726], d_loss: 1.1165, g_loss: 1.2981, Mean D(x): 0.57, Mean D(G(z)): 0.30\n",
      "Epoch [6/10], Step[500/8726], d_loss: 1.0908, g_loss: 1.1829, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[600/8726], d_loss: 1.0725, g_loss: 1.2020, Mean D(x): 0.66, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[700/8726], d_loss: 1.0715, g_loss: 1.2677, Mean D(x): 0.66, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[800/8726], d_loss: 1.1778, g_loss: 1.0513, Mean D(x): 0.62, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[900/8726], d_loss: 1.0220, g_loss: 1.1778, Mean D(x): 0.66, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[1000/8726], d_loss: 1.2088, g_loss: 0.9721, Mean D(x): 0.64, Mean D(G(z)): 0.45\n",
      "Epoch [6/10], Step[1100/8726], d_loss: 1.0428, g_loss: 1.3186, Mean D(x): 0.60, Mean D(G(z)): 0.31\n",
      "Epoch [6/10], Step[1200/8726], d_loss: 1.0741, g_loss: 1.0853, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[1300/8726], d_loss: 1.0328, g_loss: 1.0060, Mean D(x): 0.68, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[1400/8726], d_loss: 1.0870, g_loss: 1.1509, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[1500/8726], d_loss: 1.0684, g_loss: 1.3130, Mean D(x): 0.59, Mean D(G(z)): 0.30\n",
      "Epoch [6/10], Step[1600/8726], d_loss: 1.0083, g_loss: 1.4076, Mean D(x): 0.62, Mean D(G(z)): 0.31\n",
      "Epoch [6/10], Step[1700/8726], d_loss: 1.0487, g_loss: 1.3596, Mean D(x): 0.62, Mean D(G(z)): 0.31\n",
      "Epoch [6/10], Step[1800/8726], d_loss: 0.9874, g_loss: 1.0542, Mean D(x): 0.70, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[1900/8726], d_loss: 1.1087, g_loss: 1.0031, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[2000/8726], d_loss: 1.1846, g_loss: 1.2567, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[2100/8726], d_loss: 1.0761, g_loss: 1.1419, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[2200/8726], d_loss: 1.1048, g_loss: 1.1911, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[2300/8726], d_loss: 1.1763, g_loss: 1.3108, Mean D(x): 0.54, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[2400/8726], d_loss: 1.0513, g_loss: 1.3694, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[2500/8726], d_loss: 1.2491, g_loss: 1.1330, Mean D(x): 0.58, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[2600/8726], d_loss: 1.2723, g_loss: 1.0968, Mean D(x): 0.58, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[2700/8726], d_loss: 1.1471, g_loss: 1.1812, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[2800/8726], d_loss: 1.0419, g_loss: 1.4832, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[2900/8726], d_loss: 1.2740, g_loss: 1.2480, Mean D(x): 0.56, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[3000/8726], d_loss: 1.1044, g_loss: 1.1557, Mean D(x): 0.60, Mean D(G(z)): 0.32\n",
      "Epoch [6/10], Step[3100/8726], d_loss: 1.2863, g_loss: 1.3488, Mean D(x): 0.58, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[3200/8726], d_loss: 1.0560, g_loss: 1.1422, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[3300/8726], d_loss: 1.2614, g_loss: 1.0488, Mean D(x): 0.60, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[3400/8726], d_loss: 1.0821, g_loss: 1.2551, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[3500/8726], d_loss: 1.0657, g_loss: 1.2499, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[3600/8726], d_loss: 1.1096, g_loss: 1.4516, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[3700/8726], d_loss: 1.2579, g_loss: 1.0360, Mean D(x): 0.61, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[3800/8726], d_loss: 1.0071, g_loss: 1.0315, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[3900/8726], d_loss: 1.1143, g_loss: 1.2102, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[4000/8726], d_loss: 1.0705, g_loss: 1.1940, Mean D(x): 0.60, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[4100/8726], d_loss: 1.0506, g_loss: 1.0435, Mean D(x): 0.66, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[4200/8726], d_loss: 1.1291, g_loss: 1.2731, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[4300/8726], d_loss: 1.1282, g_loss: 1.3227, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[4400/8726], d_loss: 1.1476, g_loss: 1.3176, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[4500/8726], d_loss: 1.0560, g_loss: 1.2242, Mean D(x): 0.65, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[4600/8726], d_loss: 1.0611, g_loss: 1.1416, Mean D(x): 0.68, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[4700/8726], d_loss: 1.2206, g_loss: 1.0481, Mean D(x): 0.68, Mean D(G(z)): 0.48\n",
      "Epoch [6/10], Step[4800/8726], d_loss: 1.1321, g_loss: 1.1169, Mean D(x): 0.67, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[4900/8726], d_loss: 1.3902, g_loss: 1.0367, Mean D(x): 0.57, Mean D(G(z)): 0.45\n",
      "Epoch [6/10], Step[5000/8726], d_loss: 1.0993, g_loss: 1.0577, Mean D(x): 0.64, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[5100/8726], d_loss: 1.2008, g_loss: 0.9568, Mean D(x): 0.60, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[5200/8726], d_loss: 1.0970, g_loss: 1.0081, Mean D(x): 0.65, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[5300/8726], d_loss: 1.1866, g_loss: 1.1286, Mean D(x): 0.57, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[5400/8726], d_loss: 1.0952, g_loss: 0.9685, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[5500/8726], d_loss: 1.1004, g_loss: 1.2198, Mean D(x): 0.60, Mean D(G(z)): 0.33\n",
      "Epoch [6/10], Step[5600/8726], d_loss: 0.9824, g_loss: 1.2392, Mean D(x): 0.69, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[5700/8726], d_loss: 1.1246, g_loss: 1.0160, Mean D(x): 0.69, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[5800/8726], d_loss: 1.0817, g_loss: 1.1594, Mean D(x): 0.65, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[5900/8726], d_loss: 1.1350, g_loss: 1.2601, Mean D(x): 0.66, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[6000/8726], d_loss: 1.1372, g_loss: 1.0960, Mean D(x): 0.66, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[6100/8726], d_loss: 1.1695, g_loss: 1.0703, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[6200/8726], d_loss: 1.0603, g_loss: 1.5908, Mean D(x): 0.61, Mean D(G(z)): 0.27\n",
      "Epoch [6/10], Step[6300/8726], d_loss: 1.1333, g_loss: 1.0290, Mean D(x): 0.65, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[6400/8726], d_loss: 1.1779, g_loss: 1.0655, Mean D(x): 0.62, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[6500/8726], d_loss: 1.1144, g_loss: 0.9455, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[6600/8726], d_loss: 1.1604, g_loss: 1.2096, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[6700/8726], d_loss: 1.1651, g_loss: 1.1994, Mean D(x): 0.58, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[6800/8726], d_loss: 1.1170, g_loss: 1.2814, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[6900/8726], d_loss: 1.1394, g_loss: 1.2014, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[7000/8726], d_loss: 1.0954, g_loss: 1.3646, Mean D(x): 0.65, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[7100/8726], d_loss: 1.2929, g_loss: 1.0608, Mean D(x): 0.57, Mean D(G(z)): 0.42\n",
      "Epoch [6/10], Step[7200/8726], d_loss: 1.2451, g_loss: 0.9224, Mean D(x): 0.58, Mean D(G(z)): 0.40\n",
      "Epoch [6/10], Step[7300/8726], d_loss: 1.2682, g_loss: 1.2305, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[7400/8726], d_loss: 1.2429, g_loss: 1.0399, Mean D(x): 0.60, Mean D(G(z)): 0.41\n",
      "Epoch [6/10], Step[7500/8726], d_loss: 0.9314, g_loss: 1.3088, Mean D(x): 0.72, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[7600/8726], d_loss: 1.1825, g_loss: 1.0192, Mean D(x): 0.55, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[7700/8726], d_loss: 1.0240, g_loss: 1.0119, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [6/10], Step[7800/8726], d_loss: 1.1157, g_loss: 1.3182, Mean D(x): 0.59, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[7900/8726], d_loss: 1.1955, g_loss: 1.1702, Mean D(x): 0.58, Mean D(G(z)): 0.34\n",
      "Epoch [6/10], Step[8000/8726], d_loss: 1.0841, g_loss: 1.1927, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [6/10], Step[8100/8726], d_loss: 1.1498, g_loss: 1.1975, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[8200/8726], d_loss: 1.2257, g_loss: 1.2397, Mean D(x): 0.59, Mean D(G(z)): 0.37\n",
      "Epoch [6/10], Step[8300/8726], d_loss: 1.0015, g_loss: 1.1479, Mean D(x): 0.65, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[8400/8726], d_loss: 1.3257, g_loss: 1.0892, Mean D(x): 0.63, Mean D(G(z)): 0.43\n",
      "Epoch [6/10], Step[8500/8726], d_loss: 0.9812, g_loss: 1.2438, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [6/10], Step[8600/8726], d_loss: 1.1742, g_loss: 0.9678, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [6/10], Step[8700/8726], d_loss: 1.0702, g_loss: 1.2988, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[100/8726], d_loss: 1.1180, g_loss: 1.0162, Mean D(x): 0.65, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[200/8726], d_loss: 1.2498, g_loss: 1.0658, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[300/8726], d_loss: 1.1634, g_loss: 1.2760, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[400/8726], d_loss: 1.0755, g_loss: 1.1527, Mean D(x): 0.64, Mean D(G(z)): 0.34\n",
      "Epoch [7/10], Step[500/8726], d_loss: 1.1182, g_loss: 1.0817, Mean D(x): 0.66, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[600/8726], d_loss: 1.1075, g_loss: 1.1046, Mean D(x): 0.65, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[700/8726], d_loss: 0.9948, g_loss: 1.1721, Mean D(x): 0.69, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[800/8726], d_loss: 0.9987, g_loss: 0.9173, Mean D(x): 0.67, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[900/8726], d_loss: 1.0820, g_loss: 1.0979, Mean D(x): 0.67, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[1000/8726], d_loss: 1.0199, g_loss: 1.2763, Mean D(x): 0.72, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[1100/8726], d_loss: 1.0968, g_loss: 1.0948, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[1200/8726], d_loss: 1.1697, g_loss: 0.9244, Mean D(x): 0.63, Mean D(G(z)): 0.44\n",
      "Epoch [7/10], Step[1300/8726], d_loss: 1.1282, g_loss: 1.2383, Mean D(x): 0.69, Mean D(G(z)): 0.43\n",
      "Epoch [7/10], Step[1400/8726], d_loss: 1.1422, g_loss: 0.9287, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[1500/8726], d_loss: 1.1001, g_loss: 1.1060, Mean D(x): 0.59, Mean D(G(z)): 0.34\n",
      "Epoch [7/10], Step[1600/8726], d_loss: 1.2010, g_loss: 0.8801, Mean D(x): 0.61, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[1700/8726], d_loss: 1.2245, g_loss: 1.0779, Mean D(x): 0.61, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[1800/8726], d_loss: 1.1744, g_loss: 1.1744, Mean D(x): 0.59, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[1900/8726], d_loss: 1.2576, g_loss: 0.9149, Mean D(x): 0.65, Mean D(G(z)): 0.47\n",
      "Epoch [7/10], Step[2000/8726], d_loss: 1.2352, g_loss: 1.4048, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[2100/8726], d_loss: 1.0766, g_loss: 1.1351, Mean D(x): 0.70, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[2200/8726], d_loss: 1.1648, g_loss: 1.2308, Mean D(x): 0.54, Mean D(G(z)): 0.32\n",
      "Epoch [7/10], Step[2300/8726], d_loss: 1.1370, g_loss: 1.0563, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[2400/8726], d_loss: 1.1109, g_loss: 1.1285, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[2500/8726], d_loss: 1.1993, g_loss: 1.1192, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[2600/8726], d_loss: 1.0101, g_loss: 1.1816, Mean D(x): 0.68, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[2700/8726], d_loss: 1.1941, g_loss: 1.1846, Mean D(x): 0.57, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[2800/8726], d_loss: 0.9903, g_loss: 1.1670, Mean D(x): 0.72, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[2900/8726], d_loss: 1.2150, g_loss: 1.0792, Mean D(x): 0.55, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[3000/8726], d_loss: 1.1717, g_loss: 1.0646, Mean D(x): 0.58, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[3100/8726], d_loss: 1.1326, g_loss: 1.1291, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[3200/8726], d_loss: 1.0903, g_loss: 1.0417, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[3300/8726], d_loss: 1.2666, g_loss: 0.9288, Mean D(x): 0.57, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[3400/8726], d_loss: 1.2611, g_loss: 1.2479, Mean D(x): 0.55, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[3500/8726], d_loss: 1.1149, g_loss: 1.2612, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[3600/8726], d_loss: 1.1323, g_loss: 1.2294, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[3700/8726], d_loss: 1.1351, g_loss: 0.9767, Mean D(x): 0.65, Mean D(G(z)): 0.43\n",
      "Epoch [7/10], Step[3800/8726], d_loss: 1.0581, g_loss: 1.1350, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[3900/8726], d_loss: 1.2269, g_loss: 1.0402, Mean D(x): 0.64, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[4000/8726], d_loss: 1.2626, g_loss: 1.1960, Mean D(x): 0.59, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[4100/8726], d_loss: 1.1196, g_loss: 1.0520, Mean D(x): 0.65, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[4200/8726], d_loss: 1.1088, g_loss: 1.3002, Mean D(x): 0.62, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[4300/8726], d_loss: 1.2193, g_loss: 0.9931, Mean D(x): 0.63, Mean D(G(z)): 0.43\n",
      "Epoch [7/10], Step[4400/8726], d_loss: 1.0799, g_loss: 1.1987, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[4500/8726], d_loss: 1.1899, g_loss: 1.1477, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[4600/8726], d_loss: 1.1933, g_loss: 0.8556, Mean D(x): 0.64, Mean D(G(z)): 0.45\n",
      "Epoch [7/10], Step[4700/8726], d_loss: 1.1859, g_loss: 1.1502, Mean D(x): 0.57, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[4800/8726], d_loss: 1.0610, g_loss: 1.2322, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[4900/8726], d_loss: 1.1813, g_loss: 1.0148, Mean D(x): 0.57, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[5000/8726], d_loss: 0.9662, g_loss: 1.1500, Mean D(x): 0.67, Mean D(G(z)): 0.35\n",
      "Epoch [7/10], Step[5100/8726], d_loss: 1.1048, g_loss: 1.0583, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[5200/8726], d_loss: 1.1743, g_loss: 1.2617, Mean D(x): 0.60, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[5300/8726], d_loss: 1.2199, g_loss: 1.2086, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[5400/8726], d_loss: 1.0619, g_loss: 1.3090, Mean D(x): 0.58, Mean D(G(z)): 0.33\n",
      "Epoch [7/10], Step[5500/8726], d_loss: 1.0120, g_loss: 1.2862, Mean D(x): 0.65, Mean D(G(z)): 0.35\n",
      "Epoch [7/10], Step[5600/8726], d_loss: 1.1974, g_loss: 1.0890, Mean D(x): 0.62, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[5700/8726], d_loss: 1.1942, g_loss: 1.2964, Mean D(x): 0.58, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[5800/8726], d_loss: 1.1112, g_loss: 1.0730, Mean D(x): 0.59, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[5900/8726], d_loss: 1.1460, g_loss: 1.1469, Mean D(x): 0.68, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[6000/8726], d_loss: 1.2647, g_loss: 0.9947, Mean D(x): 0.62, Mean D(G(z)): 0.45\n",
      "Epoch [7/10], Step[6100/8726], d_loss: 1.0687, g_loss: 1.3571, Mean D(x): 0.61, Mean D(G(z)): 0.34\n",
      "Epoch [7/10], Step[6200/8726], d_loss: 0.9983, g_loss: 1.1536, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[6300/8726], d_loss: 1.1639, g_loss: 1.1447, Mean D(x): 0.64, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[6400/8726], d_loss: 1.0013, g_loss: 1.2198, Mean D(x): 0.61, Mean D(G(z)): 0.30\n",
      "Epoch [7/10], Step[6500/8726], d_loss: 0.9973, g_loss: 0.9204, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [7/10], Step[6600/8726], d_loss: 1.1200, g_loss: 1.3747, Mean D(x): 0.58, Mean D(G(z)): 0.32\n",
      "Epoch [7/10], Step[6700/8726], d_loss: 0.9918, g_loss: 1.2198, Mean D(x): 0.66, Mean D(G(z)): 0.32\n",
      "Epoch [7/10], Step[6800/8726], d_loss: 1.0906, g_loss: 1.1227, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[6900/8726], d_loss: 1.1620, g_loss: 1.1891, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [7/10], Step[7000/8726], d_loss: 1.1062, g_loss: 1.0042, Mean D(x): 0.69, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[7100/8726], d_loss: 1.0801, g_loss: 1.1102, Mean D(x): 0.62, Mean D(G(z)): 0.35\n",
      "Epoch [7/10], Step[7200/8726], d_loss: 1.1300, g_loss: 1.1381, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[7300/8726], d_loss: 1.1546, g_loss: 1.0787, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[7400/8726], d_loss: 1.1174, g_loss: 1.0755, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[7500/8726], d_loss: 1.1683, g_loss: 0.9854, Mean D(x): 0.59, Mean D(G(z)): 0.42\n",
      "Epoch [7/10], Step[7600/8726], d_loss: 1.1953, g_loss: 1.1425, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [7/10], Step[7700/8726], d_loss: 1.1823, g_loss: 1.2040, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[7800/8726], d_loss: 1.0577, g_loss: 1.0030, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [7/10], Step[7900/8726], d_loss: 1.1442, g_loss: 0.9654, Mean D(x): 0.62, Mean D(G(z)): 0.41\n",
      "Epoch [7/10], Step[8000/8726], d_loss: 1.2886, g_loss: 1.0845, Mean D(x): 0.58, Mean D(G(z)): 0.44\n",
      "Epoch [7/10], Step[8100/8726], d_loss: 1.1719, g_loss: 1.2120, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [7/10], Step[8200/8726], d_loss: 1.2745, g_loss: 1.0592, Mean D(x): 0.61, Mean D(G(z)): 0.44\n",
      "Epoch [7/10], Step[8300/8726], d_loss: 1.1065, g_loss: 1.0565, Mean D(x): 0.60, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[8400/8726], d_loss: 1.0963, g_loss: 0.8884, Mean D(x): 0.68, Mean D(G(z)): 0.43\n",
      "Epoch [7/10], Step[8500/8726], d_loss: 0.9891, g_loss: 1.2313, Mean D(x): 0.66, Mean D(G(z)): 0.36\n",
      "Epoch [7/10], Step[8600/8726], d_loss: 1.1725, g_loss: 1.1208, Mean D(x): 0.55, Mean D(G(z)): 0.33\n",
      "Epoch [7/10], Step[8700/8726], d_loss: 0.9578, g_loss: 1.2277, Mean D(x): 0.69, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[100/8726], d_loss: 1.0341, g_loss: 1.1991, Mean D(x): 0.61, Mean D(G(z)): 0.34\n",
