BRAIN-LARGE-stability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from model import scVI_final as scVI\n",
    "from model.vae import format_time\n",
    "import tensorflow as tf\n",
    "from benchmarking import *\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/home/ubuntu/single-cell/10x1Mcells/data.hdf5\"\n",
    "import h5py\n",
    "f = h5py.File(data_path)\n",
    "expression_train = f[\"data_train\"][:10000]\n",
    "expression_test = f[\"data_test\"][:10000]\n",
    "import matplotlib.pyplot as plt\n",
    "log_library_size = np.log(np.sum(expression_train, axis=1))\n",
    "mean, var = np.mean(log_library_size), np.var(log_library_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_batch(data, batch_size):\n",
    "    index = np.random.choice(np.arange(data.shape[0]), size=batch_size)\n",
    "    return data[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_likelihood(data, sess, kl=1):\n",
    "    s = 0\n",
    "    for i in range(10):\n",
    "        dic_full = {expression: data[1000 * i: 1000 * (i+ 1)], training_phase:False, kl_scalar:kl}\n",
    "        s += sess.run(model.loss, feed_dict=dic_full) \n",
    "    return s / 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(num_epochs, expression_train, expression_test, step, batch_size, impute=False):\n",
    "    iterep = int(expression_train.shape[0]/float(batch_size))-1\n",
    "    training_history = {\"t_loss\":[], \"v_loss\":[], \"time\":[], \"epoch\":[], \"impute\":[]}\n",
    "    training_history[\"n_hidden\"] = model.n_hidden\n",
    "    training_history[\"model\"] = model.__class__.__name__\n",
    "    training_history[\"n_input\"] = model.n_input\n",
    "    training_history[\"dropout_nn\"] = model.dropout_rate\n",
    "    training_history[\"learning_rate\"] = learning_rate\n",
    "    training_history[\"dispersion\"] = model.complex_dispersion\n",
    "    training_history[\"epsilon\"] = epsilon\n",
    "    training_history[\"n_layers\"] = model.n_layers\n",
    "    begin = time.time()\n",
    "    \n",
    "    \n",
    "    for t in range(iterep * num_epochs):\n",
    "    \n",
    "        # arange data in batches\n",
    "        #x_train, b_train = next_batch(expression_train, batch_train, batch_size)\n",
    "        #x_test, b_test = next_batch(expression_test, batch_test, batch_size)\n",
    "\n",
    "        #prepare data dictionaries\n",
    "        #dic_train = {expression: x_train, batch:b_train.A, training_phase:True, kl_scalar:0}\n",
    "        #dic_test = {expression: x_test, batch:b_test.A, training_phase:False, kl_scalar:0}\n",
    "        \n",
    "        # arange data in batches\n",
    "        #x_train = next_batch(expression_train, batch_size).astype(np.float32)\n",
    "        x_train = expression_train[batch_size * (t%iterep) :batch_size * (t%iterep + 1) ].astype(np.float32)\n",
    "        x_test = next_batch(expression_test, batch_size).astype(np.float32)\n",
    "\n",
    "        #prepare data dictionaries\n",
    "        dic_train = {expression: x_train, training_phase:True, kl_scalar:0}\n",
    "        dic_test = {expression: x_test,  training_phase:False, kl_scalar:0} \n",
    "        \n",
    "        # run an optimization set\n",
    "        _, l_tr = sess.run([step, model.loss], feed_dict=dic_train)\n",
    "        end_epoch, epoch = t % iterep == 0, t / iterep\n",
    "\n",
    "        if end_epoch:\n",
    "            now = time.time()\n",
    "            print epoch\n",
    "\n",
    "            l_t = sess.run((model.loss), feed_dict=dic_test)\n",
    "\n",
    "            training_history[\"t_loss\"].append(l_tr)\n",
    "            training_history[\"v_loss\"].append(l_t)\n",
    "            training_history[\"time\"].append(format_time(int(now-begin)))\n",
    "            training_history[\"epoch\"].append(epoch)\n",
    "            \n",
    "            print 'Train / Test performance:', l_tr, l_t\n",
    "            print \"Elapsed Time:\", training_history[\"time\"][-1]\n",
    "            \n",
    "            if impute:\n",
    "                training_history[\"impute\"].append(eval_imputed_data(model))\n",
    "            if np.isnan(l_tr):\n",
    "                break\n",
    "    return training_history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vizualisation of one training curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n",
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    },
    {
     "name": "stdout",
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      "Elapsed Time: 0:01:34\n",
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      "131\n",
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      "162\n",
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      "Elapsed Time: 0:02:05\n",
      "168\n",
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      "Elapsed Time: 0:02:06\n",
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      "Elapsed Time: 0:02:19\n",
