bond_gan_mk2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook addresses the question, \"Can we represent a molecule as a graph via a 1D column vector or a 2D matrix of fixed length, with maximum number of atoms n_rows?\" Then, can we use this representation to learn neural fingerprints? E.g., can we make an aromatic ring detector? \n",
    "\n",
    "Scheme:\n",
    "feature_matrix = X\n",
    "for each ligand:\n",
    "    choose a central atom. this can be the atom (node) that minimizes distance to furthest heavy atom in graph.\n",
    "    set first row of X to be this central atom\n",
    "    set next four rows to be the atoms bonded to that centrl atom\n",
    "        set zeros for rows where row ind > n_bonds of atom\n",
    "    for each of those atoms:\n",
    "        repeat. find their neighbors. add to matrix.\n",
    "\n",
    "algorithm: breadth-first search:\n",
    "1. create networkx graph based on molecule\n",
    "2. find \"central\" atom (different strategies)\n",
    "3. define atom matrix of size (1+4+4*3^(L-1)) x (n_features_per_atom)\n",
    "4. start atom queue q\n",
    "5. central_atom.layer = 0; central_atom.row_idx = 0;\n",
    "6. q.enqueue(central_atom)\n",
    "7. define adjacency matrix of size (1+4+4*3^(L-1)) x 4\n",
    "\n",
    "def get_row_idx(curr_layer, prev_row_idx, curr_neighbor_idx):\n",
    "    if curr_layer == 0:\n",
    "        return(0)\n",
    "    if curr_layer == 1:\n",
    "        row_idx = 1 + curr_neighbor_idx\n",
    "    if layer == 2:\n",
    "        last_max = 5\n",
    "        row_idx = last_max + (3*(prev_row_idx-last_max)) + curr_neighbor_idx\n",
    "    if layer > 2:\n",
    "        last_max = 5 + 4*3^(curr_layer-2) \n",
    "        row_idx = last_max + 3*(prev_row_idx-last_max) + curr_neighbor_idx\n",
    "    return(row_idx)\n",
    "    \n",
    "\n",
    "while q.is_not_empty():\n",
    "    a = q.dequeue()\n",
    "    a.visited = True\n",
    "    for n_idx, n in enumerate(a.neighbors()):\n",
    "        if not n.visited:\n",
    "            row_idx = c\n",
    "            n.layer = a.layer + 1\n",
    "            row_idx = get_row_idx(n.layer, a.row_idx, n_idx)\n",
    "            n.row_idx = row_idx\n",
    "            adj_matrix[a.row_idx][n_idx] = n.row_idx\n",
    "            atom_matrix[row_idx][elem_to_idx[n.elem]] = 1\n",
    "\n",
    "input_matrix = tf.concat([atom_matrix, atom_matrix[adj_matrix[:,0]], atom_matrix[adj_matrix[:,1]], atom_matrix[adj_matrix[:,2]], atom_matrix[adj_matrix[:,3]]\n",
    "\n",
    "neural net:\n",
    "h1 = relu([tf.zeros([n_features_per_atom, 4]) * input_matrix + bias))\n",
    "h1_conc = tf.concat([h1, h1[adj_matrix[:,0], ..., h1[adj_matrix[:,3])\n",
    "\n",
    "repeat h1 to get h2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dihedral predictor pseudocode:\n",
    "\n",
    "get bonds for molecule\n",
    "create networkx graph out of molecule (use atom indices)\n",
    "\n",
    "for each edge:\n",
    "   for neighbor_i in atom_i.neighbors():\n",
    "       if neighbor_i == atom_j: continue\n",
    "       for neighbor_j in atom_j.neighbors():\n",
    "           if neighbor_j == atom_i: continue\n",
    "           dihedrals.append((neighbor_i, atom_i, neighbor_j, atom_j))\n",
    "           check to make sure (atom_j, neighbor_j, atom_i, neighbor_i)) not already in list\n",
    "\n",
    "for dihedral in dihedrals:\n",
    "    angle =  rdMolTransforms.GetDihedralDeg(c, 0,1,2,3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_torsions_angles(mol, n_bonds=100, n_atoms=200):\n",
    "    c = mol.GetConformer(0)\n",
    "\n",
    "    torsions = []\n",
    "    torsion_tuples = []\n",
    "    \n",
    "    torsion_matrix = np.zeros((n_bonds,6))\n",
    "    torsion_indices = np.zeros((n_bonds,n_atoms,24)).astype(np.uint8)\n",
    "    \n",
    "    bond_adjacency = np.zeros((n_bonds,n_bonds)).astype(np.uint8)\n",
    "    for idx, bond in enumerate(mol.GetBonds()):\n",
    "        if bond.IsInRing(): \n",
    "            continue\n",
    "\n",
    "        def calc_torsions(atom_i, atom_j, bond_idx, start=0):        \n",
    "            exist_dihed = False\n",
    "            for neighbor_j in atom_j.GetNeighbors():\n",
    "                if neighbor_j.GetIdx() == atom_i.GetIdx():\n",
    "                    continue\n",
    "\n",
    "                dihed_idx = 0\n",
    "                for neighbor_i in atom_i.GetNeighbors():\n",
    "                    if neighbor_i.GetIdx() == atom_j.GetIdx():\n",
    "                        continue\n",
    "\n",
    "                    exist_dihed=True\n",
    "                    torsion_tuple = (neighbor_i.GetIdx(), atom_i.GetIdx(), atom_j.GetIdx(), neighbor_j.GetIdx())\n",
    "\n",
    "                    torsion_matrix[bond_idx][3*start+dihed_idx] = np.cos(rdMolTransforms.GetDihedralRad(c, *torsion_tuple))\n",
    "                    torsion_indices[bond_idx][torsion_tuple[0]][12*start+dihed_idx*4] = 1\n",
    "                    torsion_indices[bond_idx][torsion_tuple[1]][12*start+dihed_idx*4+1] = 1\n",
    "                    torsion_indices[bond_idx][torsion_tuple[2]][12*start+dihed_idx*4+2] = 1\n",
    "                    torsion_indices[bond_idx][torsion_tuple[3]][12*start+dihed_idx*4+3] = 1\n",
    "                    dihed_idx += 1\n",
    "                #torsion_matrix[bond_idx][3] = 3.14159 - np.abs(np.abs(torsion_matrix[bond_idx][2]-torsion_matrix[bond_idx][1])-3.14159)\n",
    "                #torsion_matrix[bond_idx][4] = 3.14159 - np.abs(np.abs(torsion_matrix[bond_idx][2]-torsion_matrix[bond_idx][0])-3.14159)\n",
    "                #torsion_matrix[bond_idx][5] = 3.14159 - np.abs(np.abs(torsion_matrix[bond_idx][1]-torsion_matrix[bond_idx][0])-3.14159)\n",
    "                break\n",
    "        \n",
    "        calc_torsions(bond.GetBeginAtom(), bond.GetEndAtom(), idx, 0)\n",
    "        calc_torsions(bond.GetEndAtom(), bond.GetBeginAtom(), idx, 1)\n",
    "    \n",
    "        atom_i_bonds = bond.GetBeginAtom().GetBonds()\n",
    "        atom_j_bonds = bond.GetEndAtom().GetBonds()\n",
    "\n",
    "        for bond_i in atom_i_bonds:\n",
    "            if bond_i.GetIdx() != bond.GetIdx():\n",
    "                bond_adjacency[bond.GetIdx()][bond_i.GetIdx()] = 1.\n",
    "                bond_adjacency[bond_i.GetIdx()][bond.GetIdx()] = 1.\n",
    "        for bond_j in atom_j_bonds:\n",
    "            if bond_j.GetIdx() != bond.GetIdx():\n",
    "                bond_adjacency[bond.GetIdx()][bond_j.GetIdx()] = 1.\n",
    "                bond_adjacency[bond_j.GetIdx()][bond.GetIdx()] = 1.    \n",
    "    \n",
    "    \n",
    "    return((torsion_indices, csr_matrix(torsion_matrix), csr_matrix(bond_adjacency)))\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def featurize_mols(mol_files):\n",
    "    featurizer = AdjacencyFingerprint(max_n_atoms=200)\n",
    "    features = []\n",
    "    for idx, mol_file in enumerate(mol_files):\n",
    "        if idx % 100 == 0:\n",
    "            print(idx)\n",
    "        if 1==1:\n",
    "            mol = Chem.MolFromMol2File(mol_file)\n",
    "            if mol is None:\n",