      "Epoch [8/10], Step[200/8726], d_loss: 1.0494, g_loss: 1.1795, Mean D(x): 0.65, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[300/8726], d_loss: 1.0725, g_loss: 1.0396, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[400/8726], d_loss: 1.2812, g_loss: 0.9968, Mean D(x): 0.63, Mean D(G(z)): 0.45\n",
      "Epoch [8/10], Step[500/8726], d_loss: 1.0552, g_loss: 1.1227, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[600/8726], d_loss: 1.2082, g_loss: 0.9912, Mean D(x): 0.59, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[700/8726], d_loss: 1.1055, g_loss: 1.1769, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[800/8726], d_loss: 1.3495, g_loss: 1.1322, Mean D(x): 0.53, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[900/8726], d_loss: 1.0995, g_loss: 1.1112, Mean D(x): 0.66, Mean D(G(z)): 0.42\n",
      "Epoch [8/10], Step[1000/8726], d_loss: 1.1719, g_loss: 0.9953, Mean D(x): 0.66, Mean D(G(z)): 0.44\n",
      "Epoch [8/10], Step[1100/8726], d_loss: 1.1432, g_loss: 1.0905, Mean D(x): 0.57, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[1200/8726], d_loss: 0.9800, g_loss: 1.3974, Mean D(x): 0.65, Mean D(G(z)): 0.32\n",
      "Epoch [8/10], Step[1300/8726], d_loss: 1.0878, g_loss: 0.9925, Mean D(x): 0.70, Mean D(G(z)): 0.44\n",
      "Epoch [8/10], Step[1400/8726], d_loss: 1.0811, g_loss: 1.1616, Mean D(x): 0.67, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[1500/8726], d_loss: 1.2797, g_loss: 1.1978, Mean D(x): 0.54, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[1600/8726], d_loss: 1.0526, g_loss: 1.3761, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[1700/8726], d_loss: 1.0827, g_loss: 0.9834, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[1800/8726], d_loss: 1.1251, g_loss: 1.1389, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[1900/8726], d_loss: 1.2091, g_loss: 0.8375, Mean D(x): 0.59, Mean D(G(z)): 0.43\n",
      "Epoch [8/10], Step[2000/8726], d_loss: 1.0944, g_loss: 1.0560, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[2100/8726], d_loss: 1.1320, g_loss: 1.1569, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[2200/8726], d_loss: 1.1963, g_loss: 1.0595, Mean D(x): 0.66, Mean D(G(z)): 0.44\n",
      "Epoch [8/10], Step[2300/8726], d_loss: 1.0522, g_loss: 1.0813, Mean D(x): 0.68, Mean D(G(z)): 0.41\n",
      "Epoch [8/10], Step[2400/8726], d_loss: 1.1453, g_loss: 1.1134, Mean D(x): 0.62, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[2500/8726], d_loss: 1.1129, g_loss: 1.2701, Mean D(x): 0.59, Mean D(G(z)): 0.31\n",
      "Epoch [8/10], Step[2600/8726], d_loss: 0.9565, g_loss: 1.0936, Mean D(x): 0.68, Mean D(G(z)): 0.35\n",
      "Epoch [8/10], Step[2700/8726], d_loss: 1.2452, g_loss: 1.0804, Mean D(x): 0.60, Mean D(G(z)): 0.41\n",
      "Epoch [8/10], Step[2800/8726], d_loss: 1.1374, g_loss: 1.1172, Mean D(x): 0.59, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[2900/8726], d_loss: 1.1254, g_loss: 1.2812, Mean D(x): 0.67, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[3000/8726], d_loss: 1.2503, g_loss: 1.1841, Mean D(x): 0.58, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[3100/8726], d_loss: 1.2184, g_loss: 1.2031, Mean D(x): 0.59, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[3200/8726], d_loss: 1.1754, g_loss: 1.1073, Mean D(x): 0.62, Mean D(G(z)): 0.41\n",
      "Epoch [8/10], Step[3300/8726], d_loss: 1.0363, g_loss: 1.1372, Mean D(x): 0.67, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[3400/8726], d_loss: 1.1420, g_loss: 1.1009, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[3500/8726], d_loss: 1.1694, g_loss: 1.3320, Mean D(x): 0.54, Mean D(G(z)): 0.30\n",
      "Epoch [8/10], Step[3600/8726], d_loss: 1.0724, g_loss: 0.9730, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[3700/8726], d_loss: 1.1375, g_loss: 1.0504, Mean D(x): 0.63, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[3800/8726], d_loss: 1.0928, g_loss: 1.1091, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[3900/8726], d_loss: 1.1800, g_loss: 1.0449, Mean D(x): 0.60, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[4000/8726], d_loss: 1.2031, g_loss: 1.0845, Mean D(x): 0.62, Mean D(G(z)): 0.42\n",
      "Epoch [8/10], Step[4100/8726], d_loss: 1.1206, g_loss: 1.2199, Mean D(x): 0.61, Mean D(G(z)): 0.32\n",
      "Epoch [8/10], Step[4200/8726], d_loss: 1.2064, g_loss: 1.0754, Mean D(x): 0.66, Mean D(G(z)): 0.42\n",
      "Epoch [8/10], Step[4300/8726], d_loss: 1.2987, g_loss: 1.0309, Mean D(x): 0.65, Mean D(G(z)): 0.47\n",
      "Epoch [8/10], Step[4400/8726], d_loss: 1.1014, g_loss: 1.2682, Mean D(x): 0.69, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[4500/8726], d_loss: 1.2076, g_loss: 1.2155, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[4600/8726], d_loss: 1.0937, g_loss: 1.1165, Mean D(x): 0.64, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[4700/8726], d_loss: 1.1435, g_loss: 1.1006, Mean D(x): 0.69, Mean D(G(z)): 0.44\n",
      "Epoch [8/10], Step[4800/8726], d_loss: 1.1154, g_loss: 1.2383, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[4900/8726], d_loss: 1.0457, g_loss: 1.0779, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[5000/8726], d_loss: 1.2419, g_loss: 1.0684, Mean D(x): 0.54, Mean D(G(z)): 0.35\n",
      "Epoch [8/10], Step[5100/8726], d_loss: 1.2446, g_loss: 1.1105, Mean D(x): 0.58, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[5200/8726], d_loss: 1.1023, g_loss: 1.0151, Mean D(x): 0.67, Mean D(G(z)): 0.44\n",
      "Epoch [8/10], Step[5300/8726], d_loss: 1.1095, g_loss: 1.0097, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[5400/8726], d_loss: 1.2016, g_loss: 1.0150, Mean D(x): 0.62, Mean D(G(z)): 0.42\n",
      "Epoch [8/10], Step[5500/8726], d_loss: 1.0326, g_loss: 0.9491, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[5600/8726], d_loss: 1.0931, g_loss: 1.0639, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[5700/8726], d_loss: 1.1221, g_loss: 1.1680, Mean D(x): 0.65, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[5800/8726], d_loss: 1.0688, g_loss: 1.0487, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[5900/8726], d_loss: 1.2165, g_loss: 1.2573, Mean D(x): 0.53, Mean D(G(z)): 0.34\n",
      "Epoch [8/10], Step[6000/8726], d_loss: 1.0219, g_loss: 1.3770, Mean D(x): 0.68, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[6100/8726], d_loss: 1.0976, g_loss: 1.2668, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [8/10], Step[6200/8726], d_loss: 1.0512, g_loss: 1.4148, Mean D(x): 0.67, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[6300/8726], d_loss: 1.0954, g_loss: 1.3887, Mean D(x): 0.59, Mean D(G(z)): 0.30\n",
      "Epoch [8/10], Step[6400/8726], d_loss: 1.1270, g_loss: 1.1115, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[6500/8726], d_loss: 1.1284, g_loss: 1.1718, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[6600/8726], d_loss: 1.1274, g_loss: 1.0008, Mean D(x): 0.61, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[6700/8726], d_loss: 1.0551, g_loss: 1.0890, Mean D(x): 0.58, Mean D(G(z)): 0.31\n",
      "Epoch [8/10], Step[6800/8726], d_loss: 1.1091, g_loss: 1.2733, Mean D(x): 0.62, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[6900/8726], d_loss: 1.0200, g_loss: 1.2041, Mean D(x): 0.65, Mean D(G(z)): 0.35\n",
      "Epoch [8/10], Step[7000/8726], d_loss: 1.1310, g_loss: 1.1036, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[7100/8726], d_loss: 1.3369, g_loss: 1.0168, Mean D(x): 0.58, Mean D(G(z)): 0.43\n",
      "Epoch [8/10], Step[7200/8726], d_loss: 1.0411, g_loss: 1.0792, Mean D(x): 0.64, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[7300/8726], d_loss: 1.1134, g_loss: 1.2068, Mean D(x): 0.64, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[7400/8726], d_loss: 1.0728, g_loss: 0.9282, Mean D(x): 0.66, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[7500/8726], d_loss: 1.1527, g_loss: 1.0694, Mean D(x): 0.65, Mean D(G(z)): 0.43\n",
      "Epoch [8/10], Step[7600/8726], d_loss: 0.9879, g_loss: 0.9918, Mean D(x): 0.69, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[7700/8726], d_loss: 1.1529, g_loss: 1.1774, Mean D(x): 0.57, Mean D(G(z)): 0.34\n",
      "Epoch [8/10], Step[7800/8726], d_loss: 1.1894, g_loss: 1.0046, Mean D(x): 0.59, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[7900/8726], d_loss: 1.0786, g_loss: 1.0412, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [8/10], Step[8000/8726], d_loss: 1.1673, g_loss: 1.0536, Mean D(x): 0.64, Mean D(G(z)): 0.43\n",
      "Epoch [8/10], Step[8100/8726], d_loss: 1.0829, g_loss: 1.2812, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[8200/8726], d_loss: 1.1790, g_loss: 1.0894, Mean D(x): 0.64, Mean D(G(z)): 0.42\n",
      "Epoch [8/10], Step[8300/8726], d_loss: 1.1853, g_loss: 1.1146, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[8400/8726], d_loss: 1.3503, g_loss: 1.1394, Mean D(x): 0.54, Mean D(G(z)): 0.38\n",
      "Epoch [8/10], Step[8500/8726], d_loss: 1.1270, g_loss: 1.0191, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [8/10], Step[8600/8726], d_loss: 1.1557, g_loss: 1.0955, Mean D(x): 0.61, Mean D(G(z)): 0.39\n",
      "Epoch [8/10], Step[8700/8726], d_loss: 1.1665, g_loss: 1.1274, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[100/8726], d_loss: 1.1198, g_loss: 1.0466, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[200/8726], d_loss: 1.0745, g_loss: 1.0228, Mean D(x): 0.67, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[300/8726], d_loss: 1.1791, g_loss: 1.0921, Mean D(x): 0.59, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[400/8726], d_loss: 1.2313, g_loss: 1.1633, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[500/8726], d_loss: 1.0511, g_loss: 1.1731, Mean D(x): 0.70, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[600/8726], d_loss: 1.0600, g_loss: 1.0529, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[700/8726], d_loss: 1.0397, g_loss: 1.5286, Mean D(x): 0.68, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[800/8726], d_loss: 1.2009, g_loss: 0.8860, Mean D(x): 0.63, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[900/8726], d_loss: 1.1669, g_loss: 1.0816, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[1000/8726], d_loss: 1.1820, g_loss: 0.9658, Mean D(x): 0.59, Mean D(G(z)): 0.42\n",
      "Epoch [9/10], Step[1100/8726], d_loss: 1.0449, g_loss: 1.1096, Mean D(x): 0.65, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[1200/8726], d_loss: 1.0667, g_loss: 1.1661, Mean D(x): 0.67, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[1300/8726], d_loss: 1.1008, g_loss: 1.2459, Mean D(x): 0.62, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[1400/8726], d_loss: 1.1745, g_loss: 1.2461, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[1500/8726], d_loss: 1.2032, g_loss: 1.0178, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[1600/8726], d_loss: 1.1746, g_loss: 1.0723, Mean D(x): 0.61, Mean D(G(z)): 0.42\n",
      "Epoch [9/10], Step[1700/8726], d_loss: 1.0977, g_loss: 1.0977, Mean D(x): 0.61, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[1800/8726], d_loss: 1.1989, g_loss: 1.1017, Mean D(x): 0.55, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[1900/8726], d_loss: 1.1179, g_loss: 1.1428, Mean D(x): 0.64, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[2000/8726], d_loss: 1.1990, g_loss: 1.1229, Mean D(x): 0.60, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[2100/8726], d_loss: 1.2007, g_loss: 1.2275, Mean D(x): 0.61, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[2200/8726], d_loss: 1.0616, g_loss: 1.1345, Mean D(x): 0.63, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[2300/8726], d_loss: 1.1571, g_loss: 1.0008, Mean D(x): 0.59, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[2400/8726], d_loss: 1.1894, g_loss: 1.2840, Mean D(x): 0.64, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[2500/8726], d_loss: 1.2185, g_loss: 0.9010, Mean D(x): 0.62, Mean D(G(z)): 0.44\n",
      "Epoch [9/10], Step[2600/8726], d_loss: 1.1575, g_loss: 1.1559, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[2700/8726], d_loss: 1.2284, g_loss: 0.9205, Mean D(x): 0.58, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[2800/8726], d_loss: 1.1108, g_loss: 1.1015, Mean D(x): 0.60, Mean D(G(z)): 0.34\n",
      "Epoch [9/10], Step[2900/8726], d_loss: 1.0432, g_loss: 0.9146, Mean D(x): 0.66, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[3000/8726], d_loss: 1.1598, g_loss: 1.0693, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[3100/8726], d_loss: 1.1564, g_loss: 1.0642, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[3200/8726], d_loss: 1.2710, g_loss: 1.0241, Mean D(x): 0.54, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[3300/8726], d_loss: 1.2379, g_loss: 1.0789, Mean D(x): 0.59, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[3400/8726], d_loss: 1.2020, g_loss: 0.9813, Mean D(x): 0.63, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[3500/8726], d_loss: 1.2215, g_loss: 1.0393, Mean D(x): 0.64, Mean D(G(z)): 0.46\n",
      "Epoch [9/10], Step[3600/8726], d_loss: 1.2066, g_loss: 1.0118, Mean D(x): 0.56, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[3700/8726], d_loss: 1.2676, g_loss: 1.0986, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[3800/8726], d_loss: 1.1953, g_loss: 1.1546, Mean D(x): 0.53, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[3900/8726], d_loss: 1.2597, g_loss: 1.1796, Mean D(x): 0.61, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[4000/8726], d_loss: 1.3251, g_loss: 1.0157, Mean D(x): 0.59, Mean D(G(z)): 0.45\n",
      "Epoch [9/10], Step[4100/8726], d_loss: 1.0627, g_loss: 1.2338, Mean D(x): 0.61, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[4200/8726], d_loss: 1.2002, g_loss: 1.1228, Mean D(x): 0.58, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[4300/8726], d_loss: 1.2540, g_loss: 1.1553, Mean D(x): 0.56, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[4400/8726], d_loss: 1.1322, g_loss: 1.0977, Mean D(x): 0.59, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[4500/8726], d_loss: 1.0981, g_loss: 1.0877, Mean D(x): 0.62, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[4600/8726], d_loss: 1.1699, g_loss: 1.1017, Mean D(x): 0.63, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[4700/8726], d_loss: 1.0810, g_loss: 1.2394, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[4800/8726], d_loss: 1.1194, g_loss: 1.1089, Mean D(x): 0.60, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[4900/8726], d_loss: 0.9758, g_loss: 1.0991, Mean D(x): 0.68, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[5000/8726], d_loss: 1.0857, g_loss: 1.0156, Mean D(x): 0.63, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[5100/8726], d_loss: 1.1856, g_loss: 1.3064, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[5200/8726], d_loss: 1.0271, g_loss: 1.0717, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[5300/8726], d_loss: 1.0562, g_loss: 1.1677, Mean D(x): 0.64, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[5400/8726], d_loss: 1.3438, g_loss: 0.8663, Mean D(x): 0.56, Mean D(G(z)): 0.44\n",
      "Epoch [9/10], Step[5500/8726], d_loss: 1.2414, g_loss: 1.1381, Mean D(x): 0.62, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[5600/8726], d_loss: 1.1504, g_loss: 1.0176, Mean D(x): 0.62, Mean D(G(z)): 0.40\n",
      "Epoch [9/10], Step[5700/8726], d_loss: 1.1045, g_loss: 1.0654, Mean D(x): 0.61, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[5800/8726], d_loss: 1.2351, g_loss: 1.1436, Mean D(x): 0.54, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[5900/8726], d_loss: 1.2921, g_loss: 1.0551, Mean D(x): 0.56, Mean D(G(z)): 0.42\n",
      "Epoch [9/10], Step[6000/8726], d_loss: 1.1538, g_loss: 1.1255, Mean D(x): 0.55, Mean D(G(z)): 0.34\n",
      "Epoch [9/10], Step[6100/8726], d_loss: 1.3253, g_loss: 1.0531, Mean D(x): 0.56, Mean D(G(z)): 0.42\n",
      "Epoch [9/10], Step[6200/8726], d_loss: 0.9822, g_loss: 1.1624, Mean D(x): 0.66, Mean D(G(z)): 0.34\n",
      "Epoch [9/10], Step[6300/8726], d_loss: 1.0953, g_loss: 1.2578, Mean D(x): 0.63, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[6400/8726], d_loss: 1.1054, g_loss: 1.2441, Mean D(x): 0.64, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[6500/8726], d_loss: 1.0464, g_loss: 1.2478, Mean D(x): 0.62, Mean D(G(z)): 0.34\n",
      "Epoch [9/10], Step[6600/8726], d_loss: 1.0085, g_loss: 1.2399, Mean D(x): 0.66, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[6700/8726], d_loss: 1.0392, g_loss: 1.1645, Mean D(x): 0.63, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[6800/8726], d_loss: 1.0671, g_loss: 1.0898, Mean D(x): 0.64, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[6900/8726], d_loss: 1.1692, g_loss: 1.2177, Mean D(x): 0.58, Mean D(G(z)): 0.36\n",
      "Epoch [9/10], Step[7000/8726], d_loss: 1.1451, g_loss: 1.0584, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[7100/8726], d_loss: 1.2066, g_loss: 1.2200, Mean D(x): 0.56, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[7200/8726], d_loss: 1.0919, g_loss: 1.0749, Mean D(x): 0.63, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[7300/8726], d_loss: 1.2102, g_loss: 0.8106, Mean D(x): 0.65, Mean D(G(z)): 0.48\n",
      "Epoch [9/10], Step[7400/8726], d_loss: 1.2811, g_loss: 0.9001, Mean D(x): 0.58, Mean D(G(z)): 0.45\n",
      "Epoch [9/10], Step[7500/8726], d_loss: 1.2324, g_loss: 1.0860, Mean D(x): 0.61, Mean D(G(z)): 0.45\n",
      "Epoch [9/10], Step[7600/8726], d_loss: 1.1909, g_loss: 1.0610, Mean D(x): 0.62, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[7700/8726], d_loss: 1.1863, g_loss: 1.0690, Mean D(x): 0.61, Mean D(G(z)): 0.41\n",
      "Epoch [9/10], Step[7800/8726], d_loss: 1.1769, g_loss: 1.1385, Mean D(x): 0.58, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[7900/8726], d_loss: 1.2477, g_loss: 1.1429, Mean D(x): 0.54, Mean D(G(z)): 0.37\n",
      "Epoch [9/10], Step[8000/8726], d_loss: 1.1940, g_loss: 1.0764, Mean D(x): 0.60, Mean D(G(z)): 0.43\n",
      "Epoch [9/10], Step[8100/8726], d_loss: 1.1742, g_loss: 1.2691, Mean D(x): 0.57, Mean D(G(z)): 0.35\n",
      "Epoch [9/10], Step[8200/8726], d_loss: 1.1412, g_loss: 1.0721, Mean D(x): 0.68, Mean D(G(z)): 0.42\n",
      "Epoch [9/10], Step[8300/8726], d_loss: 1.2465, g_loss: 1.0986, Mean D(x): 0.55, Mean D(G(z)): 0.39\n",
      "Epoch [9/10], Step[8400/8726], d_loss: 1.1815, g_loss: 1.0039, Mean D(x): 0.67, Mean D(G(z)): 0.45\n",
