voxel_gan_mk1.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, rdMolTransforms\n",
    "import os\n",
    "import fnmatch\n",
    "import numpy as np\n",
    "import deepchem as dc\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.mixture import GMM\n",
    "import random\n",
    "from tflearn.activations import leaky_relu\n",
    "from deepchem.utils.rdkit_util import get_xyz_from_mol\n",
    "from deepchem.feat.rdkit_grid_featurizer import convert_atom_to_voxel, compute_centroid, rotate_molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from deepchem.feat.graph_features import ConvMolFeaturizer\n",
    "from deepchem.feat.adjacency_fingerprints import AdjacencyFingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "elem_to_idx_dict = {1: 0, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6, 15: 7, 16: 8, 17: 9, 35: 10, 53: 11, 30: 12, 14: 13, 80:14, 81:15}\n",
    "\n",
    "def featurize_mols(mol_files, max_n_atoms, box_width, voxel_width, elems_dict):\n",
    "    n_elems = len(list(elems_dict.keys()))\n",
    "\n",
    "    featurizer = AdjacencyFingerprint(max_n_atoms=max_n_atoms)\n",
    "    features = []\n",
    "    for idx, mol_file in enumerate(mol_files):\n",
    "        if idx % 32 == 0:\n",
    "            print(idx)\n",
    "        try:\n",
    "            atoms_per_type = np.zeros((len(elems_dict.keys())))\n",
    "\n",
    "            mol = Chem.MolFromMol2File(mol_file)\n",
    "            mol_xyz = get_xyz_from_mol(mol)\n",
    "            centroid = compute_centroid(mol_xyz)\n",
    "            mol_xyz -= centroid\n",
    "            #mol_xyz = rotate_molecules([mol_xyz])[0]\n",
    "            \n",
    "            \n",
    "\n",
    "            if mol is None:\n",
    "                features.append(None)\n",
    "                continue\n",
    "                \n",
    "            temp = np.zeros((max_n_atoms,3))\n",
    "            n_atoms = mol_xyz.shape[0]\n",
    "            temp[:n_atoms] = mol_xyz\n",
    "            mol_xyz = temp\n",
    "            \n",
    "            voxel_grid = np.zeros([int(box_width/voxel_width)]*3 + [n_elems]).astype(np.uint8)\n",
    "            #voxel_grid[:,:,:,-1] += 1\n",
    "            \n",
    "            for atom_idx in range(n_atoms):\n",
    "                voxel_inds = convert_atom_to_voxel(mol_xyz,\n",
    "                                                   atom_idx,\n",
    "                                                   box_width,\n",
    "                                                   voxel_width)[0].tolist()   \n",
    "                elem = mol.GetAtomWithIdx(atom_idx).GetAtomicNum()\n",
    "                if elem in elem_to_idx_dict.keys():\n",
    "                    channel = elem_to_idx_dict[elem]\n",
    "                    atoms_per_type[channel] += 1.\n",
    "                else:\n",
    "                    pass\n",
    "                    #channel = -2\n",
    "                    #atoms_per_type[channel] += 1.\n",
    "                voxel_grid[voxel_inds[0], voxel_inds[1], voxel_inds[2], channel] = 1\n",
    "                voxel_grid[voxel_inds[0], voxel_inds[1], voxel_inds[2], -1] = 0\n",
    "            \n",
    "            for i in range(0, voxel_grid.shape[0]):\n",
    "                for j in range(0, voxel_grid.shape[1]):\n",
    "                    for k in range(0, voxel_grid.shape[2]):\n",
    "                        if voxel_grid[i,j,k,-1] == 1.:\n",
    "                            #atoms_per_type[-1] += 1\n",
    "                            pass\n",
    "            \n",
    "            graph_feat = featurizer.featurize([mol])[0]\n",
    "            \n",
    "            features.append((mol_file, voxel_grid, graph_feat, atoms_per_type))\n",
    "        except:\n",
    "            features.append(None)\n",
    "    return(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.indices((3,4)).reshape(2,-1).T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "feature_file = \"./voxel_features_pdbbind.pkl\"\n",
    "#if not os.path.exists(feature_file):\n",
    "if 1== 1:\n",
    "    pdbbind_dir = \"/home/evan/Documents/deep_docking/datasets/v2015/\"\n",
    "    def find_files(directory, pattern):\n",
    "        for root, dirs, files in os.walk(directory):\n",
    "            for basename in files:\n",
    "                if fnmatch.fnmatch(basename, pattern):\n",
    "                    filename = os.path.join(root, basename)\n",
    "                    yield filename\n",
    "    ligand_files = []\n",
    "    for f in find_files(pdbbind_dir, \"*ligand.mol2\"):\n",
    "        ligand_files += [f]\n",
    "    ligand_files = ligand_files[8:9]*32\n",
    "    features = featurize_mols(ligand_files, 128,  4, 0.25, elem_to_idx_dict)\n",
    "    with open(feature_file, \"wb\") as f:\n",
    "        pickle.dump(features, f, protocol=2)\n",
    "else:\n",
    "    with open(feature_file, \"rb\") as f:\n",
    "        features = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = [f for f in features if f is not None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
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     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16, 16, 16)"
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     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "features[0][1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(features[0][1][:,:,:,:] == 1)[0].shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 116,
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    {
     "data": {
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       "('/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2',\n",
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       "          [0, 0, 0, ..., 0, 0, 0],\n",
       "          [0, 0, 0, ..., 0, 0, 0]]]], dtype=uint8),\n",
       " (array([[1, 1, 0, ..., 0, 0, 0],\n",
       "         [1, 1, 1, ..., 0, 0, 0],\n",
       "         [0, 1, 1, ..., 0, 0, 0],\n",
       "         ..., \n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       "  array([[1, 0, 0, ..., 0, 0, 0],\n",
       "         [1, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ..., \n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)),\n",
       " array([ 0.,  0.,  1.,  2.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.]))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.reset_default_graph()\n",
    "alpha = 0.01\n",
    "n_layers = 0\n",
    "\n",
    "S = 32\n",
    "\n",
    "B = 128\n",
    "p = 75\n",
    "\n",
    "z_dim = 32\n",
    "\n",
    "L_list = [p, 64, 64, 128, 256]\n",
    "\n",
    "\n",
    "n_bonds = 50\n",
    "dihed_per_bond = 1\n",
    "valence = 4\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.device('/gpu:0'):\n",
    "    x = tf.placeholder(tf.float32, [S, B, L_list[0]], name=\"atom_features\")\n",
    "    keep_prob = tf.constant(1.)\n",
    "    \n",
    "    adj_matrix = tf.placeholder(tf.float32, shape=[S, B, B], name=\"adj_matrix\")\n",
    "    \n",
    "    z = tf.random_normal([S, 2, 2, 2, z_dim], mean=0, stddev=1)\n",
    "    \n",
    "    #z = tf.placeholder(tf.float32,\n",
    "    #                          [None, B, L_list[0]])\n",
    "    \n",
    "    mol_grids = tf.cast(tf.placeholder(tf.uint8, [S, 16, 16, 16, 16]), tf.float32)\n",
    "    \n",
    "    atoms_per_type = tf.placeholder(tf.float32, shape=[S,len(elem_to_idx_dict.keys())])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building clades and adj\n",
      "Building hidden layers\n"
     ]
    }
   ],
   "source": [
    "GW_list = [None for i in range(n_layers)]\n",
    "Gb_list = [None for i in range(n_layers)]\n",
    "\n",
    "DW_list = [None for i in range(n_layers)]\n",
    "Db_list = [None for i in range(n_layers)]\n",
    "\n",
    "\n",
    "Gh_list = [x]\n",
    "Dh_list = [x]\n",
    "\n",
    "adj_list = [adj_matrix]\n",
    "B_list = [B]\n",
    "clades_list = []\n",
    "\n",
    "print(\"building clades and adj\")\n",
    "for i in range(n_layers):\n",
    "    print(i)\n",
    "    B_list.append(int(np.ceil(B_list[i]/2.)))\n",
    "    print(B_list)\n",
    "    clades_list.append(tf.stack([tf.one_hot(range(0,B_list[i],2), depth=B_list[i])]*S, axis=0)) \n",
    "\n",
    "\n",
    "    adj_temp = tf.matmul(clades_list[i], adj_list[i])\n",
    "    sub_adj = tf.matmul(adj_temp, tf.transpose(adj_temp, perm=[0, 2, 1]))\n",
    "    sub_adj = tf.minimum(sub_adj, 1.)\n",
    "    adj_list.append(sub_adj)\n",
    "\n",
    "print(\"Building hidden layers\")\n",
    "for layer_idx in range(n_layers):\n",
    "    GW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"GW_list%d\" %layer_idx)\n",
    "    Gb_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "    \n",
    "    DW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"DW_list%d\" %layer_idx)\n",
    "    Db_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "    \n",
    "g1 = tf.Variable(tf.truncated_normal([4, 4, 4, 32, L_list[n_layers]+z_dim]))\n",
    "g_b1 = tf.Variable(tf.ones([1, 1, 1, 1, 32]))\n",
    "\n",
    "g2 = tf.Variable(tf.truncated_normal([4, 4, 4, 16, 32]))\n",
    "\n",
    "g_b2 = tf.Variable(tf.ones([1,1, 1, 1, 16]))\n",
    "\n",
    "g_b3 = tf.Variable(tf.ones([]))\n",
    "\n",
    "\n",
    "d1 = tf.Variable(tf.truncated_normal([4, 4, 4, 16, 32]))\n",
    "d_b1 = tf.Variable(tf.ones([1, 1, 1, 1, 32]))\n",
    "\n",
    "d2 = tf.Variable(tf.truncated_normal([4, 4, 4, 32, 64]))\n",
    "d_b2 = tf.Variable(tf.ones([1,1,1,1,64]))\n",
    "\n",
    "d3 = tf.Variable(tf.truncated_normal([L_list[n_layers] + 64, 10]))\n",
    "d_b3  = tf.Variable(tf.ones([1, 10]))\n",
    "\n",
    "d4 = tf.Variable(tf.truncated_normal([10, 1]))\n",
    "\n",
    "d_b4  = tf.Variable(tf.ones([1, 1]))\n",
    "\n",
    "arr = np.ones([1, 1, 1, 1, 16])\n",
    "arr -= 50.\n",
    "arr = arr.astype(np.float32)\n",
    "g_types = tf.Variable(tf.constant(arr))\n",
    "types1 = tf.Variable(tf.constant(1., shape=[1,1]))\n",
    "types2 = tf.Variable(tf.constant(1., shape=[14,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'adj_matrix:0' shape=(32, 128, 128) dtype=float32>"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adjacency_conv_layer(x, W, b, adj, clades, L_in, L_out, layer_idx, S, B):\n",
    "    with tf.device('/gpu:0'):\n",
    "        print(\"layer_idx: %d\" %(layer_idx))\n",
    "        h = tf.matmul(adj, x, name=\"adj_mult_%d\" %layer_idx)\n",
    "        h = tf.reshape(h, shape=(S*B, L_in), name=\"adj_reshape_1_%d\" %layer_idx)\n",
    "\n",
    "        h = tf.matmul(h, W, name=\"adjconv_%d\" %layer_idx) + b\n",
    "    \n",
    "        h = tf.nn.tanh(h)\n",
    "        h = tf.reshape(h, (S, B, L_out), name=\"adj_reshape_2_%d\" %layer_idx)  \n",
    "\n",
    "        h = tf.matmul(clades, h)\n",
    "\n",
    "        print(\"within func h:\")\n",
    "        print(h)                                                                                                                              \n",
    "\n",
    "    return(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#t = sess.run(tf.matmul(dihed_indices[:,:,:,0], x))\n",
    "#t = np.reshape(t, [t.shape[0]*t.shape[1],t.shape[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(W_list, b_list, h_list, L_list, n_layers):\n",
    "    for layer_idx in range(n_layers):\n",
    "        h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                                     b_list[layer_idx], adj_list[layer_idx], \n",
    "                                                     clades_list[layer_idx],\n",
    "                                                     L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                                     layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "    L_final = L_list[n_layers]  \n",
    "    \n",
    "    print(\"h_list[-1]\")\n",
    "    print(h_list[-1])\n",
    "    \n",
    "    print(\"B_list\")\n",
    "    print(B_list)\n",
    "    \n",
    "    h_final = tf.reduce_sum(h_list[-1], axis=1, keep_dims=True)\n",
    "    h_final = tf.multiply(0.125, tf.tile(tf.reshape(h_final, [S, 1,1, 1, L_final]), [1, 2, 2, 2, 1]))\n",
    "    #shape = (S, 1, 1, 1, 64)\n",
    "    \n",
    "    #h_final = tf.reshape(h_list[-1], [S] + [int(np.cbrt(B_list[-1]))]*3 + [L_final])\n",
    "    h_final = tf.concat([h_final, z], axis=4)\n",
    "    #shape = (S, 1, 1, 1, 96)\n",
    "    \n",
    "    print(\"h_final\")\n",
    "    print(h_final)\n",
    "    \n",
    "    g_dc1 = tf.nn.conv3d_transpose(h_final, g1, [S, 8, 8, 8, 32], [1,4,4,4, 1], padding='SAME', name=None)\n",
    "    #shape = (S, 4, 4, 4, 32)\n",
    "    g_dc1 = tf.nn.tanh(g_dc1) + g_b1#, dim=-1)\n",
    "    \n",
    "    print(\"g_dc1\")\n",
    "    print(g_dc1)\n",
    "    g_dc2 = tf.nn.conv3d_transpose(g_dc1, g2, [S, 16, 16, 16, 16], [1,2, 2, 2, 1], padding='SAME') + g_b2\n",
    "    #shape = (S, 16, 16, 16)\n",
    "    \n",
    "    #g_dc2 = tf.nn.sigmoid(tf.multiply(g_dc2, 2.))\n",
    "    \n",
    "\n",
    "    #g_dc2 = tf.multiply(g_types, g_dc2)\n",
    "    g_dc2 = tf.nn.tanh(tf.multiply(tf.add(g_dc2, g_types), 10.))\n",
    "    g_dc2 = tf.maximum(g_dc2, 0.)\n",
    "    #g_dc2 = tf.minimum(0.00001*g_dc2, g_dc2)\n",
    "    #g_dc2 = tf.nn.softmax(tf.matmul, tf.constant(2., shape=[S,16,16,16,16])))\n",
    "    #g_dc2 = tf.nn.softmax(g_dc2)\n",
    "    #a = tf.constant(0.8, shape=[1,1])\n",
    "    #g_dc2 = tf.subtract(g_dc2, a)\n",
    "    #g_dc2 = tf.clip_by_value(g_dc2, 0.01, 1.)\n",
    "    #g_dc2 = tf.divide(g_dc2, tf.reduce_max(g_dc2))\n",
    "    \n",
    "    \n",
    "    #g_dc1\n",
    "    #output = tf.one_hot(where_max, 16)\n",
    "\n",
    "    \n",
    "    return(g_dc2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator(W_list, b_list, h_list, L_list, n_layers, mol):\n",
    "    with tf.device('/gpu:0'):\n",
    "        #pred_atoms_per_type = tf.nn.softmax(tf.matmul(mol, tf.constant(2., shape=[S,16,16,16,1])))\n",
    "        print(\"mol\")\n",
    "        print(mol)\n",
    "        pred_atoms_per_type = tf.reshape(tf.reduce_sum(mol, axis=[1,2,3]), [S,16])[:, :]\n",
    "        corr_atoms_per_type = tf.reshape(atoms_per_type[:,:], [S,16]) \n",
    "        differences = tf.matmul(tf.reshape(tf.reduce_sum(tf.square(tf.subtract(pred_atoms_per_type, corr_atoms_per_type)), axis=1), (S,1)), types1)\n",
    "\n",
    "\n",
    "        d_c1 = tf.nn.conv3d(mol, d1, [1, 4, 4, 4, 1], padding='VALID', name=None) + d_b1\n",
    "        \n",
    "        print(\"d_c1\")\n",
    "        print(d_c1)\n",
    "        d_c1 = tf.reshape(d_c1, [S, 4, 4, 4, 32])\n",
    "        \n",
    "        \n",
    "        d_c2 = tf.nn.conv3d(d_c1, d2, [1, 4, 4, 4, 1], padding='VALID', name=None) + d_b2     \n",
    "        print(\"d_c2\")\n",
    "        print(d_c2)\n",
    "        d_c2 = tf.reshape(d_c2, [S, 64])\n",
    "\n",
    "        \n",
    "        for layer_idx in range(n_layers):\n",
    "            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                             b_list[layer_idx], adj_list[layer_idx], \n",
    "                                             clades_list[layer_idx],\n",
    "                                             L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                             layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "        \n",
    "        h_final = tf.reshape(tf.reduce_sum(h_list[-1], axis=1), [S,L_list[n_layers]])\n",
    "        print(\"d_h_final\")\n",
    "        print(h_final)\n",
    "            \n",
    "        d_c3 = tf.concat([d_c2, h_final], axis=1)\n",
    "        print(\"d_c3\")\n",
    "        print(d_c3)\n",
    "    \n",
    "        d_c4 = tf.nn.relu(tf.matmul(d_c3, d3)) + d_b3\n",
    "        print(\"d_c4\")\n",
    "        print(d_c4)\n",
    "        \n",
    "        d_c5 = tf.nn.relu(tf.matmul(d_c4, d4)) + d_b4\n",
    "        \n",
    "        print(\"d_c5\")\n",
    "        print(d_c5)\n",
    "        \n",
    "    \n",
    "    return(tf.add(d_c5, 0.), differences,corr_atoms_per_type, pred_atoms_per_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "theta_D = DW_list[:n_layers] + Db_list[:n_layers] + [d1, d_b1, d2, d_b2, d3, d_b3, d4, d_b4, types1]\n",
    "\n",
    "theta_G =  DW_list[:n_layers] + Db_list[:n_layers] + [g1, g_b1, g2, g_b2, g_types]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h_list[-1]\n",
      "Tensor(\"atom_features:0\", shape=(32, 128, 75), dtype=float32, device=/device:GPU:0)\n",
      "B_list\n",
      "[128]\n",
      "h_final\n",
      "Tensor(\"concat:0\", shape=(32, 2, 2, 2, 107), dtype=float32, device=/device:GPU:0)\n",
      "g_dc1\n",
      "Tensor(\"add:0\", shape=(32, 8, 8, 8, 32), dtype=float32, device=/device:GPU:0)\n",
      "mol\n",
      "Tensor(\"Cast:0\", shape=(32, 16, 16, 16, 16), dtype=float32, device=/device:GPU:0)\n",
      "d_c1\n",
      "Tensor(\"add_2:0\", shape=(32, 4, 4, 4, 32), dtype=float32, device=/device:GPU:0)\n",
      "d_c2\n",
      "Tensor(\"add_3:0\", shape=(32, 1, 1, 1, 64), dtype=float32, device=/device:GPU:0)\n",
      "d_h_final\n",
      "Tensor(\"Reshape_6:0\", shape=(32, 75), dtype=float32, device=/device:GPU:0)\n",