    "                features.append(None)\n",
    "                continue\n",
    "            torsions = get_torsions_angles(mol)\n",
    "            graph_feat = featurizer.featurize([mol])[0]\n",
    "            features.append((mol_file, torsions, graph_feat))\n",
    "        else:\n",
    "            features.append(None)\n",
    "    return(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "feature_file = \"./dihedral_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 = [f for f in find_files(pdbbind_dir, \"*ligand.mol2\")][:1]\n",
    "    features = featurize_mols(ligand_files)\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": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = [f for f in features if f is not None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][1][1].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "alpha = 0.01\n",
    "n_layers = 0\n",
    "S = 64\n",
    "B = 200\n",
    "L_list = [50, 50, 50, 50]\n",
    "p = 75\n",
    "n_bonds=100\n",
    "\n",
    "x = tf.placeholder(tf.float32, shape=[S, B, p])\n",
    "\n",
    "non_zero_inds = tf.placeholder(tf.int32, shape=[None, S*n_bonds])\n",
    "\n",
    "adj_matrix = tf.placeholder(tf.float32, shape=[S, B, B])\n",
    "dihed_indices = tf.placeholder(tf.float32, shape=[S, n_bonds, B, 12])\n",
    "\n",
    "label_placeholder = tf.placeholder(\n",
    "    dtype='float32', shape=[None,3], name=\"label_placeholder\")\n",
    "\n",
    "phase = tf.placeholder(dtype='bool', name='phase')\n",
    "\n",
    "z = tf.placeholder(tf.float32,\n",
    "                          [None, 200, 75], name='z')\n",
    "\n",
    "z_ini = tf.placeholder(tf.float32,\n",
    "                          [None, 200, 75], name='z_ini')\n",
    "\n",
    "ones_like = tf.placeholder(tf.float32, shape=[None,1])\n",
    "zeros_like = tf.placeholder(tf.float32, shape=[None,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DISCRIMINATOR\n",
    "\n",
    "\n",
    "D_W_list = [None for i in range(n_layers)]\n",
    "D_b_list = [None for i in range(n_layers)]\n",
    "D_h_list = [None for i in range(n_layers)]\n",
    "\n",
    "\n",
    "\n",
    "for layer_idx in range(n_layers):\n",
    "    if layer_idx == 0:\n",
    "      L_in = p\n",
    "      L_out = L_list[0]\n",
    "      atom_matrix = x\n",
    "    else:\n",
    "      L_in = L_list[layer_idx-1]\n",
    "      L_out = L_list[layer_idx]\n",
    "      atom_matrix = D_h_list[layer_idx-1]\n",
    "\n",
    "    D_W_list[layer_idx] = tf.Variable(tf.truncated_normal([L_in, L_out], seed=2017), name=\"D_W_list%d\" %layer_idx)\n",
    "    D_b_list[layer_idx] = tf.Variable(tf.zeros([1, L_out]))\n",
    "\n",
    "    \n",
    "D_W2 = tf.Variable(tf.truncated_normal([L_list[-1]*12+3, 100]))\n",
    "D_b2 = tf.Variable(tf.zeros([1, 100]))\n",
    "\n",
    "D_W3 = tf.Variable(tf.truncated_normal([100, 3]))\n",
    "D_b3 = tf.Variable(tf.zeros([1, 3]))\n",
    "\n",
    "D_W4 = tf.Variable(tf.truncated_normal([3, 10]))\n",
    "D_b4 = tf.Variable(tf.zeros([1, 10]))\n",
    "\n",
    "D_W5 = tf.Variable(tf.truncated_normal([10, 1]))\n",
    "D_b5 = tf.Variable(tf.zeros([1, 1]))\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "D_W0 = tf.Variable(tf.truncated_normal([3, 100]))\n",
    "D_b0 = tf.Variable(tf.zeros([1,100]))\n",
    "\n",
    "D_W1 = tf.Variable(tf.truncated_normal([100, 100]))\n",
    "D_b1 = tf.Variable(tf.zeros([1,100]))\n",
    "\n",
    "D_W2 = tf.Variable(tf.truncated_normal([100, 1]))\n",
    "D_b2 = tf.Variable(tf.zeros([1,1]))\n",
    "\n",
    "def adjacency_conv_layer(atom_matrix, W, b, L_in, L_out, layer_idx, is_training=True):\n",
    "    print(\"layer_idx: %d\" %(layer_idx))\n",
    "    h = tf.matmul(adj_matrix, atom_matrix, name=\"adj_conv1\")\n",
    "    h = tf.reshape(h, shape=(S*B, L_in))\n",
    "\n",
    "    h = tf.matmul(h, W) + b\n",
    "    h = tf.maximum(alpha*h, h)\n",
    "    \n",
    "    h = tf.reshape(h, (S, B, L_out))\n",
    "    #h = tf.contrib.layers.batch_norm(h, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "\n",
    "    return(h)\n",
    "\n",
    "def discriminator(angles, is_training=True):\n",
    "    D_h0 = tf.matmul(angles, D_W0) + D_b0\n",
    "    D_h0 = tf.maximum(alpha*D_h0, D_h0)\n",
    "\n",
    "    D_h1 = tf.matmul(D_h0, D_W1) + D_b1\n",
    "    D_h1 = tf.maximum(alpha*D_h1, D_h1)\n",
    "    \n",
    "    D_h2 = tf.matmul(D_h1, D_W2) + D_b2\n",
    "    #D_h2 = tf.maximum(alpha*D_h2, D_h2)\n",
    "    \n",
    "    return(D_h2)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    for layer_idx in range(n_layers):\n",
    "        if layer_idx == 0:\n",
    "          L_in = p\n",
    "          L_out = L_list[0]\n",
    "          atom_matrix = x\n",
    "        else:\n",
    "          L_in = L_list[layer_idx-1]\n",
    "          L_out = L_list[layer_idx]\n",
    "          atom_matrix = D_h_list[layer_idx-1]\n",
    "        \n",
    "        D_h_list[layer_idx] = adjacency_conv_layer(atom_matrix, D_W_list[layer_idx], D_b_list[layer_idx], L_in, L_out, layer_idx)\n",
    "\n",
    "    L_final = L_list[n_layers-1]\n",
    "    h_final = tf.reshape(D_h_list[layer_idx], (S, B, L_final))\n",
    "\n",
    "    #add dihedral regressor layers\n",
    "\n",
    "    d0 = []\n",
    "    for i in range(0, S):\n",
    "        mol_tuple = []\n",
    "        for j in range(0, 12):\n",
    "          entry = h_final[i]\n",
    "          indices = dihed_indices[i][:,:,j]\n",
    "          atom_list = tf.matmul(indices, entry, name=\"disc1\")\n",
    "          atom_list = tf.reshape(atom_list, (n_bonds, L_final))\n",
    "          mol_tuple.append(atom_list)\n",
    "        mol_tuple = tf.reshape(tf.stack(mol_tuple, axis=1), (n_bonds, L_final*12))\n",
    "        d0.append(mol_tuple)\n",
    "\n",
    "    d0 = tf.concat(d0, axis=0)\n",
    "    d0 = tf.concat([d0, angles], axis=1)\n",
    "    d0 = tf.matmul(tf.cast(non_zero_inds, tf.float32), d0)\n",
    "\n",
    "\n",
    "    d2 = tf.matmul(d0, D_W2, name=\"disc2\") + D_b2\n",
    "    d2 = tf.maximum(d2, alpha*d2)\n",
    "\n",
    "    #d2 = tf.contrib.layers.batch_norm(d2, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "    \n",
    "    d3 = tf.matmul(d2, D_W3, name=\"disc3\") + D_b3\n",
    "    d3 = tf.maximum(d3, alpha*d3)\n",
    "    \n",
    "    #d3 = tf.nn.tanh(d3)\n",
    "\n",
    "    #d3 = tf.contrib.layers.batch_norm(d3, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "    \n",
    "                                                                                                                                                               \n",
    "    d4 = tf.matmul(d3, D_W4, name=\"disc3\") + D_b4\n",
    "    \n",
    "    #\n",
    "    output = tf.matmul(d4, D_W5, name=\"disc4\") + D_b5\n",
    "    \n",
    "    #D_prob = tf.nn.sigmoid(D_logit)\n",
    "    return(output)\n",
    "    \"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def atan2(y, x, epsilon=1.0e-12):\n",
    "  # Add a small number to all zeros, to avoid division by zero:\n",
    "  x = tf.where(tf.equal(x, 0.0), x+epsilon, x)\n",
    "  y = tf.where(tf.equal(y, 0.0), y+epsilon, y)\n",
    "\n",