      "Epoch [9/10], Step[8500/8726], d_loss: 1.0694, g_loss: 1.1005, Mean D(x): 0.64, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[8600/8726], d_loss: 1.1352, g_loss: 1.1116, Mean D(x): 0.60, Mean D(G(z)): 0.38\n",
      "Epoch [9/10], Step[8700/8726], d_loss: 1.1065, g_loss: 1.0697, Mean D(x): 0.68, Mean D(G(z)): 0.43\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 10\n",
    "# allow for manual keyboard interrupt\n",
    "try: \n",
    "    # loop through epochs\n",
    "    for epoch in range(N_EPOCHS):\n",
    "        # loop through batches (no need for class labels right now)\n",
    "        for batch_number, (images, _) in enumerate(data_loader): \n",
    "\n",
    "            # Reshape training dataset images from (batch_size, 25, 25) to (batch_size, 25*25) for \n",
    "            # processing through fully-connected net \n",
    "            batch_size = images.shape[0] # this specific batch size (last one may not be equal to BATCH_SIZE)\n",
    "            images = to_var(images.view(batch_size, -1))\n",
    "\n",
    "            # Create targets for the discriminator network D\n",
    "            # (can use label flipping or label smoothing)\n",
    "            real_labels = to_var(torch.ones(batch_size, 1)) \n",
    "            fake_labels = to_var(torch.zeros(batch_size, 1))\n",
    "\n",
    "            # 1) TRAIN DISCRIMINATOR\n",
    "            # Evaluate the discriminator on the real input images\n",
    "            outputs = D(images) # or D(add_instance_noise(images))\n",
    "            real_score = outputs\n",
    "            # Compute the discriminator loss with respect to the real labels (1s)\n",
    "            d_loss_real = criterion(outputs, real_labels)\n",
    "\n",
    "            # Draw random 64-dimensional noise vectors as inputs to the generator network\n",
    "            z = to_var(torch.randn(batch_size, 64)) # the latents space is 64D\n",
    "            # Transform the noise through the generator network to get synthetic images\n",
    "            fake_images = G(z)\n",
    "            # Evaluate the discriminator on the fake images\n",
    "            outputs = D(fake_images) # or D(add_instance_noise(fake_images))\n",
    "            fake_score = outputs\n",
    "            # Compute the discriminator loss with respect to the fake labels (0s)\n",
    "            d_loss_fake = criterion(outputs, fake_labels)\n",
    "\n",
    "            # Backprop + Optimize the discriminator\n",
    "            d_loss = d_loss_real + d_loss_fake\n",
    "            D.zero_grad()\n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2) TRAIN GENERATOR\n",
    "            # Draw random 64-dimensional noise vectors as inputs to the generator network\n",
    "            z = to_var(torch.randn(batch_size, 64))\n",
    "            # Transform the noise through the generator network to get synthetic images\n",
    "            fake_images = G(z)\n",
    "            # Evaluate the (new) discriminator on the fake images\n",
    "            outputs = D(fake_images)\n",
    "\n",
    "            # Compute the cross-entropy loss with \"real\" as target (1s). This is what the G wants to do\n",
    "            g_loss = criterion(outputs, real_labels)\n",
    "\n",
    "            # Backprop + Optimize the generator\n",
    "            D.zero_grad() # probably unnecessary?\n",
    "            G.zero_grad()\n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()\n",
    "\n",
    "            if (batch_number + 1) % 100 == 0:\n",
    "                print('Epoch [%d/%d], Step[%d/%d], d_loss: %.4f, '\n",
    "                      'g_loss: %.4f, Mean D(x): %.2f, Mean D(G(z)): %.2f' \n",
    "                      %(epoch,\n",
    "                        N_EPOCHS,\n",
    "                        batch_number + 1,\n",
    "                        n_batches,\n",
    "                        d_loss.data[0],\n",
    "                        g_loss.data[0],\n",
    "                        real_score.data.mean(),\n",
    "                        fake_score.data.mean())\n",
    "                )\n",
    "\n",
    "        # Save the trained parameters \n",
    "        torch.save(G.state_dict(), './weights/generator-%0.3d.pkl' %(epoch + 1))\n",
    "        torch.save(D.state_dict(), './weights/discriminator-%0.3d.pkl' %(epoch + 1))\n",
    "        \n",
    "except KeyboardInterrupt:\n",
    "    print('Training ended early.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# reload weights from any epoch\n",
    "G.load_state_dict(torch.load('weights/generator-001.pkl')) # 009, 020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check image quality and properties at that epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "z = to_var(torch.randn(batch_size, 64))\n",
    "fake_images = G(z).data.numpy().reshape(-1, 25, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195c3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot random images from the GAN-generated dataset\n",
    "plot_random_jetimages(fake_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vzdc/NSHI3tfjqRbPO73L/iB7vqFHkB1T9UqQzXn63UG28fUTMsY3D1/c4hqQvysfnxpk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\ncTIcNNWjVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c18610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare them to random images from the training set\n",
    "plot_random_jetimages(data_loader.dataset.images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Euclidean distances between each pair of images in each dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def distances(images):\n",
    "    d = []\n",
    "    for i in images:\n",
    "        for j in images:\n",
    "            d.append(np.sum((i-j)**2))\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d376f50>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4Ot4bQGv91kN4dlwrt12VTSEO2RnH9Wn5i5JOS8vDyf5orm2z7zORPcuAiPib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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca613d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distances for the real and GAN-generated dataset\n",
    "bins = np.linspace(0, 55000, 15)\n",
    "_ = plt.hist(distances(data_loader.dataset.images[:batch_size]),\n",
    "             bins=bins, histtype='stepfilled', label='real', color='green', alpha=0.1)\n",
    "_ = plt.hist(distances(fake_images), bins=bins, histtype='step', label='GAN', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Pairwise Euclidean distance')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c7926d0>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1193358d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(40, 120, 15)\n",
    "_ = plt.hist(discrete_mass(data_loader.dataset.images[:batch_size]),\n",
    "             histtype='stepfilled', bins=bins, label='GAN', color='green', alpha=0.1)\n",
    "_ = plt.hist(discrete_mass(fake_images), histtype='step', bins=bins, label='real', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Jet mass')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11dab3ad0>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb65790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(150, 400, 15)\n",
    "_ = plt.hist(discrete_pt(data_loader.dataset.images[:batch_size]),\n",
    "             histtype='stepfilled', bins=bins, label='real', color='green', alpha=0.1)\n",
    "_ = plt.hist(discrete_pt(fake_images), histtype='step', bins=bins, label='GAN', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Jet pT')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d0575d0>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8r/Q7fgncDpyWbnIu8KOI2N2H4zDbywnCms0BaYnktcAfSWrz9HQp8FHghYhY\nmra9naTG0D0kJaIPIanNU6ufR0RXROwENgJtZbZ5G3AcsCb9nreRlE6ApDtsRfp8HdCePv8VsETS\nfJLFrnpzLfC+9Pn7gP/Th2Mwe5kRvW9iVijPp/87r6YF2AO8TtKwSKp8Crg0IlaVbqjal258oeT5\nS5T/tyXg2xHx0TLv7Y59dW/2fj4iLpQ0k+SqYJ2k46oFERG/SgfQTyRZHa3XgXSzSnwFYUNK2vW0\nGJhHUh3zH9O3VgEXlfTfH55WzB2o3SVjAD8Hzpb02vQ7XiOp3JVGabxvjIi7IuKTwJO8vDw1wDMk\ny2uW+g7wb/jqwQbICcKGmo8Bv4yI/0eSHP5HOjZwLUnX0N3p9NVvUZ8r7EXAvZK+FxEbgY+TrAh4\nL8kqYb0t1/llSfelMf2apJJvqeXAmd2D1Gnb94BXk1ScNes3V3M1azKSzgbmRsS7847Fis1jEGZN\nRNK/kMzeemfesVjx+QrCzMzK8hiEmZmV5QRhZmZlOUGYmVlZThBmZlaWE4SZmZXlBGFmZmX9f9W4\n4+Q8ymQOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d316d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.linspace(0, 300, 50)\n",
    "_ = plt.hist(data_loader.dataset.images[:batch_size].ravel(),\n",
    "             bins=bins, histtype='stepfilled', color='green', alpha=0.1, label='real')\n",
    "_ = plt.hist(fake_images.ravel(), bins=bins, histtype='step', color='red', label='GAN')\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5oSRptKayWEiSRstiIUlqZbGQJLWyWEiSWlksJEmtLBaSpFYWC0lSK4uFJKmVxUKS1Mpi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\nC0lSK4uFJKmVxUKS1MpiIUlq9f8BMcpN8Ukwqh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11da98c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.logspace(-21, 3, 50)\n",
    "_ = plt.hist(data_loader.dataset.images[:batch_size].ravel(),\n",
    "             bins=bins, histtype='stepfilled', color='green', alpha=0.1, label='real')\n",
    "_ = plt.hist(fake_images.ravel(), bins=bins, histtype='step', color='red', label='GAN')\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can go back up and see if things change after different epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the evolution of images over the epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 001\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1257a4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 002\n"
     ]
    },
    {
     "data": {
      "image/png": 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rt/+rtudvClxpliRJkqSKSKjKSvNNwLXAzWMNETEIXAesAIaBLRFxJzAIrB13\n/prM3FOPr6qfB7AIeLAed2UiTJolSZIkqSoqsnt2Zt4TEUPjms8CtmfmToCI2AhckJlrgfPH9xER\nAXwc+HJmfrfePEwtcf4+XaqcNmmWJEmSpIp4kec3f330lgU9Hsa8iNhaOl6XmetaOG8hsKt0PAyc\nfYDnfxh4D/D6iDghMz8F3A5cGxHvA77Y5rinxKRZkiRJkioiM1f2egzdkpnXANeMa/sl8O+7OQ43\nApMkSZIkdcNuYHHpeFG9ra+ZNEuSJEmSumELsDwilkbEXGAVcGePxzQpk2ZJkiRJUkdFxAbgPuDE\niBiOiEszcx9wBbAZeBS4JTMf7uU4WxGZrd1nS5IkSZKk2caVZkmSJEmSmjBpliRJkiSpCZNmSZIk\nSZKaMGmWJEmSJKkJk2ZJkiRJkpowaZYkSZIkqQmTZkmSJEmSmjBpliRJkiSpCZNmSZIkSZKa+P84\nmCWMrGNUIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125555c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 003\n"
     ]
    },
    {
     "data": {
      "image/png": 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B57Z8bfy2RX2exlERsa3yfENmbmjhusXAnsrzUeD8Q7z+t4H3Aq+NiJMz85PA\nHcD1EfE+4G/bnHdHDJolSZIkqRCZuabfc+iVzLwOuG5K38+Bf9/LeVgITJIkSZLUC08BSyvPl9T7\n5jSDZkmSJElSL2wFVkXE8ohYCKwF7urznGZk0CxJkiRJ6qqI2ATcC5wSEaMRcWVmHgCuBrYADwO3\nZeaD/ZxnKyKztftsSZIkSZI037jTLEmSJElSEwbNkiRJkiQ1YdAsSZIkSVITBs2SJEmSJDVh0CxJ\nkiRJUhMGzZIkSZIkNWHQLEmSJElSEwbNkiRJkiQ1YdAsSZIkSVIT/x9B5xNaHzjFvgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc64d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 004\n"
     ]
    },
    {
     "data": {
      "image/png": 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sIuYDa4Db+xzTjEyaJUmSJEkdFRGbgHuAkyJiNCIuy8z9wBXAVuAR4ObMfKif\ncTYjMpt7zpYkSZIkSXONK82SJEmSJDVg0ixJkiRJUgMmzZIkSZIkNWDSLEmSJElSAybNkiRJkiQ1\nYNIsSZIkSVIDJs2SJEmSJDVg0ixJkiRJUgMmzZIkSZIkNfD/Ac1c2bHjJCfbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f751890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 005\n"
     ]
    },
    {
     "data": {
      "image/png": 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wOeAbmfmDevUItcD5R/Qpc9qgWZIkSZIK8SxPb/72+PULB9yNBRGxtXK9LjPX\ntfDcIuDxyvUIcOZ+7v8k8AHgyIg4ITOvBm4EroiIDwFfb7PfHTFoliRJkqRCZOaqQfehXzLzcuDy\nSXXPAf+yn/1wIzBJkiRJUj/sBpZUrhfX62Y0g2ZJkiRJUj9sAVZExLKIOBi4ALh5wH2alkGzJEmS\nJKmrImIjcAdwYkSMRMRFmbkXuATYDNwPXJ+Z9w6yn62IzNbO2ZIkSZIkaa5xplmSJEmSpCYMmiVJ\nkiRJasKgWZIkSZKkJgyaJUmSJElqwqBZkiRJkqQmDJolSZIkSWrCoFmSJEmSpCYMmiVJkiRJasKg\nWZIkSZKkJv4/tmUV93RuFgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122528b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 006\n"
     ]
    },
    {
     "data": {
      "image/png": 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AVkXE8ohYAFwO3DXgPk3LoFmSJEmS1FURsQW4F1gdESMRcWVmHgKuAbYDDwK3\nZebuQfazFZHZ2j5bkiRJkiTNNc40S5IkSZLUhEGzJEmSJElNGDRLkiRJktSEQbMkSZIkSU0YNEuS\nJEmS1IRBsyRJkiRJTRg0S5IkSZLUhEGzJEmSJElNGDRLkiRJktTE/wdiSh0/SPHTrQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c7c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 007\n"
     ]
    },
    {
     "data": {
      "image/png": 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m7gaIiC3AJZm5Hrh4chsREcD1wJcy81v16hFqgfO36VHmtEGzJEmSJBXipzy/\n7a7xWxf1uRtHRMSOyvGGzNzQxnWLgb2V4xHg3IOc/wHgHcDREXFiZn4cuB24MSLeCXyxw37PiEGz\nJEmSJBUiM1f3uw+9kpk3ADdMqnsB+A+97IcbgUmSJEmSemEfsLRyvKReN9AMmiVJkiRJvbAdWBkR\nyyPiMGANcGef+zQtg2ZJkiRJUldFxGbgXuDkiBiJiCsz8wBwNbANeAS4NTMf6mc/2xGZ7T1nS5Ik\nSZKk+caZZkmSJEmSWjBoliRJkiSpBYNmSZIkSZJaMGiWJEmSJKkFg2ZJkiRJklowaJYkSZIkqQWD\nZkmSJEmSWjBoliRJkiSpBYNmSZIkSZJa+P/cJGPD5fY9GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fbcd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 008\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1203593d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 009\n"
     ]
    },
    {
     "data": {
      "image/png": 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lYB9tiIhNEbE3Iu6eUL86Iu6PiF0RcelB/6uZN2Tmh4CLgX9Tr34vcF29/l90\nPH7T4EyzJEmSJBUioZSZ5quBK4BrxisiYgi4ElgFjADbI+ImYAjYMOH69Zm5t17+WP06gCXAXfVy\nXwbCoFmSJEmSSlHI7tmZeXNEDE+oPhvYlZm7ASJiC7AmMzcA501sIyIC+ATw1cy8s149Qi1w/gF9\nypw2aJYkSZKkQjzL09u+MXbtogF344iIuKNyvDEzN7Zx3WLgkcrxCHDOQc7/feAdwDERcVJmfga4\nHrgiIt4NfKXDfk+LQbMkSZIkFSIzVw+6D/2SmZcDl0+o+yXw7/rZDzcCkyRJkiT1w6PA0srxknrd\njGbQLEmSJEnqh+3AyohYHhELgLXATQPu05QMmiVJkiRJXRURm4FbgZMjYiQiLsrMA8AlwDbgXuDa\nzNw5yH62IzLbu8+WJEmSJElzjTPNkiRJkiS1YNAsSZIkSVILBs2SJEmSJLVg0CxJkiRJUgsGzZIk\nSZIktWDQLEmSJElSCwbNkiRJkiS1YNAsSZIkSVILBs2SJEmSJLXw/wHeuGAdOMBGoQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127f85b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 010\n"
     ]
    },
    {
     "data": {
      "image/png": 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daENEDABXASuAIWBzRNwCDABrx52/JjN31+NP1s8DWAT8sB73ZCJMmiVJkiSp\nFIXsnp2Zt0fE4Ljm04DtmbkDICKuB87JzLXA2eP7iIgAPg18LTO/U28eopY4f48eVU6bNEuSJElS\nIZ7n2U3fGrlhQZ+HcVBEbKkcr8vMdS2ctxDYVTkeAk7fx/M/BrwPODQijs3MzwI3AVdGxAeBr7Y5\n7o6YNEuSJElSITJzZb/H0CuZeQVwxbi2nwO/1ctxuBGYJEmSJKkXHgMWV44X1dumNZNmSZIkSVIv\nbAaWR8TSiJgHrAJu6fOYJmXSLEmSJEnqqojYANwJHBcRQxFxUWbuBS4FNgH3Azdk5r39HGcrIrO1\n+2xJkiRJkjTbuNIsSZIkSVITJs2SJEmSJDVh0ixJkiRJUhMmzZIkSZIkNWHSLEmSJElSEybNkiRJ\nkiQ1YdIsSZIkSVITJs2SJEmSJDVh0ixJkiRJUhP/H5MaQgYsdGuoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1274b7f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 011\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124eae590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 012\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126432f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 013\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126d0df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 014\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b83f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126f71050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 016\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1276edad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 017\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12691d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 018\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12735af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 019\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124eed310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 020\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ecdda10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch final\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127731c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "for _w in glob.glob('./weights/generator-*'):\n",
    "    print('Epoch ' + _w.split('-')[-1].split('.')[0])\n",
    "    \n",
    "    # load weights into G\n",
    "    G.load_state_dict(torch.load(_w))\n",
    "    \n",
    "    # produce a batch of fake images\n",
    "    batch_size = 100\n",
    "    z = to_var(torch.randn(batch_size, 64))\n",
    "    fake_images = G(z).data.numpy().reshape(-1, 25, 25)\n",
    "    \n",
    "    # plot a few of them\n",
    "    plot_random_jetimages(fake_images, njets=10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is certainly evidence of mode collapse in this specific instance of the training after a few epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Wasserstein GANs\n",
    "\n",
    "A recently proposed solution to the general instability of GANs is the Wasserstein GAN, or WGAN. It addresses the issues with non-overlapping supports.\n",
    "\n",
    "In lieu of the non-saturating heuristic that Vanilla GANs optimize, \n",
    "\n",
    "$$\n",
    "\\mathbb{E}_{x\\sim p_{\\mathsf{data}}(x)}[\\log D(x;\\theta_D)] + \\mathbb{E}_{z\\sim p_{z}(z)}[\\log (1 - D(G(x;\\theta_G);\\theta_D))]\n",
    "$$\n",
    "\n",
    "WGANs propose to directly minimize the Wasserstein distance (or Earth Movers distance) between fake and real data.\n",
    "\n",
    "Determining the Wasserstein distance between two distributions, $W(p,q)$, can be efficiently computed using the Kantorovich Rubenstein duality, allowing for minibatch sampled expectatins from $p$ and $q$. \n",
    "\n",
    "$$\n",
    "K \\cdot W(p,q)=\\sup_{f : \\Vert f\\Vert_{L \\leq K}} (\\mathbb{E}_{x \\sim P_r}[f(x)] - \\mathbb{E}_{x \\sim P_g}[f(x)])\n",
    "$$\n",
    "\n",
    "The \"discriminator\" from a normal GAN now becomes this $f$ here, and the parameters of $f$ are clamped to make it's weights compact, and this the overal neural network $K$-Lipschitz. \n",
    "\n",
    "In practice, WGANs aim to maximize\n",
    "\n",
    "$$\n",
    "\\mathbb{E}_{x \\sim p_{\\mathsf{data}}(x)}[f(x)] - \\mathbb{E}_{\\hat{x} \\sim p_{\\mathsf{model}}(\\hat{x};\\theta)}[f(\\hat{x})]\n",
    "$$\n",
    "\n",
    "Implementing this in PyTorch is quite simple - we will change parlance from \"discriminator\" to \"critic\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Critic\n",
    "# Note that we just have a linear output here\n",
    "critic = nn.Sequential(\n",
    "    nn.Linear(25 * 25, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 1)\n",
    ")\n",
    "\n",