      "d_c3\n",
      "Tensor(\"concat_1:0\", shape=(32, 139), dtype=float32, device=/device:GPU:0)\n",
      "d_c4\n",
      "Tensor(\"add_4:0\", shape=(32, 10), dtype=float32, device=/device:GPU:0)\n",
      "d_c5\n",
      "Tensor(\"add_5:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "mol\n",
      "Tensor(\"Maximum:0\", shape=(32, 16, 16, 16, 16), dtype=float32, device=/device:GPU:0)\n",
      "d_c1\n",
      "Tensor(\"add_6:0\", shape=(32, 4, 4, 4, 32), dtype=float32, device=/device:GPU:0)\n",
      "d_c2\n",
      "Tensor(\"add_7:0\", shape=(32, 1, 1, 1, 64), dtype=float32, device=/device:GPU:0)\n",
      "d_h_final\n",
      "Tensor(\"Reshape_12:0\", shape=(32, 75), dtype=float32, device=/device:GPU:0)\n",
      "d_c3\n",
      "Tensor(\"concat_2:0\", shape=(32, 139), dtype=float32, device=/device:GPU:0)\n",
      "d_c4\n",
      "Tensor(\"add_8:0\", shape=(32, 10), dtype=float32, device=/device:GPU:0)\n",
      "d_c5\n",
      "Tensor(\"add_9:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "\n",
    "with tf.device('/gpu:0'):    \n",
    "\n",
    "    G_sample = generator(DW_list, Db_list, Dh_list, L_list, n_layers)\n",
    "\n",
    "    D_real,diff_real,  corr_real, pred_real = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, mol_grids)\n",
    "    D_fake, diff_fake, corr_fake, pred_fake = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, G_sample)\n",
    "\n",
    "\n",
    "    D_loss = tf.reduce_mean(D_real) - tf.reduce_mean(D_fake)\n",
    "    G_loss = -tf.reduce_mean(D_fake)\n",
    "    clip_D = [p.assign(tf.clip_by_value(p, -0.01, 0.01)) for p in theta_D]\n",
    "    #clip_types = types1.assign(tf.clip_by_value(types1, -0.1, 0.1))\n",
    "\n",
    "    #D_solver = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(D_loss, var_list=theta_D)\n",
    "    #G_solver = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(G_loss, var_list=theta_G)\n",
    "\n",
    "    D_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-3)\n",
    "                .minimize(-D_loss, var_list=theta_D))\n",
    "    G_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-3)\n",
    "                .minimize(G_loss, var_list=theta_G))\n",
    "\n",
    "mb_size = S\n",
    "\n",
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "preds = []\n",
    "\n",
    "d_losses = []\n",
    "g_losses = []\n",
    "#print(sess.run(label_placeholder))\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def construct_feed_dict(X, start=None,\n",
    "                      stop=None, y=None,\n",
    "                      keep_prob=1.0, train=True,\n",
    "                       shuffle_inds=True):\n",
    "    a = time.time()\n",
    "    if start is None:\n",
    "      start = 0\n",
    "      stop = len(X)\n",
    "    \n",
    "    inds = range(start, stop)\n",
    "    if shuffle_inds:\n",
    "        random.shuffle(inds)\n",
    "\n",
    "    atom_adj_batch = [X[idx][2][0] for idx in inds]\n",
    "    A_batch = np.array([X[idx][2][1] for idx in inds])\n",
    "    atoms_per_type_batch = np.array([X[idx][3] for idx in inds])\n",
    "    y_batch = [X[idx][1] for idx in inds]\n",
    "\n",
    "    feed_dict = {x: A_batch,\n",
    "                 adj_matrix: atom_adj_batch,\n",
    "                 mol_grids: y_batch,\n",
    "                 atoms_per_type: atoms_per_type_batch\n",
    "                }\n",
    "    t = time.time()-a\n",
    "    #print(\"Construct feed dict: %f\" %(t))\n",
    "    return(feed_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import periodictable as pt\n",
    "\n",
    "index_to_elem = {}\n",
    "for key,value in elem_to_idx_dict.iteritems():\n",
    "    index_to_elem[value] = key\n",
    "\n",
    "def convert_to_molecule(grid, index_to_elem, filename):\n",
    "    with open(filename, \"wb\") as f:\n",
    "        f.write(\"4\\n\")\n",
    "        f.write(\"comment\\n\")\n",
    "        atom_inds = np.argmax(grid, axis=3)\n",
    "        for x in range(16):\n",
    "            for y in range(16):\n",
    "                for z in range(16):\n",
    "                    if np.max(grid[x,y,z,:]) < 0.5:\n",
    "                        continue\n",
    "                    elem_idx = atom_inds[x][y][z]\n",
    "\n",
    "                    an = index_to_elem[elem_idx]\n",
    "                    elem = str(pt.elements[an])\n",
    "\n",
    "                    x_pos = -2.0 + x*0.25\n",
    "                    y_pos = -2.0 + y*0.25\n",
    "                    z_pos = -2.0 + z*0.25\n",
    "                    f.write(\"%s %f %f %f \\n\" %(elem, x_pos, y_pos, z_pos))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.136533975601\n",
      "Training epoch 0\n",
      "Iter: 0\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.98658419    4.99910545   23.6946373    10.98026466    1.            2.\n",
      "   36.05406952    3.          151.46418762   16.9995594    41.92592621\n",
      "   39.77760315    0.            2.            3.99999738    6.99999475]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 261.88357544]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0730841159821\n",
      "Training epoch 100\n",
      "Iter: 100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99999952    5.           32.90704346   12.32230186    0.\n",
      "    5.99940968   19.93741226    5.96741581  120.04033661   16.22336197\n",
      "   53.99990082   46.30736542    0.            1.            1.99998534\n",
      "    6.9617219 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 281.41760254]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073637008667\n",
      "Training epoch 200\n",
      "Iter: 200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            4.           35.27838898    5.99999952    0.            5.\n",
      "   17.98255157    0.          113.85257721   17.9478817    35.53355408\n",
      "   38.73313904    0.            2.            2.            6.9998889 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.60348511]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725719928741\n",
      "Training epoch 300\n",
      "Iter: 300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            7.           47.95392227   12.87430859    0.            3.\n",
      "   25.68321228    2.          125.25502777   18.94861603   56.45592117\n",
      "   54.65939713    0.            3.99688792    5.98368025    7.99998522]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 290.18756104]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0748929977417\n",
      "Training epoch 400\n",
      "Iter: 400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.            4.           53.99126053    7.            0.\n",
      "    2.99966002   26.99109459    3.99919462  118.28759003   21.69379425\n",
      "   45.44551849   43.18209839    1.            3.            2.\n",
      "    8.99965668]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 232.46081543]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0753560066223\n",
      "Training epoch 500\n",
      "Iter: 500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.87063026    2.99999976   46.46268463   11.            0.\n",
      "    2.99923253   30.99991798    2.          119.29141235   15.9998703\n",
      "   40.97499084   46.76366425    0.            1.            3.10231876\n",
      "    6.99999094]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 232.18969727]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0738551616669\n",
      "Training epoch 600\n",
      "Iter: 600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99817657    1.99928784   39.0290947     6.92761326    1.            4.\n",
      "   25.            2.          115.76834106   15.5874157    41.92383194\n",
      "   55.17697525    0.            2.            2.99999905    2.99999976]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 236.37368774]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740990638733\n",
      "Training epoch 700\n",
      "Iter: 700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            2.75634789   38.85082245    9.99992275    0.\n",
      "    5.99849272   26.92419434    3.981493    132.90370178   13.93615055\n",
      "   50.37518692   53.99328232    1.            3.99380803    6.82307673\n",
      "    9.89955616]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 239.59483337]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726380348206\n",
      "Training epoch 800\n",
      "Iter: 800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.98993731    4.99992371   44.99661636    7.09383011    0.\n",
      "    5.06666327   29.69771576    7.27766323  119.74912262   17.99975967\n",
      "   37.99209595   53.97934723    0.            2.            4.           12.9872303 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 203.61181641]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725269317627\n",
      "Training epoch 900\n",
      "Iter: 900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   9.99514008    5.           37.6395874    11.            2.            4.\n",
      "   30.91189003    3.          127.87172699   19.44311142   45.80460739\n",
      "   48.57223892    1.            1.9999764     2.99998617    8.91799927]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 259.53656006]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726959705353\n",
      "Training epoch 1000\n",
      "Iter: 1000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99999142    6.44502354   41.91984558   13.99983597    1.\n",
      "    2.85655165   26.99961853    1.95988059  145.97105408   10.34221554\n",
      "   47.96796036   51.80295181    1.            2.99999976    1.88338041\n",
      "    6.99998379]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 287.14630127]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728840827942\n",
      "Training epoch 1100\n",
      "Iter: 1100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99673152    2.           42.79301453   15.99999046    0.\n",
      "    6.99994612   27.51834488    2.          144.31637573   22.89976692\n",
      "   47.99997711   52.9108429     1.            2.97877526    4.99999905\n",
      "    4.24493313]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 244.41069031]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729451179504\n",
      "Training epoch 1200\n",
      "Iter: 1200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            7.68588781   31.99786949   16.9999733     0.            2.\n",
      "   18.99292374    2.          133.97557068   20.98005104   36.41943359\n",
      "   49.74618149    2.            1.99925423    1.           12.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 230.50900269]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735759735107\n",
      "Training epoch 1300\n",
      "Iter: 1300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.9672718     4.99935198   48.37393188   11.98603439    0.\n",
      "    2.99850988   34.35829926    6.          148.30685425   22.83089066\n",
      "   59.99800873   50.98468018    0.            5.99964809    3.            6.9988637 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 233.46969604]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0723559856415\n",
      "Training epoch 1400\n",
      "Iter: 1400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.99959326   46.89078522   18.09662819    0.\n",
      "    3.99812126   36.99432373    0.          134.96383667    9.53197002\n",
      "   49.7495575    38.98293304    0.            1.9936645     2.50097823\n",
      "    7.91828823]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 265.40887451]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741698741913\n",
      "Training epoch 1500\n",
      "Iter: 1500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.99980164   29.99999619   11.99999905    0.            2.\n",
      "   33.71268845    0.          150.39727783   13.99999046   36.0638504\n",
      "   48.14773941    0.            4.            0.44673496    8.99998569]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 254.87927246]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0727670192719\n",
      "Training epoch 1600\n",
      "Iter: 1600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.99998665    5.5691452    46.11080551    9.99517727    0.            3.\n",
      "   20.95746994    0.          127.4247818    12.96675873   51.89516449\n",
      "   56.99838638    0.            0.            5.96137333   14.99999523]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 237.9336853]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0732171535492\n",
      "Training epoch 1700\n",
      "Iter: 1700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.72047472    5.97105694   46.79829788   12.            0.            0.\n",
      "   24.99798393    2.          117.25858307   13.67766094   39.00848389\n",
      "   59.98908234    0.            1.            1.98431134    6.9906621 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 177.82446289]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731530189514\n",
      "Training epoch 1800\n",
      "Iter: 1800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            1.           41.8910141     7.            0.\n",
      "    6.86263609   26.99995995    0.99999839  110.17736053   18.03742218\n",
      "   49.52277374   52.94982529    0.            1.98528254    5.            6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 299.9609375]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728340148926\n",
      "Training epoch 1900\n",
      "Iter: 1900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            6.99999475   50.98793793    5.99909353    0.            2.\n",
      "   34.87569427    1.          133.0539093    18.37942886   42.99549103\n",
      "   55.08800888    0.            2.            2.            9.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 225.9765625]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072448015213\n",
      "Training epoch 2000\n",
      "Iter: 2000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  11.90207672    6.           44.88632584    5.99999905    2.\n",
      "    5.98247099   24.59749794    1.99999988  121.08797455   17.10585594\n",
      "   35.58929443   44.99993896    0.            4.            5.\n",
      "   12.99989414]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 272.38208008]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740430355072\n",
      "Training epoch 2100\n",
      "Iter: 2100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.           47.86282349    4.73682117    0.            3.\n",
      "   25.93157196    0.          116.75263977   14.91246223   51.22380066\n",
      "   72.48086548    0.            4.99744844    4.           11.74755859]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.64561462]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740020275116\n",
      "Training epoch 2200\n",
      "Iter: 2200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.99999666   35.            9.65760994    0.\n",
      "    3.99869943   33.93269348    1.          121.39386749   14.91849136\n",
      "   33.16154099   53.96155548    0.            5.91108465    2.99999833\n",
      "    6.9341054 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 253.69685364]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0722050666809\n",
      "Training epoch 2300\n",
      "Iter: 2300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99998713    7.           30.69980812    5.97821045    0.            2.\n",
      "   28.99820137    0.          126.91635132    2.87824821   45.54611588\n",
      "   39.73009872    0.            3.            2.           11.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 275.07147217]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0789110660553\n",
      "Training epoch 2400\n",
      "Iter: 2400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.79116583    1.           25.93989754    9.99996376    0.\n",
      "    0.99681169   24.73583031    0.          123.99542999   16.45061111\n",
      "   56.24308014   53.97354126    0.            4.            2.99693036\n",
      "    5.46039963]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 297.43585205]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0750150680542\n",
      "Training epoch 2500\n",
      "Iter: 2500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.            8.           37.99747467    8.            0.            3.\n",
      "   29.98534775    2.          114.90898895    4.44027567   46.88538361\n",
      "   42.99391174    0.            5.52004814    3.            4.99999905]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 223.01150513]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726459026337\n",
      "Training epoch 2600\n",
      "Iter: 2600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.2893939    11.36824417   28.9624691     9.99819469    0.\n",
      "    1.99291694   31.99170876    1.44033074  149.58256531    9.19371414\n",
      "   47.62044907   44.0861969     0.            2.            2.92821217\n",