    "  angle = tf.where(tf.greater(x,0.0), tf.atan(y/x), tf.zeros_like(x))\n",
    "  angle = tf.where(tf.logical_and(tf.less(x,0.0),  tf.greater_equal(y,0.0)), tf.atan(y/x) + np.pi, angle)\n",
    "  angle = tf.where(tf.logical_and(tf.less(x,0.0),  tf.less(y,0.0)), tf.atan(y/x) - np.pi, angle)\n",
    "  angle = tf.where(tf.logical_and(tf.equal(x,0.0), tf.greater(y,0.0)), 0.5*np.pi * tf.ones_like(x), angle)\n",
    "  angle = tf.where(tf.logical_and(tf.equal(x,0.0), tf.less(y,0.0)), -0.5*np.pi * tf.ones_like(x), angle)\n",
    "  angle = tf.where(tf.logical_and(tf.equal(x,0.0), tf.equal(y,0.0)), tf.zeros_like(x), angle)\n",
    "  return angle\n",
    "\n",
    "#GENERATOR\n",
    "\n",
    "G_W_list = [None for i in range(n_layers+1)]\n",
    "G_b_list = [None for i in range(n_layers+1)]\n",
    "G_h_list = [None for i in range(n_layers+1)]\n",
    "\n",
    "\n",
    "L_final = p\n",
    "for layer_idx in range(n_layers):\n",
    "    if layer_idx == 0:\n",
    "      L_in = p\n",
    "      L_out = L_list[0]\n",
    "      atom_matrix = x\n",
    "    else:\n",
    "      L_in = L_list[layer_idx-1]\n",
    "      L_out = L_list[layer_idx]\n",
    "      atom_matrix = G_h_list[layer_idx-1]\n",
    "\n",
    "    G_W_list[layer_idx] = tf.Variable(tf.truncated_normal([L_in, L_out], seed=2017), name=\"G_W_list%d\" %layer_idx)\n",
    "    G_b_list[layer_idx] = tf.Variable(tf.zeros([1, L_out]))\n",
    "\n",
    "    \n",
    "G_W2 = tf.Variable(tf.truncated_normal([L_final*24, 6]))\n",
    "G_b2 = tf.Variable(tf.zeros([1, 6]))\n",
    "\n",
    "#G_W3 = tf.Variable(tf.truncated_normal([100, 3]))\n",
    "#G_b3 = tf.Variable(tf.zeros([1, 3]))\n",
    "\n",
    "#G_W4 = tf.Variable(tf.truncated_normal([100, 3]))\n",
    "#G_b4 = tf.Variable(tf.zeros([1, 3]))\n",
    "\n",
    "\n",
    "def adjacency_conv_layer(atom_matrix, W, b, L_in, L_out, layer_idx, is_training):\n",
    "    print(\"layer_idx: %d\" %(layer_idx))\n",
    "    h = tf.matmul(adj_matrix, atom_matrix, name=\"gen0_%d\" %layer_idx)\n",
    "    h = tf.reshape(h, shape=(S*B, L_in))\n",
    "\n",
    "    h = tf.matmul(h, W, name=\"gen1\") + b\n",
    "    #h = tf.maximum(alpha*h, h)\n",
    "    h = tf.nn.tanh(h)\n",
    "    \n",
    "    h = tf.reshape(h, (S, B, L_out))\n",
    "    #h = tf.contrib.layers.batch_norm(h, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "\n",
    "    return(h)\n",
    "\n",
    "def generator(z, is_training=True):\n",
    "    layer_idx = 0\n",
    "    atom_matrix = tf.add(x, z)\n",
    "    G_h_list[0] = atom_matrix\n",
    "    \n",
    "    L_final = p\n",
    "    \n",
    "    for layer_idx in range(n_layers):\n",
    "        if layer_idx == 0:\n",
    "          L_in = p\n",
    "          L_out = L_list[0]\n",
    "\n",
    "        else:\n",
    "          L_in = L_list[layer_idx-1]\n",
    "          L_out = L_list[layer_idx]\n",
    "          atom_matrix = G_h_list[layer_idx-1]\n",
    "        \n",
    "        G_h_list[layer_idx+1] = gen_adjacency_conv_layer(atom_matrix, G_W_list[layer_idx], G_b_list[layer_idx], L_in, L_out, layer_idx, is_training)\n",
    "    \n",
    "    g_h_final = tf.reshape(G_h_list[layer_idx], (S, B, L_final))\n",
    "\n",
    "    #add dihedral regressor layers\n",
    "\n",
    "    def mult(idx):\n",
    "        return(tf.matmul(dihed_indices[:,:,:,tf.cast(idx, tf.int32)], g_h_final))\n",
    "\n",
    "    elems = np.arange(24).astype(np.float32)\n",
    "    g_d0 = tf.concat(tf.map_fn(mult, elems), axis=2)\n",
    "    g_d0 = tf.reshape(g_d0, (S*n_bonds, L_final*24))\n",
    "    \n",
    "    \n",
    "    #g_d0 = []\n",
    "    #for l in range(0,12):\n",
    "    #    indices = dihed_indices[:,:,:,l]\n",
    "    #    g_d0.append(tf.matmul(indices, g_h_final))\n",
    "    \n",
    "    #for i in range(0, S):\n",
    "    #    mol_tuple = []\n",
    "    #    for j in range(0, 12):\n",
    "    #      entry = g_h_final[i]\n",
    "    #      indices = dihed_indices[i][:,:,j]\n",
    "    #      atom_list = tf.matmul(indices, entry, name='gen2')\n",
    "    #      atom_list = tf.reshape(atom_list, (n_bonds, L_final))\n",
    "    #      mol_tuple.append(atom_list)\n",
    "    #    mol_tuple = tf.reshape(tf.stack(mol_tuple, axis=1), (n_bonds, L_final*12))\n",
    "    #    g_d0.append(mol_tuple)\n",
    "\n",
    "    #g_d0 = tf.concat(g_d0, axis=0)\n",
    "    \n",
    "    g_d2 = tf.matmul(g_d0, G_W2, name='gen3') + G_b2\n",
    "    #g_d2 = tf.tanh(g_d2)\n",
    "    #g_d2 = tf.maximum(alpha*g_d2, g_d2)\n",
    "\n",
    "    \n",
    "    #g_d2 = tf.contrib.layers.batch_norm(g_d2, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "    \n",
    "    #g_d3 = tf.matmul(g_d2, G_W3) + G_b3\n",
    "    #g_d3 = tf.nn.tanh(g_d3)\n",
    "\n",
    "    #g_d3 = tf.contrib.layers.batch_norm(g_d3, \n",
    "    #                                    center=True, scale=False, \n",
    "    #                                    is_training=is_training)\n",
    "                                                                                                                                                               \n",
    "    #g_d4 = tf.matmul(g_d3, G_W4) + G_b4\n",
    "    \n",
    "    output_i = g_d2\n",
    "    output = tf.matmul(tf.cast(non_zero_inds, tf.float32), output_i)\n",
    "\n",
    "    #d3_cos = tf.cos(g_d4)\n",
    "    #d3_sin = tf.sin(g_d4)\n",
    "    #output = atan2(d3_sin, d3_cos)\n",
    "    \n",
    "    #output = tf.concat([output,\n",
    "    #                    tf.reshape(3.14159 - tf.abs(tf.abs(output[:,2]-output[:,1])-3.14159), (-1,1)),\n",
    "    #                    tf.reshape(3.14159 - tf.abs(tf.abs(output[:,2]-output[:,0])-3.14159), (-1,1)),\n",
    "    #                    tf.reshape(3.14159 - tf.abs(tf.abs(output[:,1]-output[:,0])-3.14159), (-1,1))],\n",
    "    #                   axis=1)\n",
    "    print(\"output\")\n",
    "    print(output)\n",
    "    \n",
    "    G_logit = tf.nn.sigmoid(output)\n",
    "\n",
    "    return(output, output_i)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theta_D = [D_W0, D_W1, D_W2, D_b0, D_b1, D_b2]\n",
    "#theta_D = [D_W_list[0], D_W2, D_W3, D_W4, D_W5, D_b_list[0], D_b2, D_b3, D_b4, D_b5]\n",
    "\n",
    "theta_G = [G_W2, G_b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gmm = GMM(2, n_iter=1., covariance_type=\"spherical\")\n",
    "\n",
    "gmm.means_ = np.array([[-10], [10]])\n",
    "gmm.covars_ = np.array([[0.0001], [0.0001]])\n",
    "\n",
    "def construct_feed_dict(X, start=None,\n",
    "                      stop=None, y=None,\n",
    "                      keep_prob=1.0, train=False):\n",
    "    if start is None:\n",
    "      start = 0\n",
    "      stop = len(X)\n",
    "\n",
    "    atom_adj_batch = [X[idx][2][0].toarray().astype(np.float32) for idx in range(start, stop)]\n",
    "    A_batch = [X[idx][2][1].toarray() for idx in range(start, stop)]\n",
    "    D_batch = [X[idx][1][0] for idx in range(start, stop)]\n",
    "    y_batch = [X[idx][1][1].toarray() for idx in range(start, stop)]\n",
    "    bond_adj_batch = [X[idx][1][2].toarray() for idx in range(start, stop)]\n",
    "\n",
    "    \n",
    "    y_batch = np.concatenate(y_batch, axis=0)\n",