    "# Generator \n",
    "generator = nn.Sequential(\n",
    "    nn.Linear(64, 256),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(256, 512),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(512, 25 * 25),\n",
    "    nn.ReLU()\n",
    ")\n",
    "\n",
    "# if you're running on GPU, move models there\n",
    "if torch.cuda.is_available():\n",
    "    critic.cuda()\n",
    "    generator.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# These are the recommended defaults (sauf N_EPOCHS) from the WGAN paper.\n",
    "LR = 0.0005\n",
    "N_EPOCHS = 20\n",
    "N_CRITIC_STEPS = 5\n",
    "CLAMP = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "critic_optimizer = torch.optim.RMSprop(critic.parameters(), lr=LR)\n",
    "generator_optimizer = torch.optim.RMSprop(generator.parameters(), lr=LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "For the training loop, we want to alternate between `N_CRITIC_STEPS` critic updates and a single generator update. We'll keep a counter that will help us know where we are.\n",
    "\n",
    "This is due to the fact that unlike normal GANs, we would like our critic to convervge before each generator update so that it's current estimate of the mutliplicatively scaled Wasserstein distance is more accurate and informative for the Generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If you want to, you can keep track of the history of the training by saving all per-epoch losses\n",
    "g_loss = []\n",
    "c_loss = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0/20], Step[300/8726], d_loss: -3.7239, g_loss: -2.7260, Mean D(x): 6.20, Mean D(G(z)): 2.47\n",
      "Epoch [0/20], Step[600/8726], d_loss: -2.0668, g_loss: -0.9106, Mean D(x): -0.70, Mean D(G(z)): -2.77\n",
      "Epoch [0/20], Step[900/8726], d_loss: -3.4252, g_loss: 0.5271, Mean D(x): 3.07, Mean D(G(z)): -0.36\n",
      "Epoch [0/20], Step[1200/8726], d_loss: -4.1013, g_loss: 0.1144, Mean D(x): 4.76, Mean D(G(z)): 0.66\n",
      "Epoch [0/20], Step[1500/8726], d_loss: -4.2233, g_loss: 1.7200, Mean D(x): 4.47, Mean D(G(z)): 0.25\n",
      "Epoch [0/20], Step[1800/8726], d_loss: -3.7967, g_loss: -1.3661, Mean D(x): 3.58, Mean D(G(z)): -0.21\n",
      "Epoch [0/20], Step[2100/8726], d_loss: -4.1264, g_loss: 0.3313, Mean D(x): 3.13, Mean D(G(z)): -0.99\n",
      "Epoch [0/20], Step[2400/8726], d_loss: -3.5116, g_loss: -0.6166, Mean D(x): 4.54, Mean D(G(z)): 1.02\n",
      "Epoch [0/20], Step[2700/8726], d_loss: -3.1929, g_loss: -2.0354, Mean D(x): 3.37, Mean D(G(z)): 0.18\n",
      "Epoch [0/20], Step[3000/8726], d_loss: -2.9032, g_loss: 1.4251, Mean D(x): 3.37, Mean D(G(z)): 0.47\n",
      "Epoch [0/20], Step[3300/8726], d_loss: -2.8407, g_loss: 1.9706, Mean D(x): 2.16, Mean D(G(z)): -0.68\n",
      "Epoch [0/20], Step[3600/8726], d_loss: -1.8666, g_loss: -1.2010, Mean D(x): 1.44, Mean D(G(z)): -0.43\n",
      "Epoch [0/20], Step[3900/8726], d_loss: -1.9044, g_loss: -1.0975, Mean D(x): 2.11, Mean D(G(z)): 0.21\n",
      "Epoch [0/20], Step[4200/8726], d_loss: -1.4596, g_loss: -0.3041, Mean D(x): 1.42, Mean D(G(z)): -0.04\n",
      "Epoch [0/20], Step[4500/8726], d_loss: -1.5190, g_loss: 0.0132, Mean D(x): 1.30, Mean D(G(z)): -0.22\n",
      "Epoch [0/20], Step[4800/8726], d_loss: -1.4532, g_loss: 0.2772, Mean D(x): 0.72, Mean D(G(z)): -0.73\n",
      "Epoch [0/20], Step[5100/8726], d_loss: -0.7305, g_loss: 0.1480, Mean D(x): 1.36, Mean D(G(z)): 0.62\n",
      "Epoch [0/20], Step[5400/8726], d_loss: -1.5827, g_loss: 0.1664, Mean D(x): 2.36, Mean D(G(z)): 0.78\n",
      "Epoch [0/20], Step[5700/8726], d_loss: -1.5862, g_loss: -0.0694, Mean D(x): 1.81, Mean D(G(z)): 0.23\n",
      "Epoch [0/20], Step[6000/8726], d_loss: -2.0408, g_loss: -0.8181, Mean D(x): 1.30, Mean D(G(z)): -0.74\n",
      "Epoch [0/20], Step[6300/8726], d_loss: -1.9565, g_loss: -0.4805, Mean D(x): 2.03, Mean D(G(z)): 0.08\n",
      "Epoch [0/20], Step[6600/8726], d_loss: -2.3819, g_loss: 1.4132, Mean D(x): 0.63, Mean D(G(z)): -1.75\n",
      "Epoch [0/20], Step[6900/8726], d_loss: -2.2546, g_loss: -0.4196, Mean D(x): 2.44, Mean D(G(z)): 0.19\n",
      "Epoch [0/20], Step[7200/8726], d_loss: -2.1912, g_loss: -0.2657, Mean D(x): 2.27, Mean D(G(z)): 0.08\n",
      "Epoch [0/20], Step[7500/8726], d_loss: -1.8903, g_loss: -0.0859, Mean D(x): 2.26, Mean D(G(z)): 0.37\n",
      "Epoch [0/20], Step[7800/8726], d_loss: -1.9174, g_loss: -0.0064, Mean D(x): 1.97, Mean D(G(z)): 0.05\n",
      "Epoch [0/20], Step[8100/8726], d_loss: -1.4678, g_loss: 0.0694, Mean D(x): 0.88, Mean D(G(z)): -0.59\n",
      "Epoch [0/20], Step[8400/8726], d_loss: -1.2920, g_loss: -0.4086, Mean D(x): 1.74, Mean D(G(z)): 0.45\n",
      "Epoch [0/20], Step[8700/8726], d_loss: -1.1998, g_loss: -0.7893, Mean D(x): 1.17, Mean D(G(z)): -0.03\n",
      "Epoch [1/20], Step[300/8726], d_loss: -1.4425, g_loss: -0.0931, Mean D(x): 1.95, Mean D(G(z)): 0.50\n",
      "Epoch [1/20], Step[600/8726], d_loss: -1.8043, g_loss: -0.1720, Mean D(x): 1.90, Mean D(G(z)): 0.09\n",
      "Epoch [1/20], Step[900/8726], d_loss: -1.5246, g_loss: -0.4831, Mean D(x): 1.93, Mean D(G(z)): 0.41\n",
      "Epoch [1/20], Step[1200/8726], d_loss: -1.5540, g_loss: 0.2599, Mean D(x): 1.69, Mean D(G(z)): 0.14\n",
      "Epoch [1/20], Step[1500/8726], d_loss: -1.9395, g_loss: -0.4020, Mean D(x): 2.21, Mean D(G(z)): 0.27\n",
      "Epoch [1/20], Step[1800/8726], d_loss: -1.2576, g_loss: -0.1564, Mean D(x): 1.28, Mean D(G(z)): 0.03\n",
      "Epoch [1/20], Step[2100/8726], d_loss: -1.1540, g_loss: 0.0545, Mean D(x): 1.94, Mean D(G(z)): 0.79\n",
      "Epoch [1/20], Step[2400/8726], d_loss: -1.3566, g_loss: -0.3876, Mean D(x): 1.30, Mean D(G(z)): -0.06\n",
      "Epoch [1/20], Step[2700/8726], d_loss: -1.6128, g_loss: 0.5967, Mean D(x): 1.15, Mean D(G(z)): -0.46\n",
      "Epoch [1/20], Step[3000/8726], d_loss: -1.1326, g_loss: -0.3739, Mean D(x): 1.51, Mean D(G(z)): 0.37\n",
      "Epoch [1/20], Step[3300/8726], d_loss: -1.3752, g_loss: 0.6222, Mean D(x): 1.42, Mean D(G(z)): 0.04\n",
      "Epoch [1/20], Step[3600/8726], d_loss: -1.1281, g_loss: -0.3305, Mean D(x): 1.42, Mean D(G(z)): 0.29\n",
      "Epoch [1/20], Step[3900/8726], d_loss: -0.8233, g_loss: -0.1967, Mean D(x): 1.50, Mean D(G(z)): 0.68\n",
      "Epoch [1/20], Step[4200/8726], d_loss: -0.9829, g_loss: -0.7513, Mean D(x): 1.16, Mean D(G(z)): 0.17\n",
      "Epoch [1/20], Step[4500/8726], d_loss: -1.0082, g_loss: -1.2308, Mean D(x): 1.76, Mean D(G(z)): 0.75\n",
      "Epoch [1/20], Step[4800/8726], d_loss: -1.4248, g_loss: -0.5787, Mean D(x): 1.58, Mean D(G(z)): 0.15\n",
      "Epoch [1/20], Step[5100/8726], d_loss: -1.2514, g_loss: 1.1576, Mean D(x): 0.44, Mean D(G(z)): -0.81\n",
      "Epoch [1/20], Step[5400/8726], d_loss: -0.9498, g_loss: 1.0821, Mean D(x): 0.54, Mean D(G(z)): -0.41\n",
      "Epoch [1/20], Step[5700/8726], d_loss: -0.9895, g_loss: -0.5152, Mean D(x): 1.27, Mean D(G(z)): 0.28\n",
      "Epoch [1/20], Step[6000/8726], d_loss: -1.1827, g_loss: -0.2335, Mean D(x): 2.02, Mean D(G(z)): 0.84\n",
      "Epoch [1/20], Step[6300/8726], d_loss: -0.8677, g_loss: -0.6355, Mean D(x): 1.15, Mean D(G(z)): 0.28\n",
      "Epoch [1/20], Step[6600/8726], d_loss: -1.2853, g_loss: -0.0704, Mean D(x): 1.44, Mean D(G(z)): 0.15\n",
      "Epoch [1/20], Step[6900/8726], d_loss: -1.2001, g_loss: 0.4516, Mean D(x): 0.69, Mean D(G(z)): -0.51\n",
      "Epoch [1/20], Step[7200/8726], d_loss: -1.1306, g_loss: 0.8094, Mean D(x): 0.88, Mean D(G(z)): -0.26\n",
      "Epoch [1/20], Step[7500/8726], d_loss: -1.1784, g_loss: 0.0203, Mean D(x): 0.62, Mean D(G(z)): -0.56\n",
      "Epoch [1/20], Step[7800/8726], d_loss: -1.0610, g_loss: 1.2124, Mean D(x): 0.49, Mean D(G(z)): -0.57\n",
      "Epoch [1/20], Step[8100/8726], d_loss: -1.2117, g_loss: 0.6872, Mean D(x): 1.31, Mean D(G(z)): 0.10\n",
      "Epoch [1/20], Step[8400/8726], d_loss: -1.0186, g_loss: -0.1210, Mean D(x): 1.58, Mean D(G(z)): 0.56\n",
      "Epoch [1/20], Step[8700/8726], d_loss: -0.9625, g_loss: -0.7116, Mean D(x): 1.27, Mean D(G(z)): 0.31\n",
      "Epoch [2/20], Step[300/8726], d_loss: -1.1314, g_loss: 0.2605, Mean D(x): 1.12, Mean D(G(z)): -0.01\n",
      "Epoch [2/20], Step[600/8726], d_loss: -0.8371, g_loss: -0.3942, Mean D(x): 1.63, Mean D(G(z)): 0.79\n",
      "Epoch [2/20], Step[900/8726], d_loss: -0.7822, g_loss: -0.2507, Mean D(x): 1.14, Mean D(G(z)): 0.36\n",
      "Epoch [2/20], Step[1200/8726], d_loss: -0.8995, g_loss: 1.0782, Mean D(x): 0.84, Mean D(G(z)): -0.06\n",
      "Epoch [2/20], Step[1500/8726], d_loss: -1.0400, g_loss: -0.4922, Mean D(x): 1.13, Mean D(G(z)): 0.09\n",
      "Epoch [2/20], Step[1800/8726], d_loss: -1.3547, g_loss: 0.1205, Mean D(x): 0.87, Mean D(G(z)): -0.48\n",
      "Epoch [2/20], Step[2100/8726], d_loss: -0.8913, g_loss: -0.4816, Mean D(x): 2.31, Mean D(G(z)): 1.41\n",
      "Epoch [2/20], Step[2400/8726], d_loss: -1.1575, g_loss: 1.7644, Mean D(x): 0.51, Mean D(G(z)): -0.64\n",
      "Epoch [2/20], Step[2700/8726], d_loss: -0.8580, g_loss: -0.4030, Mean D(x): 0.86, Mean D(G(z)): 0.01\n",
      "Epoch [2/20], Step[3000/8726], d_loss: -0.6618, g_loss: 0.7232, Mean D(x): 0.72, Mean D(G(z)): 0.06\n",
      "Epoch [2/20], Step[3300/8726], d_loss: -0.6359, g_loss: -0.3688, Mean D(x): 0.79, Mean D(G(z)): 0.15\n",
      "Epoch [2/20], Step[3600/8726], d_loss: -0.8709, g_loss: 0.3191, Mean D(x): 1.22, Mean D(G(z)): 0.35\n",
      "Epoch [2/20], Step[3900/8726], d_loss: -0.8135, g_loss: 0.4557, Mean D(x): 0.70, Mean D(G(z)): -0.12\n",
      "Epoch [2/20], Step[4200/8726], d_loss: -0.8808, g_loss: 0.3200, Mean D(x): 0.98, Mean D(G(z)): 0.10\n",
      "Epoch [2/20], Step[4500/8726], d_loss: -1.0385, g_loss: 0.0308, Mean D(x): 1.10, Mean D(G(z)): 0.06\n",
      "Epoch [2/20], Step[4800/8726], d_loss: -1.9351, g_loss: 1.6633, Mean D(x): 1.28, Mean D(G(z)): -0.65\n",
      "Epoch [2/20], Step[5100/8726], d_loss: -1.5945, g_loss: 1.0696, Mean D(x): 1.02, Mean D(G(z)): -0.58\n",
      "Epoch [2/20], Step[5400/8726], d_loss: -0.7408, g_loss: -0.2315, Mean D(x): 2.23, Mean D(G(z)): 1.49\n",
      "Epoch [2/20], Step[5700/8726], d_loss: -0.9538, g_loss: -0.1154, Mean D(x): 0.70, Mean D(G(z)): -0.25\n",
      "Epoch [2/20], Step[6000/8726], d_loss: -0.5778, g_loss: 0.3995, Mean D(x): 1.63, Mean D(G(z)): 1.05\n",
      "Epoch [2/20], Step[6300/8726], d_loss: -0.8259, g_loss: 2.0522, Mean D(x): 1.05, Mean D(G(z)): 0.22\n",
      "Epoch [2/20], Step[6600/8726], d_loss: -0.8114, g_loss: 1.3371, Mean D(x): 1.08, Mean D(G(z)): 0.27\n",
      "Epoch [2/20], Step[6900/8726], d_loss: -0.5011, g_loss: 0.1893, Mean D(x): 0.20, Mean D(G(z)): -0.30\n",
      "Epoch [2/20], Step[7200/8726], d_loss: -0.5418, g_loss: -0.4439, Mean D(x): 0.52, Mean D(G(z)): -0.02\n",
      "Epoch [2/20], Step[7500/8726], d_loss: -0.7019, g_loss: 0.5892, Mean D(x): 0.53, Mean D(G(z)): -0.17\n",
      "Epoch [2/20], Step[7800/8726], d_loss: -0.5868, g_loss: 0.5132, Mean D(x): 0.32, Mean D(G(z)): -0.27\n",
      "Epoch [2/20], Step[8100/8726], d_loss: -0.7879, g_loss: -0.0782, Mean D(x): 0.84, Mean D(G(z)): 0.05\n",
      "Epoch [2/20], Step[8400/8726], d_loss: -0.7492, g_loss: 0.3172, Mean D(x): 0.41, Mean D(G(z)): -0.34\n",
      "Epoch [2/20], Step[8700/8726], d_loss: -0.5303, g_loss: 0.2719, Mean D(x): 0.15, Mean D(G(z)): -0.38\n",
      "Epoch [3/20], Step[300/8726], d_loss: -0.9443, g_loss: -1.5933, Mean D(x): 1.59, Mean D(G(z)): 0.64\n",
      "Epoch [3/20], Step[600/8726], d_loss: -0.7265, g_loss: 1.2368, Mean D(x): -0.18, Mean D(G(z)): -0.91\n",
      "Epoch [3/20], Step[900/8726], d_loss: -0.6859, g_loss: 0.6462, Mean D(x): 0.63, Mean D(G(z)): -0.06\n",
      "Epoch [3/20], Step[1200/8726], d_loss: -0.6917, g_loss: -0.2357, Mean D(x): 0.48, Mean D(G(z)): -0.21\n",
      "Epoch [3/20], Step[1500/8726], d_loss: -0.5954, g_loss: 0.5311, Mean D(x): 0.23, Mean D(G(z)): -0.36\n",
      "Epoch [3/20], Step[1800/8726], d_loss: -0.6263, g_loss: 0.7470, Mean D(x): 0.02, Mean D(G(z)): -0.61\n",
      "Epoch [3/20], Step[2100/8726], d_loss: -0.5671, g_loss: -0.0168, Mean D(x): 0.73, Mean D(G(z)): 0.16\n",
      "Epoch [3/20], Step[2400/8726], d_loss: -0.4913, g_loss: -0.1683, Mean D(x): 0.53, Mean D(G(z)): 0.03\n",
      "Epoch [3/20], Step[2700/8726], d_loss: -0.5345, g_loss: -0.9674, Mean D(x): 0.61, Mean D(G(z)): 0.08\n",
      "Epoch [3/20], Step[3000/8726], d_loss: -0.5265, g_loss: 0.6045, Mean D(x): 0.03, Mean D(G(z)): -0.50\n",
      "Epoch [3/20], Step[3300/8726], d_loss: -0.4964, g_loss: 0.0389, Mean D(x): 0.05, Mean D(G(z)): -0.44\n",
      "Epoch [3/20], Step[3600/8726], d_loss: -0.5487, g_loss: -0.4391, Mean D(x): 1.09, Mean D(G(z)): 0.54\n",
      "Epoch [3/20], Step[3900/8726], d_loss: -0.4642, g_loss: 0.2674, Mean D(x): 0.05, Mean D(G(z)): -0.41\n",
      "Epoch [3/20], Step[4200/8726], d_loss: -0.3995, g_loss: -1.2404, Mean D(x): 0.25, Mean D(G(z)): -0.15\n",
      "Epoch [3/20], Step[4500/8726], d_loss: -0.6563, g_loss: -1.0692, Mean D(x): 0.82, Mean D(G(z)): 0.16\n",
      "Epoch [3/20], Step[4800/8726], d_loss: -0.3151, g_loss: 0.6145, Mean D(x): 0.57, Mean D(G(z)): 0.26\n",
      "Epoch [3/20], Step[5100/8726], d_loss: -0.7122, g_loss: 0.5728, Mean D(x): 0.65, Mean D(G(z)): -0.06\n",
      "Epoch [3/20], Step[5400/8726], d_loss: -0.6675, g_loss: -0.3324, Mean D(x): 0.74, Mean D(G(z)): 0.07\n",
      "Epoch [3/20], Step[5700/8726], d_loss: -0.4885, g_loss: -0.7097, Mean D(x): -0.33, Mean D(G(z)): -0.81\n",
      "Epoch [3/20], Step[6000/8726], d_loss: -0.7134, g_loss: -0.0140, Mean D(x): 1.30, Mean D(G(z)): 0.59\n",
      "Epoch [3/20], Step[6300/8726], d_loss: -0.4828, g_loss: 0.3656, Mean D(x): 0.16, Mean D(G(z)): -0.33\n",
      "Epoch [3/20], Step[6600/8726], d_loss: -0.4511, g_loss: 0.3602, Mean D(x): 0.08, Mean D(G(z)): -0.37\n",
      "Epoch [3/20], Step[6900/8726], d_loss: -0.4660, g_loss: 0.5471, Mean D(x): 0.68, Mean D(G(z)): 0.22\n",
      "Epoch [3/20], Step[7200/8726], d_loss: -0.3245, g_loss: 0.2368, Mean D(x): -0.16, Mean D(G(z)): -0.49\n",
      "Epoch [3/20], Step[7500/8726], d_loss: -0.3773, g_loss: -0.3686, Mean D(x): 0.19, Mean D(G(z)): -0.19\n",
      "Epoch [3/20], Step[7800/8726], d_loss: -0.7700, g_loss: -1.1272, Mean D(x): 0.87, Mean D(G(z)): 0.10\n",
      "Epoch [3/20], Step[8100/8726], d_loss: -0.4799, g_loss: -0.3604, Mean D(x): 0.90, Mean D(G(z)): 0.42\n",
      "Epoch [3/20], Step[8400/8726], d_loss: -0.4166, g_loss: -0.2950, Mean D(x): 0.74, Mean D(G(z)): 0.33\n",
      "Epoch [3/20], Step[8700/8726], d_loss: -0.5814, g_loss: 0.4688, Mean D(x): 0.54, Mean D(G(z)): -0.04\n",
      "Epoch [4/20], Step[300/8726], d_loss: -0.6202, g_loss: 0.6632, Mean D(x): 0.26, Mean D(G(z)): -0.36\n",
      "Epoch [4/20], Step[600/8726], d_loss: -0.4342, g_loss: -0.0407, Mean D(x): 0.56, Mean D(G(z)): 0.12\n",
      "Epoch [4/20], Step[900/8726], d_loss: -0.3050, g_loss: -0.9889, Mean D(x): 1.16, Mean D(G(z)): 0.86\n",
      "Epoch [4/20], Step[1200/8726], d_loss: -0.4143, g_loss: -0.0859, Mean D(x): 0.45, Mean D(G(z)): 0.04\n",
      "Epoch [4/20], Step[1500/8726], d_loss: -0.1992, g_loss: 0.9660, Mean D(x): 0.92, Mean D(G(z)): 0.72\n",
      "Epoch [4/20], Step[1800/8726], d_loss: -0.2824, g_loss: -0.7787, Mean D(x): 1.16, Mean D(G(z)): 0.88\n",
      "Epoch [4/20], Step[2100/8726], d_loss: -0.3347, g_loss: -0.3364, Mean D(x): 0.17, Mean D(G(z)): -0.16\n",
      "Epoch [4/20], Step[2400/8726], d_loss: -0.2735, g_loss: 0.1174, Mean D(x): 0.12, Mean D(G(z)): -0.16\n",
      "Epoch [4/20], Step[2700/8726], d_loss: -0.3667, g_loss: 0.2435, Mean D(x): 0.89, Mean D(G(z)): 0.53\n",
      "Epoch [4/20], Step[3000/8726], d_loss: -0.1049, g_loss: 0.1428, Mean D(x): 0.73, Mean D(G(z)): 0.63\n",
      "Epoch [4/20], Step[3300/8726], d_loss: -0.3187, g_loss: 0.9932, Mean D(x): 0.11, Mean D(G(z)): -0.21\n",
      "Epoch [4/20], Step[3600/8726], d_loss: -0.3731, g_loss: 0.4542, Mean D(x): 0.60, Mean D(G(z)): 0.23\n",
      "Epoch [4/20], Step[3900/8726], d_loss: -0.4435, g_loss: 0.3543, Mean D(x): 0.23, Mean D(G(z)): -0.21\n",
      "Epoch [4/20], Step[4200/8726], d_loss: -0.3787, g_loss: -0.0727, Mean D(x): 0.38, Mean D(G(z)): -0.00\n",
      "Epoch [4/20], Step[4500/8726], d_loss: -0.2601, g_loss: 0.2747, Mean D(x): 0.69, Mean D(G(z)): 0.43\n",
      "Epoch [4/20], Step[4800/8726], d_loss: -0.3831, g_loss: -0.2376, Mean D(x): 0.29, Mean D(G(z)): -0.10\n",
      "Epoch [4/20], Step[5100/8726], d_loss: -0.3256, g_loss: -1.3533, Mean D(x): 0.89, Mean D(G(z)): 0.56\n",
      "Epoch [4/20], Step[5400/8726], d_loss: -0.4680, g_loss: 0.2321, Mean D(x): 0.64, Mean D(G(z)): 0.17\n",
      "Epoch [4/20], Step[5700/8726], d_loss: -0.5053, g_loss: 0.2856, Mean D(x): 0.10, Mean D(G(z)): -0.40\n",
      "Epoch [4/20], Step[6000/8726], d_loss: -0.5662, g_loss: 0.5560, Mean D(x): 0.24, Mean D(G(z)): -0.33\n",
      "Epoch [4/20], Step[6300/8726], d_loss: -0.2150, g_loss: 0.4330, Mean D(x): 0.02, Mean D(G(z)): -0.20\n",
      "Epoch [4/20], Step[6600/8726], d_loss: -0.2477, g_loss: -0.9210, Mean D(x): 0.86, Mean D(G(z)): 0.61\n",
      "Epoch [4/20], Step[6900/8726], d_loss: -0.3821, g_loss: 0.2241, Mean D(x): 0.25, Mean D(G(z)): -0.13\n",
      "Epoch [4/20], Step[7200/8726], d_loss: -0.4412, g_loss: 0.1651, Mean D(x): 0.26, Mean D(G(z)): -0.18\n",
      "Epoch [4/20], Step[7500/8726], d_loss: -0.3796, g_loss: 1.3450, Mean D(x): -0.67, Mean D(G(z)): -1.05\n",
      "Epoch [4/20], Step[7800/8726], d_loss: -0.4118, g_loss: 0.8468, Mean D(x): -0.15, Mean D(G(z)): -0.56\n",
      "Epoch [4/20], Step[8100/8726], d_loss: -0.3000, g_loss: 0.4193, Mean D(x): 0.38, Mean D(G(z)): 0.08\n",
      "Epoch [4/20], Step[8400/8726], d_loss: -0.6008, g_loss: -1.2597, Mean D(x): 1.29, Mean D(G(z)): 0.69\n",
      "Epoch [4/20], Step[8700/8726], d_loss: -0.2376, g_loss: -0.3337, Mean D(x): 0.85, Mean D(G(z)): 0.62\n",
      "Epoch [5/20], Step[300/8726], d_loss: -0.3201, g_loss: 0.0251, Mean D(x): 0.30, Mean D(G(z)): -0.02\n",
      "Epoch [5/20], Step[600/8726], d_loss: -0.3966, g_loss: -0.0242, Mean D(x): 0.54, Mean D(G(z)): 0.15\n",
      "Epoch [5/20], Step[900/8726], d_loss: -0.2928, g_loss: 0.4828, Mean D(x): -0.33, Mean D(G(z)): -0.62\n",
      "Epoch [5/20], Step[1200/8726], d_loss: -0.3033, g_loss: -0.3513, Mean D(x): 0.28, Mean D(G(z)): -0.02\n",
      "Epoch [5/20], Step[1500/8726], d_loss: -0.2908, g_loss: 0.3433, Mean D(x): 0.09, Mean D(G(z)): -0.21\n",
      "Epoch [5/20], Step[1800/8726], d_loss: -0.2145, g_loss: 0.0446, Mean D(x): 0.25, Mean D(G(z)): 0.04\n",
      "Epoch [5/20], Step[2100/8726], d_loss: -0.2586, g_loss: -0.0212, Mean D(x): 0.28, Mean D(G(z)): 0.02\n",