      "    8.99999905]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 231.63082886]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0871269702911\n",
      "Training epoch 2700\n",
      "Iter: 2700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.            6.99986458   41.99958801    6.83321428    1.            2.\n",
      "   15.99993515    2.          122.34297943   16.98955154   47.79014969\n",
      "   47.06554794    0.            4.99946022    2.99999475    4.99929905]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 286.60614014]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0750629901886\n",
      "Training epoch 2800\n",
      "Iter: 2800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   0.            1.           35.24237442    8.94594669    0.            1.\n",
      "   21.81687164    1.99600124  121.01317596   18.99865913   36.9990387\n",
      "   49.2636528     0.            2.            1.            6.99999952]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 213.92054749]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0871748924255\n",
      "Training epoch 2900\n",
      "Iter: 2900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            2.99422383   41.98717499    7.            0.            2.9911828\n",
      "   24.99841118    1.          131.71917725   13.76893044   41.83227921\n",
      "   53.62798309    0.            2.            3.99863982    8.41046715]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 209.14120483]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0788881778717\n",
      "Training epoch 3000\n",
      "Iter: 3000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.99926901    3.           47.79851913   12.99999619    0.            4.\n",
      "   32.48249054    1.          107.04242706   13.76613522   49.35309601\n",
      "   50.87625504    0.            1.            7.           19.83810425]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 249.67448425]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0770790576935\n",
      "Training epoch 3100\n",
      "Iter: 3100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            3.72102046   33.53080368   12.99999428    1.            2.\n",
      "   23.01079559    1.99995828  147.41299438   13.           50.98487091\n",
      "   47.6123085     0.            3.            4.99997187   10.99472713]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 280.54956055]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0749659538269\n",
      "Training epoch 3200\n",
      "Iter: 3200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.            2.           36.91801071    5.99466848    0.            2.\n",
      "   35.96124649    2.06804776  130.90499878   17.40620422   49.79302216\n",
      "   51.9956665     0.            1.95302725    3.99995136    7.99105549]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 264.2489624]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0836088657379\n",
      "Training epoch 3300\n",
      "Iter: 3300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99995637   10.           46.51072311    6.            0.            3.9999938\n",
      "   24.90405273    2.          120.06716919   15.           40.97919464\n",
      "   45.9977951     2.            1.            4.            7.99999332]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 210.92985535]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0836589336395\n",
      "Training epoch 3400\n",
      "Iter: 3400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            6.39227581   34.14173126    9.58007431    0.\n",
      "    6.95270967   24.95623589    2.          136.22564697   16.29606056\n",
      "   42.96941376   54.88051224    0.            0.14159736    3.99995852\n",
      "    3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 220.28071594]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0756840705872\n",
      "Training epoch 3500\n",
      "Iter: 3500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            5.99999809   45.61009216    6.99997425    0.            1.\n",
      "   25.75901413    2.          115.90689087   19.78109741   44.99686432\n",
      "   59.75761795    1.            2.            4.97315168   11.99949455]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 185.8039093]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0752279758453\n",
      "Training epoch 3600\n",
      "Iter: 3600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.86250877    4.           49.51598358   14.89781761    0.\n",
      "    2.95009279   23.932621      1.47222042  120.57467651   13.90695763\n",
      "   39.99946213   53.8024025     0.            1.            1.99793768\n",
      "    8.97091198]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 201.09541321]\n",
      "[ 0.01]\n",
      "()\n",
      "0.100593090057\n",
      "Training epoch 3700\n",
      "Iter: 3700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            1.99927163   37.98646545    4.99999475    0.            1.\n",
      "   23.98685074    2.          145.55886841   15.96453094   48.98408508\n",
      "   52.40251923    0.            3.            3.57488775   12.99834919]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 265.43804932]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745451450348\n",
      "Training epoch 3800\n",
      "Iter: 3800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.2161119     4.           36.           12.01346207    0.            8.\n",
      "   27.37828064    0.96187598  127.98995972   18.44720459   54.94092941\n",
      "   50.8792305     0.            2.            0.99999994   10.99110413]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 275.59909058]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073881149292\n",
      "Training epoch 3900\n",
      "Iter: 3900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            7.9986949    35.78491592   11.95406342    0.\n",
      "    2.99963379   32.0552063     2.99999928  129.77252197   14.49504185\n",
      "   44.97234726   47.99588776    0.            2.            2.99999571\n",
      "    6.85867262]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 249.59347534]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740580558777\n",
      "Training epoch 4000\n",
      "Iter: 4000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            5.99999952   42.76156998    9.99335766    1.            1.\n",
      "   29.8843956     3.          134.27890015   16.73893356   44.85575867\n",
      "   52.97748184    0.99904186    2.            3.           10.92462444]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 240.59942627]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0752170085907\n",
      "Training epoch 4100\n",
      "Iter: 4100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            6.           47.            7.            0.            0.\n",
      "   29.93745995    2.          132.98278809   18.9991703    48.18284225\n",
      "   49.89525604    0.            1.99914014    2.65501738   13.81222057]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 232.75492859]\n",
      "[ 0.01]\n",
      "()\n",
      "0.074000120163\n",
      "Training epoch 4200\n",
      "Iter: 4200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.33891439   10.95046043   41.99809265   14.99999428    0.            1.\n",
      "   26.24912453    0.99717802  123.18343353   23.99448013   39.99972916\n",
      "   41.40111923    1.            5.99956846    4.05662584   13.96959877]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 255.4694519]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0748319625854\n",
      "Training epoch 4300\n",
      "Iter: 4300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            2.           33.99997711   10.41744137    0.            2.\n",
      "   27.94135475    3.83971524  115.83965302   14.           44.1348381\n",
      "   53.41788101    0.            2.            3.            9.95051193]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 219.24145508]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0762460231781\n",
      "Training epoch 4400\n",
      "Iter: 4400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99997425    6.           36.68042755    4.            0.            6.\n",
      "   26.36026192    1.64692748  119.36536407   19.19831467   54.25912857\n",
      "   51.44813919    0.            1.57253325    2.97970605    5.98574114]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 257.04055786]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0742030143738\n",
      "Training epoch 4500\n",
      "Iter: 4500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.82629919    6.05609322   48.63251495   13.            0.            6.\n",
      "   29.99223518    1.          115.9998703    18.80589294   43.83128738\n",
      "   67.5141983     0.            2.            1.            3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 310.45690918]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735301971436\n",
      "Training epoch 4600\n",
      "Iter: 4600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.9952755     4.           52.25878143   10.54356861    0.\n",
      "    6.99974298   35.06903458    3.          116.48841858   14.\n",
      "   48.33013916   48.24818802    1.            1.04985487    1.9970603     8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 261.19070435]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0749309062958\n",
      "Training epoch 4700\n",
      "Iter: 4700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99862099    3.           38.63701248   10.            0.            2.\n",
      "   31.99996758    3.          124.9985199    13.93889809   54.54174423\n",
      "   53.76624298    0.            2.            1.9989996     3.99466586]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 230.59765625]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0749127864838\n",
      "Training epoch 4800\n",
      "Iter: 4800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.90326548    3.99999881   48.97546768   15.44752312    1.            3.\n",
      "   27.39013481    5.          146.82366943   17.61222076   45.99998474\n",
      "   50.21211624    0.            5.            1.            8.90468788]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 245.42871094]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0753009319305\n",
      "Training epoch 4900\n",
      "Iter: 4900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.11194754    8.           38.21790314    8.            0.\n",
      "    5.99999952   36.01583862    2.          143.04582214   18.99996567\n",
      "   51.44664001   50.78213882    1.            2.            2.99944711\n",
      "    8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 201.02105713]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741350650787\n",
      "Training epoch 5000\n",
      "Iter: 5000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            5.           39.91226959   13.34345913    0.\n",
      "    3.99830961   20.95724678    2.          120.10562134   11.88242912\n",
      "   38.19802475   53.47625732    0.            5.99999952    3.\n",
      "    4.98270273]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 268.43560791]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740358829498\n",
      "Training epoch 5100\n",
      "Iter: 5100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.9852643     3.           50.9969902    10.            0.\n",
      "    1.74480689   22.99994278    0.99999982  126.97055817   18.81060791\n",
      "   51.50331497   45.99353027    1.            7.            0.            5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 225.54977417]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0749311447144\n",
      "Training epoch 5200\n",
      "Iter: 5200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            7.           32.45175552    6.99621153    0.            3.\n",
      "   15.95447636    1.          120.47867584   15.85194397   45.61618423\n",
      "   44.99417877    0.            2.            3.99999952    6.99739408]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 261.72106934]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735821723938\n",
      "Training epoch 5300\n",
      "Iter: 5300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            3.           27.8164196    11.30599785    1.            2.\n",
      "   30.83543587    1.99998796  131.965271     15.99896622   40.92640686\n",
      "   58.04132843    0.            1.99988484    3.            3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 287.14401245]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0751419067383\n",
      "Training epoch 5400\n",
      "Iter: 5400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.97357273    5.           44.31818771   14.            0.            1.\n",
      "   25.            1.98723269  115.98821259   13.87287998   45.33999252\n",
      "   52.99597931    0.            3.            2.            7.33362103]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 216.0736084]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0737671852112\n",
      "Training epoch 5500\n",
      "Iter: 5500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99241543    5.99999809   28.99928474   14.99999809    0.            3.\n",
      "   26.99785233    4.          133.04598999   24.6380825    41.21935272\n",
      "   55.            0.            3.            2.           13.3179512 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 201.18508911]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0755770206451\n",
      "Training epoch 5600\n",
      "Iter: 5600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.45883465    2.           33.96624756   14.99995708    0.\n",
      "    5.86455631   30.98545074    3.99999475  130.9822998    20.\n",
      "   51.53852081   55.79354095    1.            2.            1.99999928\n",
      "   10.9614296 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 216.19979858]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745120048523\n",
      "Training epoch 5700\n",
      "Iter: 5700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.77355886    7.58173084   32.82299042   12.            0.            2.\n",
      "   21.14516449    2.          125.77744293    8.99982643   37.96691513\n",
      "   40.91259384    0.            1.            3.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 213.05235291]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0759770870209\n",
      "Training epoch 5800\n",
      "Iter: 5800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.21294928    9.           41.109478     10.52154541    0.            1.\n",
      "   33.99447632    1.          116.65208435   13.           41.93276596\n",
      "   51.99958038    0.            1.99989474    0.            8.99979401]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 196.54460144]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745260715485\n",
      "Training epoch 5900\n",
      "Iter: 5900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.            1.           42.9997673     9.6987915     0.\n",
      "    2.63961554   28.50181389    2.          128.02703857   21.2992363\n",
      "   38.99865723   51.99761963    0.            3.            2.\n",
      "    9.87455177]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 217.80691528]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745458602905\n",
      "Training epoch 6000\n",
      "Iter: 6000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  10.99969864    3.99913836   43.98784637   11.99405479    0.\n",
      "    7.94543028   19.89622307    3.99960899  120.70580292   11.99978256\n",
      "   52.39947891   54.95930481    0.            3.            1.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 237.81069946]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0754280090332\n",
      "Training epoch 6100\n",
      "Iter: 6100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99999619    2.46045947   37.85448074    5.9988718     0.\n",
      "    4.99998951   23.75011063    2.99995661  127.51590729   16.85963821\n",
      "   49.64635468   59.69887543    0.99774885    1.            1.99916077\n",
      "   14.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 280.91864014]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0768010616302\n",
      "Training epoch 6200\n",
      "Iter: 6200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99994183    3.99996805   55.99974823    8.99969482    0.\n",
      "    4.96734095   16.70963669    1.          111.48506165   18.98589897\n",
      "   43.16795731   46.04233932    0.            4.99968433    5.99998236\n",
      "    6.95294666]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 233.01922607]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0749299526215\n",
      "Training epoch 6300\n",