    "    non_zero_batch = np.where(y_batch[:,0] != 0.)[0]\n",
    "    y_batch = y_batch[non_zero_batch]\n",
    "\n",
    "    y_batch = y_batch + np.random.normal(0, 0.03, size=y_batch.shape)\n",
    "\n",
    "    onehotter = OneHotEncoder(n_values = S*X[0][1][1].shape[0])\n",
    "    non_zero_onehot = onehotter.fit_transform(non_zero_batch).toarray().reshape((len(non_zero_batch),S*X[0][1][1].shape[0]))\n",
    "    \n",
    "    z_batch = np.random.uniform(-0.1, 0.1, size=(S, adj[0].shape[0],75))\n",
    "    \n",
    "    feed_dict = {x: A_batch,\n",
    "                 adj_matrix: atom_adj_batch,\n",
    "                 phase: train,\n",
    "                 label_placeholder: y_batch,\n",
    "                 non_zero_inds: non_zero_onehot,\n",
    "                 dihed_indices: D_batch,\n",
    "                 z:z_batch,\n",
    "                 bond_matrix: bond_adj_batch\n",
    "                }\n",
    "    return(feed_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output\n",
      "Tensor(\"MatMul:0\", shape=(?, 3), dtype=float32)\n",
      "output\n",
      "Tensor(\"MatMul_1:0\", shape=(?, 3), dtype=float32)\n",
      "Training epoch 0\n",
      "Iter: 0\n",
      "D loss: -0.0001181\n",
      "G_loss: -0.0001256\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe3bce610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe00acfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd87691d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.30132711  0.18542333  1.07594001]\n",
      "Training epoch 100\n",
      "Iter: 100\n",
      "D loss: 0.01982\n",
      "G_loss: 0.01766\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niiDoAFAEQQeAIgg6ABRB0AGgiP8B/f/j3zWhmJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd87aa990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb803cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb32c5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.31420338  0.0982305   0.91644073]\n",
      "Training epoch 200\n",
      "Iter: 200\n",
      "D loss: 0.01734\n",
      "G_loss: 0.01518\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb8047850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf/CP1DMgACM8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb8033590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9df9af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.03601849 -0.09766396  0.80952001]\n",
      "Training epoch 300\n",
      "Iter: 300\n",
      "D loss: 0.01421\n",
      "G_loss: 0.01188\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dfd2450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dd8aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZKaMOiS1IRBl6QmDLokNfF/QndmESuo/hUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dcd1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.71485353 -0.51566565  0.5694415 ]\n",
      "Training epoch 400\n",
      "Iter: 400\n",
      "D loss: 0.01283\n",
      "G_loss: 0.009909\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9df46ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nasKgS1ITBl2Smvgv7gRY+TrVdPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb168fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9ddab4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.47331899 -0.68668354  0.41492677]\n",
      "Training epoch 500\n",
      "Iter: 500\n",
      "D loss: 0.01002\n",
      "G_loss: 0.006708\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADdJJREFUeJzt3X+o3fV9x/Hna0ZptbK25PSXenfdsIJz1pbbYifbWrUl\nq9JsYwNlFl2FC2N1dggSK5uMMchmaVfooFxqZmEhpVhdi7arWWcXBjZtTKNGY3/QOY3TJU62thTq\nMt/74x4hXm5yzznf770n9+PzASH3+z3fez4vLsmLz/2c749UFZKk9e/nph1AktQPC12SGmGhS1Ij\nLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUiA1rOdjGjRtrdnZ2LYeUpHXvwQcffK6qBisdt6aF\nPjs7y549e9ZySEla95L8+yjHueQiSY2w0CWpERa6JDXCQpekRljoktSIFQs9ybYkh5LsX7L/+iSP\nJ3k0yV+vXkRJ0ihGmaHfAWw6ekeS9wKbgbdV1S8DH+8/miRpHCsWelXtAp5fsvsPga1V9bPhMYdW\nIZskaQyTrqG/Ffi1JLuT/EuSd/YZSpI0vkmvFN0AvB64CHgn8IUkv1jLPHE6yTwwDzAzMzNpTkk9\nm91y79TGfmLr5VMbu2WTztAPAnfVom8BLwIblzuwqhaqaq6q5gaDFW9FIEma0KSF/g/AewGSvBU4\nBXiur1CSpPGtuOSSZAfwHmBjkoPArcA2YNvwVMYXgGuWW26RJK2dFQu9qq46xktX95xFktSBV4pK\nUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1\nwkKXpEZY6JLUCAtdkhphoUtSI1Ys9CTbkhwaPm5u6Ws3Jqkkyz4gWpK0dkaZod8BbFq6M8lZwPuB\nJ3vOJEmawIqFXlW7gOeXeemTwE2AD4eWpBPARGvoSTYDT1fVQz3nkSRNaMO435DkVOBjLC63jHL8\nPDAPMDMzM+5wkqQRTTJD/yXgbOChJE8AZwJ7k7xpuYOraqGq5qpqbjAYTJ5UknRcY8/Qq+oR4A0v\nbQ9Lfa6qnusxlyRpTKOctrgDeAA4N8nBJNetfixJ0rhWnKFX1VUrvD7bWxpJ0sS8UlSSGmGhS1Ij\nLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJC\nl6RGWOiS1AgLXZIaMcoj6LYlOZRk/1H7bkvyeJKHk9yd5LWrG1OStJJRZuh3AJuW7NsJnF9VFwDf\nA27uOZckaUwrFnpV7QKeX7Lvvqo6Mtz8JnDmKmSTJI2hjzX0DwNfPdaLSeaT7Emy5/Dhwz0MJ0la\nTqdCT3ILcATYfqxjqmqhquaqam4wGHQZTpJ0HBsm/cYk1wJXAJdWVfWWSJI0kYkKPckm4CbgN6rq\np/1GkiRNYpTTFncADwDnJjmY5Drg08DpwM4k+5J8ZpVzSpJWsOIMvaquWmb37auQRZLUgVeKSlIj\nLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJC\nl6RGWOiS1AgLXZIaYaFLUiNGeQTdtiSHkuw/at/rk+xM8v3h369b3ZiSpJWMMkO/A9i0ZN8W4OtV\ndQ7w9eG2JGmKViz0qtoFPL9k92bgc8OvPwf8Vs+5JEljWvEh0cfwxqp6Zvj1s8Abj3VgknlgHmBm\nZmbC4fRKMbvl3qmM+8TWy6cyrtSnzh+KVlUBdZzXF6pqrqrmBoNB1+EkSccwaaH/Z5I3Awz/PtRf\nJEnSJCYt9C8D1wy/vgb4Uj9xJEmTGuW0xR3AA8C5SQ4muQ7YCrwvyfeBy4bbkqQpWvFD0aq66hgv\nXdpzFklSB14pKkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrok\nNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY3oVOhJ/iTJo0n2J9mR5FV9BZMkjWfiQk9yBvDH\nwFxVnQ+cBFzZVzBJ0ni6LrlsAF6dZANwKvAf3SNJkiYxcaFX1dPAx4EngWeA/6mq+5Yel2Q+yZ4k\new4fPjx5UknScXVZcnkdsBk4G3gLcFqSq5ceV1ULVTVXVXODwWDypJKk4+qy5HIZ8G9Vdbiq/he4\nC/jVfmJJksbVpdCfBC5KcmqSAJcCB/qJJUkaV5c19N3AncBe4JHhey30lEuSNKYNXb65qm4Fbu0p\niySpA68UlaRGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpEZ3OQ19Ls1vundrYT2y9fGpjSy2a\n1v/n1v8vO0OXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNaJToSd5bZI7kzye5ECS\nd/cVTJI0nq5Xin4K+Meq+t0kpwCn9pBJkjSBiQs9yc8Dvw5cC1BVLwAv9BNLkjSuLksuZwOHgb9L\n8p0kn01yWk+5JElj6rLksgF4B3B9Ve1O8ilgC/CnRx+UZB6YB5iZmekw3PR4IyFJ60GXGfpB4GBV\n7R5u38liwb9MVS1U1VxVzQ0Ggw7DSZKOZ+JCr6pngaeSnDvcdSnwWC+pJElj63qWy/XA9uEZLj8E\n/qB7JEnSJDoVelXtA+Z6yiJJ6sArRSWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmN\n6HqlqCStG9O60R6szc32nKFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGtG50JOc\nlOQ7Se7pI5AkaTJ9zNBvAA708D6SpA46FXqSM4HLgc/2E0eSNKmuM/S/AW4CXuwhiySpg4lvzpXk\nCuBQVT2Y5D3HOW4emAeYmZmZdDitoWnewOiVyJ+3+tJlhn4x8MEkTwCfBy5J8vdLD6qqhaqaq6q5\nwWDQYThJ0vFMXOhVdXNVnVlVs8CVwD9X1dW9JZMkjcXz0CWpEb084KKqvgF8o4/3kiRNxhm6JDXC\nQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqRG9XFgkrXfeIEstcIYuSY2w0CWpERa6JDXC\nQpekRljoktQIC12SGmGhS1IjLHRJasTEhZ7krCT3J3ksyaNJbugzmCRpPF2uFD0C3FhVe5OcDjyY\nZGdVPdZTNknSGCaeoVfVM1W1d/j1j4EDwBl9BZMkjaeXNfQks8Dbgd19vJ8kaXydb86V5DXAF4GP\nVtWPlnl9HpgHmJmZ6TrcK4o3jJI0jk4z9CQns1jm26vqruWOqaqFqpqrqrnBYNBlOEnScXQ5yyXA\n7cCBqvpEf5EkSZPoMkO/GPgQcEmSfcM/H+gplyRpTBOvoVfVvwLpMYskqQOvFJWkRljoktQIC12S\nGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakR\nFrokNcJCl6RGdH1I9KYk303ygyRb+golSRpfl4dEnwT8LfCbwHnAVUnO6yuYJGk8XWbo7wJ+UFU/\nrKoXgM8Dm/uJJUkaV5dCPwN46qjtg8N9kqQp2LDaAySZB+aHmz9J8l/Ac6s97irYiLnXkrnXlrlX\nWf7qZZvj5v6FUQ7qUuhPA2cdtX3mcN/LVNUCsPDSdpI9VTXXYdypMPfaMvfaMvfaWq3cXZZcvg2c\nk+TsJKcAVwJf7ieWJGlcE8/Qq+pIko8AXwNOArZV1aO9JZMkjaXTGnpVfQX4ypjftrDyISckc68t\nc68tc6+tVcmdqlqN95UkrTEv/ZekRky10JPcmKSSbJxmjlEl+YskDyfZl+S+JG+ZdqZRJLktyePD\n7Hcnee20M40iye8leTTJi0lO+DMZ1uOtMJJsS3Ioyf5pZxlHkrOS3J/kseG/kRumnWkUSV6V5FtJ\nHhrm/vM+339qhZ7kLOD9wJPTyjCB26rqgqq6ELgH+LNpBxrRTuD8qroA+B5w85TzjGo/8DvArmkH\nWck6vhXGHcCmaYeYwBHgxqo6D7gI+KN18vP+GXBJVb0NuBDYlOSivt58mjP0TwI3AetmEb+qfnTU\n5mmsk+xVdV9VHRlufpPFawZOeFV1oKq+O+0cI1qXt8Koql3A89POMa6qeqaq9g6//jFwgHVwpXot\n+slw8+Thn956ZCqFnmQz8HRVPTSN8btI8pdJngJ+n/UzQz/ah4GvTjtEg7wVxpQkmQXeDuyebpLR\nJDkpyT7gELCzqnrLvWqX/if5J+BNy7x0C/AxFpdbTjjHy11VX6qqW4BbktwMfAS4dU0DHsNKuYfH\n3MLir6rb1zLb8YySWzqWJK8Bvgh8dMlv0Cesqvo/4MLhZ1l3Jzm/qnr5DGPVCr2qLltuf5JfAc4G\nHkoCi7/+703yrqp6drXyjOpYuZexncVz8E+IQl8pd5JrgSuAS+sEOld1jJ/3iW6kW2GoP0lOZrHM\nt1fVXdPOM66q+u8k97P4GUYvhb7mSy5V9UhVvaGqZqtqlsVfTd9xIpT5SpKcc9TmZuDxaWUZR5JN\nLH5e8cGq+um08zTKW2GsoSzOBm8HDlTVJ6adZ1RJBi+dZZbk1cD76LFHPA99PFuT7E/yMItLRuvi\nVCng08DpwM7hKZefmXagUST57SQHgXcD9yb52rQzHcvwQ+eXboVxAPjCergVRpIdwAPAuUkOJrlu\n2plGdDHwIeCS4b/pfUk+MO1QI3gzcP+wQ77N4hr6PX29uVeKSlIjnKFLUiMsdElqhIUuSY2w0CWp\nERa6JDXCQpekRljoktQIC12SGvH/H2VT6evAA04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9db18790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9ddab7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndIAhBB1gCEEHGELQAYb4f5IMQEin8VMFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9da90150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.11978109 -0.95677364  0.36146411]\n",
      "Training epoch 600\n",
      "Iter: 600\n",
      "D loss: 0.008184\n",
      "G_loss: 0.005165\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dc28e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd870f410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9deb2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.07549416 -1.35025263  0.37304261]\n",
      "Training epoch 700\n",
      "Iter: 700\n",
      "D loss: 0.006084\n",
      "G_loss: 0.00324\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwqBLUhP/By5Vn68NrLL/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb169f790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dba3990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntTI8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3224b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.54156828 -1.60223603  0.50121295]\n",
      "Training epoch 800\n",
      "Iter: 800\n",
      "D loss: 0.00491\n",
      "G_loss: 0.002202\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3382490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKRzqhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd877ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80fe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.71857083 -1.5971539   0.43989319]\n",
      "Training epoch 900\n",
      "Iter: 900\n",
      "D loss: 0.004552\n",
      "G_loss: 0.003993\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb327e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb166fad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXthgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3272f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.03681803 -1.21801603  0.26552609]\n",
      "Training epoch 1000\n",
      "Iter: 1000\n",
      "D loss: 0.004949\n",
      "G_loss: 0.003944\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb802a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQdABoIh/A2NAg77FFB93AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb32d6d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe3502f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.20803821 -1.14556003  0.11391169]\n",
      "Training epoch 1100\n",
      "Iter: 1100\n",
      "D loss: 0.00333\n",
      "G_loss: 0.002158\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe00c1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9d9c3050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3231550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.21659768 -1.25505292  0.04524595]\n",
      "Training epoch 1200\n",
      "Iter: 1200\n",
      "D loss: 0.003083\n",
      "G_loss: 0.00151\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe34ce650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb16dced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dac91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.28216577 -1.07958174 -0.02440737]\n",
      "Training epoch 1300\n",
      "Iter: 1300\n",
      "D loss: 0.003541\n",
      "G_loss: 0.002314\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dc8a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80d13d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dd692d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.44679928 -0.90137827  0.19066785]\n",
      "Training epoch 1400\n",
      "Iter: 1400\n",
      "D loss: 0.002023\n",
      "G_loss: 0.0003641\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dd9e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dded950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dc34f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.54654789 -0.99883056  0.38628727]\n",
      "Training epoch 1500\n",
      "Iter: 1500\n",
      "D loss: 0.00106\n",
      "G_loss: -0.0009896\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb164aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80aaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb167bbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.79292989 -0.97180986  0.51098967]\n",
      "Training epoch 1600\n",
      "Iter: 1600\n",
      "D loss: 0.002953\n",
      "G_loss: -0.002461\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb17f3090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb17954d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd87e7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.82164252 -0.94051939  0.50860089]\n",
      "Training epoch 1700\n",
      "Iter: 1700\n",
      "D loss: 0.008033\n",
      "G_loss: 0.004372\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb17b8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dea71d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqQmDLklNGHRJasKgS1ITBl2SmvgfcLHeSmXYw28AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dfde190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.58536446 -1.04804301  0.51924175]\n",
      "Training epoch 1800\n",
      "Iter: 1800\n",
      "D loss: 0.006754\n",
      "G_loss: 0.002748\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb328f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb332efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb804ad90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.26493788 -1.24722457  0.54286826]\n",
      "Training epoch 1900\n",
      "Iter: 1900\n",
      "D loss: 0.005539\n",
      "G_loss: 0.0007035\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxMO+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9d9f96d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dfea250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUhH/B1XlFHeh8Xs5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe00c9350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.01008201 -1.35460746  0.66635871]\n",