      "Epoch [5/20], Step[2400/8726], d_loss: -0.2438, g_loss: 0.5397, Mean D(x): -0.11, Mean D(G(z)): -0.36\n",
      "Epoch [5/20], Step[2700/8726], d_loss: -0.3097, g_loss: -0.4505, Mean D(x): 0.55, Mean D(G(z)): 0.24\n",
      "Epoch [5/20], Step[3000/8726], d_loss: -0.4819, g_loss: 1.3317, Mean D(x): -0.17, Mean D(G(z)): -0.65\n",
      "Epoch [5/20], Step[3300/8726], d_loss: -0.2164, g_loss: 0.6059, Mean D(x): 0.50, Mean D(G(z)): 0.28\n",
      "Epoch [5/20], Step[3600/8726], d_loss: -0.3418, g_loss: -0.2882, Mean D(x): 0.41, Mean D(G(z)): 0.07\n",
      "Epoch [5/20], Step[3900/8726], d_loss: -0.3968, g_loss: 0.1465, Mean D(x): 0.17, Mean D(G(z)): -0.23\n",
      "Epoch [5/20], Step[4200/8726], d_loss: -0.3412, g_loss: 0.8905, Mean D(x): -0.82, Mean D(G(z)): -1.17\n",
      "Epoch [5/20], Step[4500/8726], d_loss: -0.2233, g_loss: 0.3131, Mean D(x): 0.41, Mean D(G(z)): 0.18\n",
      "Epoch [5/20], Step[4800/8726], d_loss: -0.2984, g_loss: -0.0808, Mean D(x): 0.89, Mean D(G(z)): 0.59\n",
      "Epoch [5/20], Step[5100/8726], d_loss: -0.2151, g_loss: -0.3050, Mean D(x): 0.81, Mean D(G(z)): 0.59\n",
      "Epoch [5/20], Step[5400/8726], d_loss: -0.1813, g_loss: -0.2590, Mean D(x): 0.16, Mean D(G(z)): -0.02\n",
      "Epoch [5/20], Step[5700/8726], d_loss: -0.3753, g_loss: 0.2928, Mean D(x): 0.16, Mean D(G(z)): -0.21\n",
      "Epoch [5/20], Step[6000/8726], d_loss: -0.1763, g_loss: -0.3787, Mean D(x): -0.02, Mean D(G(z)): -0.19\n",
      "Epoch [5/20], Step[6300/8726], d_loss: -0.3313, g_loss: -0.3453, Mean D(x): 0.87, Mean D(G(z)): 0.54\n",
      "Epoch [5/20], Step[6600/8726], d_loss: -0.2739, g_loss: -0.9577, Mean D(x): 1.37, Mean D(G(z)): 1.09\n",
      "Epoch [5/20], Step[6900/8726], d_loss: -0.0869, g_loss: 0.0407, Mean D(x): 0.28, Mean D(G(z)): 0.19\n",
      "Epoch [5/20], Step[7200/8726], d_loss: -0.3866, g_loss: -0.6035, Mean D(x): 0.45, Mean D(G(z)): 0.06\n",
      "Epoch [5/20], Step[7500/8726], d_loss: -0.3842, g_loss: -0.3093, Mean D(x): 0.18, Mean D(G(z)): -0.21\n",
      "Epoch [5/20], Step[7800/8726], d_loss: -0.2105, g_loss: -0.3476, Mean D(x): 0.49, Mean D(G(z)): 0.28\n",
      "Epoch [5/20], Step[8100/8726], d_loss: -0.1218, g_loss: 0.3996, Mean D(x): 0.32, Mean D(G(z)): 0.19\n",
      "Epoch [5/20], Step[8400/8726], d_loss: -0.2440, g_loss: -0.8210, Mean D(x): 0.77, Mean D(G(z)): 0.52\n",
      "Epoch [5/20], Step[8700/8726], d_loss: -0.1977, g_loss: -0.0866, Mean D(x): 0.06, Mean D(G(z)): -0.14\n",
      "Epoch [6/20], Step[300/8726], d_loss: -0.1605, g_loss: -0.3655, Mean D(x): 0.43, Mean D(G(z)): 0.27\n",
      "Epoch [6/20], Step[600/8726], d_loss: -0.4620, g_loss: 1.3932, Mean D(x): -0.10, Mean D(G(z)): -0.56\n",
      "Epoch [6/20], Step[900/8726], d_loss: -0.3523, g_loss: 0.8135, Mean D(x): -0.79, Mean D(G(z)): -1.14\n",
      "Epoch [6/20], Step[1200/8726], d_loss: -0.3006, g_loss: 0.3874, Mean D(x): 0.02, Mean D(G(z)): -0.28\n",
      "Epoch [6/20], Step[1500/8726], d_loss: -0.2970, g_loss: 0.9591, Mean D(x): 0.42, Mean D(G(z)): 0.12\n",
      "Epoch [6/20], Step[1800/8726], d_loss: -0.2874, g_loss: 0.0874, Mean D(x): 0.58, Mean D(G(z)): 0.29\n",
      "Epoch [6/20], Step[2100/8726], d_loss: -0.0254, g_loss: -0.3597, Mean D(x): -0.40, Mean D(G(z)): -0.42\n",
      "Epoch [6/20], Step[2400/8726], d_loss: -0.3898, g_loss: 0.9907, Mean D(x): -0.16, Mean D(G(z)): -0.55\n",
      "Epoch [6/20], Step[2700/8726], d_loss: -0.3410, g_loss: 0.1579, Mean D(x): 1.48, Mean D(G(z)): 1.14\n",
      "Epoch [6/20], Step[3000/8726], d_loss: -0.2711, g_loss: 0.4062, Mean D(x): -0.22, Mean D(G(z)): -0.49\n",
      "Epoch [6/20], Step[3300/8726], d_loss: -0.1873, g_loss: 0.6780, Mean D(x): -0.10, Mean D(G(z)): -0.29\n",
      "Epoch [6/20], Step[3600/8726], d_loss: -0.4413, g_loss: -0.8035, Mean D(x): 0.02, Mean D(G(z)): -0.43\n",
      "Epoch [6/20], Step[3900/8726], d_loss: -0.1060, g_loss: -0.2951, Mean D(x): 0.44, Mean D(G(z)): 0.34\n",
      "Epoch [6/20], Step[4200/8726], d_loss: -0.3699, g_loss: -0.7779, Mean D(x): 0.55, Mean D(G(z)): 0.18\n",
      "Epoch [6/20], Step[4500/8726], d_loss: -0.3630, g_loss: 0.3286, Mean D(x): 0.77, Mean D(G(z)): 0.41\n",
      "Epoch [6/20], Step[4800/8726], d_loss: -0.2348, g_loss: 0.0885, Mean D(x): 0.86, Mean D(G(z)): 0.63\n",
      "Epoch [6/20], Step[5100/8726], d_loss: -0.3056, g_loss: -0.2380, Mean D(x): 0.56, Mean D(G(z)): 0.26\n",
      "Epoch [6/20], Step[5400/8726], d_loss: -0.1402, g_loss: 0.2889, Mean D(x): -0.02, Mean D(G(z)): -0.16\n",
      "Epoch [6/20], Step[5700/8726], d_loss: -0.3048, g_loss: 0.7868, Mean D(x): -0.27, Mean D(G(z)): -0.57\n",
      "Epoch [6/20], Step[6000/8726], d_loss: -0.0279, g_loss: -0.2117, Mean D(x): 0.97, Mean D(G(z)): 0.94\n",
      "Epoch [6/20], Step[6300/8726], d_loss: -0.0596, g_loss: 0.7774, Mean D(x): -0.97, Mean D(G(z)): -1.03\n",
      "Epoch [6/20], Step[6600/8726], d_loss: -0.1476, g_loss: 0.6842, Mean D(x): 0.48, Mean D(G(z)): 0.33\n",
      "Epoch [6/20], Step[6900/8726], d_loss: -0.3812, g_loss: 0.3406, Mean D(x): -0.06, Mean D(G(z)): -0.44\n",
      "Epoch [6/20], Step[7200/8726], d_loss: -0.2543, g_loss: 0.6861, Mean D(x): -0.00, Mean D(G(z)): -0.26\n",
      "Epoch [6/20], Step[7500/8726], d_loss: -0.0914, g_loss: 0.0105, Mean D(x): 0.13, Mean D(G(z)): 0.04\n",
      "Epoch [6/20], Step[7800/8726], d_loss: -0.1386, g_loss: -0.7227, Mean D(x): -0.23, Mean D(G(z)): -0.37\n",
      "Epoch [6/20], Step[8100/8726], d_loss: -0.1021, g_loss: -0.0143, Mean D(x): 0.90, Mean D(G(z)): 0.80\n",
      "Epoch [6/20], Step[8400/8726], d_loss: -0.1792, g_loss: -0.4049, Mean D(x): 0.29, Mean D(G(z)): 0.11\n",
      "Epoch [6/20], Step[8700/8726], d_loss: -0.1602, g_loss: -1.7139, Mean D(x): 1.51, Mean D(G(z)): 1.35\n",
      "Epoch [7/20], Step[300/8726], d_loss: -0.2433, g_loss: -0.0952, Mean D(x): -0.33, Mean D(G(z)): -0.58\n",
      "Epoch [7/20], Step[600/8726], d_loss: -0.3716, g_loss: -0.4390, Mean D(x): 0.96, Mean D(G(z)): 0.58\n",
      "Epoch [7/20], Step[900/8726], d_loss: -0.4329, g_loss: 0.2082, Mean D(x): 0.43, Mean D(G(z)): -0.01\n",
      "Epoch [7/20], Step[1200/8726], d_loss: -0.1420, g_loss: -0.5660, Mean D(x): 1.52, Mean D(G(z)): 1.38\n",
      "Epoch [7/20], Step[1500/8726], d_loss: -0.2618, g_loss: 0.0556, Mean D(x): -0.40, Mean D(G(z)): -0.66\n",
      "Epoch [7/20], Step[1800/8726], d_loss: -0.2029, g_loss: -0.0740, Mean D(x): 0.04, Mean D(G(z)): -0.16\n",
      "Epoch [7/20], Step[2100/8726], d_loss: -0.2862, g_loss: 1.3390, Mean D(x): -0.12, Mean D(G(z)): -0.41\n",
      "Epoch [7/20], Step[2400/8726], d_loss: -0.3530, g_loss: 0.3950, Mean D(x): 0.25, Mean D(G(z)): -0.10\n",
      "Epoch [7/20], Step[2700/8726], d_loss: -0.0256, g_loss: 0.1468, Mean D(x): 1.00, Mean D(G(z)): 0.97\n",
      "Epoch [7/20], Step[3000/8726], d_loss: -0.1947, g_loss: 0.3274, Mean D(x): -0.25, Mean D(G(z)): -0.45\n",
      "Epoch [7/20], Step[3300/8726], d_loss: -0.1477, g_loss: -0.6735, Mean D(x): 1.52, Mean D(G(z)): 1.37\n",
      "Epoch [7/20], Step[3600/8726], d_loss: -0.2310, g_loss: 0.6699, Mean D(x): 0.13, Mean D(G(z)): -0.10\n",
      "Epoch [7/20], Step[3900/8726], d_loss: -0.1798, g_loss: 0.6456, Mean D(x): 0.25, Mean D(G(z)): 0.07\n",
      "Epoch [7/20], Step[4200/8726], d_loss: -0.2610, g_loss: -1.7730, Mean D(x): 1.43, Mean D(G(z)): 1.17\n",
      "Epoch [7/20], Step[4500/8726], d_loss: -0.1638, g_loss: -0.6102, Mean D(x): 0.19, Mean D(G(z)): 0.03\n",
      "Epoch [7/20], Step[4800/8726], d_loss: -0.3632, g_loss: -1.0713, Mean D(x): -0.11, Mean D(G(z)): -0.47\n",
      "Epoch [7/20], Step[5100/8726], d_loss: -0.1474, g_loss: 0.7534, Mean D(x): 0.26, Mean D(G(z)): 0.11\n",
      "Epoch [7/20], Step[5400/8726], d_loss: 0.0057, g_loss: 0.5904, Mean D(x): 0.37, Mean D(G(z)): 0.38\n",
      "Epoch [7/20], Step[5700/8726], d_loss: -0.0302, g_loss: 0.8524, Mean D(x): -0.98, Mean D(G(z)): -1.01\n",
      "Epoch [7/20], Step[6000/8726], d_loss: -0.3248, g_loss: -0.8266, Mean D(x): 0.76, Mean D(G(z)): 0.44\n",
      "Epoch [7/20], Step[6300/8726], d_loss: -0.3864, g_loss: -0.3724, Mean D(x): 0.06, Mean D(G(z)): -0.32\n",
      "Epoch [7/20], Step[6600/8726], d_loss: -0.1690, g_loss: -1.2313, Mean D(x): 0.03, Mean D(G(z)): -0.14\n",
      "Epoch [7/20], Step[6900/8726], d_loss: -0.3736, g_loss: -0.9572, Mean D(x): -0.13, Mean D(G(z)): -0.51\n",
      "Epoch [7/20], Step[7200/8726], d_loss: -0.1766, g_loss: -0.3420, Mean D(x): 0.60, Mean D(G(z)): 0.42\n",
      "Epoch [7/20], Step[7500/8726], d_loss: -0.2540, g_loss: 0.5445, Mean D(x): 0.01, Mean D(G(z)): -0.25\n",
      "Epoch [7/20], Step[7800/8726], d_loss: -0.2599, g_loss: 0.7567, Mean D(x): 0.31, Mean D(G(z)): 0.05\n",
      "Epoch [7/20], Step[8100/8726], d_loss: -0.2158, g_loss: -0.9258, Mean D(x): -0.31, Mean D(G(z)): -0.53\n",
      "Epoch [7/20], Step[8400/8726], d_loss: -0.2046, g_loss: -1.6616, Mean D(x): 0.51, Mean D(G(z)): 0.31\n",
      "Epoch [7/20], Step[8700/8726], d_loss: -0.2936, g_loss: -0.1500, Mean D(x): -0.06, Mean D(G(z)): -0.35\n",
      "Epoch [8/20], Step[300/8726], d_loss: 0.2663, g_loss: -0.6265, Mean D(x): 0.75, Mean D(G(z)): 1.02\n",
      "Epoch [8/20], Step[600/8726], d_loss: -0.3907, g_loss: 0.8221, Mean D(x): 0.12, Mean D(G(z)): -0.27\n",
      "Epoch [8/20], Step[900/8726], d_loss: -0.0701, g_loss: 0.6899, Mean D(x): 0.78, Mean D(G(z)): 0.71\n",
      "Epoch [8/20], Step[1200/8726], d_loss: 0.0897, g_loss: 0.3174, Mean D(x): -0.73, Mean D(G(z)): -0.64\n",
      "Epoch [8/20], Step[1500/8726], d_loss: -0.2569, g_loss: 0.6881, Mean D(x): -0.27, Mean D(G(z)): -0.52\n",
      "Epoch [8/20], Step[1800/8726], d_loss: -0.1387, g_loss: -0.8647, Mean D(x): 0.82, Mean D(G(z)): 0.68\n",
      "Epoch [8/20], Step[2100/8726], d_loss: -0.3102, g_loss: -0.4131, Mean D(x): 1.00, Mean D(G(z)): 0.69\n",
      "Epoch [8/20], Step[2400/8726], d_loss: -0.2035, g_loss: 0.8081, Mean D(x): 0.04, Mean D(G(z)): -0.16\n",
      "Epoch [8/20], Step[2700/8726], d_loss: -0.1755, g_loss: -0.2044, Mean D(x): 0.25, Mean D(G(z)): 0.08\n",
      "Epoch [8/20], Step[3000/8726], d_loss: -0.3219, g_loss: 0.1198, Mean D(x): -0.33, Mean D(G(z)): -0.65\n",
      "Epoch [8/20], Step[3300/8726], d_loss: -0.1470, g_loss: 0.7166, Mean D(x): -0.42, Mean D(G(z)): -0.56\n",
      "Epoch [8/20], Step[3600/8726], d_loss: -0.2128, g_loss: -0.0095, Mean D(x): -0.14, Mean D(G(z)): -0.35\n",
      "Epoch [8/20], Step[3900/8726], d_loss: -0.2872, g_loss: -0.5071, Mean D(x): -0.48, Mean D(G(z)): -0.76\n",
      "Epoch [8/20], Step[4200/8726], d_loss: -0.2019, g_loss: 1.0320, Mean D(x): 0.03, Mean D(G(z)): -0.17\n",
      "Epoch [8/20], Step[4500/8726], d_loss: -0.3039, g_loss: 0.7814, Mean D(x): -0.31, Mean D(G(z)): -0.61\n",
      "Epoch [8/20], Step[4800/8726], d_loss: -0.2656, g_loss: 0.0481, Mean D(x): -0.17, Mean D(G(z)): -0.43\n",
      "Epoch [8/20], Step[5100/8726], d_loss: -0.1641, g_loss: -0.5477, Mean D(x): 0.91, Mean D(G(z)): 0.74\n",
      "Epoch [8/20], Step[5400/8726], d_loss: -0.2397, g_loss: -1.0252, Mean D(x): 1.02, Mean D(G(z)): 0.78\n",
      "Epoch [8/20], Step[5700/8726], d_loss: -0.0389, g_loss: 0.3502, Mean D(x): -0.39, Mean D(G(z)): -0.43\n",
      "Epoch [8/20], Step[6000/8726], d_loss: -0.1236, g_loss: 0.8124, Mean D(x): -0.66, Mean D(G(z)): -0.78\n",
      "Epoch [8/20], Step[6300/8726], d_loss: 0.0118, g_loss: -0.4184, Mean D(x): 0.88, Mean D(G(z)): 0.90\n",
      "Epoch [8/20], Step[6600/8726], d_loss: -0.2351, g_loss: 0.4224, Mean D(x): -0.64, Mean D(G(z)): -0.88\n",
      "Epoch [8/20], Step[6900/8726], d_loss: -0.3710, g_loss: 0.9286, Mean D(x): -0.36, Mean D(G(z)): -0.73\n",
      "Epoch [8/20], Step[7200/8726], d_loss: -0.1533, g_loss: 0.5263, Mean D(x): 0.37, Mean D(G(z)): 0.21\n",
      "Epoch [8/20], Step[7500/8726], d_loss: -0.0993, g_loss: -0.0724, Mean D(x): -0.63, Mean D(G(z)): -0.73\n",
      "Epoch [8/20], Step[7800/8726], d_loss: 0.0176, g_loss: 0.5988, Mean D(x): -0.68, Mean D(G(z)): -0.67\n",
      "Epoch [8/20], Step[8100/8726], d_loss: -0.1458, g_loss: -0.8692, Mean D(x): 0.22, Mean D(G(z)): 0.08\n",
      "Epoch [8/20], Step[8400/8726], d_loss: -0.2283, g_loss: 0.3369, Mean D(x): -0.16, Mean D(G(z)): -0.39\n",
      "Epoch [8/20], Step[8700/8726], d_loss: -0.0392, g_loss: -0.9384, Mean D(x): 0.74, Mean D(G(z)): 0.70\n",
      "Epoch [9/20], Step[300/8726], d_loss: -0.0955, g_loss: 1.0312, Mean D(x): 0.08, Mean D(G(z)): -0.02\n",
      "Epoch [9/20], Step[600/8726], d_loss: -0.4911, g_loss: 0.0233, Mean D(x): -0.16, Mean D(G(z)): -0.65\n",
      "Epoch [9/20], Step[900/8726], d_loss: -0.1797, g_loss: -0.9672, Mean D(x): 0.79, Mean D(G(z)): 0.61\n",
      "Epoch [9/20], Step[1200/8726], d_loss: -0.0598, g_loss: -0.5332, Mean D(x): 0.78, Mean D(G(z)): 0.72\n",
      "Epoch [9/20], Step[1500/8726], d_loss: -0.0790, g_loss: 0.0984, Mean D(x): -0.67, Mean D(G(z)): -0.75\n",
      "Epoch [9/20], Step[1800/8726], d_loss: -0.1372, g_loss: 0.1801, Mean D(x): 0.21, Mean D(G(z)): 0.07\n",
      "Epoch [9/20], Step[2100/8726], d_loss: -0.2558, g_loss: -0.7678, Mean D(x): 0.23, Mean D(G(z)): -0.03\n",
      "Epoch [9/20], Step[2400/8726], d_loss: -0.1326, g_loss: -0.6399, Mean D(x): 0.21, Mean D(G(z)): 0.08\n",
      "Epoch [9/20], Step[2700/8726], d_loss: -0.2487, g_loss: 0.8988, Mean D(x): 0.05, Mean D(G(z)): -0.20\n",
      "Epoch [9/20], Step[3000/8726], d_loss: -0.0239, g_loss: 0.6840, Mean D(x): -0.49, Mean D(G(z)): -0.51\n",
      "Epoch [9/20], Step[3300/8726], d_loss: -0.1657, g_loss: -0.0393, Mean D(x): -0.17, Mean D(G(z)): -0.34\n",
      "Epoch [9/20], Step[3600/8726], d_loss: -0.1940, g_loss: 0.0942, Mean D(x): 0.34, Mean D(G(z)): 0.14\n",
      "Epoch [9/20], Step[3900/8726], d_loss: -0.2251, g_loss: 0.8848, Mean D(x): -0.26, Mean D(G(z)): -0.49\n",
      "Epoch [9/20], Step[4200/8726], d_loss: -0.2846, g_loss: 0.9819, Mean D(x): -0.29, Mean D(G(z)): -0.57\n",
      "Epoch [9/20], Step[4500/8726], d_loss: -0.3062, g_loss: 0.9298, Mean D(x): -0.67, Mean D(G(z)): -0.98\n",
      "Epoch [9/20], Step[4800/8726], d_loss: -0.1564, g_loss: 0.2001, Mean D(x): 0.09, Mean D(G(z)): -0.06\n",
      "Epoch [9/20], Step[5100/8726], d_loss: -0.1964, g_loss: 0.4822, Mean D(x): 0.25, Mean D(G(z)): 0.05\n",
      "Epoch [9/20], Step[5400/8726], d_loss: -0.0763, g_loss: 0.0363, Mean D(x): 0.04, Mean D(G(z)): -0.04\n",
      "Epoch [9/20], Step[5700/8726], d_loss: -0.1737, g_loss: 0.1359, Mean D(x): 0.60, Mean D(G(z)): 0.43\n",
      "Epoch [9/20], Step[6000/8726], d_loss: -0.1829, g_loss: 0.3680, Mean D(x): 0.46, Mean D(G(z)): 0.28\n",
      "Epoch [9/20], Step[6300/8726], d_loss: -0.2682, g_loss: 0.8811, Mean D(x): -0.05, Mean D(G(z)): -0.32\n",
      "Epoch [9/20], Step[6600/8726], d_loss: -0.3343, g_loss: -0.2846, Mean D(x): 0.21, Mean D(G(z)): -0.13\n",
      "Epoch [9/20], Step[6900/8726], d_loss: -0.1444, g_loss: -0.9847, Mean D(x): 1.51, Mean D(G(z)): 1.37\n",
      "Epoch [9/20], Step[7200/8726], d_loss: -0.2557, g_loss: 0.2832, Mean D(x): 0.08, Mean D(G(z)): -0.18\n",
      "Epoch [9/20], Step[7500/8726], d_loss: -0.2355, g_loss: 0.6234, Mean D(x): -0.63, Mean D(G(z)): -0.87\n",
      "Epoch [9/20], Step[7800/8726], d_loss: -0.1650, g_loss: 0.1741, Mean D(x): 0.06, Mean D(G(z)): -0.11\n",
      "Epoch [9/20], Step[8100/8726], d_loss: -0.0465, g_loss: -0.8841, Mean D(x): 1.01, Mean D(G(z)): 0.96\n",
      "Epoch [9/20], Step[8400/8726], d_loss: -0.1017, g_loss: 0.5850, Mean D(x): 0.20, Mean D(G(z)): 0.10\n",
      "Epoch [9/20], Step[8700/8726], d_loss: -0.0818, g_loss: -0.7963, Mean D(x): 0.14, Mean D(G(z)): 0.06\n",
      "Epoch [10/20], Step[300/8726], d_loss: -0.0370, g_loss: -0.4362, Mean D(x): -0.46, Mean D(G(z)): -0.50\n",
      "Epoch [10/20], Step[600/8726], d_loss: -0.1042, g_loss: 0.7643, Mean D(x): -0.38, Mean D(G(z)): -0.48\n",
      "Epoch [10/20], Step[900/8726], d_loss: -0.2665, g_loss: 0.6051, Mean D(x): -0.47, Mean D(G(z)): -0.74\n",
      "Epoch [10/20], Step[1200/8726], d_loss: -0.1728, g_loss: 0.0670, Mean D(x): 0.01, Mean D(G(z)): -0.16\n",
      "Epoch [10/20], Step[1500/8726], d_loss: -0.1545, g_loss: 1.0635, Mean D(x): 0.41, Mean D(G(z)): 0.26\n",
      "Epoch [10/20], Step[1800/8726], d_loss: -0.0772, g_loss: 1.0876, Mean D(x): -0.84, Mean D(G(z)): -0.92\n",
      "Epoch [10/20], Step[2100/8726], d_loss: -0.2141, g_loss: 1.4240, Mean D(x): -0.99, Mean D(G(z)): -1.20\n",
      "Epoch [10/20], Step[2400/8726], d_loss: -0.1818, g_loss: -0.7538, Mean D(x): 0.03, Mean D(G(z)): -0.16\n",
      "Epoch [10/20], Step[2700/8726], d_loss: -0.0247, g_loss: -0.7303, Mean D(x): 0.83, Mean D(G(z)): 0.80\n",
      "Epoch [10/20], Step[3000/8726], d_loss: -0.2718, g_loss: -0.0223, Mean D(x): 0.01, Mean D(G(z)): -0.27\n",
      "Epoch [10/20], Step[3300/8726], d_loss: 0.1194, g_loss: -0.8808, Mean D(x): 1.22, Mean D(G(z)): 1.34\n",
      "Epoch [10/20], Step[3600/8726], d_loss: -0.0318, g_loss: 0.7764, Mean D(x): -0.32, Mean D(G(z)): -0.35\n",
      "Epoch [10/20], Step[3900/8726], d_loss: -0.1343, g_loss: -0.2143, Mean D(x): 0.22, Mean D(G(z)): 0.08\n",
      "Epoch [10/20], Step[4200/8726], d_loss: -0.1176, g_loss: -2.2001, Mean D(x): 1.74, Mean D(G(z)): 1.63\n",
      "Epoch [10/20], Step[4500/8726], d_loss: -0.0205, g_loss: -0.5593, Mean D(x): 0.42, Mean D(G(z)): 0.40\n",
      "Epoch [10/20], Step[4800/8726], d_loss: -0.0181, g_loss: -0.5964, Mean D(x): 0.97, Mean D(G(z)): 0.96\n",
      "Epoch [10/20], Step[5100/8726], d_loss: 0.0929, g_loss: 0.3300, Mean D(x): -0.48, Mean D(G(z)): -0.38\n",
      "Epoch [10/20], Step[5400/8726], d_loss: -0.0611, g_loss: -0.1065, Mean D(x): 0.12, Mean D(G(z)): 0.06\n",
      "Epoch [10/20], Step[5700/8726], d_loss: -0.0648, g_loss: -0.1748, Mean D(x): -0.05, Mean D(G(z)): -0.11\n",
      "Epoch [10/20], Step[6000/8726], d_loss: -0.2164, g_loss: 0.8424, Mean D(x): -0.65, Mean D(G(z)): -0.86\n",
      "Epoch [10/20], Step[6300/8726], d_loss: -0.2041, g_loss: 0.9835, Mean D(x): -0.09, Mean D(G(z)): -0.29\n",
      "Epoch [10/20], Step[6600/8726], d_loss: -0.1102, g_loss: -0.7622, Mean D(x): -0.26, Mean D(G(z)): -0.37\n",
      "Epoch [10/20], Step[6900/8726], d_loss: -0.2202, g_loss: 0.7130, Mean D(x): -0.15, Mean D(G(z)): -0.37\n",
      "Epoch [10/20], Step[7200/8726], d_loss: -0.1217, g_loss: -0.5015, Mean D(x): 0.78, Mean D(G(z)): 0.66\n",
      "Epoch [10/20], Step[7500/8726], d_loss: -0.1722, g_loss: -0.0107, Mean D(x): -0.34, Mean D(G(z)): -0.51\n",
      "Epoch [10/20], Step[7800/8726], d_loss: -0.2849, g_loss: -1.1220, Mean D(x): -0.09, Mean D(G(z)): -0.38\n",
      "Epoch [10/20], Step[8100/8726], d_loss: -0.2405, g_loss: 0.2984, Mean D(x): -0.27, Mean D(G(z)): -0.51\n",