      "Iter: 6300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.7310338     5.           50.64681244   13.89008713    0.            6.\n",
      "   24.96504211    3.          132.40802002   11.99999619   44.58223724\n",
      "   47.99943542    0.            4.            4.            9.90226269]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 212.1915741]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0736298561096\n",
      "Training epoch 6400\n",
      "Iter: 6400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.93581581    3.           47.78991699   15.99999619    0.            5.\n",
      "   36.92414856    6.63425827  121.37813568   16.04141998   40.73303986\n",
      "   56.30131912    0.            2.91277075    1.99999928    8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 273.6026001]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0747289657593\n",
      "Training epoch 6500\n",
      "Iter: 6500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            6.98871279   41.82799911    7.            0.\n",
      "    0.99999809   30.26626778    1.32962477  121.99632263   14.9187336\n",
      "   44.98468018   41.13684464    0.            2.96597171    3.68267941\n",
      "   11.99823189]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 208.81057739]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0738368034363\n",
      "Training epoch 6600\n",
      "Iter: 6600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            3.99961638   40.28490067    8.95572376    0.\n",
      "    4.18457985   22.96785545    1.          133.20272827   17.99106979\n",
      "   58.9207077    39.76757431    0.            2.            5.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 224.34649658]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0752789974213\n",
      "Training epoch 6700\n",
      "Iter: 6700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99998951    2.           40.53290558    5.99999905    0.            4.\n",
      "   27.38024712    1.          123.44087219   14.98797607   42.53824997\n",
      "   50.29379272    0.            0.            2.           12.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 160.11154175]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735011100769\n",
      "Training epoch 6800\n",
      "Iter: 6800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.24752188    4.           38.21492004    6.            0.\n",
      "    1.99958241   22.36417389    1.          129.09429932   22.99919891\n",
      "   43.39020538   50.86695862    0.            2.35720062    4.\n",
      "    7.99865866]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 202.23025513]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0739381313324\n",
      "Training epoch 6900\n",
      "Iter: 6900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   0.            3.99641943   42.24656296   16.99999237    0.            1.\n",
      "   20.9593544     2.          143.20794678   15.99999809   37.99244308\n",
      "   51.74765015    0.            0.99662066    3.           11.86522102]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 266.43719482]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0732779502869\n",
      "Training epoch 7000\n",
      "Iter: 7000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  10.78341579    6.95683956   42.04151917   14.            0.\n",
      "    3.48937368   20.99827576    0.          114.37716675   23.\n",
      "   49.98616791   46.74910355    0.            1.            5.\n",
      "    7.99999952]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 251.71369934]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073902130127\n",
      "Training epoch 7100\n",
      "Iter: 7100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.97192001    5.97432709   40.94970703    7.97380066    0.99999607\n",
      "    2.           31.45025253    2.          138.04109192   18.07206154\n",
      "   43.82713699   56.72369003    0.            3.            4.99999952\n",
      "    6.91694927]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 292.30197144]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0733950138092\n",
      "Training epoch 7200\n",
      "Iter: 7200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            5.           41.98665619   12.86040211    1.            4.\n",
      "   25.89593506    1.          117.97776031   14.99964619   32.99996567\n",
      "   44.47314835    0.            0.99414241    1.            6.92344999]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.71612549]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0742580890656\n",
      "Training epoch 7300\n",
      "Iter: 7300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            6.           50.93272018    6.93676043    0.\n",
      "    1.99842978   24.95792961    3.          120.12121582   13.\n",
      "   44.68996429   52.81618118    0.            4.99999523    4.99980021\n",
      "    7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 205.58854675]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741460323334\n",
      "Training epoch 7400\n",
      "Iter: 7400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            0.99997389   37.99842834   14.44429779    1.            1.\n",
      "   32.89100266    0.          111.97290802   19.97058678   47.87958145\n",
      "   52.54709625    0.            2.99999714    4.            3.99264312]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 285.66262817]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0743470191956\n",
      "Training epoch 7500\n",
      "Iter: 7500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.98783302    7.99999905   36.93306732    8.93859959    1.            4.\n",
      "   26.70876884    0.          130.07215881   20.88523674   40.74175262\n",
      "   52.99061203    0.            4.99472094    4.98837948    6.46271896]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 206.87738037]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740549564362\n",
      "Training epoch 7600\n",
      "Iter: 7600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            7.           48.96416092    6.            0.            1.9871521\n",
      "   38.91827011    3.          148.36129761   13.99146938   53.96702576\n",
      "   53.99997711    0.            3.            0.99782753    4.98194647]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 264.53060913]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0743498802185\n",
      "Training epoch 7700\n",
      "Iter: 7700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99703932    3.99997807   50.75832748    8.21414757    1.99999607\n",
      "    3.95118713   19.72197151    2.          123.81708527   20.26580429\n",
      "   48.53820801   44.45129776    0.            1.            0.\n",
      "    6.99987173]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 202.34199524]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0747220516205\n",
      "Training epoch 7800\n",
      "Iter: 7800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            3.           34.99588013   15.            0.            1.\n",
      "   23.14071083    2.          139.81991577   13.9977932    27.97883415\n",
      "   53.31717682    0.99993974    7.43536425    2.           14.96104717]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 229.63368225]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0755310058594\n",
      "Training epoch 7900\n",
      "Iter: 7900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99998236    3.           36.44329834    9.77882004    0.\n",
      "    3.95091653   26.99829865    2.93590569  131.54161072   10.99986839\n",
      "   48.99879837   56.94350815    0.            3.            4.67345858\n",
      "   14.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 224.40718079]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0739731788635\n",
      "Training epoch 8000\n",
      "Iter: 8000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            5.99906588   39.14704132   10.99993038    0.\n",
      "    2.99999976   23.99934387    2.          118.61672974   17.99903488\n",
      "   44.99131393   52.994133      0.            7.9970727     1.\n",
      "    5.99241543]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 288.85440063]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740740299225\n",
      "Training epoch 8100\n",
      "Iter: 8100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.96147156    7.41298199   28.14756775    6.90311527    0.            1.\n",
      "   32.39209366    2.          129.1401825    14.99176598   41.99612427\n",
      "   50.05565262    0.97515541    1.            2.           11.99999714]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 237.46534729]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735681056976\n",
      "Training epoch 8200\n",
      "Iter: 8200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            7.99990177   39.99999237    6.99912643    0.\n",
      "    4.99999762   27.99493027    1.          104.44407654   17.99944878\n",
      "   43.46737289   49.99999619    0.            2.99999952    4.\n",
      "   10.83224106]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 255.55108643]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745720863342\n",
      "Training epoch 8300\n",
      "Iter: 8300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99983692    4.9933157    38.91318512    7.            0.            3.\n",
      "   17.96151161    0.99993759  113.92151642   15.           34.99773026\n",
      "   42.15464783    0.            2.96679354    3.            6.93873787]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 250.87702942]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0733368396759\n",
      "Training epoch 8400\n",
      "Iter: 8400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.40320897    4.           38.95593643    9.99870014    1.\n",
      "    2.99971366   24.99967575    5.9681263   125.74688721   20.72200775\n",
      "   35.79703522   42.99936676    0.            3.99999976    4.           10.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 249.95664978]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735900402069\n",
      "Training epoch 8500\n",
      "Iter: 8500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.99999917    6.99996376   41.03383255    9.94942474    0.\n",
      "    4.99999905   28.            3.99888158  118.36706543   14.99789238\n",
      "   44.66171265   41.98574829    0.            1.            3.99999714\n",
      "    8.39731121]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 227.65887451]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729620456696\n",
      "Training epoch 8600\n",
      "Iter: 8600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.            9.9917593    43.97353745   12.99577332    1.            1.\n",
      "   28.99945641    1.99999976  120.58638      13.99703693   48.84749222\n",
      "   53.72595215    0.            3.            4.99993134    4.99999809]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 281.56100464]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0975520610809\n",
      "Training epoch 8700\n",
      "Iter: 8700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.91913104    5.81546116   47.80646515   14.99619484    0.            4.\n",
      "   37.80587769    1.99945283  128.95355225   10.99938774   48.29771423\n",
      "   56.13496399    0.            3.97990894    4.99773884    6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 260.87127686]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735929012299\n",
      "Training epoch 8800\n",
      "Iter: 8800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99980998    1.           48.79236221   10.99998569    1.\n",
      "    1.97575283   24.99999619    5.          112.91571808   14.0893774\n",
      "   40.8547287    53.84941864    0.            1.99993336    3.99999952\n",
      "    8.9999876 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 208.23298645]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0833139419556\n",
      "Training epoch 8900\n",
      "Iter: 8900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            3.           28.99999809   11.            0.\n",
      "    0.99997276   31.95669937    5.          115.41279602   18.79085159\n",
      "   42.94979477   52.76096725    1.            5.            2.28805494\n",
      "    7.95431852]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 240.83761597]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0738558769226\n",
      "Training epoch 9000\n",
      "Iter: 9000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            3.           39.91814041   15.            2.\n",
      "    4.67941046   31.70679474    4.99961853  122.02697754   15.\n",
      "   44.87149811   44.97603607    0.            3.            4.99998379\n",
      "    8.99730682]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 223.6656189]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736939907074\n",
      "Training epoch 9100\n",
      "Iter: 9100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.           10.99995232   33.27671051    7.97704792    1.            2.\n",
      "   20.98682594    2.          122.95755768   13.98474598   33.99998856\n",
      "   50.76557922    0.            1.99879193    1.            9.10437012]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 209.24996948]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736331939697\n",
      "Training epoch 9200\n",
      "Iter: 9200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99999285    4.7412138    40.08401871   10.            0.\n",
      "    1.99979556   29.            3.99073648  125.40399933   13.60834599\n",
      "   44.97422028   57.33962631    1.            2.72519541    1.71056724\n",
      "    9.99859905]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 241.78538513]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741140842438\n",
      "Training epoch 9300\n",
      "Iter: 9300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99999094    4.8491025    36.99950409   15.69666481    0.            1.\n",
      "   31.50083923    2.99998236  123.9115448    15.82017517   33.99998093\n",
      "   42.99916077    1.            5.            4.            5.99813032]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 178.95120239]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736398696899\n",
      "Training epoch 9400\n",
      "Iter: 9400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.96975517    7.           45.72286606    7.            0.\n",
      "    2.99999976   31.66076851    2.99999952  142.29449463   13.\n",
      "   38.91306686   49.00740433    0.            5.85762024    0.\n",
      "    9.98228931]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 197.74346924]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740580558777\n",
      "Training epoch 9500\n",
      "Iter: 9500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99989939    3.           41.            9.99999905    0.            1.\n",
      "   33.99247742    3.          123.54562378   11.99857903   38.87471008\n",
      "   51.13685608    0.            1.99999928    1.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 250.68528748]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0737040042877\n",
      "Training epoch 9600\n",
      "Iter: 9600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            5.81071472   45.84991455   11.99993706    1.\n",
      "    1.99505615   29.62607574    2.          130.85070801   13.94234848\n",
      "   57.95806503   47.43414307    0.            3.95908213    2.            5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 208.39094543]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740101337433\n",
      "Training epoch 9700\n",
      "Iter: 9700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            6.99877357   32.5044632    10.92641163    0.            1.\n",
      "   23.98191643    1.          139.81007385   17.99555588   47.92848206\n",
      "   43.91415405    0.            2.            2.9872191     6.99999809]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.42401123]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736339092255\n",
      "Training epoch 9800\n",
      "Iter: 9800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   9.            2.99999857   57.96920776   12.86839676    0.            2.\n",
      "   32.87869263    1.          108.85005951   13.99376678   43.98658752\n",
      "   59.82535172    1.            6.            4.97680426    5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 220.40830994]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0738670825958\n",
      "Training epoch 9900\n",
      "Iter: 9900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.83486891    4.86572695   38.97310638   12.99978161    1.            5.\n",
      "   20.07821083    2.          140.93769836   15.           57.98134232\n",