      "Training epoch 2000\n",
      "Iter: 2000\n",
      "D loss: 0.004782\n",
      "G_loss: -0.0006933\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9da77050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe005d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd871cbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.78025752 -1.23265254  0.58919692]\n",
      "Training epoch 2100\n",
      "Iter: 2100\n",
      "D loss: 0.004036\n",
      "G_loss: -0.000306\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe005b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9db50e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3363150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.49555561 -1.10714269  0.51629198]\n",
      "Training epoch 2200\n",
      "Iter: 2200\n",
      "D loss: 0.002441\n",
      "G_loss: -0.002929\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9db72cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADQZJREFUeJzt3X+MZfVdxvHnkZUoSATcW0pZxqFNs5E0Jm1uDFqjpovN\nypJuTTSBpAqWZNI/1GqakKn80X8Xa/wVjc2kRTASaoIlJS5aFiwhJkDcpQvssrTQOtJdF3YJSf3R\nRNz08Y85Ncs4M/fec87MnfvZ9yu5mXPOPXO/n+/9zj77nXPPOeMkAgDMvh+YdgEAgH4Q6ABQBIEO\nAEUQ6ABQBIEOAEUQ6ABQBIEOAEUQ6ABQBIEOAEXs2MrGdu7cmfn5+a1sEgBm3pEjR95IMhi135YG\n+vz8vA4fPryVTQLAzLP9r+PsxyEXACiCQAeAIgh0ACiCQAeAIgh0AChiZKDbvsf2GdvH1njuU7Zj\ne+fmlAcAGNc4M/R7Je1dvdH2tZI+LOnVnmsCALQwMtCTPCnpzTWe+iNJd0rib9gBwDbQ6hi67f2S\nTiV5rud6AAAtTXylqO1LJP2eVg63jLP/gqQFSZqbm5u0OWBLzC8enFrbywf2Ta1t1NJmhv4eSddJ\nes72sqRdkp61/c61dk6ylGSYZDgYjLwVAQCgpYln6ElekPSO7683oT5M8kaPdQEAJjTOaYsPSHpK\n0m7bJ23fsfllAQAmNXKGnuTWEc/P91YNAKA1rhQFgCIIdAAogkAHgCIIdAAogkAHgCIIdAAogkAH\ngCIIdAAogkAHgCIIdAAogkAHgCIIdAAogkAHgCIIdAAogkAHgCIIdAAogkAHgCIIdAAogkAHgCII\ndAAogkAHgCJGBrrte2yfsX3svG2ftf2S7edtP2T78s0tEwAwyjgz9Hsl7V217ZCk9yX5SUnfkPTp\nnusCAExoZKAneVLSm6u2PZrkXLP6tKRdm1AbAGACO3p4jY9L+pv1nrS9IGlBkubm5npoDqhlfvHg\nVNpdPrBvKu1i83T6UNT2XZLOSbp/vX2SLCUZJhkOBoMuzQEANtB6hm77dkk3S9qTJL1VBABopVWg\n294r6U5JP5/ku/2WBABoY5zTFh+Q9JSk3bZP2r5D0p9JukzSIdtHbX9uk+sEAIwwcoae5NY1Nn9h\nE2oBAHTAlaIAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASB\nDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFjAx02/fYPmP72HnbrrR9yPbL\nzdcrNrdMAMAo48zQ75W0d9W2RUmPJ3mvpMebdQDAFI0M9CRPSnpz1eb9ku5rlu+T9NGe6wIATKjt\nMfSrkpxull+TdFVP9QAAWtrR9QWSxHbWe972gqQFSZqbm+vaHLbA/OLBqbW9fGDf1NoGZl3bGfrr\ntq+WpObrmfV2TLKUZJhkOBgMWjYHABilbaA/LOm2Zvk2SV/upxwAQFvjnLb4gKSnJO22fdL2HZIO\nSPpF2y9LurFZBwBM0chj6EluXeepPT3XAgDogCtFAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQ\nAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAI\nAh0AiiDQAaCIToFu+3dtH7d9zPYDtn+or8IAAJNpHei2r5H025KGSd4n6SJJt/RVGABgMl0PueyQ\n9MO2d0i6RNK/dS8JANBG60BPckrSH0h6VdJpSd9J8ujq/Wwv2D5s+/DZs2fbVwoA2FCXQy5XSNov\n6TpJ75J0qe2Prd4vyVKSYZLhYDBoXykAYENdDrncKOlfkpxN8j+SviTpZ/opCwAwqS6B/qqkG2xf\nYtuS9kg60U9ZAIBJdTmG/oykByU9K+mF5rWWeqoLADChHV2+OclnJH2mp1oAAB1wpSgAFEGgA0AR\nBDoAFEGgA0ARBDoAFEGgA0ARBDoAFEGgA0ARBDoAFNHpSlFsrvnFg9MuAYVN8+dr+cC+qbVdGTN0\nACiCQAeAIgh0ACiCQAeAIgh0ACiCQAeAIgh0ACiCQAeAIgh0ACiCQAeAIjoFuu3LbT9o+yXbJ2z/\ndF+FAQAm0/VeLn8i6R+S/IrtiyVd0kNNAIAWWge67R+V9HOSbpekJG9JequfsgAAk+pyyOU6SWcl\n/aXtr9n+vO1Le6oLADChLoG+Q9IHJP1FkvdL+i9Ji6t3sr1g+7Dtw2fPnu3QHABgI10C/aSkk0me\nadYf1ErAv02SpSTDJMPBYNChOQDARloHepLXJH3b9u5m0x5JL/ZSFQBgYl3PcvktSfc3Z7h8S9Jv\ndC8JANBGp0BPclTSsKdaAAAdcKUoABRBoANAEQQ6ABRBoANAEQQ6ABRBoANAEQQ6ABRBoANAEQQ6\nABRBoANAEV3v5XJBmF88OO0SAGAkZugAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBF\nEOgAUASBDgBFdA502xfZ/prtv+ujIABAO33M0D8p6UQPrwMA6KBToNveJWmfpM/3Uw4AoK2uM/Q/\nlnSnpO/1UAsAoIPWt8+1fbOkM0mO2P6FDfZbkLQgSXNzc22bA1DItG5JvXxg31Ta3SpdZugflPQR\n28uSvijpQ7b/evVOSZaSDJMMB4NBh+YAABtpHehJPp1kV5J5SbdI+sckH+utMgDARDgPHQCK6OVP\n0CV5QtITfbwWAKAdZugAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBF\nEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUASBDgBFEOgAUETrQLd9re2v\n2n7R9nHbn+yzMADAZHZ0+N5zkj6V5Fnbl0k6YvtQkhd7qg0AMIHWM/Qkp5M82yz/h6QTkq7pqzAA\nwGS6zND/j+15Se+X9Mwazy1IWpCkubm5PppDYfOLB6ddAgqb5s/X8oF9m95G5w9Fbf+IpL+V9DtJ\n/n3180mWkgyTDAeDQdfmAADr6BTotn9QK2F+f5Iv9VMSAKCNLme5WNIXJJ1I8of9lQQAaKPLDP2D\nkn5N0odsH20eN/VUFwBgQq0/FE3yT5LcYy0AgA64UhQAiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAI\nAh0AiiDQAaAIAh0Aiujl9rlbgduqAsDGmKEDQBEEOgAUQaADQBEEOgAUQaADQBEEOgAUQaADQBEE\nOgAUQaADQBEEOgAU0SnQbe+1/XXbr9he7KsoAMDkWge67Ysk/bmkX5J0vaRbbV/fV2EAgMl0maH/\nlKRXknwryVuSvihpfz9lAQAm1SXQr5H07fPWTzbbAABTsOm3z7W9IGmhWf1P21/f7DZb2inpjWkX\n0ZMqfaEf20+Vvmx5P3x3p2//8XF26hLopyRde976rmbb2yRZkrTUoZ0tYftwkuG06+hDlb7Qj+2n\nSl+q9GO1Lodc/lnSe21fZ/tiSbdIerifsgAAk2o9Q09yzvZvSvqKpIsk3ZPkeG+VAQAm0ukYepJH\nJD3SUy3Ttu0PC02gSl/ox/ZTpS9V+vE2TjLtGgAAPeDSfwAo4oINdNuftf2S7edtP2T78nX2W7b9\ngu2jtg9vdZ2jTNCPbX+bBtu/avu47e/ZXvcMhBkYk3H7MQtjcqXtQ7Zfbr5esc5+23JMRr3HXvGn\nzfPP2/7ANOrsTZIL8iHpw5J2NMt3S7p7nf2WJe2cdr1d+qGVD62/Kendki6W9Jyk66dd+xp1/oSk\n3ZKekDTcYL/tPiYj+zFDY/L7khab5cVZ+ncyznss6SZJfy/Jkm6Q9My06+7yuGBn6EkeTXKuWX1a\nK+fRz5wx+zETt2lIciLJdr3wbGxj9mMmxkQrNd3XLN8n6aNTrGVS47zH+yX9VVY8Lely21dvdaF9\nuWADfZWPa+V/6bVE0mO2jzRXvW5n6/Wj2m0aZmlM1jMrY3JVktPN8muSrlpnv+04JuO8x7MyDmPZ\n9Ev/p8n2Y5LeucZTdyX5crPPXZLOSbp/nZf52SSnbL9D0iHbLyV5cnMqXltP/dgWxunLGGZiTGbF\nRn05fyVJbK93WtzUxwTFAz3JjRs9b/t2STdL2pPmgNoar3Gq+XrG9kNa+TVuS39Qe+jHWLdp2Aqj\n+jLma2z7MRnDTIyJ7ddtX53kdHMo4sw6rzH1MVnDOO/xthmHPlywh1xs75V0p6SPJPnuOvtcavuy\n7y9r5QPIY1tX5Wjj9EOFbtMwC2MyplkZk4cl3dYs3ybp//32sY3HZJz3+GFJv96c7XKDpO+cd4hp\n9kz7U9lpPSS9opVjZ0ebx+ea7e+S9Eiz/G6tfDL+nKTjWvl1euq1T9qPZv0mSd/Qyqf+264fTY2/\nrJVjmP8t6XVJX5nRMRnZjxkakx+T9LiklyU9JunKWRqTtd5jSZ+Q9Ilm2Vr5Qz3flPSCNji7ahYe\nXCkKAEVcsIdcAKAaAh0AiiDQAaAIAh0AiiDQAaAIAh0AiiDQAaAIAh0AivhfWiifkQdnbGgAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9ddf8d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rmc2boqP8vJ4FfGo49gRgH/CcOWd6L/Cnw8ensFKc22fw81pk/TcfZ3qsj5Fr5sf7KLlW\njevleN+SK/RN7AI+mORy4JvAbwEkeRpwVVVdBLwIeA1wd5I7h3/urbVyZWtvap3bHyT5/eH+97Hy\n7vVFrBxQP2ZlZTU1I2b6E+BngL8Zrg6O1JRvYDRirpkbJVdV7U/ySeBLwCOsHGcbfhRt2pmAPwOu\nTnI3KwX65qqa6l0Fk1wHnA9sT3IQuBJ43FGZZnqsj5Fr5sf7iLn6n3P4r4Mk6Rh3LH7KRZK0Bgtd\nkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RG/B+FYx16/ijolQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dcb6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.20300867 -1.16329014  0.53777868]\n",
      "Training epoch 2300\n",
      "Iter: 2300\n",
      "D loss: 0.001937\n",
      "G_loss: -0.004058\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb17c0f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dcb6490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dc54590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02757948 -1.07353973  0.5281412 ]\n",
      "Training epoch 2400\n",
      "Iter: 2400\n",
      "D loss: 0.003768\n",
      "G_loss: -0.0003566\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80d9c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb3244350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w0orpLwB7Zpjz8fz0OpHnAHcN23ZHbc9hvcewfA73xFlsz2GMBdZ/oXDm++aEOWe+b06Y\ns+m+uWX/iJ32Bfw88BngTuDTwCnD/CcAVw/Tz2f5dMxXgRuHr/OGZX8L3Dws+zgrSr9RvvNYfjfP\nN4G3DfNeC7x2mA7LH9LyzSHH4sM9dwu340Y5LwG+t2L7HRzmP2nYKW8Cbt0BOV835LiJ5Rfhznm4\n584q5/D41aw6qNjO7cnyXxD3AD9i+TzwRTt039wo507ZNzfK2XTf9IpXSepYV+fkJUkPZclLUscs\neUnqmCUvSR2z5CWpY5a8JHXMkpekjlnyktSx/weBUukHm2ELDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe005e850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.1604237  -1.03399849  0.51664531]\n",