      "Epoch [10/20], Step[8400/8726], d_loss: -0.1143, g_loss: -0.2952, Mean D(x): 0.97, Mean D(G(z)): 0.85\n",
      "Epoch [10/20], Step[8700/8726], d_loss: -0.1193, g_loss: 0.4638, Mean D(x): -0.75, Mean D(G(z)): -0.87\n",
      "Epoch [11/20], Step[300/8726], d_loss: -0.1022, g_loss: -0.0683, Mean D(x): 0.62, Mean D(G(z)): 0.52\n",
      "Epoch [11/20], Step[600/8726], d_loss: -0.1221, g_loss: -0.9834, Mean D(x): -0.23, Mean D(G(z)): -0.35\n",
      "Epoch [11/20], Step[900/8726], d_loss: -0.0766, g_loss: 0.4049, Mean D(x): 0.13, Mean D(G(z)): 0.06\n",
      "Epoch [11/20], Step[1200/8726], d_loss: -0.1107, g_loss: 0.1261, Mean D(x): 0.18, Mean D(G(z)): 0.07\n",
      "Epoch [11/20], Step[1500/8726], d_loss: -0.1061, g_loss: 1.1410, Mean D(x): -0.94, Mean D(G(z)): -1.04\n",
      "Epoch [11/20], Step[1800/8726], d_loss: -0.2548, g_loss: -0.9742, Mean D(x): 1.24, Mean D(G(z)): 0.98\n",
      "Epoch [11/20], Step[2100/8726], d_loss: -0.2141, g_loss: -0.2318, Mean D(x): 0.65, Mean D(G(z)): 0.44\n",
      "Epoch [11/20], Step[2400/8726], d_loss: -0.0931, g_loss: 1.2584, Mean D(x): -1.16, Mean D(G(z)): -1.25\n",
      "Epoch [11/20], Step[2700/8726], d_loss: -0.2143, g_loss: 0.4453, Mean D(x): 0.41, Mean D(G(z)): 0.19\n",
      "Epoch [11/20], Step[3000/8726], d_loss: -0.1918, g_loss: 1.2917, Mean D(x): -0.54, Mean D(G(z)): -0.73\n",
      "Epoch [11/20], Step[3300/8726], d_loss: -0.1444, g_loss: -0.4551, Mean D(x): 0.47, Mean D(G(z)): 0.33\n",
      "Epoch [11/20], Step[3600/8726], d_loss: -0.1189, g_loss: 0.0662, Mean D(x): 0.47, Mean D(G(z)): 0.35\n",
      "Epoch [11/20], Step[3900/8726], d_loss: -0.0414, g_loss: -0.6169, Mean D(x): 0.59, Mean D(G(z)): 0.55\n",
      "Epoch [11/20], Step[4200/8726], d_loss: 0.0118, g_loss: -0.2933, Mean D(x): 0.67, Mean D(G(z)): 0.68\n",
      "Epoch [11/20], Step[4500/8726], d_loss: -0.1347, g_loss: 0.0145, Mean D(x): 0.36, Mean D(G(z)): 0.23\n",
      "Epoch [11/20], Step[4800/8726], d_loss: -0.0684, g_loss: -0.7389, Mean D(x): 0.04, Mean D(G(z)): -0.03\n",
      "Epoch [11/20], Step[5100/8726], d_loss: -0.0259, g_loss: -0.7018, Mean D(x): -0.01, Mean D(G(z)): -0.04\n",
      "Epoch [11/20], Step[5400/8726], d_loss: -0.0401, g_loss: -0.4826, Mean D(x): 0.60, Mean D(G(z)): 0.56\n",
      "Epoch [11/20], Step[5700/8726], d_loss: -0.1364, g_loss: 0.5276, Mean D(x): 0.31, Mean D(G(z)): 0.17\n",
      "Epoch [11/20], Step[6000/8726], d_loss: -0.2523, g_loss: 0.4124, Mean D(x): -0.49, Mean D(G(z)): -0.74\n",
      "Epoch [11/20], Step[6300/8726], d_loss: -0.0792, g_loss: 0.8353, Mean D(x): -0.86, Mean D(G(z)): -0.94\n",
      "Epoch [11/20], Step[6600/8726], d_loss: -0.0424, g_loss: 0.6199, Mean D(x): -0.58, Mean D(G(z)): -0.62\n",
      "Epoch [11/20], Step[6900/8726], d_loss: -0.1265, g_loss: -0.9656, Mean D(x): 0.34, Mean D(G(z)): 0.21\n",
      "Epoch [11/20], Step[7200/8726], d_loss: -0.0811, g_loss: -0.1964, Mean D(x): 0.10, Mean D(G(z)): 0.02\n",
      "Epoch [11/20], Step[7500/8726], d_loss: -0.0497, g_loss: -0.6768, Mean D(x): 0.09, Mean D(G(z)): 0.04\n",
      "Epoch [11/20], Step[7800/8726], d_loss: -0.2131, g_loss: -0.8778, Mean D(x): 0.52, Mean D(G(z)): 0.30\n",
      "Epoch [11/20], Step[8100/8726], d_loss: -0.2275, g_loss: 0.4478, Mean D(x): -0.50, Mean D(G(z)): -0.73\n",
      "Epoch [11/20], Step[8400/8726], d_loss: -0.1159, g_loss: 1.2613, Mean D(x): -0.98, Mean D(G(z)): -1.10\n",
      "Epoch [11/20], Step[8700/8726], d_loss: -0.1331, g_loss: -0.8809, Mean D(x): 0.71, Mean D(G(z)): 0.58\n",
      "Epoch [12/20], Step[300/8726], d_loss: -0.0548, g_loss: -0.0820, Mean D(x): 0.30, Mean D(G(z)): 0.25\n",
      "Epoch [12/20], Step[600/8726], d_loss: -0.0322, g_loss: -0.6866, Mean D(x): 0.82, Mean D(G(z)): 0.78\n",
      "Epoch [12/20], Step[900/8726], d_loss: -0.0661, g_loss: 0.1113, Mean D(x): -0.13, Mean D(G(z)): -0.20\n",
      "Epoch [12/20], Step[1200/8726], d_loss: -0.0814, g_loss: -0.7147, Mean D(x): -0.42, Mean D(G(z)): -0.50\n",
      "Epoch [12/20], Step[1500/8726], d_loss: -0.0820, g_loss: -0.5755, Mean D(x): -0.12, Mean D(G(z)): -0.20\n",
      "Epoch [12/20], Step[1800/8726], d_loss: -0.0464, g_loss: -0.3343, Mean D(x): 0.27, Mean D(G(z)): 0.23\n",
      "Epoch [12/20], Step[2100/8726], d_loss: -0.2246, g_loss: 1.6750, Mean D(x): -0.89, Mean D(G(z)): -1.11\n",
      "Epoch [12/20], Step[2400/8726], d_loss: -0.0819, g_loss: -0.5130, Mean D(x): 0.62, Mean D(G(z)): 0.54\n",
      "Epoch [12/20], Step[2700/8726], d_loss: -0.0527, g_loss: 0.6479, Mean D(x): 0.00, Mean D(G(z)): -0.05\n",
      "Epoch [12/20], Step[3000/8726], d_loss: -0.1433, g_loss: 0.3836, Mean D(x): -0.18, Mean D(G(z)): -0.33\n",
      "Epoch [12/20], Step[3300/8726], d_loss: -0.0880, g_loss: 1.1636, Mean D(x): -1.02, Mean D(G(z)): -1.11\n",
      "Epoch [12/20], Step[3600/8726], d_loss: -0.0305, g_loss: 0.7481, Mean D(x): -0.02, Mean D(G(z)): -0.05\n",
      "Epoch [12/20], Step[3900/8726], d_loss: -0.0888, g_loss: -1.0739, Mean D(x): 1.13, Mean D(G(z)): 1.04\n",
      "Epoch [12/20], Step[4200/8726], d_loss: -0.2929, g_loss: 0.2072, Mean D(x): 0.42, Mean D(G(z)): 0.13\n",
      "Epoch [12/20], Step[4500/8726], d_loss: -0.0629, g_loss: 0.8149, Mean D(x): -0.72, Mean D(G(z)): -0.78\n",
      "Epoch [12/20], Step[4800/8726], d_loss: 0.0676, g_loss: 1.0175, Mean D(x): -1.14, Mean D(G(z)): -1.07\n",
      "Epoch [12/20], Step[5100/8726], d_loss: -0.2680, g_loss: -0.1177, Mean D(x): 0.65, Mean D(G(z)): 0.38\n",
      "Epoch [12/20], Step[5400/8726], d_loss: -0.2079, g_loss: -0.9582, Mean D(x): 0.80, Mean D(G(z)): 0.60\n",
      "Epoch [12/20], Step[5700/8726], d_loss: -0.0181, g_loss: 0.4691, Mean D(x): -0.46, Mean D(G(z)): -0.48\n",
      "Epoch [12/20], Step[6000/8726], d_loss: 0.0175, g_loss: 0.2635, Mean D(x): -0.26, Mean D(G(z)): -0.24\n",
      "Epoch [12/20], Step[6300/8726], d_loss: -0.0220, g_loss: 0.7088, Mean D(x): -0.76, Mean D(G(z)): -0.78\n",
      "Epoch [12/20], Step[6600/8726], d_loss: -0.0161, g_loss: -0.3072, Mean D(x): 0.43, Mean D(G(z)): 0.42\n",
      "Epoch [12/20], Step[6900/8726], d_loss: -0.1251, g_loss: 1.2980, Mean D(x): -0.87, Mean D(G(z)): -1.00\n",
      "Epoch [12/20], Step[7200/8726], d_loss: -0.0840, g_loss: -0.1437, Mean D(x): 0.02, Mean D(G(z)): -0.06\n",
      "Epoch [12/20], Step[7500/8726], d_loss: -0.1990, g_loss: 3.0453, Mean D(x): -2.63, Mean D(G(z)): -2.83\n",
      "Epoch [12/20], Step[7800/8726], d_loss: -0.0450, g_loss: 2.4197, Mean D(x): -2.54, Mean D(G(z)): -2.59\n",
      "Epoch [12/20], Step[8100/8726], d_loss: -0.0083, g_loss: -0.0153, Mean D(x): -0.08, Mean D(G(z)): -0.09\n",
      "Epoch [12/20], Step[8400/8726], d_loss: -0.0266, g_loss: -0.6891, Mean D(x): 0.47, Mean D(G(z)): 0.45\n",
      "Epoch [12/20], Step[8700/8726], d_loss: 0.0341, g_loss: -1.9674, Mean D(x): 2.03, Mean D(G(z)): 2.06\n",
      "Epoch [13/20], Step[300/8726], d_loss: -0.2397, g_loss: 1.3115, Mean D(x): -0.61, Mean D(G(z)): -0.85\n",
      "Epoch [13/20], Step[600/8726], d_loss: -0.0278, g_loss: 0.1310, Mean D(x): -0.09, Mean D(G(z)): -0.12\n",
      "Epoch [13/20], Step[900/8726], d_loss: -0.0091, g_loss: -0.0981, Mean D(x): -0.02, Mean D(G(z)): -0.03\n",
      "Epoch [13/20], Step[1200/8726], d_loss: 0.1270, g_loss: 0.4657, Mean D(x): -0.34, Mean D(G(z)): -0.21\n",
      "Epoch [13/20], Step[1500/8726], d_loss: -0.2842, g_loss: 1.8435, Mean D(x): -0.92, Mean D(G(z)): -1.20\n",
      "Epoch [13/20], Step[1800/8726], d_loss: -0.2651, g_loss: 1.8420, Mean D(x): -0.19, Mean D(G(z)): -0.45\n",
      "Epoch [13/20], Step[2100/8726], d_loss: -0.0892, g_loss: -1.9623, Mean D(x): 1.84, Mean D(G(z)): 1.75\n",
      "Epoch [13/20], Step[2400/8726], d_loss: -0.0476, g_loss: 0.1669, Mean D(x): -0.13, Mean D(G(z)): -0.18\n",
      "Epoch [13/20], Step[2700/8726], d_loss: -0.0448, g_loss: 0.6230, Mean D(x): -0.43, Mean D(G(z)): -0.48\n",
      "Epoch [13/20], Step[3000/8726], d_loss: -0.2867, g_loss: -3.2821, Mean D(x): 2.09, Mean D(G(z)): 1.80\n",
      "Epoch [13/20], Step[3300/8726], d_loss: -0.0516, g_loss: 0.7488, Mean D(x): 0.29, Mean D(G(z)): 0.24\n",
      "Epoch [13/20], Step[3600/8726], d_loss: -0.2211, g_loss: -1.8011, Mean D(x): 1.47, Mean D(G(z)): 1.25\n",
      "Epoch [13/20], Step[3900/8726], d_loss: -0.0802, g_loss: -0.9939, Mean D(x): 1.29, Mean D(G(z)): 1.21\n",
      "Epoch [13/20], Step[4200/8726], d_loss: -0.0960, g_loss: -2.4379, Mean D(x): 2.71, Mean D(G(z)): 2.62\n",
      "Epoch [13/20], Step[4500/8726], d_loss: -0.2179, g_loss: 0.1876, Mean D(x): -0.08, Mean D(G(z)): -0.30\n",
      "Epoch [13/20], Step[4800/8726], d_loss: -0.1088, g_loss: 0.3508, Mean D(x): -0.69, Mean D(G(z)): -0.80\n",
      "Epoch [13/20], Step[5100/8726], d_loss: -0.1538, g_loss: 0.5263, Mean D(x): -0.05, Mean D(G(z)): -0.21\n",
      "Epoch [13/20], Step[5400/8726], d_loss: 0.0184, g_loss: 0.1901, Mean D(x): -1.33, Mean D(G(z)): -1.31\n",
      "Epoch [13/20], Step[5700/8726], d_loss: -0.1188, g_loss: 1.9691, Mean D(x): -1.52, Mean D(G(z)): -1.64\n",
      "Epoch [13/20], Step[6000/8726], d_loss: 0.0328, g_loss: -1.5592, Mean D(x): 1.81, Mean D(G(z)): 1.85\n",
      "Epoch [13/20], Step[6300/8726], d_loss: -0.1480, g_loss: -0.1472, Mean D(x): 0.31, Mean D(G(z)): 0.16\n",
      "Epoch [13/20], Step[6600/8726], d_loss: -0.1203, g_loss: -1.1229, Mean D(x): 1.49, Mean D(G(z)): 1.37\n",
      "Epoch [13/20], Step[6900/8726], d_loss: 0.1258, g_loss: -0.2348, Mean D(x): 1.14, Mean D(G(z)): 1.27\n",
      "Epoch [13/20], Step[7200/8726], d_loss: 0.2141, g_loss: 0.3175, Mean D(x): -1.42, Mean D(G(z)): -1.20\n",
      "Epoch [13/20], Step[7500/8726], d_loss: -0.0649, g_loss: 1.2850, Mean D(x): -1.21, Mean D(G(z)): -1.28\n",
      "Epoch [13/20], Step[7800/8726], d_loss: 0.0032, g_loss: 0.1286, Mean D(x): -0.45, Mean D(G(z)): -0.44\n",
      "Epoch [13/20], Step[8100/8726], d_loss: 0.0374, g_loss: 0.4720, Mean D(x): 0.14, Mean D(G(z)): 0.18\n",
      "Epoch [13/20], Step[8400/8726], d_loss: -0.2831, g_loss: -0.4265, Mean D(x): 0.59, Mean D(G(z)): 0.31\n",
      "Epoch [13/20], Step[8700/8726], d_loss: -0.2545, g_loss: -2.1989, Mean D(x): 1.98, Mean D(G(z)): 1.72\n",
      "Epoch [14/20], Step[300/8726], d_loss: -0.0898, g_loss: 0.1414, Mean D(x): -0.44, Mean D(G(z)): -0.53\n",
      "Epoch [14/20], Step[600/8726], d_loss: 0.1260, g_loss: -0.0859, Mean D(x): 0.49, Mean D(G(z)): 0.61\n",
      "Epoch [14/20], Step[900/8726], d_loss: -0.1347, g_loss: 2.3877, Mean D(x): -1.98, Mean D(G(z)): -2.12\n",
      "Epoch [14/20], Step[1200/8726], d_loss: 0.0709, g_loss: -0.2024, Mean D(x): -0.51, Mean D(G(z)): -0.44\n",
      "Epoch [14/20], Step[1500/8726], d_loss: -0.0500, g_loss: 0.1387, Mean D(x): -0.55, Mean D(G(z)): -0.60\n",
      "Epoch [14/20], Step[1800/8726], d_loss: -0.3234, g_loss: -2.9131, Mean D(x): 2.61, Mean D(G(z)): 2.28\n",
      "Epoch [14/20], Step[2100/8726], d_loss: -0.0265, g_loss: 0.8740, Mean D(x): -0.58, Mean D(G(z)): -0.61\n",
      "Epoch [14/20], Step[2400/8726], d_loss: -0.1000, g_loss: -2.3330, Mean D(x): 1.98, Mean D(G(z)): 1.88\n",
      "Epoch [14/20], Step[2700/8726], d_loss: -0.0520, g_loss: 0.3830, Mean D(x): -0.31, Mean D(G(z)): -0.36\n",
      "Epoch [14/20], Step[3000/8726], d_loss: -0.0565, g_loss: 0.0325, Mean D(x): 0.03, Mean D(G(z)): -0.02\n",
      "Epoch [14/20], Step[3300/8726], d_loss: 0.0545, g_loss: 0.1756, Mean D(x): -0.39, Mean D(G(z)): -0.34\n",
      "Epoch [14/20], Step[3600/8726], d_loss: 0.1081, g_loss: -1.0497, Mean D(x): 1.70, Mean D(G(z)): 1.81\n",
      "Epoch [14/20], Step[3900/8726], d_loss: 0.0154, g_loss: -0.2109, Mean D(x): 0.36, Mean D(G(z)): 0.38\n",
      "Epoch [14/20], Step[4200/8726], d_loss: 0.0106, g_loss: 0.4387, Mean D(x): -0.46, Mean D(G(z)): -0.45\n",
      "Epoch [14/20], Step[4500/8726], d_loss: -0.2908, g_loss: 3.4148, Mean D(x): -1.43, Mean D(G(z)): -1.72\n",
      "Epoch [14/20], Step[4800/8726], d_loss: -0.0636, g_loss: 0.2322, Mean D(x): 0.09, Mean D(G(z)): 0.03\n",
      "Epoch [14/20], Step[5100/8726], d_loss: -0.0267, g_loss: 1.8733, Mean D(x): -1.69, Mean D(G(z)): -1.71\n",
      "Epoch [14/20], Step[5400/8726], d_loss: 0.3530, g_loss: -1.0315, Mean D(x): 1.98, Mean D(G(z)): 2.33\n",
      "Epoch [14/20], Step[5700/8726], d_loss: -0.0376, g_loss: 0.3065, Mean D(x): -0.09, Mean D(G(z)): -0.13\n",
      "Epoch [14/20], Step[6000/8726], d_loss: 0.0368, g_loss: -0.1187, Mean D(x): -0.01, Mean D(G(z)): 0.03\n",
      "Epoch [14/20], Step[6300/8726], d_loss: -0.2672, g_loss: -2.1026, Mean D(x): 0.85, Mean D(G(z)): 0.58\n",
      "Epoch [14/20], Step[6600/8726], d_loss: 0.0111, g_loss: -0.2846, Mean D(x): 0.30, Mean D(G(z)): 0.31\n",
      "Epoch [14/20], Step[6900/8726], d_loss: -0.0861, g_loss: -0.0175, Mean D(x): 0.38, Mean D(G(z)): 0.29\n",
      "Epoch [14/20], Step[7200/8726], d_loss: -0.0878, g_loss: -1.2100, Mean D(x): 0.52, Mean D(G(z)): 0.44\n",
      "Epoch [14/20], Step[7500/8726], d_loss: -0.0135, g_loss: 0.1904, Mean D(x): -0.12, Mean D(G(z)): -0.13\n",
      "Epoch [14/20], Step[7800/8726], d_loss: -0.0085, g_loss: -0.1976, Mean D(x): 0.30, Mean D(G(z)): 0.29\n",
      "Epoch [14/20], Step[8100/8726], d_loss: -0.0221, g_loss: -0.0085, Mean D(x): -0.10, Mean D(G(z)): -0.12\n",
      "Epoch [14/20], Step[8400/8726], d_loss: 0.0227, g_loss: 1.3948, Mean D(x): -1.56, Mean D(G(z)): -1.54\n",
      "Epoch [14/20], Step[8700/8726], d_loss: -0.0641, g_loss: -0.3641, Mean D(x): 0.27, Mean D(G(z)): 0.21\n",
      "Epoch [15/20], Step[300/8726], d_loss: -0.1419, g_loss: -0.7685, Mean D(x): 0.69, Mean D(G(z)): 0.55\n",
      "Epoch [15/20], Step[600/8726], d_loss: -0.2858, g_loss: -1.2426, Mean D(x): 0.30, Mean D(G(z)): 0.02\n",
      "Epoch [15/20], Step[900/8726], d_loss: -0.2083, g_loss: -2.7978, Mean D(x): 1.20, Mean D(G(z)): 0.99\n",
      "Epoch [15/20], Step[1200/8726], d_loss: -0.2561, g_loss: -2.7848, Mean D(x): 2.34, Mean D(G(z)): 2.09\n",
      "Epoch [15/20], Step[1500/8726], d_loss: -0.2138, g_loss: -1.4832, Mean D(x): 0.80, Mean D(G(z)): 0.59\n",
      "Epoch [15/20], Step[1800/8726], d_loss: -0.2807, g_loss: 0.5912, Mean D(x): 0.41, Mean D(G(z)): 0.13\n",
      "Epoch [15/20], Step[2100/8726], d_loss: -0.2073, g_loss: 0.5988, Mean D(x): -0.65, Mean D(G(z)): -0.86\n",
      "Epoch [15/20], Step[2400/8726], d_loss: -0.1002, g_loss: 0.7353, Mean D(x): -0.44, Mean D(G(z)): -0.54\n",
      "Epoch [15/20], Step[2700/8726], d_loss: -0.1142, g_loss: -0.3064, Mean D(x): -0.02, Mean D(G(z)): -0.13\n",
      "Epoch [15/20], Step[3000/8726], d_loss: -0.0048, g_loss: -0.2009, Mean D(x): 1.13, Mean D(G(z)): 1.13\n",
      "Epoch [15/20], Step[3300/8726], d_loss: -0.2690, g_loss: -1.4949, Mean D(x): 0.55, Mean D(G(z)): 0.28\n",
      "Epoch [15/20], Step[3600/8726], d_loss: -0.1332, g_loss: 1.1775, Mean D(x): -0.27, Mean D(G(z)): -0.40\n",
      "Epoch [15/20], Step[3900/8726], d_loss: -0.5306, g_loss: 4.1615, Mean D(x): -2.21, Mean D(G(z)): -2.74\n",
      "Epoch [15/20], Step[4200/8726], d_loss: -0.0068, g_loss: -0.0143, Mean D(x): -0.54, Mean D(G(z)): -0.54\n",
      "Epoch [15/20], Step[4500/8726], d_loss: -0.1709, g_loss: -3.3670, Mean D(x): 2.98, Mean D(G(z)): 2.81\n",
      "Epoch [15/20], Step[4800/8726], d_loss: -0.1257, g_loss: 1.2177, Mean D(x): -1.29, Mean D(G(z)): -1.42\n",
      "Epoch [15/20], Step[5100/8726], d_loss: -0.0156, g_loss: -0.1076, Mean D(x): 0.07, Mean D(G(z)): 0.05\n",
      "Epoch [15/20], Step[5400/8726], d_loss: -0.0816, g_loss: -0.1490, Mean D(x): 0.28, Mean D(G(z)): 0.20\n",
      "Epoch [15/20], Step[5700/8726], d_loss: -0.1307, g_loss: 2.8718, Mean D(x): -2.49, Mean D(G(z)): -2.62\n",
      "Epoch [15/20], Step[6000/8726], d_loss: -0.0760, g_loss: -0.6221, Mean D(x): 0.37, Mean D(G(z)): 0.29\n",
      "Epoch [15/20], Step[6300/8726], d_loss: -0.2660, g_loss: -2.7333, Mean D(x): 1.07, Mean D(G(z)): 0.80\n",
      "Epoch [15/20], Step[6600/8726], d_loss: -0.0005, g_loss: -0.0989, Mean D(x): 0.15, Mean D(G(z)): 0.15\n",
      "Epoch [15/20], Step[6900/8726], d_loss: 0.1564, g_loss: -1.1522, Mean D(x): 2.10, Mean D(G(z)): 2.26\n",
      "Epoch [15/20], Step[7200/8726], d_loss: -0.3513, g_loss: -3.2235, Mean D(x): 2.46, Mean D(G(z)): 2.11\n",
      "Epoch [15/20], Step[7500/8726], d_loss: -0.0128, g_loss: -0.1442, Mean D(x): 0.02, Mean D(G(z)): 0.00\n",
      "Epoch [15/20], Step[7800/8726], d_loss: -0.0091, g_loss: -0.7669, Mean D(x): 0.84, Mean D(G(z)): 0.83\n",
      "Epoch [15/20], Step[8100/8726], d_loss: -0.0685, g_loss: -0.5949, Mean D(x): 0.59, Mean D(G(z)): 0.52\n",
      "Epoch [15/20], Step[8400/8726], d_loss: -0.3112, g_loss: -2.8011, Mean D(x): 1.14, Mean D(G(z)): 0.83\n",
      "Epoch [15/20], Step[8700/8726], d_loss: -0.1487, g_loss: 0.2936, Mean D(x): -0.25, Mean D(G(z)): -0.39\n",
      "Epoch [16/20], Step[300/8726], d_loss: 0.0145, g_loss: -0.1336, Mean D(x): 0.21, Mean D(G(z)): 0.23\n",
      "Epoch [16/20], Step[600/8726], d_loss: -0.0026, g_loss: -0.0583, Mean D(x): 0.07, Mean D(G(z)): 0.07\n",
      "Epoch [16/20], Step[900/8726], d_loss: -0.2111, g_loss: 1.4028, Mean D(x): -0.42, Mean D(G(z)): -0.63\n",
      "Epoch [16/20], Step[1200/8726], d_loss: 0.1539, g_loss: -0.2867, Mean D(x): 0.68, Mean D(G(z)): 0.84\n",
      "Epoch [16/20], Step[1500/8726], d_loss: -0.0220, g_loss: 0.0136, Mean D(x): 0.09, Mean D(G(z)): 0.07\n",
      "Epoch [16/20], Step[1800/8726], d_loss: -0.0716, g_loss: -0.8573, Mean D(x): 0.52, Mean D(G(z)): 0.45\n",
      "Epoch [16/20], Step[2100/8726], d_loss: -0.1893, g_loss: 0.9530, Mean D(x): -0.52, Mean D(G(z)): -0.71\n",
      "Epoch [16/20], Step[2400/8726], d_loss: -0.2415, g_loss: -2.1519, Mean D(x): 1.28, Mean D(G(z)): 1.04\n",
      "Epoch [16/20], Step[2700/8726], d_loss: -0.0528, g_loss: -0.3017, Mean D(x): 0.44, Mean D(G(z)): 0.39\n",
      "Epoch [16/20], Step[3000/8726], d_loss: -0.0007, g_loss: -1.0431, Mean D(x): 1.50, Mean D(G(z)): 1.50\n",
      "Epoch [16/20], Step[3300/8726], d_loss: -0.0613, g_loss: -0.0625, Mean D(x): -0.25, Mean D(G(z)): -0.31\n",
      "Epoch [16/20], Step[3600/8726], d_loss: -0.0309, g_loss: 0.6777, Mean D(x): -1.04, Mean D(G(z)): -1.07\n",
      "Epoch [16/20], Step[3900/8726], d_loss: -0.1945, g_loss: 0.2458, Mean D(x): -0.28, Mean D(G(z)): -0.47\n",
      "Epoch [16/20], Step[4200/8726], d_loss: -0.0843, g_loss: 1.6366, Mean D(x): -1.40, Mean D(G(z)): -1.48\n",
      "Epoch [16/20], Step[4500/8726], d_loss: -0.0697, g_loss: 0.5260, Mean D(x): 0.21, Mean D(G(z)): 0.14\n",
      "Epoch [16/20], Step[4800/8726], d_loss: -0.1281, g_loss: 2.0849, Mean D(x): -1.85, Mean D(G(z)): -1.98\n",