      "   49.89028168    0.            3.            4.81249285    8.30354118]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.02694702]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735988616943\n",
      "Training epoch 10000\n",
      "Iter: 10000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.72524261    4.44661236   40.9699173     8.            1.\n",
      "    3.86252332   28.97380447    3.99528289  119.89141846   13.99522972\n",
      "   46.31004333   44.99967194    0.            5.            1.            6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 236.55300903]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740358829498\n",
      "Training epoch 10100\n",
      "Iter: 10100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            1.69406199   40.82161713   11.41537952    1.            3.\n",
      "   26.99661636    2.          129.04119873    8.99559593   43.8835907\n",
      "   38.95284653    0.            3.89289308    4.            3.35611439]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 225.61032104]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073390007019\n",
      "Training epoch 10200\n",
      "Iter: 10200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.9191556     5.99998951   41.78592682   15.48817062    0.            2.\n",
      "   37.83263779    2.9934907   112.98152924   14.           59.36426926\n",
      "   58.99618912    1.            2.84330273    3.99709153    9.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 197.13514709]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735919475555\n",
      "Training epoch 10300\n",
      "Iter: 10300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            5.           37.96261978   10.            1.\n",
      "    2.99141788   24.99999237    2.          123.47506714   10.13785744\n",
      "   38.09492493   46.61296463    0.            4.            2.            3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 250.71589661]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073940038681\n",
      "Training epoch 10400\n",
      "Iter: 10400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            5.           37.98782349    9.99628353    1.\n",
      "    5.88640928   23.73407745    3.          128.14935303   17.95250702\n",
      "   53.4563179    57.07735443    0.            4.            3.\n",
      "    8.74821186]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.04653931]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735130310059\n",
      "Training epoch 10500\n",
      "Iter: 10500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            2.99988198   49.9989357    15.60852242    0.            4.\n",
      "   26.99384689    0.          130.91836548   17.99929428   52.18434525\n",
      "   45.99909592    0.            3.89047408    2.99999952   12.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 259.01965332]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736811161041\n",
      "Training epoch 10600\n",
      "Iter: 10600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.96192694    5.           38.96269226    9.            1.            2.\n",
      "   28.92804527    3.99999928  141.19732666   16.99999237   47.66016769\n",
      "   46.9257164     0.            1.            2.99813938   12.32236481]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 269.06530762]\n",
      "[ 0.01]\n",
      "()\n",
      "0.074107170105\n",
      "Training epoch 10700\n",
      "Iter: 10700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99999976    3.92123675   47.95762634    8.            0.\n",
      "    7.99962139   28.99999046    0.          120.78105927   24.99997902\n",
      "   38.93045807   59.9685936     0.            2.0617075     3.            6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 214.87417603]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073429107666\n",
      "Training epoch 10800\n",
      "Iter: 10800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.76383734    2.99274182   45.99998474   10.65377426    0.\n",
      "    2.29982567   25.99935913    2.          129.66835022   17.\n",
      "   39.97639847   38.43450928    0.            2.            2.\n",
      "    9.99995613]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 229.23242188]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727100372314\n",
      "Training epoch 10900\n",
      "Iter: 10900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            9.01861191   45.76930237   13.99999809    0.99999994\n",
      "    2.           23.92484856    1.99904966  108.59610748   14.\n",
      "   51.96290207   49.55773544    0.            2.            2.           12.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 309.34957886]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0779750347137\n",
      "Training epoch 11000\n",
      "Iter: 11000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.            2.           46.91210175    7.94148827    0.\n",
      "    2.64660048   26.99912262    0.99999994  126.77467346   20.\n",
      "   48.46527863   44.99999619    0.            5.            2.99921322\n",
      "   14.90411186]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 202.96881104]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0790228843689\n",
      "Training epoch 11100\n",
      "Iter: 11100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99979639    7.99997139   35.70918274    7.97817564    1.            3.\n",
      "   21.99996376    3.          131.77095032   24.99145699   48.93805313\n",
      "   56.9966011     1.            2.99999571    2.99997449    6.99998903]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 200.81504822]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0749611854553\n",
      "Training epoch 11200\n",
      "Iter: 11200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.99964762    8.03116798   27.95394516    7.99255562    0.            1.\n",
      "   23.83893776    3.          138.04438782    9.99344444   46.93475342\n",
      "   46.82327652    0.            3.            3.99999619    9.99983978]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 305.19616699]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072741985321\n",
      "Training epoch 11300\n",
      "Iter: 11300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            2.           53.44374084   11.99993801    0.\n",
      "    1.99998248   37.91635513    0.          115.38417053    7.99977493\n",
      "   47.06413269   58.97151566    0.            1.9827497     1.\n",
      "    6.00686646]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 215.32044983]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0766339302063\n",
      "Training epoch 11400\n",
      "Iter: 11400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            4.1668787    42.65450287    7.99999952    0.87347186\n",
      "    2.99951148   18.80404854    0.          121.25147247   23.99290657\n",
      "   44.99666595   55.16571426    0.            3.            2.\n",
      "    7.87725115]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 224.77557373]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0761330127716\n",
      "Training epoch 11500\n",
      "Iter: 11500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.9960978    37.97543716   21.93563652    0.            1.\n",
      "   32.99417114    1.99997222  117.16083527   17.91394043   56.83024979\n",
      "   46.49225998    0.            3.            3.            8.99897003]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 235.75837708]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0758860111237\n",
      "Training epoch 11600\n",
      "Iter: 11600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            3.16721535   52.97988129   12.96289253    0.\n",
      "    6.87186146   21.            2.66887212  127.28617096   15.06812477\n",
      "   51.7444191    60.49182892    0.            3.94254804    2.\n",
      "    6.13729572]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 282.46347046]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0763959884644\n",
      "Training epoch 11700\n",
      "Iter: 11700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99746752    5.99997044   44.22180939   15.84261608    1.            3.\n",
      "   24.79589653    1.49570394  125.61301422   17.49420166   58.97307968\n",
      "   54.97185898    2.            1.            2.99999285    8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 239.86436462]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0750069618225\n",
      "Training epoch 11800\n",
      "Iter: 11800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.            5.99999332   31.83693504   12.58580208    0.\n",
      "    4.87389565   31.47854614    0.          126.98665619   22.04917336\n",
      "   42.92072678   47.9535408     1.            3.            3.\n",
      "   12.33736706]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 267.13485718]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0772039890289\n",
      "Training epoch 11900\n",
      "Iter: 11900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            6.           30.73487854   10.17245483    0.9934783\n",
      "    1.99987006   22.99999809    2.          130.99694824   13.99667549\n",
      "   43.65658951   49.63977432    0.            5.9691906     1.\n",
      "    8.05356312]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 254.67726135]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741741657257\n",
      "Training epoch 12000\n",
      "Iter: 12000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.15322256    6.           46.76813507   11.99999142    0.            5.\n",
      "   31.99643898    1.97626102  126.64032745   22.96857071   47.99728012\n",
      "   60.36911011    0.            4.            2.            5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 183.07836914]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731749534607\n",
      "Training epoch 12100\n",
      "Iter: 12100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99934912    5.98962545   36.91094589    9.99998283    0.            4.9616127\n",
      "   30.99998474    0.          132.51750183   14.35754585   42.99012375\n",
      "   44.99909973    0.            3.99992085    0.96350974    7.73897505]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 255.84428406]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0771689414978\n",
      "Training epoch 12200\n",
      "Iter: 12200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            3.99999404   42.89470673   12.99104881    0.            5.\n",
      "   28.99975014    2.99986458  120.86816406   16.99892998   45.95134735\n",
      "   42.71833038    0.            0.            2.99967909    6.99999332]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 203.94129944]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0765120983124\n",
      "Training epoch 12300\n",
      "Iter: 12300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.93503952    4.98547745   45.60353088    4.99997425    1.\n",
      "    0.92443955   24.82409477    0.99956083  131.70896912   16.99124718\n",
      "   48.03054047   54.2156105     2.            0.92304617    1.5864172\n",
      "    7.97816277]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 311.29141235]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745258331299\n",
      "Training epoch 12400\n",
      "Iter: 12400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99922514    2.93620038   47.65826797    9.34142303    0.            3.\n",
      "   21.99987221    1.          125.99835968   15.73610592   47.31156158\n",
      "   46.20595932    0.            3.            0.           11.99990845]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 223.04045105]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0758638381958\n",
      "Training epoch 12500\n",
      "Iter: 12500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            4.           34.98996735   14.99998856    0.            1.\n",
      "   33.98321533    0.9971211   122.94585419   11.97979736   42.92076111\n",
      "   47.38961029    0.            4.            4.99997473    8.98630524]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 212.71842957]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728690624237\n",
      "Training epoch 12600\n",
      "Iter: 12600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            4.96354771   42.92350769    8.86914635    0.            3.\n",
      "   22.36503029    2.          112.03684998   14.2861557    39.23087692\n",
      "   40.52771759    0.            1.            1.99299574   11.99978542]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 239.93049622]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745980739594\n",
      "Training epoch 12700\n",
      "Iter: 12700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99993515    4.           34.00030518   11.            0.            1.\n",
      "   20.47299385    0.99999988  123.76641846   11.           42.95357513\n",
      "   50.17361069    1.            4.92821217    5.            9.99955177]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 275.92654419]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0765690803528\n",
      "Training epoch 12800\n",
      "Iter: 12800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99964714    8.99988461   40.29813766    7.99997807    0.\n",
      "    3.99817586   22.99996185    1.          137.85803223    7.99999952\n",
      "   51.93492508   54.876091      0.99999964    3.            7.            3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 269.6625061]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0744850635529\n",
      "Training epoch 12900\n",
      "Iter: 12900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99992561    2.           34.           11.            1.\n",
      "    2.98285651   23.84930038    1.          121.67285156   15.99999905\n",
      "   40.85676193   47.83024597    0.            1.            2.            5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 244.95578003]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729088783264\n",
      "Training epoch 13000\n",
      "Iter: 13000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            4.           30.51129341   12.99999905    0.            1.\n",
      "   36.99982452    1.99999034  141.73930359   12.99998856   50.87247086\n",
      "   47.91366959    1.            2.            1.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 270.18063354]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0743918418884\n",
      "Training epoch 13100\n",
      "Iter: 13100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            8.87216377   46.5664978    11.98684502    0.            3.\n",
      "   26.34423065    3.          123.67193604   21.99971199   34.67448044\n",
      "   51.73698425    0.99999565    2.82154012    4.            8.99839592]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 210.57635498]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736620426178\n",
      "Training epoch 13200\n",
      "Iter: 13200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  11.99997807    5.45410395   29.72569847    8.99876213    0.            5.5766139\n",
      "   23.93577766    2.          154.91334534   10.99340057   34.27554321\n",
      "   53.97873306    0.            4.99988031    3.            3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 237.25854492]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0757901668549\n",
      "Training epoch 13300\n",
      "Iter: 13300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            6.99983311   45.0193634     6.99968338    1.            2.\n",
      "   27.99990845    2.          119.72281647   21.00252914   34.92570496\n",
      "   56.38438416    0.            3.            5.            7.28292465]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 194.04954529]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0768320560455\n",
      "Training epoch 13400\n",
      "Iter: 13400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            4.59216881   36.93679047   11.9975214     0.\n",
      "    1.99999976   24.99843025    2.          126.72103882   10.30024719\n",
      "   48.99044418   58.97296906    0.            3.99942255    3.92139792\n",
      "   10.83935165]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 271.87304688]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073539018631\n",
      "Training epoch 13500\n",
      "Iter: 13500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            3.98857379   44.19036865   10.54772854    0.            3.\n",
      "   33.05235291    5.73812866  135.88870239   15.89675903   42.98134232\n",
      "   59.90958023    0.98979574    0.            5.            8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 241.11764526]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0759100914001\n",
      "Training epoch 13600\n",
      "Iter: 13600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.99993479    5.99999952   46.7575531     8.68910599    0.\n",