      "Training epoch 2500\n",
      "Iter: 2500\n",
      "D loss: 0.003976\n",
      "G_loss: -0.00029\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fd87299d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80b4350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe005db90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.06920671 -0.89499158  0.44140276]\n",
      "Training epoch 2600\n",
      "Iter: 2600\n",
      "D loss: 0.002914\n",
      "G_loss: -0.001571\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb337cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dfc2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dacc290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.34926867 -1.07029819  0.56313717]\n",
      "Training epoch 2700\n",
      "Iter: 2700\n",
      "D loss: 0.002428\n",
      "G_loss: -0.001861\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9db11cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb16aa0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9daf2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.57017493 -1.18510818  0.30485779]\n",
      "Training epoch 2800\n",
      "Iter: 2800\n",
      "D loss: 0.002618\n",
      "G_loss: -0.00168\n",
      "()\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dfa4050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9d9c4350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9de4c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.58154434 -0.96689051  0.44317442]\n",
      "Training epoch 2900\n",
      "Iter: 2900\n",
      "D loss: 0.001627\n",
      "G_loss: -0.002505\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADKxJREFUeJzt3W+MZYVdxvHnkaFRkAjN3kLLch1iCEklpm0mTW2NYsFm\nyzZuXzQGkiqtJBNN1GqakKlNJPHVGo3/omkzoetixK0GixAXK1vaBk0ocXe7LQtLhdQt3RW6iySt\n2Ca48fHFHMx2mJl77zln5u79zfeTTOb+OXPPbw+HL2fP3HtwEgEAZt8PTHsAAEA/CDoAFEHQAaAI\ngg4ARRB0ACiCoANAEQQdAIog6ABQBEEHgCLmtnJlO3bsyPz8/FauEgBm3pEjR15MMhi13JYGfX5+\nXocPH97KVQLAzLP9jXGW45QLABRB0AGgCIIOAEUQdAAogqADQBEjg257n+0zto+v8dxHbcf2js0Z\nDwAwrnGO0PdL2rX6QdvXSHqPpOd6ngkA0MLIoCd5VNJLazz1R5LulMT/ww4ALgCtzqHb3iPpdJKv\n9DwPAKCliT8pavsSSb+tldMt4yy/KGlRkobD4aSrA7bE/NLBqa375N7dU1s3amlzhP5jkq6V9BXb\nJyXtlHTU9lVrLZxkOclCkoXBYOSlCAAALU18hJ7kCUlvePV+E/WFJC/2OBcAYELjvG3xgKTHJF1v\n+5TtOzZ/LADApEYeoSe5bcTz871NAwBojU+KAkARBB0AiiDoAFAEQQeAIgg6ABRB0AGgCIIOAEUQ\ndAAogqADQBEEHQCKIOgAUARBB4AiCDoAFEHQAaAIgg4ARRB0ACiCoANAEQQdAIog6ABQBEEHgCII\nOgAUMTLotvfZPmP7+HmP/b7tp21/1fb9ti/f3DEBAKOMc4S+X9KuVY8dknRDkp+Q9G+SPtbzXACA\nCY0MepJHJb206rGHk5xr7n5J0s5NmA0AMIG5Hl7jlyX9zXpP2l6UtChJw+Gwh9UB6MP80sGprfvk\n3t1TW3dlnX4pavvjks5June9ZZIsJ1lIsjAYDLqsDgCwgdZH6LY/JOl9km5Kkt4mAgC00irotndJ\nulPSzyT5br8jAQDaGOdtiwckPSbpetunbN8h6c8kXSbpkO1jtj+5yXMCAEYYeYSe5LY1Hv7UJswC\nAOiAT4oCQBEEHQCKIOgAUARBB4AiCDoAFEHQAaAIgg4ARRB0ACiCoANAEQQdAIog6ABQBEEHgCII\nOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0AiiDoAFAEQQeAIkYG3fY+22dsHz/vsdfbPmT7meb7FZs7\nJgBglHGO0PdL2rXqsSVJjyS5TtIjzX0AwBSNDHqSRyW9tOrhPZLuaW7fI+n9Pc8FAJhQ23PoVyZ5\nvrn9gqQre5oHANDSXNcXSBLbWe9524uSFiVpOBx2XR2Km186OO0RgJnV9gj9W7bfKEnN9zPrLZhk\nOclCkoXBYNBydQCAUdoG/UFJtze3b5f0QD/jAADaGudtiwckPSbpetunbN8haa+kn7P9jKSbm/sA\ngCkaeQ49yW3rPHVTz7MAADrgk6IAUARBB4AiCDoAFEHQAaAIgg4ARRB0ACiCoANAEQQdAIog6ABQ\nBEEHgCIIOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0AiiDoAFAEQQeAIgg6ABRB0AGgCIIOAEV0Crrt\n37L9pO3jtg/Y/sG+BgMATKZ10G1fLek3JC0kuUHSRZJu7WswAMBkup5ymZP0Q7bnJF0i6T+6jwQA\naGOu7Q8mOW37DyQ9J+l7kh5O8vDq5WwvSlqUpOFw2HZ12ELzSwenPQKAFrqccrlC0h5J10p6k6RL\nbX9w9XJJlpMsJFkYDAbtJwUAbKjLKZebJf17krNJ/kfSZyS9s5+xAACT6hL05yS9w/Ylti3pJkkn\n+hkLADCp1kFP8rik+yQdlfRE81rLPc0FAJhQ61+KSlKSuyTd1dMsAIAO+KQoABRB0AGgCIIOAEUQ\ndAAogqADQBEEHQCKIOgAUARBB4AiCDoAFNHpk6IAuuNyxegLR+gAUARBB4AiCDoAFEHQAaAIgg4A\nRRB0ACiCoANAEQQdAIog6ABQBEEHgCI6Bd325bbvs/207RO2f7KvwQAAk+l6LZc/kfTZJB+w/TpJ\nl/QwEwCghdZBt/0jkn5a0ockKckrkl7pZywAwKS6nHK5VtJZSX9h+8u277Z9aU9zAQAm1CXoc5Le\nJukTSd4q6b8lLa1eyPai7cO2D589e7bD6gAAG+kS9FOSTiV5vLl/n1YC/32SLCdZSLIwGAw6rA4A\nsJHWQU/ygqRv2r6+eegmSU/1MhUAYGJd3+Xy65Lubd7h8nVJH+4+EgCgjU5BT3JM0kJPswAAOuCT\nogBQBEEHgCIIOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0AiiDoAFAEQQeAIgg6ABRB0AGgCIIOAEUQ\ndAAogqADQBEEHQCKIOgAUARBB4AiCDoAFEHQAaAIgg4ARXQOuu2LbH/Z9j/0MRAAoJ0+jtA/IulE\nD68DAOigU9Bt75S0W9Ld/YwDAGir6xH6H0u6U9L/9jALAKCDubY/aPt9ks4kOWL7xg2WW5S0KEnD\n4bDt6ral+aWD0x4B2BTT2rdP7t09lfVulS5H6O+S9PO2T0r6tKR32/6r1QslWU6ykGRhMBh0WB0A\nYCOtg57kY0l2JpmXdKukzyf5YG+TAQAmwvvQAaCI1ufQz5fki5K+2MdrAQDa4QgdAIog6ABQBEEH\ngCIIOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0AiiDoAFAEQQeAIgg6ABRB0AGgCIIOAEUQdAAogqAD\nQBEEHQCKIOgAUARBB4AiCDoAFEHQAaCI1kG3fY3tL9h+yvaTtj/S52AAgMnMdfjZc5I+muSo7csk\nHbF9KMlTPc0GAJhA6yP0JM8nOdrc/i9JJyRd3ddgAIDJdDlC/3+25yW9VdLjazy3KGlRkobDYet1\nzC8dbP2zALAddP6lqO0flvR3kn4zyXdWP59kOclCkoXBYNB1dQCAdXQKuu2LtRLze5N8pp+RAABt\ndHmXiyV9StKJJH/Y30gAgDa6HKG/S9IvSnq37WPN1y09zQUAmFDrX4om+RdJ7nEWAEAHfFIUAIog\n6ABQBEEHgCIIOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0Aiujl8rkAMAumeRnuk3t3b/o6OEIHgCII\nOgAUQdABoAiCDgBFEHQAKIKgA0ARBB0AiiDoAFAEQQeAIgg6ABTRKei2d9n+mu1nbS/1NRQAYHKt\ng277Ikl/Lum9kt4s6Tbbb+5rMADAZLocob9d0rNJvp7kFUmflrSnn7EAAJPqEvSrJX3zvPunmscA\nAFOw6ZfPtb0oabG5+7Ltr/W8ih2SXuz5NStgu7wW22RtbJe19bpd/HudfvxHx1moS9BPS7rmvPs7\nm8e+T5JlScsd1rMh24eTLGzW688qtstrsU3WxnZZ2yxuly6nXP5V0nW2r7X9Okm3Snqwn7EAAJNq\nfYSe5JztX5P0T5IukrQvyZO9TQYAmEinc+hJHpL0UE+ztLVpp3NmHNvltdgma2O7rG3mtouTTHsG\nAEAP+Og/ABQxc0G3/Xrbh2w/03y/Yp3lTtp+wvYx24e3es6tMOrSC17xp83zX7X9tmnMudXG2C43\n2v52s28cs/0705hzK9neZ/uM7ePrPL9d95VR22Wm9pWZC7qkJUmPJLlO0iPN/fX8bJK3zNpbj8Yx\n5qUX3ivpuuZrUdIntnTIKZjgkhT/3Owbb0nyu1s65HTsl7Rrg+e33b7S2K+Nt4s0Q/vKLAZ9j6R7\nmtv3SHr/FGeZpnEuvbBH0l9mxZckXW77jVs96BbjkhRrSPKopJc2WGQ77ivjbJeZMotBvzLJ883t\nFyRduc5ykfQ520eaT6tWM86lF7bj5RnG/TO/szm18I+2f3xrRrugbcd9ZVwzs69s+kf/27D9OUlX\nrfHUx8+/kyS213ubzk8lOW37DZIO2X66+a8xcFTSMMnLtm+R9PdaOdUArDZT+8oFeYSe5OYkN6zx\n9YCkb736V8Hm+5l1XuN08/2MpPu18lfxSsa59MJYl2coZuSfOcl3krzc3H5I0sW2d2zdiBek7biv\njDRr+8oFGfQRHpR0e3P7dkkPrF7A9qW2L3v1tqT3SFrzt9gzbJxLLzwo6ZeadzC8Q9K3zztdVdXI\n7WL7Kttubr9dK/8e/OeWT3ph2Y77ykiztq9ckKdcRtgr6W9t3yHpG5J+QZJsv0nS3Ulu0cp59fub\nfw5zkv46yWenNO+mWO/SC7Z/pXn+k1r5FO8tkp6V9F1JH57WvFtlzO3yAUm/avucpO9JujXFP2Fn\n+4CkGyXtsH1K0l2SLpa2774ijbVdZmpf4ZOiAFDELJ5yAQCsgaADQBEEHQCKIOgAUARBB4AiCDoA\nFEHQAaAIgg4ARfwfsxgr+t6FctAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9de75fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe34c9f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9df75f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.63106841 -0.90578157  0.52533048]\n",
      "Training epoch 3000\n",
      "Iter: 3000\n",
      "D loss: 0.001797\n",
      "G_loss: -0.003126\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dda8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dc77a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9deb4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.63167834 -0.96474922  0.62452638]\n",
      "Training epoch 3100\n",
      "Iter: 3100\n",
      "D loss: 0.001525\n",
      "G_loss: -0.002937\n",