      "Epoch [16/20], Step[5100/8726], d_loss: -0.0363, g_loss: -0.8663, Mean D(x): 0.78, Mean D(G(z)): 0.74\n",
      "Epoch [16/20], Step[5400/8726], d_loss: -0.0165, g_loss: 0.1601, Mean D(x): -0.26, Mean D(G(z)): -0.28\n",
      "Epoch [16/20], Step[5700/8726], d_loss: -0.1181, g_loss: -0.6554, Mean D(x): 0.50, Mean D(G(z)): 0.38\n",
      "Epoch [16/20], Step[6000/8726], d_loss: -0.0650, g_loss: -0.4440, Mean D(x): 0.19, Mean D(G(z)): 0.13\n",
      "Epoch [16/20], Step[6300/8726], d_loss: 0.0177, g_loss: 0.2069, Mean D(x): -0.05, Mean D(G(z)): -0.04\n",
      "Epoch [16/20], Step[6600/8726], d_loss: -0.0895, g_loss: -0.0773, Mean D(x): 0.36, Mean D(G(z)): 0.27\n",
      "Epoch [16/20], Step[6900/8726], d_loss: -0.0742, g_loss: 0.1098, Mean D(x): 0.20, Mean D(G(z)): 0.12\n",
      "Epoch [16/20], Step[7200/8726], d_loss: 0.1300, g_loss: -0.9909, Mean D(x): 1.42, Mean D(G(z)): 1.55\n",
      "Epoch [16/20], Step[7500/8726], d_loss: 0.0850, g_loss: -0.6358, Mean D(x): 0.75, Mean D(G(z)): 0.84\n",
      "Epoch [16/20], Step[7800/8726], d_loss: 0.0057, g_loss: 0.2184, Mean D(x): -0.24, Mean D(G(z)): -0.23\n",
      "Epoch [16/20], Step[8100/8726], d_loss: 0.2135, g_loss: 0.5381, Mean D(x): -1.11, Mean D(G(z)): -0.90\n",
      "Epoch [16/20], Step[8400/8726], d_loss: -0.0922, g_loss: -0.6794, Mean D(x): 0.25, Mean D(G(z)): 0.16\n",
      "Epoch [16/20], Step[8700/8726], d_loss: -0.3377, g_loss: -2.9159, Mean D(x): 1.71, Mean D(G(z)): 1.37\n",
      "Epoch [17/20], Step[300/8726], d_loss: -0.0057, g_loss: 0.0098, Mean D(x): -0.01, Mean D(G(z)): -0.01\n",
      "Epoch [17/20], Step[600/8726], d_loss: -0.1614, g_loss: -1.3122, Mean D(x): 0.85, Mean D(G(z)): 0.69\n",
      "Epoch [17/20], Step[900/8726], d_loss: -0.0106, g_loss: 0.4013, Mean D(x): -0.17, Mean D(G(z)): -0.18\n",
      "Epoch [17/20], Step[1200/8726], d_loss: -0.0927, g_loss: -1.1430, Mean D(x): 0.72, Mean D(G(z)): 0.63\n",
      "Epoch [17/20], Step[1500/8726], d_loss: -0.0888, g_loss: -0.2984, Mean D(x): 0.29, Mean D(G(z)): 0.20\n",
      "Epoch [17/20], Step[1800/8726], d_loss: -0.0137, g_loss: 0.6568, Mean D(x): -0.63, Mean D(G(z)): -0.64\n",
      "Epoch [17/20], Step[2100/8726], d_loss: 0.0016, g_loss: 4.1910, Mean D(x): -3.81, Mean D(G(z)): -3.81\n",
      "Epoch [17/20], Step[2400/8726], d_loss: 0.0075, g_loss: -0.0550, Mean D(x): 0.03, Mean D(G(z)): 0.04\n",
      "Epoch [17/20], Step[2700/8726], d_loss: -0.0108, g_loss: -0.0472, Mean D(x): 0.04, Mean D(G(z)): 0.03\n",
      "Epoch [17/20], Step[3000/8726], d_loss: 0.0947, g_loss: 0.1360, Mean D(x): -0.54, Mean D(G(z)): -0.45\n",
      "Epoch [17/20], Step[3300/8726], d_loss: 0.0120, g_loss: 0.4010, Mean D(x): -0.38, Mean D(G(z)): -0.37\n",
      "Epoch [17/20], Step[3600/8726], d_loss: 0.1482, g_loss: 0.2819, Mean D(x): -0.96, Mean D(G(z)): -0.81\n",
      "Epoch [17/20], Step[3900/8726], d_loss: -0.0622, g_loss: -1.3131, Mean D(x): 1.36, Mean D(G(z)): 1.30\n",
      "Epoch [17/20], Step[4200/8726], d_loss: -0.0749, g_loss: 0.9002, Mean D(x): -0.90, Mean D(G(z)): -0.98\n",
      "Epoch [17/20], Step[4500/8726], d_loss: -0.3020, g_loss: -2.0801, Mean D(x): 1.26, Mean D(G(z)): 0.96\n",
      "Epoch [17/20], Step[4800/8726], d_loss: -0.1609, g_loss: 0.2405, Mean D(x): -0.35, Mean D(G(z)): -0.51\n",
      "Epoch [17/20], Step[5100/8726], d_loss: -0.0192, g_loss: -0.0072, Mean D(x): 0.09, Mean D(G(z)): 0.07\n",
      "Epoch [17/20], Step[5400/8726], d_loss: 0.0360, g_loss: -0.5217, Mean D(x): 0.75, Mean D(G(z)): 0.79\n",
      "Epoch [17/20], Step[5700/8726], d_loss: -0.0219, g_loss: 0.0572, Mean D(x): -0.05, Mean D(G(z)): -0.07\n",
      "Epoch [17/20], Step[6000/8726], d_loss: 0.0352, g_loss: -0.4193, Mean D(x): 0.43, Mean D(G(z)): 0.47\n",
      "Epoch [17/20], Step[6300/8726], d_loss: -0.1586, g_loss: -1.1193, Mean D(x): 0.65, Mean D(G(z)): 0.49\n",
      "Epoch [17/20], Step[6600/8726], d_loss: 0.0136, g_loss: 1.0054, Mean D(x): -1.04, Mean D(G(z)): -1.03\n",
      "Epoch [17/20], Step[6900/8726], d_loss: -0.0152, g_loss: 0.2821, Mean D(x): -0.36, Mean D(G(z)): -0.38\n",
      "Epoch [17/20], Step[7200/8726], d_loss: -0.0191, g_loss: -0.1610, Mean D(x): 0.02, Mean D(G(z)): -0.00\n",
      "Epoch [17/20], Step[7500/8726], d_loss: -0.1520, g_loss: 0.1800, Mean D(x): 0.16, Mean D(G(z)): 0.01\n",
      "Epoch [17/20], Step[7800/8726], d_loss: -0.0460, g_loss: -0.1228, Mean D(x): 0.34, Mean D(G(z)): 0.29\n",
      "Epoch [17/20], Step[8100/8726], d_loss: 0.0500, g_loss: -1.7426, Mean D(x): 1.97, Mean D(G(z)): 2.02\n",
      "Epoch [17/20], Step[8400/8726], d_loss: -0.0916, g_loss: -2.2449, Mean D(x): 2.05, Mean D(G(z)): 1.96\n",
      "Epoch [17/20], Step[8700/8726], d_loss: -0.0683, g_loss: -0.5487, Mean D(x): 0.68, Mean D(G(z)): 0.61\n",
      "Epoch [18/20], Step[300/8726], d_loss: -0.2099, g_loss: 1.0964, Mean D(x): -0.47, Mean D(G(z)): -0.68\n",
      "Epoch [18/20], Step[600/8726], d_loss: -0.1320, g_loss: 1.4370, Mean D(x): -1.12, Mean D(G(z)): -1.25\n",
      "Epoch [18/20], Step[900/8726], d_loss: -0.0156, g_loss: -0.4446, Mean D(x): 0.17, Mean D(G(z)): 0.16\n",
      "Epoch [18/20], Step[1200/8726], d_loss: 0.0520, g_loss: 0.0034, Mean D(x): 0.17, Mean D(G(z)): 0.23\n",
      "Epoch [18/20], Step[1500/8726], d_loss: -0.0954, g_loss: -0.5912, Mean D(x): 0.47, Mean D(G(z)): 0.38\n",
      "Epoch [18/20], Step[1800/8726], d_loss: 0.0248, g_loss: 0.3840, Mean D(x): -0.65, Mean D(G(z)): -0.62\n",
      "Epoch [18/20], Step[2100/8726], d_loss: -0.1061, g_loss: -0.2925, Mean D(x): 0.38, Mean D(G(z)): 0.28\n",
      "Epoch [18/20], Step[2400/8726], d_loss: 0.0062, g_loss: 0.0681, Mean D(x): 0.07, Mean D(G(z)): 0.07\n",
      "Epoch [18/20], Step[2700/8726], d_loss: 0.0030, g_loss: -0.3626, Mean D(x): 0.32, Mean D(G(z)): 0.32\n",
      "Epoch [18/20], Step[3000/8726], d_loss: 0.0348, g_loss: -0.3735, Mean D(x): 1.18, Mean D(G(z)): 1.21\n",
      "Epoch [18/20], Step[3300/8726], d_loss: -0.0024, g_loss: 0.1632, Mean D(x): -0.15, Mean D(G(z)): -0.16\n",
      "Epoch [18/20], Step[3600/8726], d_loss: -0.2005, g_loss: -0.7692, Mean D(x): 0.33, Mean D(G(z)): 0.12\n",
      "Epoch [18/20], Step[3900/8726], d_loss: -0.0847, g_loss: 1.0383, Mean D(x): -0.82, Mean D(G(z)): -0.90\n",
      "Epoch [18/20], Step[4200/8726], d_loss: -0.1319, g_loss: -0.8965, Mean D(x): 0.75, Mean D(G(z)): 0.62\n",
      "Epoch [18/20], Step[4500/8726], d_loss: -0.0171, g_loss: 0.2439, Mean D(x): -0.21, Mean D(G(z)): -0.23\n",
      "Epoch [18/20], Step[4800/8726], d_loss: 0.0575, g_loss: -0.5560, Mean D(x): 0.57, Mean D(G(z)): 0.63\n",
      "Epoch [18/20], Step[5100/8726], d_loss: -0.0124, g_loss: -0.2296, Mean D(x): 0.20, Mean D(G(z)): 0.19\n",
      "Epoch [18/20], Step[5400/8726], d_loss: 0.1340, g_loss: 1.7927, Mean D(x): -2.16, Mean D(G(z)): -2.03\n",
      "Epoch [18/20], Step[5700/8726], d_loss: -0.0049, g_loss: -0.9587, Mean D(x): 1.19, Mean D(G(z)): 1.19\n",
      "Epoch [18/20], Step[6000/8726], d_loss: -0.0101, g_loss: 0.0000, Mean D(x): 0.07, Mean D(G(z)): 0.06\n",
      "Epoch [18/20], Step[6300/8726], d_loss: -0.0021, g_loss: -0.5123, Mean D(x): 0.51, Mean D(G(z)): 0.51\n",
      "Epoch [18/20], Step[6600/8726], d_loss: -0.1416, g_loss: -1.3518, Mean D(x): 1.07, Mean D(G(z)): 0.93\n",
      "Epoch [18/20], Step[6900/8726], d_loss: 0.0642, g_loss: -0.3057, Mean D(x): 0.54, Mean D(G(z)): 0.60\n",
      "Epoch [18/20], Step[7200/8726], d_loss: 0.0686, g_loss: 0.0401, Mean D(x): -0.28, Mean D(G(z)): -0.21\n",
      "Epoch [18/20], Step[7500/8726], d_loss: -0.1597, g_loss: -1.6469, Mean D(x): 0.60, Mean D(G(z)): 0.44\n",
      "Epoch [18/20], Step[7800/8726], d_loss: -0.2625, g_loss: 0.7738, Mean D(x): -0.43, Mean D(G(z)): -0.69\n",
      "Epoch [18/20], Step[8100/8726], d_loss: -0.4512, g_loss: -3.3277, Mean D(x): 1.97, Mean D(G(z)): 1.52\n",
      "Epoch [18/20], Step[8400/8726], d_loss: 0.0056, g_loss: -1.0888, Mean D(x): 1.22, Mean D(G(z)): 1.23\n",
      "Epoch [18/20], Step[8700/8726], d_loss: -0.1621, g_loss: -0.9656, Mean D(x): 0.47, Mean D(G(z)): 0.30\n",
      "Epoch [19/20], Step[300/8726], d_loss: -0.0197, g_loss: -0.1982, Mean D(x): 0.05, Mean D(G(z)): 0.04\n",
      "Epoch [19/20], Step[600/8726], d_loss: -0.0351, g_loss: 0.2515, Mean D(x): -0.16, Mean D(G(z)): -0.20\n",
      "Epoch [19/20], Step[900/8726], d_loss: 0.0306, g_loss: 0.0607, Mean D(x): -0.15, Mean D(G(z)): -0.12\n",
      "Epoch [19/20], Step[1200/8726], d_loss: -0.0032, g_loss: 0.1654, Mean D(x): -0.21, Mean D(G(z)): -0.21\n",
      "Epoch [19/20], Step[1500/8726], d_loss: -0.0024, g_loss: -0.5557, Mean D(x): 0.55, Mean D(G(z)): 0.54\n",
      "Epoch [19/20], Step[1800/8726], d_loss: -0.0508, g_loss: -0.2092, Mean D(x): 0.32, Mean D(G(z)): 0.27\n",
      "Epoch [19/20], Step[2100/8726], d_loss: -0.0054, g_loss: 0.3329, Mean D(x): -0.26, Mean D(G(z)): -0.27\n",
      "Epoch [19/20], Step[2400/8726], d_loss: 0.0142, g_loss: -0.3191, Mean D(x): 0.28, Mean D(G(z)): 0.29\n",
      "Epoch [19/20], Step[2700/8726], d_loss: 0.2542, g_loss: -0.0675, Mean D(x): -1.15, Mean D(G(z)): -0.90\n",
      "Epoch [19/20], Step[3000/8726], d_loss: -0.0090, g_loss: -0.4024, Mean D(x): 0.39, Mean D(G(z)): 0.38\n",
      "Epoch [19/20], Step[3300/8726], d_loss: -0.0064, g_loss: -0.0416, Mean D(x): 0.06, Mean D(G(z)): 0.05\n",
      "Epoch [19/20], Step[3600/8726], d_loss: -0.0916, g_loss: 2.5877, Mean D(x): -1.80, Mean D(G(z)): -1.89\n",
      "Epoch [19/20], Step[3900/8726], d_loss: -0.0478, g_loss: 0.0724, Mean D(x): 0.07, Mean D(G(z)): 0.02\n",
      "Epoch [19/20], Step[4200/8726], d_loss: -0.0693, g_loss: -0.0394, Mean D(x): -0.09, Mean D(G(z)): -0.16\n",
      "Epoch [19/20], Step[4500/8726], d_loss: -0.0716, g_loss: -0.4190, Mean D(x): 0.47, Mean D(G(z)): 0.40\n",
      "Epoch [19/20], Step[4800/8726], d_loss: 0.1933, g_loss: -1.3998, Mean D(x): 1.77, Mean D(G(z)): 1.96\n",
      "Epoch [19/20], Step[5100/8726], d_loss: -0.0164, g_loss: 0.1043, Mean D(x): -0.00, Mean D(G(z)): -0.02\n",
      "Epoch [19/20], Step[5400/8726], d_loss: -0.1323, g_loss: 0.0229, Mean D(x): 0.20, Mean D(G(z)): 0.06\n",
      "Epoch [19/20], Step[5700/8726], d_loss: -0.0251, g_loss: -0.0587, Mean D(x): 0.11, Mean D(G(z)): 0.08\n",
      "Epoch [19/20], Step[6000/8726], d_loss: -0.1375, g_loss: 0.0128, Mean D(x): 0.00, Mean D(G(z)): -0.13\n",
      "Epoch [19/20], Step[6300/8726], d_loss: -0.0456, g_loss: 0.1054, Mean D(x): -0.11, Mean D(G(z)): -0.16\n",
      "Epoch [19/20], Step[6600/8726], d_loss: -0.0342, g_loss: -0.3203, Mean D(x): 0.32, Mean D(G(z)): 0.29\n",
      "Epoch [19/20], Step[6900/8726], d_loss: -0.0903, g_loss: -0.7574, Mean D(x): 0.81, Mean D(G(z)): 0.72\n",
      "Epoch [19/20], Step[7200/8726], d_loss: -0.0149, g_loss: 0.0664, Mean D(x): -0.00, Mean D(G(z)): -0.02\n",
      "Epoch [19/20], Step[7500/8726], d_loss: -0.0707, g_loss: -0.5526, Mean D(x): 0.52, Mean D(G(z)): 0.45\n",
      "Epoch [19/20], Step[7800/8726], d_loss: -0.0441, g_loss: -0.1435, Mean D(x): 0.16, Mean D(G(z)): 0.12\n",
      "Epoch [19/20], Step[8100/8726], d_loss: -0.4029, g_loss: 3.3889, Mean D(x): -1.64, Mean D(G(z)): -2.05\n",
      "Epoch [19/20], Step[8400/8726], d_loss: -0.0108, g_loss: -0.1125, Mean D(x): 0.13, Mean D(G(z)): 0.12\n",
      "Epoch [19/20], Step[8700/8726], d_loss: 0.0154, g_loss: -0.1293, Mean D(x): 0.16, Mean D(G(z)): 0.18\n"
     ]
    }
   ],
   "source": [
    "# allow for manual keyboard interrupt\n",
    "try: \n",
    "    # loop through epochs\n",
    "    for epoch in range(N_EPOCHS):\n",
    "        \n",
    "        # Keep track of what critic step we're on\n",
    "        crit_steps = 0\n",
    "        \n",
    "        # Keep track of the per-epoch losses\n",
    "        epoch_g_loss = 0\n",
    "        epoch_c_loss = 0\n",
    "        \n",
    "        # loop through batches (no need for class labels right now)\n",
    "        for batch_number, (images, _) in enumerate(data_loader): \n",
    "            \n",
    "            # this specific batch size (last one may not be equal to BATCH_SIZE)\n",
    "            batch_size = images.shape[0] \n",
    "            \n",
    "            # Reshape training dataset images from (batch_size, 25, 25) to (batch_size, 25*25) for \n",
    "            # processing through fully-connected net \n",
    "            images = to_var(images.view(batch_size, -1))\n",
    "\n",
    "            # 1) TRAIN CRITIC\n",
    "            # If we haven't done enough steps, let's train the critic\n",
    "            if crit_steps < N_CRITIC_STEPS:\n",
    "                critic.zero_grad()\n",
    "                generator.zero_grad()\n",
    "        \n",
    "                # clamp parameters to a cube\n",
    "                for p in critic.parameters():\n",
    "                    p.data.clamp_(-CLAMP, CLAMP)\n",
    "\n",
    "                # Accum predictions with real images (we want the critic to output a high value here)\n",
    "                pred_real = torch.mean(critic(images))\n",
    "            \n",
    "                # Draw random 64-dimensional noise vectors as inputs to the generator network\n",
    "                z = to_var(torch.randn(batch_size, 64))\n",
    "                \n",
    "                # Transform the noise through the generator network to get synthetic images\n",
    "                fake_images = generator(z)\n",
    "                \n",
    "                # Accum predictions for fake images (we want the critic to output a low value here)\n",
    "                pred_fake = torch.mean(critic(fake_images)) \n",
    "                \n",
    "                # If we minimize this, we maximize pred_real - pred_fake\n",
    "                critic_loss = pred_fake - pred_real\n",
    "                critic_loss.backward()\n",
    "                critic_optimizer.step()\n",
    "                \n",
    "                # We increment our counter now that we've seen a critic update\n",
    "                # If we still haven't done sufficient critic updates, we'll end up here again\n",
    "                crit_steps += 1\n",
    "                \n",
    "            # 2) TRAIN GENERATOR\n",
    "            # Here we have done N_CRITIC_STEPS critic updates, so we do the generator now\n",
    "            else:\n",
    "                critic.zero_grad()\n",
    "                generator.zero_grad()\n",
    "\n",
    "                # Draw random 64-dimensional noise vectors as inputs to the generator network\n",
    "                z = to_var(torch.randn(batch_size, 64))\n",
    "                \n",
    "                # Transform the noise through the generator network to get synthetic images\n",
    "                fake_images = generator(z)\n",
    "                \n",
    "                # Accum grads with fake images (generator wants the critic to \n",
    "                # output a high value here, \"tricking\" it)\n",
    "                outputs = critic(fake_images)\n",
    "                \n",
    "                # We minimize this so we maximize the quantity we actually care about\n",
    "                generator_loss = -torch.mean(outputs)\n",
    "\n",
    "                # Backprop + Optimize the generator\n",
    "                generator_loss.backward()\n",
    "                generator_optimizer.step()\n",
    "                \n",
    "                # Reset the counter - the next batch will go to the critic!\n",
    "                crit_steps = 0\n",
    "\n",
    "                if (batch_number + 1) % 300 == 0:\n",
    "                    print('Epoch [%d/%d], Step[%d/%d], d_loss: %.4f, '\n",
    "                          'g_loss: %.4f, Mean D(x): %.2f, Mean D(G(z)): %.2f' \n",
    "                          %(epoch,\n",
    "                            N_EPOCHS,\n",
    "                            batch_number + 1,\n",
    "                            n_batches,\n",
    "                            critic_loss.data[0],\n",
    "                            generator_loss.data[0],\n",
    "                            pred_real.data.mean(),\n",
    "                            pred_fake.data.mean())\n",
    "                    )\n",
    "                    \n",
    "                # Keep track of the per-epoch losses\n",
    "                epoch_g_loss += generator_loss.data[0]\n",
    "                epoch_c_loss += critic_loss.data[0]\n",
    "                 \n",
    "        g_loss.append(epoch_g_loss)\n",
    "        c_loss.append(epoch_c_loss)\n",
    "        \n",
    "        # Save the trained parameters \n",
    "        torch.save(generator.state_dict(), './weights/wgan_generator-%0.3d.pkl' %(epoch + 1))\n",
    "        torch.save(critic.state_dict(), './weights/wgan_discriminator-%0.3d.pkl' %(epoch + 1))\n",
    "        \n",
    "except KeyboardInterrupt:\n",
    "    print('Training ended early.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we can take a look at how we did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vXu3u62PqfABY5O43mtlVwDvd/cowmfzS3d8Y9f2UTEa2v62LB54PWisNew5R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\nEREZMyUTEREZMyUTEREZs/8PMD++hVpShKMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dcbe2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the history of the (scaled) Wasserstein distance\n",
    "_ = plt.plot([-l/n_batches for l in c_loss])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel(\"Negative Critic's Loss\")\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 001\n"
     ]
    },
    {
     "data": {
      "image/png": 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fjH9X++EVrz4xf+aXfmNYTw/AXZ/5wH2ZeeZ0j2ueaf6rzHxT8/57gTWZ+Z+b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\ng/8PFI2IHmkbQlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127058490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 002\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122a63c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 003\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ea3a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 004\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1269327d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 005\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12244a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 006\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204d6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 007\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x125141b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 008\n"