      "    2.99956179   25.            3.          128.37657166   15.\n",
      "   38.92123795   56.84997559    0.            2.            2.99999976\n",
      "    5.99997663]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 256.03717041]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0763750076294\n",
      "Training epoch 13700\n",
      "Iter: 13700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.98558712    3.99933648   35.29001617   12.            1.\n",
      "    2.11484742   19.58872795    2.          141.64704895   23.10934639\n",
      "   27.99983597   35.76953125    0.            1.            1.\n",
      "    7.99980307]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 227.82666016]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0734689235687\n",
      "Training epoch 13800\n",
      "Iter: 13800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.97293377    8.           47.92886734    6.99989557    0.99913025\n",
      "    2.           27.55251503    1.99970722  115.69562531   14.99548626\n",
      "   41.99019623   39.88307953    0.            4.            2.\n",
      "    8.99999619]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 244.13002014]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0756931304932\n",
      "Training epoch 13900\n",
      "Iter: 13900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.96603823    3.54838848   47.50619888   13.28564453    1.            1.\n",
      "   30.45272827    0.          143.40823364   15.54631519   37.85970688\n",
      "   55.98610306    0.            3.9753046     1.            5.99992371]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 264.23922729]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731799602509\n",
      "Training epoch 14000\n",
      "Iter: 14000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.17536163    5.0926218    39.45656967   11.88917542    0.            3.\n",
      "   29.98571014    0.17643327  132.78526306   12.99777889   48.94632339\n",
      "   50.3162117     0.            2.57189178    1.99999011    6.9999795 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 189.89900208]\n",
      "[ 0.01]\n",
      "()\n",
      "0.075514793396\n",
      "Training epoch 14100\n",
      "Iter: 14100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            5.97416258   38.93835449    6.8029995     0.            2.\n",
      "   17.40217209    0.          125.9953537     9.99582481   50.25049591\n",
      "   48.80817413    1.            3.99863005    3.            6.98328495]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 217.51557922]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727610588074\n",
      "Training epoch 14200\n",
      "Iter: 14200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.99942493    7.99834967   36.11069107    8.54994488    0.            1.\n",
      "   20.            0.          138.00128174   21.99998474   51.94152832\n",
      "   44.04627991    1.            6.96551228    3.99987411   15.99997139]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 212.10206604]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726890563965\n",
      "Training epoch 14300\n",
      "Iter: 14300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.69693756    4.           47.36817169    8.            0.\n",
      "    1.99999881   32.99637222    0.          122.99820709   17.97159195\n",
      "   50.99066544   38.99999237    0.            2.            3.\n",
      "    7.93636847]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 255.7313385]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.075688123703\n",
      "Training epoch 14400\n",
      "Iter: 14400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.            3.97099829   46.69000244    4.            0.\n",
      "    2.62825346   28.19361877    2.          118.5405426    16.62192535\n",
      "   46.76934433   53.98166275    0.            2.99999261    2.            8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 223.81062317]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0754401683807\n",
      "Training epoch 14500\n",
      "Iter: 14500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            5.99999857   40.82804108    9.41910458    0.99999988\n",
      "    0.9995681    32.99936676    4.          120.98374939   20.3897686\n",
      "   42.99996948   52.82570267    0.            2.            1.81903195\n",
      "    7.90638113]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 238.57377625]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0744271278381\n",
      "Training epoch 14600\n",
      "Iter: 14600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            3.99999952   27.           10.99999809    0.            5.\n",
      "   31.70967674    3.          141.97932434   17.87061119   41.57627487\n",
      "   45.82306671    0.            2.67759895    2.            9.12770844]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 243.91519165]\n",
      "[ 0.01]\n",
      "()\n",
      "0.074275970459\n",
      "Training epoch 14700\n",
      "Iter: 14700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.99999714    6.           48.27233887   17.5859108     0.\n",
      "    3.80562687   29.86208725    4.          128.4303894    16.\n",
      "   60.96221924   43.99998093    0.            2.99875784    2.\n",
      "    2.99984765]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 254.28630066]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0739099979401\n",
      "Training epoch 14800\n",
      "Iter: 14800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            3.78253031   41.41622162    6.            0.            3.\n",
      "   27.            2.          115.33908081   11.94633484   43.91685104\n",
      "   53.65276718    0.            2.99992347    8.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 215.34078979]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0759470462799\n",
      "Training epoch 14900\n",
      "Iter: 14900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.98960114    9.6908226    48.59113693   12.99999428    1.\n",
      "    2.99952555   29.1599884     1.          125.14308167   17.99736595\n",
      "   44.9980011    60.60380936    0.            5.            3.99998379\n",
      "    9.98182106]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 214.3046875]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0750799179077\n",
      "Training epoch 15000\n",
      "Iter: 15000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99999809    6.           43.98318481   13.99736595    2.            0.\n",
      "   41.68853378    1.99981344  126.36154175   13.99998474   43.31521606\n",
      "   48.4857254     0.            5.53757334    1.           12.99993229]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 236.34207153]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726039409637\n",
      "Training epoch 15100\n",
      "Iter: 15100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   8.99950886    7.           53.96936798   14.            0.            3.\n",
      "   34.85423279    2.          130.37760925   13.98954201   48.5105896\n",
      "   57.63465881    0.            5.            0.            9.61943913]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.76664734]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0756268501282\n",
      "Training epoch 15200\n",
      "Iter: 15200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.02225208    9.           39.99990082    7.74938917    0.            3.\n",
      "   25.10309219    2.62151146  124.69691467   17.99898148   34.99998856\n",
      "   58.99521255    1.            2.38535094    3.99088025    8.98817062]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 240.76217651]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0765359401703\n",
      "Training epoch 15300\n",
      "Iter: 15300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.99038672    7.99999237   45.977005      8.54812431    0.\n",
      "    5.52275991   19.97795868    2.28468966  125.96942139    9.           42.\n",
      "   56.05505371    1.            1.            2.            7.9999795 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 316.8671875]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731317996979\n",
      "Training epoch 15400\n",
      "Iter: 15400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99440479    6.98171425   30.98019028   12.35365391    0.            3.\n",
      "   26.37234497    1.          143.79652405   12.99836731   43.27433014\n",
      "   47.88930511    0.            4.            3.99999785    5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 239.63465881]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0740740299225\n",
      "Training epoch 15500\n",
      "Iter: 15500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            8.           45.78796387   12.0190773     0.            5.\n",
      "   21.99695587    3.92317915  137.26080322   16.9886837    51.98018646\n",
      "   43.99989319    1.            6.            1.99988794    8.99999619]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 218.17480469]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0730450153351\n",
      "Training epoch 15600\n",
      "Iter: 15600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99982738    4.7150135    43.90767288   14.61744118    0.            3.\n",
      "   30.99143219    0.94468439  132.19863892    8.2151289    39.47839355\n",
      "   43.9985199     0.            3.            2.            8.92926025]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 257.79415894]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0738251209259\n",
      "Training epoch 15700\n",
      "Iter: 15700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.45672417    5.           49.973629     16.83024597    0.            3.\n",
      "   31.73299408    3.          124.8091507    20.9485321    42.01911545\n",
      "   43.2175827     1.            4.80555296    3.           11.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 238.00679016]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0724411010742\n",
      "Training epoch 15800\n",
      "Iter: 15800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.9999969     6.99999237   37.96996689   11.91507435    0.            3.\n",
      "   31.06085396    1.          137.18725586   14.85051537   42.16132355\n",
      "   44.01510239    1.            4.96838903    3.99999976   11.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.94503784]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728871822357\n",
      "Training epoch 15900\n",
      "Iter: 15900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.48980284    5.           50.96993256    5.99832058    0.            5.9824152\n",
      "   25.82484627    2.          112.6934433    16.99969482   44.6029129\n",
      "   46.99623108    0.            0.            2.            1.75729299]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 236.43743896]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0737719535828\n",
      "Training epoch 16000\n",
      "Iter: 16000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            8.           38.99968719   10.99684334    0.            0.\n",
      "   32.97458649    2.          127.72964478   12.02600002   48.919487\n",
      "   59.76980972    1.            2.997967      4.           11.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 203.13069153]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728330612183\n",
      "Training epoch 16100\n",
      "Iter: 16100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.8036952     4.99999952   36.99671936    8.99998951    0.            4.9999156\n",
      "   27.39231873    2.99998164  131.63772583   16.31928062   48.6451149\n",
      "   50.216362      0.            3.99838662    5.3288765     9.24287701]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 229.137146]\n",
      "[ 0.01]\n",
      "()\n",
      "0.074511051178\n",
      "Training epoch 16200\n",
      "Iter: 16200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99999857    2.4475894    62.82649231    9.            0.\n",
      "    2.99994612   17.            3.          110.51759338   14.99998379\n",
      "   49.49979019   60.97625732    1.            3.99915838    0.50254971\n",
      "    7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 246.23231506]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725631713867\n",
      "Training epoch 16300\n",
      "Iter: 16300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.33293819    8.           39.99568176   12.99999905    0.\n",
      "    1.98949635   28.99997902    2.          115.83859253   18.96162987\n",
      "   47.08324814   57.18902588    0.            2.            4.\n",
      "    4.99849319]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 271.04150391]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0732309818268\n",
      "Training epoch 16400\n",
      "Iter: 16400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            4.97543716   40.93811798   12.90355015    0.\n",
      "    2.38640022   19.30921173    1.          114.76378632   17.02890968\n",
      "   47.47887802   55.16584778    0.            7.99935627    4.18617296\n",
      "    3.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 257.2694397]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072986125946\n",
      "Training epoch 16500\n",
      "Iter: 16500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            5.99987602   41.50216675    7.            0.            2.\n",
      "   23.99509048    2.          126.62162018   18.99999619   50.11990738\n",
      "   47.11923218    1.            2.            0.99999994    8.86114502]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 202.79396057]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727920532227\n",
      "Training epoch 16600\n",
      "Iter: 16600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            9.90692806   46.31163025   10.99600601    0.            4.\n",
      "   25.99839211    0.99972522  133.18339539   11.           55.15795898\n",
      "   42.35892868    0.            3.99614167    2.            8.10130501]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 233.01495361]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0733659267426\n",
      "Training epoch 16700\n",
      "Iter: 16700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99928665    5.19426155   43.99358368    8.93911743    0.            0.\n",
      "   28.41020393    4.          121.17211151   20.91272736   39.24081039\n",
      "   51.78163528    0.            2.            3.26710367    8.90853214]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 205.3611908]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727670192719\n",
      "Training epoch 16800\n",
      "Iter: 16800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            6.99094534   41.97208786    8.99978065    1.            1.\n",
      "   33.01641846    1.          131.55056763   16.9813385    54.32562256\n",
      "   58.06355667    0.            6.99230433    3.            6.99999952]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 206.29763794]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725541114807\n",
      "Training epoch 16900\n",
      "Iter: 16900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            2.99996901   31.78718758    8.99971581    1.\n",
      "    4.99999475   19.15525436    2.95187998  111.9761734    21.38440323\n",
      "   32.92640305   36.97550964    1.99999285    1.            3.\n",
      "    8.28177452]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 190.52233887]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0733737945557\n",
      "Training epoch 17000\n",
      "Iter: 17000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.10473871    4.9271965    50.99732208    9.99855804    0.            3.\n",
      "   31.88834953    1.          119.14457703   18.85441971   45.51079559\n",
      "   49.49958038    0.            5.            2.            6.99999714]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 273.46917725]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735719203949\n",
      "Training epoch 17100\n",
      "Iter: 17100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   9.24631214    4.           42.01715469   10.96167946    0.            1.\n",
      "   35.88365173    1.75170672  146.99311829   13.85123062   38.95970917\n",
      "   49.99766541    0.            0.            3.99999619    5.98520184]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 232.42250061]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726809501648\n",
      "Training epoch 17200\n",
      "Iter: 17200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.78759384    5.           28.99988556   17.99985313    1.            1.\n",
      "   22.94945908    0.          123.78292084   12.           32.87755585\n",