      "()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f9dd74410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb333efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DP2b1eu4lu+h42y7bPI+37bLN7Xgb8+E3RSWpEbvuGrokqR8LXZIaYaFLUiMsdElqhIUu\nSY2w0CWpERa6JDXCQpekRvwv6Vx2LKyH2eIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fb80aef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.63048744 -0.94344193  0.61500883]\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-15-4832b1bf6b2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     62\u001b[0m             _, D_loss_curr, _ = sess.run(\n\u001b[1;32m     63\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mD_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip_D\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m                 feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\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    936\u001b[0m                 ' to a larger type (e.g. int64).')\n\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m           \u001b[0mnp_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubfeed_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\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/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \"\"\"\n\u001b[0;32m--> 531\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "G_pred, G_pred_output_i = generator(z, is_training=False)\n",
    "G_sample, G_sample_output_i = generator(z, is_training=True)\n",
    "\n",
    "D_real  = discriminator(label_placeholder)\n",
    "D_fake  = discriminator(G_sample)\n",
    "\n",
    "# D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))\n",
    "# G_loss = -tf.reduce_mean(tf.log(D_fake))\n",
    "\n",
    "# Alternative losses:\n",
    "# -------------------\n",
    "\n",
    "#D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=ones_like))\n",
    "#D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=zeros_like))\n",
    "#D_loss = D_loss_real + D_loss_fake\n",
    "#G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=ones_like))\n",
    "\n",
    "#D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))\n",
    "#D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))\n",
    "#D_loss = D_loss_real + D_loss_fake\n",
    "#G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))\n",
    "\n",
    "#WGAN:\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",
    "\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-4)\n",
    "            .minimize(-D_loss, var_list=theta_D))\n",
    "G_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-4)\n",
    "            .minimize(G_loss, var_list=theta_G))\n",
    "\n",
    "mb_size = S\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "train_dataset = [features[0] for i in range(0,64)]\n",
    "n_train = len(train_dataset)\n",
    "preds = []\n",
    "\n",
    "d_losses = []\n",
    "g_losses = []\n",
    "\n",
    "for it in range(100000):\n",
    "    if it % 100 == 0:\n",
    "        print(\"Training epoch %d\" %it)\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",
    "        _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict=feed_dict)\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],\n",
    "                feed_dict=feed_dict)\n",
    "            \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",
    "\n",
    "        \n",
    "    if it % 100 == 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()\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",
    "\n",
    "    if it % 100 == 0:\n",
    "        samples = sess.run(G_sample, feed_dict=feed_dict)\n",
    "        plt.hist(samples[:,0], bins=10)\n",
    "        plt.show()\n",
    "        plt.hist(samples[:,1], bins=10)\n",
    "        plt.show()\n",
    "        plt.hist(samples[:,2], bins=10)\n",
    "        plt.show()\n",
    "        print(np.mean(samples, axis=0))\n",
    "        #print(samples[:10])\n",
    "        #plt.hist(samples, bins=50)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array([ 0.61143659, -0.99900387,  0.48252666])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-aa94017d45a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdihed_indices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.where(feed_dict[dihed_indices][0] != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'feed_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-c9e9ad47ecf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel_placeholder\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'feed_dict' is not defined"
     ]
    }
   ],
   "source": [
    "feed_dict[label_placeholder]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feed_dict[non_zero_inds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " [-0.91292151,  3.09695411,  1.06725919,  2.02969492,  1.9801807 , 2.27330438],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][1][1].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(G_sample, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array([[ 0.91292151],\n",
    "       [ 3.09695411],\n",
    "       [ 1.06725919],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(sess.run(G_sample, feed_dict=feed_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "batch_sched"
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  },
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   "cell_type": "code",
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for it in range(20001):\n",
    "    if it % 100 == 0:\n",
    "        print(\"Training epoch %d\" %it)\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",
    "        _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict=feed_dict)\n",
    "        _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict=feed_dict)\n",
    "\n",
    "    if it % 1000 == 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()\n",
    "\n",
    "    if it % 1000 == 0:\n",
    "        samples = sess.run(G_sample, feed_dict=feed_dict)\n",
    "        plt.hist(samples, bins=50)\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "for k in range(0,100):\n",
    "    feed_dict = construct_feed_dict(train_dataset, 0, 1)\n",
    "    samples = sess.run(G_sample, feed_dict=feed_dict)[2]\n",
    "    preds.append(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(np.concatenate(preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_dataset[0][1][1].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = gan.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(res, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features[0][1][1].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features[0][1][1].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "for i in range(0,100):\n",
    "    preds.append(gan.predict(features)[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.mean(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features[0][2].atom_features"
   ]
  },
  {
   "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": 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
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "source": []
  }
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