     ]
    },
    {
     "data": {
      "image/png": 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UUxGxHbgLOCUiJiLi8sw8AFwJ3A48ANycmfcNcp7tiMz27rMlSZIkSdJi406z\nJEmSJEktGDRLkiRJktSCQbMkSZIkSS0YNEuSJEmS1IJBsyRJkiRJLRg0S5IkSZLUgkGzJEmSJEkt\nGDRLkiRJktSCQbMkSZIkSS38f2Bq/kekC8BDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1205ac090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 009\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127c34f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 010\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122882dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 011\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fbaf650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 012\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1206c7e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 013\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1205bdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 014\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bfc6190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127c26a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 016\n"
     ]
    },
    {
     "data": {
      "image/png": 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n5sODjLMdkdnefbYkSZIkSZprXGmWJEmSJKkFk2ZJkiRJklowaZYkSZIkqQWT\nZkmSJEmSWjBpliRJkiSpBZNmSZIkSZJaMGmWJEmSJKkFk2ZJkiRJklowaZYkSZIkqYX/D9jXvvCM\nqzFbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef1e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 017\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b3da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 018\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1259262d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 019\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f8cdd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 020\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122116990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch final\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124bd79d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual inspection \n",
    "for _w in glob.glob('./weights/wgan_generator-*'):\n",
    "    print('Epoch ' + _w.split('-')[-1].split('.')[0])\n",
    "    \n",
    "    # load weights into G\n",
    "    generator.load_state_dict(torch.load(_w))\n",
    "    \n",
    "    # produce a batch of fake images\n",
    "    batch_size = 100\n",
    "    z = to_var(torch.randn(batch_size, 64))\n",
    "    fake_images = generator(z).data.numpy().reshape(-1, 25, 25)\n",
    "    \n",
    "    # plot a few of them\n",
    "    plot_random_jetimages(fake_images, njets=10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate the performance further at a given epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load in a different epoch if you want\n",
    "generator.load_state_dict(torch.load('weights/wgan_generator-016.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "z = to_var(torch.randn(batch_size, 64)) # sample noise\n",
    "fake_images = generator(z).data # pass it thru the generator to produce images\n",
    "\n",
    "# move to cpu if running on gpu\n",
    "if torch.cuda.is_available():\n",
    "    fake_images = fake_images.cpu()\n",
    "    \n",
    "# reshape images from [batch_size, 625] to [batch_size, 25, 25]\n",
    "fake_images = fake_images.numpy().reshape(-1, 25, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The previous training was done with the image intensity rescaling, so I'm scaling back now.\n",
    "# fake_images = data_loader.dataset.inverse_image_transform(fake_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1257a0910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_jetimages(fake_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yWz+8Oshu2jQuZ3z2iHXBnAvXnRdkv3v82CB7+X/8SzFbRA348XE3BdklT00J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120562250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare to training dataset\n",
    "plot_random_jetimages(data_loader.dataset.images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a few image properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1261c1b50>"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0ayp/TNKkVP7P6XN6VNK1kvoV++9gPYMTh1VEmovoaLKraAHOioj9ya6W/YKk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\n5fL/AasIp6/GLM4dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1254b5510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distances for the real and GAN-generated dataset\n",
    "bins = np.linspace(0, 55000, 15)\n",
    "_ = plt.hist(distances(data_loader.dataset.images[:batch_size]),\n",
    "             bins=bins, histtype='stepfilled', label='real', color='green', alpha=0.1)\n",
    "_ = plt.hist(distances(fake_images), bins=bins, histtype='step', label='WGAN', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Pairwise Euclidean distance')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x125aab790>"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiCWS3tvawCRNB6YDjBw5srWrm5lZRnmONTSJpFdyPUnj8KnArST3NWhIQMOBZw+2ckTM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\nCMzMSu7/AyosF46fKOtVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1225dc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 200, 15)\n",
    "_ = plt.hist(discrete_mass(data_loader.dataset.images[:batch_size]),\n",
    "             histtype='stepfilled', bins=bins, label='WGAN', color='green', alpha=0.1)\n",
    "_ = plt.hist(discrete_mass(fake_images), histtype='step', bins=bins, label='real', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Jet mass')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x125394890>"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124ff05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(0, 1000, 15)\n",
    "_ = plt.hist(discrete_pt(data_loader.dataset.images[:batch_size]),\n",
    "             histtype='stepfilled', bins=bins, label='real', color='green', alpha=0.1)\n",
    "_ = plt.hist(discrete_pt(fake_images), histtype='step', bins=bins, label='WGAN', color='red')\n",
    "plt.legend()\n",
    "plt.xlabel('Jet pT')\n",
    "plt.ylabel('Number of events')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1250f9350>"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SHZJ+qdL6Cd9Myr8B7JXUaH6SlP3nZJ7/ByXdJGlYZ3ySvpbM//87Se9Oyi9I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\nS+UEYWZmqf4/knNkIj8vAEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12586cf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.linspace(0, 300, 50)\n",
    "_ = plt.hist(data_loader.dataset.images[:batch_size].ravel(),\n",
    "             bins=bins, histtype='stepfilled', color='green', alpha=0.1, label='real')\n",
    "_ = plt.hist(fake_images.ravel(), bins=bins, histtype='step', color='red', label='WGAN')\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x125b506d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.logspace(-21, 3, 50)\n",
    "_ = plt.hist(data_loader.dataset.images[:batch_size].ravel(),\n",
    "             bins=bins, histtype='stepfilled', color='green', alpha=0.1, label='real')\n",
    "_ = plt.hist(fake_images.ravel(), bins=bins, histtype='step', color='red', label='WGAN')\n",
    "\n",
    "plt.xlabel('Pixel Intensity')\n",
    "plt.ylabel('Number of Pixels')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suggestions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try Locally-Connected layers. While training our LAGAN model (https://arxiv.org/abs/1701.05927), we found them to perform best on jet-images, providing us with a useful compromise between convolutional and fully connected layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not merged into PyTorch's master yet, but here's the code from the PR (I haven't tested it yet):\n",
    "```python\n",
    "class Conv2dLocal(Module):\n",
    "\n",
    "    def __init__(self, in_height, in_width, in_channels, out_channels,\n",
    "                 kernel_size, stride=1, padding=0, bias=True, dilation=1):\n",
    "        # kernel_size = _pair(kernel_size)\n",
    "        # stride = _pair(stride)\n",
    "        # padding = _pair(padding)\n",
    "        # dilation = _pair(dilation)\n",
    "        # super(Conv2dLocal, self).__init__(\n",
    "        #     in_channels, out_channels, kernel_size, stride, padding, dilation,\n",
    "        #     False, _single(0), 1, bias)\n",
    "        super(Conv2dLocal, self).__init__()\n",
    "\n",
    "        self.in_channels = in_channels\n",
    "        self.out_channels = out_channels\n",
    "        self.kernel_size = _pair(kernel_size)\n",
    "        self.stride = _pair(stride)\n",
    "        self.padding = _pair(padding)\n",
    "        self.dilation = _pair(dilation)\n",
    "\n",
    "        self.in_height = in_height\n",
    "        self.in_width = in_width\n",
    "        self.out_height = int(math.floor(\n",
    "            (in_height + 2 * self.padding[0] - self.dilation[0] * (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1))\n",
    "        self.out_width = int(math.floor(\n",
    "            (in_width + 2 * self.padding[1] - self.dilation[1] * (self.kernel_size[1] - 1) - 1) / self.stride[1] + 1))\n",
    "        self.weight = Parameter(torch.Tensor(\n",
    "            self.out_height, self.out_width,\n",
    "            out_channels, in_channels, *self.kernel_size))\n",
    "        if bias:\n",
    "            self.bias = Parameter(torch.Tensor(\n",
    "                out_channels, self.out_height, self.out_width))\n",
    "        else:\n",
    "            self.register_parameter('bias', None)\n",
    "\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        n = self.in_channels\n",
    "        for k in self.kernel_size:\n",
    "            n *= k\n",
    "        stdv = 1. / math.sqrt(n)\n",
    "        self.weight.data.uniform_(-stdv, stdv)\n",
    "        if self.bias is not None:\n",
    "            self.bias.data.uniform_(-stdv, stdv)\n",
    "\n",
    "    def forward(self, input):\n",
    "        return F.conv2d_local(input, self.weight, self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our architecture for LAGAN looked like this.\n",
    "<img src=\"lagan.png\">\n",
    "If you feel like re-implementing it in PyTorch, send us a PR!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Use Gradient Penalty (WGAN-GP) instead of weight clipping, as suggested in \"Improved Training of Wasserstein GANs\".\n",
    "This snippet is adapted from https://github.com/caogang/wgan-gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LAMBDA = 10 # Gradient penalty lambda hyperparameter\n",
    "\n",
    "def calc_gradient_penalty(D, real_data, fake_data, batch_size):\n",
    "    \n",
    "    alpha = torch.rand(batch_size, 1)\n",
    "    alpha = alpha.expand(real_data.size())\n",
    "    alpha = alpha.cuda() if torch.cuda.is_available() else alpha # for gpu\n",
    "\n",
    "    # find new data points by interpolating between real and fake samples\n",
    "    interpolates = alpha * real_data + ((1 - alpha) * fake_data)\n",
    "    if torch.cuda.is_available():\n",
    "        interpolates = interpolates.cuda()  \n",
    "    interpolates = autograd.Variable(interpolates, requires_grad=True)\n",
    "\n",
    "    predictions = D(interpolates)\n",
    "\n",
    "    gradients = autograd.grad(\n",
    "        outputs=predictions,\n",
    "        inputs=interpolates,\n",
    "        grad_outputs=torch.ones(predictions.size()).cuda() if torch.cuda.is_available()\n",
    "                else torch.ones(predictions.size()),\n",
    "        create_graph=True,\n",
    "        retain_graph=True,\n",
    "        only_inputs=True)[0]\n",
    "\n",
    "    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * LAMBDA\n",
    "    return gradient_penalty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caluclate the GP while training your discriminator and add it to the D's loss:\n",
    "```python\n",
    "gradient_penalty = calc_gradient_penalty(critic, images.data, fake_images.data)\n",
    "critic_loss = pred_fake - pred_real + gradient_penalty\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tricks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_instance_noise(images, std=0.1):\n",
    "    '''\n",
    "    add normal noise to images\n",
    "    '''\n",
    "    noise = Variable(std * torch.randn(images.shape))\n",
    "    return images + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_flip(x, p=0.05):\n",
    "    '''\n",
    "    bit flip with some probability\n",
    "    Args:\n",
    "    ----\n",
    "        x = torch.FloatTensor\n",
    "        p = float, probability of flip\n",
    "    Returns:\n",
    "    --------\n",
    "        x_new = torch.FloatTensor\n",
    "    '''\n",
    "    x_new = x.clone()\n",
    "    selection = torch.rand(x_new.shape) < prob\n",
    "    x_new[selection] = (x_new[selection].type(torch.ByteTensor)^1).type(torch.FloatTensor)\n",
    "    return x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_smoothe(x, p=0.05, smoothing_param=0.3):\n",
    "    '''\n",
    "    label smoothing with some probability\n",
    "    Args:\n",
    "    ----\n",
    "        x = torch.FloatTensor\n",
    "        p = float, probability of an entry being selected for smoothing\n",
    "        smoothing_param = float, max amount of variation\n",
    "    Returns:\n",
    "    --------\n",
    "        x_new = torch.FloatTensor\n",
    "    '''\n",
    "    x_new = x.clone()\n",
    "    selection = torch.rand(x_new.shape) < prob\n",
    "    smoothing = torch.rand(selection.sum()) * smoothing_param\n",
    "    x_new[selection] = torch.abs(x_new[selection] - smoothing)\n",
    "    return x_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from t-ae (https://gist.github.com/t-ae/732f78671643de97bbe2c46519972491)\n",
    "class MinibatchDiscrimination(nn.Module):\n",
    "    def __init__(self, in_features, out_features, kernel_dims, mean=False):\n",
    "        super().__init__()\n",
    "        self.in_features = in_features\n",
    "        self.out_features = out_features\n",
    "        self.kernel_dims = kernel_dims\n",
    "        self.mean = mean\n",
    "        # trainable tensor T\n",
    "        self.T = nn.Parameter(torch.Tensor(in_features, out_features, kernel_dims))\n",
    "        init.normal(self.T, 0, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x is NxA\n",
    "        # T is AxBxC\n",
    "        matrices = x.mm(self.T.view(self.in_features, -1)) # x * T\n",
    "        matrices = matrices.view(-1, self.out_features, self.kernel_dims)\n",
    "\n",
    "        M = matrices.unsqueeze(0)  # 1xNxBxC\n",
    "        M_T = M.permute(1, 0, 2, 3)  # Nx1xBxC\n",
    "        norm = torch.abs(M - M_T).sum(3)  # NxNxB\n",
    "        expnorm = torch.exp(-norm)\n",
    "        o_b = (expnorm.sum(0) - 1)   # NxB, subtract self distance\n",
    "        if self.mean:\n",
    "            o_b /= x.size(0) - 1\n",
    "\n",
    "        x = torch.cat([x, o_b], 1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_matching(hidden, batch_weights):\n",
    "    '''\n",
    "    Augment your hidden features with batch_level statistics.\n",
    "    These can be used to help the discriminator classify real vs fake.\n",
    "    Alternatively, you can use these features to add a term to the loss of the form:\n",
    "    ||f(x_real) - f(x_fake)||^2\n",
    "    \n",
    "    If your events are weighted, make sure to use batch_weights to compute the correct mean and std.\n",
    "    '''\n",
    "    batch_mean = batch_weights.mm(hidden).expand(batch_size, hidden.shape[-1])\n",
    "    batch_second_moment = batch_weights.mm(torch.pow(hidden, 2)).expand(batch_size, hidden.shape[-1])\n",
    "    batch_std = batch_second_moment - torch.pow(batch_mean, 2)\n",
    "    \n",
    "    # concatenate all features\n",
    "    all_features = torch.cat([batch_mean, batch_std, hidden], 1)\n",
    "    return all_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Conclusions\n",
    "\n",
    "We have seen that WGANs add some stability, but out of the box models cannot take us all the way. Other tricks in the literature have helped stabilize normal GANs as well. It is likely that you'll have to adopt some of thos tricks or come up with your own to make these models work on your specific application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Conditioning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There have been several proposed ways of effectively using labels to condition generation (and sometimes improve the training altogether). \n",
    "<img src=\"https://raw.githubusercontent.com/gitlimlab/ACGAN-PyTorch/master/figs/architecture.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our DataLoader yields both images and labels for each batch. In this tutorial, we did not use class labels, but if you decide to use them for conditioning, simply relace \n",
    "```python\n",
    "for batch_number, (images, _) in enumerate(data_loader):\n",
    "```\n",
    "with\n",
    "\n",
    "```python\n",
    "for batch_number, (images, labels) in enumerate(data_loader):\n",
    "```\n",
    "\n",
    "Then, make sure to modify either your Generator (C-GAN, InfoGAN, AC-GAN) to take in a class label/embedding in addition to the input noise, or your Discriminator (Semi-Supervised GAN, InfoGAN, AC-GAN) to make sure the output is consistent with their formulation.\n",
    "\n",
    "For example, in AC-GAN: randomly sample labels to request to your Generator (or get the labels from the dataloader in the case of the real training dataset), then make sure the Discriminator can correctly classify the images according to their label (here W vs QCD). This would require adding a cross-entropy term to your losses that both G and D try to minimize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Other Resources and Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* PyTorch DCGAN (Deep Convolutional GAN): https://github.com/pytorch/examples/tree/master/dcgan\n",
    "* PyTorch VAE (Variational Auto-Encoder): https://github.com/pytorch/examples/tree/master/vae\n",
    "* \"How to Train a GAN? Tips and tricks to make GANs work\": https://github.com/soumith/ganhacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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