      "   50.9993782     1.            8.99996471    2.31340098    4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 216.4057312]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729119777679\n",
      "Training epoch 17300\n",
      "Iter: 17300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            3.           37.81848526   11.99627686    0.            4.\n",
      "   22.            0.          113.42058563   12.86591816   31.45908356\n",
      "   54.98042297    0.            6.83040953    3.99999952    9.06883812]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 236.27674866]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0737779140472\n",
      "Training epoch 17400\n",
      "Iter: 17400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.96480131    2.           44.37677002   10.            0.            1.\n",
      "   21.77536774    1.          118.95565796   16.99929428   37.66714096\n",
      "   41.7498703     0.            2.99947333    2.            8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 231.73222351]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726130008698\n",
      "Training epoch 17500\n",
      "Iter: 17500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.            3.99966264   52.68432999   14.99139309    0.            3.9987421\n",
      "   21.85139465    1.          130.01638794   14.9681282    41.99729538\n",
      "   46.38037491    0.            4.98544121    1.99999905    9.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 258.81350708]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0725491046906\n",
      "Training epoch 17600\n",
      "Iter: 17600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  10.99995613    6.           31.70203018   11.86436367    0.\n",
      "    3.93726873   38.99981308    1.          121.30290985   21.93821716\n",
      "   42.42287827   51.99044037    0.            4.99733829    2.\n",
      "    4.99985886]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 240.42626953]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729777812958\n",
      "Training epoch 17700\n",
      "Iter: 17700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99999094    2.92347956   43.7115593    14.            0.            3.\n",
      "   28.311306      1.          115.75481415   12.98889065   42.9125824\n",
      "   66.99580383    0.            3.99996424    6.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 205.23614502]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0728850364685\n",
      "Training epoch 17800\n",
      "Iter: 17800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.94762182    2.61286736   45.96776199    7.            0.            1.\n",
      "   25.97326469    0.          122.50922394   16.99152565   51.95658875\n",
      "   43.44241714    0.            0.            1.99999046    9.84648228]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 233.27909851]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072597026825\n",
      "Training epoch 17900\n",
      "Iter: 17900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.9999342     7.           57.88258743    9.53558159    0.            3.\n",
      "   22.            1.          129.61129761    9.99964333   54.9331398\n",
      "   50.94563293    0.            3.            3.99600887    8.11270142]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 248.65643311]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072762966156\n",
      "Training epoch 18000\n",
      "Iter: 18000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99941444    4.           25.9979229    13.            0.\n",
      "    5.92569447   26.90196609    1.          133.66468811   15.69736958\n",
      "   39.           59.79948425    0.99970883    2.99999952    1.\n",
      "    5.04151821]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 264.66369629]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726749897003\n",
      "Training epoch 18100\n",
      "Iter: 18100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.           14.99966335   47.89690399    6.99918795    0.\n",
      "    2.05350709   28.95709991    1.          129.48796082   14.98035812\n",
      "   39.86407471   51.09411621    0.            4.            4.99838209\n",
      "    5.99999619]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 219.28761292]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0741710662842\n",
      "Training epoch 18200\n",
      "Iter: 18200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            3.85971904   49.94533157   12.98141861    0.\n",
      "    3.96956396   24.99801064    3.99999976  108.50118256   13.99518585\n",
      "   44.09453583   59.36907578    0.            5.            1.\n",
      "   11.99987411]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 242.38148499]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729019641876\n",
      "Training epoch 18300\n",
      "Iter: 18300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            6.47200441   59.56137466    7.26412582    0.            2.\n",
      "   10.27131462    1.          111.59467316   17.82239151   32.87582016\n",
      "   51.97829437    0.            2.3165741     1.99999619   11.97029686]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 265.09561157]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0730681419373\n",
      "Training epoch 18400\n",
      "Iter: 18400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.8782053     4.75528097   54.8721962     9.50347042    0.\n",
      "    0.99816781   36.60039139    0.5694223   119.99905396   12.99996758\n",
      "   56.33569717   52.85866547    1.            3.            1.\n",
      "   10.95289135]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 272.21105957]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0732839107513\n",
      "Training epoch 18500\n",
      "Iter: 18500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.60364914   11.           45.41021347   11.76390266    0.            1.\n",
      "   26.99426842    1.          116.22351837   16.           54.7206192\n",
      "   59.61670303    0.            2.            2.            8.53898048]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 230.08590698]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0721900463104\n",
      "Training epoch 18600\n",
      "Iter: 18600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            4.           34.23546982    5.99999952    0.            3.\n",
      "   25.48722076    2.          135.32229614   19.99996185   38.06551743\n",
      "   45.00020599    0.            1.99999881    4.            6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 244.63163757]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0743989944458\n",
      "Training epoch 18700\n",
      "Iter: 18700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99971104    3.           50.71472549   11.            1.            4.\n",
      "   19.99999619    1.99999976  126.95629883   11.9191494    38.99821854\n",
      "   52.87719727    0.99960405    5.            2.99999976   12.71056652]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 224.88679504]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725119113922\n",
      "Training epoch 18800\n",
      "Iter: 18800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.98780036    2.           45.99993134   11.99999523    0.            1.\n",
      "   31.8739357     1.          132.2253418    14.87392807   54.48544693\n",
      "   39.04997635    0.            3.            0.            7.99825478]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 217.67030334]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729560852051\n",
      "Training epoch 18900\n",
      "Iter: 18900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99998951    3.95103455   46.98654938    5.            0.            5.\n",
      "   21.99998093    4.          122.99962616   16.31600571   41.78426743\n",
      "   47.91243362    0.99877268    1.91898906    3.9848609     7.1756568 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 243.18009949]\n",
      "[ 0.01]\n",
      "()\n",
      "0.089371919632\n",
      "Training epoch 19000\n",
      "Iter: 19000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.99363315    4.99996662   33.97409058    8.56896496    0.            4.\n",
      "   33.16115952    2.99129987  129.99893188   14.96649837   47.68738556\n",
      "   55.98701096    0.            6.            1.99719787   11.95180416]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 205.66110229]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745489597321\n",
      "Training epoch 19100\n",
      "Iter: 19100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  10.99123096    5.52040625   49.9993515     9.99998856    0.            3.\n",
      "   25.83119965    4.          134.71948242   21.02841187   36.63716507\n",
      "   50.89454651    0.99990028    3.99999142    2.            6.99994469]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 202.3011322]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0732951164246\n",
      "Training epoch 19200\n",
      "Iter: 19200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            6.           41.11647797   14.56966686    0.            3.\n",
      "   20.58964539    2.          131.50331116   11.58390617   41.3538208\n",
      "   57.49702454    0.            2.92862296    1.            7.94266748]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 276.2817688]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729789733887\n",
      "Training epoch 19300\n",
      "Iter: 19300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.99998569    6.64725351   28.64825439    4.83159447    1.\n",
      "    1.99998903   29.78240013    0.          148.55609131   16.99957466\n",
      "   29.98328018   49.68173981    0.            3.99996281    1.\n",
      "   12.94214916]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 248.32749939]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072564125061\n",
      "Training epoch 19400\n",
      "Iter: 19400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.            2.9987483    39.98300171    7.99867344    0.            2.\n",
      "   24.38123703    1.          125.14444733   14.           38.99160385\n",
      "   43.72735596    0.            3.99999666    2.            5.99635124]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 203.95803833]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072573184967\n",
      "Training epoch 19500\n",
      "Iter: 19500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.            3.           48.28437424    7.            0.            4.\n",
      "   33.54133606    2.          131.16700745   21.48664474   45.97260284\n",
      "   43.47577667    1.            4.            2.81544781    5.37684536]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 316.70040894]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725681781769\n",
      "Training epoch 19600\n",
      "Iter: 19600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.            2.99963284   51.91121292    7.99999905    0.            3.\n",
      "   35.73667145    1.          128.39326477   21.9638443    56.16752243\n",
      "   53.67046356    0.            5.            2.           17.72468567]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 255.66581726]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729370117188\n",
      "Training epoch 19700\n",
      "Iter: 19700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99999666    2.9998765    33.92905426   10.77098751    0.99839121\n",
      "    2.99999666   24.99598312    2.91680956  128.75901794   18.\n",
      "   38.74903107   47.77220917    0.            2.80652976    5.\n",
      "    8.53177071]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 192.60624695]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0734431743622\n",
      "Training epoch 19800\n",
      "Iter: 19800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            2.           37.99991989    9.            2.            2.8457942\n",
      "   21.96290207    1.          126.49369812   12.84459591   44.99874878\n",
      "   47.89130402    0.            2.            2.           13.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 219.40682983]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0724868774414\n",
      "Training epoch 19900\n",
      "Iter: 19900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            8.           36.76186752   13.            0.\n",
      "    1.86131287   15.            2.          112.76906586   15.\n",
      "   54.16781998   48.93203735    0.            4.            1.99999273\n",
      "    5.99966526]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 276.20117188]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0739569664001\n",
      "Training epoch 20000\n",
      "Iter: 20000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.99967861    6.70254087   32.94326019    9.99678326    0.            4.\n",
      "   25.80670738    0.          125.9733963    14.99999619   39.6277771\n",
      "   49.27851868    0.            2.9781642     0.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 262.2701416]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0755248069763\n",
      "Training epoch 20100\n",
      "Iter: 20100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.99999905    4.           54.72559738    8.            0.\n",
      "    1.99999988   32.67110443    3.          101.99960327   10.99999428\n",
      "   53.5281601    53.70140076    1.            2.99999905    4.99999905\n",
      "    6.99996042]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 230.58230591]\n",
      "[ 0.01]\n",
      "()\n",
      "0.072417974472\n",
      "Training epoch 20200\n",
      "Iter: 20200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            7.           44.95107269   10.99999905    0.91276443\n",
      "    3.           30.62113953    3.          126.1696167    11.\n",
      "   50.78983688   41.9678421     0.            2.            1.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.15690613]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0749650001526\n",
      "Training epoch 20300\n",
      "Iter: 20300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.75314617    4.           37.21408844   12.99958801    0.            1.\n",
      "   26.99125099    4.          111.99816132   19.           40.90610886\n",
      "   41.92855835    1.98588085    1.            4.99996901    5.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 251.61000061]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0724608898163\n",
      "Training epoch 20400\n",
      "Iter: 20400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.84039593    5.           30.86871338    9.43499851    0.            1.\n",
      "   25.27290344    2.          135.49465942   15.97091007   47.70381927\n",
      "   54.87298203    0.            3.99998093    0.           10.97486496]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 241.81321716]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0751149654388\n",
      "Training epoch 20500\n",
      "Iter: 20500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99738741    6.74187756   43.52689362    8.99999905    1.            6.\n",
      "   31.87097931    2.          106.62007141   15.99927902   42.38499832\n",
      "   46.40584946    0.            2.            2.99104095    6.99988937]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 311.65979004]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0742990970612\n",
      "Training epoch 20600\n",
      "Iter: 20600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            2.9534421    42.           12.45862007    0.            2.\n",
      "   22.997612      3.99876428  116.86398315   16.99996948   50.46907043\n",
      "   36.93024063    0.            1.            3.           10.99962902]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 243.59347534]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0746810436249\n",
      "Training epoch 20700\n",
      "Iter: 20700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.99997044    6.99999428   38.05055237   11.39254856    0.            5.\n",
      "   23.98241425    0.          124.89186096   11.92381287   42.99530029\n",
      "   36.78490067    0.            3.            5.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 209.71313477]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0742769241333\n",
      "Training epoch 20800\n",
      "Iter: 20800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.99932671    6.99457598   51.58696365   14.99996281    1.            2.\n",
      "   24.48328018    0.          111.74975586   15.55295944   42.70828629\n",
      "   34.99970245    0.            1.14925206    1.            9.93403053]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.2265625]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0723860263824\n",
      "Training epoch 20900\n",
      "Iter: 20900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.84888792    0.           48.58470917   13.70550632    0.66137284\n",
      "    2.96654367   27.77244568    4.95773792  140.95465088   20.99999619\n",
      "   59.68651199   51.39218521    0.            3.            1.99999952\n",
      "    8.99903488]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 289.83612061]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731918811798\n",
      "Training epoch 21000\n",
      "Iter: 21000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            5.99999809   42.97877502   14.99874878    2.\n",
      "    4.90219212   19.99954224    1.          128.38461304   15.99996471\n",
      "   35.42072296   45.18207169    0.            2.            1.\n",
      "    3.99459457]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 252.47740173]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0739760398865\n",
      "Training epoch 21100\n",
      "Iter: 21100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.989043      7.           47.92486954   13.11028767    0.            5.\n",
      "   29.63332367    0.          125.96445465    9.98090363   41.09492493\n",
      "   59.8450737     0.            1.            3.95922756   12.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 212.496521]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0742428302765\n",
      "Training epoch 21200\n",
      "Iter: 21200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.            4.           43.17421722   12.99901009    0.            1.\n",
      "   29.86192131    2.94979692  123.97442627    9.99824333   48.73729324\n",
      "   57.56138229    1.            1.            3.97786903    9.99907875]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 259.99865723]\n",
      "[ 0.01]\n",
      "()\n",
      "0.074599981308\n",
      "Training epoch 21300\n",
      "Iter: 21300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   1.9999969     2.99703169   47.80196381   13.            1.\n",
      "    3.99884892   19.97506332    0.          139.13998413   19.17470932\n",
      "   31.99965477   36.8567009     0.            5.            2.99911809\n",
      "    8.9941349 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.69400024]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727798938751\n",
      "Training epoch 21400\n",
      "Iter: 21400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.9986248     3.           44.99777603    7.06512737    0.            2.\n",
      "   30.99999809    3.          127.89373779   19.93822098   33.53144073\n",
      "   47.99546432    0.            1.99414062    1.            8.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 205.03948975]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0729629993439\n",
      "Training epoch 21500\n",
      "Iter: 21500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99997663    2.99428844   32.91772842    4.            0.            1.\n",
      "   20.95836258    1.          115.87832642   13.99999428   35.1501236\n",
      "   46.05686951    0.            2.            2.12537432    6.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 222.27101135]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735549926758\n",
      "Training epoch 21600\n",
      "Iter: 21600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99999428    3.           51.94566727    8.99999905    0.            3.\n",
      "   18.87765312    0.          119.90527344   14.96525764   50.05056381\n",
      "   45.71666336    0.            1.99927461    2.            4.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 220.29362488]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0731010437012\n",
      "Training epoch 21700\n",
      "Iter: 21700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            5.           36.           12.9174881     0.            2.\n",
      "   29.53868675    0.          140.99359131    9.           35.9943161\n",
      "   47.99980164    2.            1.99999523    2.           11.90782642]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 241.65328979]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0724878311157\n",
      "Training epoch 21800\n",
      "Iter: 21800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[  6.92093277e+00   6.97858715e+00   4.28988457e+01   9.00000000e+00\n",
      "   0.00000000e+00   3.99771214e+00   3.39294586e+01   2.00000000e+00\n",
      "   1.32210464e+02   1.30000000e+01   4.20492783e+01   4.74162064e+01\n",
      "   0.00000000e+00   1.00000000e+00   6.10343972e-03   8.00000000e+00]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 253.17300415]\n",
      "[ 0.01]\n",
      "()\n",
      "0.07293009758\n",
      "Training epoch 21900\n",
      "Iter: 21900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.96605587    6.99999332   51.90789032   12.78839397    0.            4.\n",
      "   21.74498367    2.          121.6585083    18.56686592   36.09745789\n",
      "   40.3390007     0.            1.            1.99998951    7.99978733]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 231.51600647]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0745420455933\n",
      "Training epoch 22000\n",
      "Iter: 22000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99959469    6.           51.99752045    8.9753418     1.\n",
      "    2.99993563   27.77173042    1.99999237  120.26180267   13.9864912\n",
      "   63.84118652   49.93408585    0.            4.            1.\n",
      "    9.99958897]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 232.46806335]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0743579864502\n",
      "Training epoch 22100\n",
      "Iter: 22100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            6.           32.9850235    11.99963474    0.\n",
      "    2.99999619   17.99967194    0.          132.59408569   11.79155922\n",
      "   49.9681778    46.95984268    0.            3.            0.\n",
      "   14.99998188]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 246.66835022]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0742130279541\n",
      "Training epoch 22200\n",
      "Iter: 22200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   2.            6.           42.93610382    9.22081375    1.            4.9999733\n",
      "   30.8354435     3.          123.42040253   16.99990273   53.89548111\n",
      "   51.53595734    0.            2.            1.           13.98574257]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 243.54701233]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0735290050507\n",
      "Training epoch 22300\n",
      "Iter: 22300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.96812201    8.69681358   49.0396347    10.90930367    0.\n",
      "    2.99855137   41.76833344    2.          127.35552216   18.99974442\n",
      "   34.90615082   60.44152451    1.            3.99998093    4.\n",
      "    4.10131502]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 250.78347778]\n",
      "[ 0.01]\n",
      "()\n",
      "0.092325925827\n",
      "Training epoch 22400\n",
      "Iter: 22400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99999428    6.9999609    45.99172592    6.99997091    2.            2.\n",
      "   29.90090942    0.          138.65785217   16.58882141   41.778759\n",
      "   56.97434998    1.            4.99999714    2.            7.36285162]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 279.21496582]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0736348628998\n",
      "Training epoch 22500\n",
      "Iter: 22500\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.68994713   10.99860668   28.98951149    9.6784029     0.            2.9986856\n",
      "   26.31170845    0.          137.89398193   21.           42.18826675\n",
      "   50.59272003    0.            0.            4.96462059    6.29658556]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 275.06494141]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0733981132507\n",
      "Training epoch 22600\n",
      "Iter: 22600\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.85328054    3.           40.97998047    8.99868584    1.            2.\n",
      "   23.37199211    1.          104.61321259   11.99999332   43.97294998\n",
      "   55.40261078    0.            0.99999624    2.            7.98497629]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 243.21855164]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0735580921173\n",
      "Training epoch 22700\n",
      "Iter: 22700\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.6370151     5.99999905   42.97248077   14.77476501    0.            3.\n",
      "   33.70579529    0.          126.66995239   14.99999809   44.61391449\n",
      "   47.2938652     1.            1.            2.99878931    4.99523163]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 229.54467773]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0734088420868\n",
      "Training epoch 22800\n",
      "Iter: 22800\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.99982548    7.           39.65114975   13.            0.            0.\n",
      "   33.6397171     4.99942064  125.3234024    16.99938202   48.59495163\n",
      "   44.99812317    0.99990445    4.99977684    3.99696708    4.88666439]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 233.11677551]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726339817047\n",
      "Training epoch 22900\n",
      "Iter: 22900\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   5.            4.99637747   44.99808502    9.99788189    0.            4.\n",
      "   26.99869537    0.99995673  118.2010498    14.86155415   43.01491165\n",
      "   55.67773056    0.            3.99832916    4.            9.9987011 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 221.87936401]\n",
      "[ 0.01]\n",
      "()\n",
      "0.073117017746\n",
      "Training epoch 23000\n",
      "Iter: 23000\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   7.99666929    4.99972725   39.02964783    9.99985504    0.\n",
      "    1.71531272   24.11148262    1.          117.69922638   12.8883667\n",
      "   43.87717438   57.91087723    0.            6.44774199    2.\n",
      "    5.55818176]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 264.64868164]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0726399421692\n",
      "Training epoch 23100\n",
      "Iter: 23100\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   6.            5.99996662   41.53987122   12.99775696    1.\n",
      "    2.89521742   22.99993896    1.99616051  125.40603638   18.71186638\n",
      "   45.91196823   46.13590622    0.            0.            2.\n",
      "    7.80435514]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 239.73194885]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725860595703\n",
      "Training epoch 23200\n",
      "Iter: 23200\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.99998736    7.99996948   41.9987793     8.99999523    0.\n",
      "    2.40330291   36.65995407    3.          124.42932129   17.98265839\n",
      "   42.99729156   36.83896637    1.            2.            2.            7.        ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 265.07901001]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0725340843201\n",
      "Training epoch 23300\n",
      "Iter: 23300\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   3.            3.           46.02481079   11.99200153    0.\n",
      "    4.94569683   32.67974854    0.99999994  141.76724243    7.99921608\n",
      "   44.57040405   47.99187088    0.            5.99982452    3.            5.3072257 ]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 274.13601685]\n",
      "[ 0.01]\n",
      "()\n",
      "0.0727958679199\n",
      "Training epoch 23400\n",
      "Iter: 23400\n",
      "D loss: 0.0\n",
      "G_loss: -0.01\n",
      "real:\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 0.]\n",
      "[ 0.01]\n",
      "fake:\n",
      "[   4.89158726    3.9964149    35.98981476   12.97470188    0.            3.\n",
      "   30.98699188    1.99989343  138.50007629   23.90964508   62.78471375\n",
      "   44.05011368    0.            3.99999952    0.           10.35870266]\n",
      "[ 0.  0.  1.  2.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      "[ 209.68484497]\n",
      "[ 0.01]\n",
      "()\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-316-43b9f073537b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             _, D_loss_curr, _ = sess.run(\n\u001b[0;32m---> 17\u001b[0;31m                 [D_solver, D_loss, clip_D], feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_feed_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfetch_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m   \u001b[0;31m# Captures the name of a node in an error status.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mbuild_results\u001b[0;34m(self, session, tensor_values)\u001b[0m\n\u001b[1;32m    468\u001b[0m         \u001b[0mcontaining\u001b[0m \u001b[0mtensors\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mfor\u001b[0m \u001b[0mfetched\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m     \"\"\"\n\u001b[0;32m--> 470\u001b[0;31m     \u001b[0mfull_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    471\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_fetches\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    472\u001b[0m     \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "every = 100\n",
    "train_dataset = features\n",
    "n_train = len(train_dataset)\n",
    "for it in range(1000000):\n",
    "    random.shuffle(train_dataset)\n",
    "    a = time.time()\n",
    "    batch_sched = list(range(0, n_train+1,S))\n",
    "    for j in range(0, len(batch_sched)-1):\n",
    "        start = batch_sched[j]\n",
    "        stop = batch_sched[j+1]\n",
    "        feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "        for _ in range(5):\n",
    "            feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "            _, D_loss_curr, _ = sess.run(\n",
    "                [D_solver, D_loss, clip_D], feed_dict=feed_dict)\n",
    "        \n",
    "        feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "        _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict=feed_dict)\n",
    "        \n",
    "        d_losses.append(D_loss_curr)\n",
    "        g_losses.append(G_loss_curr)\n",
    "    if it % every == 0:\n",
    "        print(time.time()-a)\n",
    "        print(\"Training epoch %d\" %it)\n",
    "\n",
    "\n",
    "        \n",
    "    if it % every == 0:\n",
    "        print('Iter: {}'.format(it))\n",
    "        print('D loss: {:.4}'. format(D_loss_curr))\n",
    "        print('G_loss: {:.4}'.format(G_loss_curr))\n",
    "        print(\"real:\")\n",
    "        print(np.array(sess.run(pred_real, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(corr_real, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(diff_real, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(D_real, feed_dict=feed_dict))[0])\n",
    "        \n",
    "        print(\"fake:\")\n",
    "\n",
    "        print(np.array(sess.run(pred_fake, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(corr_fake, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(diff_fake, feed_dict=feed_dict))[0])\n",
    "        print(np.array(sess.run(D_fake, feed_dict=feed_dict))[0])\n",
    "\n",
    "    \n",
    "        results = sess.run(G_sample, feed_dict=feed_dict)\n",
    "        convert_to_molecule(results[0], index_to_elem, \"./test.xyz\")\n",
    "        print()\n",
    "        \n",
    "        \n",
    "\n",
    "        #print(\"output_i\")\n",
    "        #print(sess.run(G_sample_output_i, feed_dict=feed_dict))\n",
    "        #print(sess.run(G_sample, feed_dict=feed_dict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.min(results[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.where(features[0][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2,  7,  9, 12]),\n",
       " array([ 4,  4, 13,  9]),\n",
       " array([ 5,  6, 11,  8]),\n",
       " array([5, 2, 3, 3]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(features[2][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = sess.run(G_sample, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(pred_real, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(results[0][:,:,:,:], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "         2.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         4.09600000e+03])"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromMol2File('/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2')\n",
    "mol.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "AllChem.Compute2DCoords(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAALDElEQVR4nO3cf6iW9f3H8ddMdDo9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\ngDIECyhDsIAyBAso438B7w9cPBQEks4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300 at 0x7F35E87B9A50>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import Draw\n",
    "\n",
    "Draw.MolToImage(mol)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}