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      "Elapsed Time: 0:02:20\n",
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      "Elapsed Time: 0:02:21\n",
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      "Elapsed Time: 0:02:22\n",
      "191\n",
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      "Elapsed Time: 0:02:23\n",
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      "Elapsed Time: 0:02:24\n",
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      "194\n",
      "Train / Test performance: 1160.46 1173.19\n",
      "Elapsed Time: 0:02:25\n",
      "195\n",
      "Train / Test performance: 1163.22 1234.88\n",
      "Elapsed Time: 0:02:26\n",
      "196\n",
      "Train / Test performance: 1160.06 1170.29\n",
      "Elapsed Time: 0:02:27\n",
      "197\n",
      "Train / Test performance: 1163.2 1157.26\n",
      "Elapsed Time: 0:02:27\n",
      "198\n",
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      "Elapsed Time: 0:02:28\n",
      "199\n",
      "Train / Test performance: 1151.74 1149.77\n",
      "Elapsed Time: 0:02:29\n"
     ]
    }
   ],
   "source": [
    "n_cells = 10000\n",
    "expression_train = f[\"data_train\"][:n_cells]\n",
    "batch_size = 128\n",
    "learning_rate = 0.005\n",
    "epsilon = 0.01\n",
    "\n",
    "res = {}\n",
    "\n",
    "tf.reset_default_graph()\n",
    "expression = tf.placeholder(tf.float32, (None, expression_train.shape[1]), name='x')\n",
    "kl_scalar = tf.placeholder(tf.float32, (), name='kl_scalar')\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, epsilon=epsilon)\n",
    "training_phase = tf.placeholder(tf.bool, (), name='training_phase')\n",
    "\n",
    "model = scVI.scVIModel(expression=expression, kl_scale=kl_scalar, \\\n",
    "                         optimize_algo=optimizer, phase=training_phase, \\\n",
    "                          library_size_mean=mean, library_size_var=var, \\\n",
    "                          n_layers=1, n_hidden=128)\n",
    "\n",
    "# Session creation\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "history = train_model(200, expression_train, expression_test, model.train_step, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9VVW/KyJ3MMSC3qr6haxGlkuhCjj9721SRGOMGUa6ZqtG937V4Qgkr4g4Fwga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\nVLxWyIJ5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2b041ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history[\"epoch\"][2:], history[\"t_loss\"][2:],  marker='o', linestyle='--',label=\"Training\", alpha=0.5)\n",
    "plt.plot(history[\"epoch\"][2:], history[\"v_loss\"][2:],  marker='o', linestyle='--',label=\"Validation\", alpha=0.5)\n",
    "plt.legend()\n",
    "plt.ylabel(\"negative log-likelihood on a minibatch\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.savefig(\"training_curves.pdf\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_latent(data, sess, model, combine=False):\n",
    "    dic_full = {expression: data, training_phase:False, kl_scalar:1}\n",
    "    if model.__class__.__name__ == 'scVIModel':\n",
    "        temp = sess.run(model.sampling_latent, feed_dict=dic_full)\n",
    "        if combine:\n",
    "            return np.hstack((temp[0], (temp[1] - mean) / np.sqrt(var)))\n",
    "        else:\n",
    "            return temp[0]\n",
    "    else:\n",
    "        return sess.run(model.sampling_latent, feed_dict=dic_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# imputation along a training curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/home/ubuntu/single-cell-scVI/data/10x1M/\"\n",
    "X_zero, i, j, ix = \\\n",
    "        np.load(data_path + \"imputation/X_zero.npy\"), np.load(data_path + \"imputation/i.npy\"),\\\n",
    "        np.load(data_path + \"imputation/j.npy\"), np.load(data_path + \"imputation/ix.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
    }
   ],
   "source": [
    "n_cells = 10000\n",
    "expression_train = f[\"data_train\"][:n_cells].astype(np.float32)\n",
    "batch_size = 128\n",
    "learning_rate = 0.005\n",
    "epsilon = 0.01\n",
    "\n",
    "tf.reset_default_graph()\n",
    "expression = tf.placeholder(tf.float32, (None, expression_train.shape[1]), name='x')\n",
    "kl_scalar = tf.placeholder(tf.float32, (), name='kl_scalar')\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, epsilon=epsilon)\n",
    "training_phase = tf.placeholder(tf.bool, (), name='training_phase')\n",
    "\n",
    "model = scVI.scVIModel(expression=expression, kl_scale=kl_scalar, \\\n",
    "                         optimize_algo=optimizer, phase=training_phase, \\\n",
    "                          library_size_mean=mean, library_size_var=var, \\\n",
    "                          n_layers=1, n_hidden=128)\n",
    "\n",
    "# Session creation\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_imputed_data(model):\n",
    "    dic_zero = {expression: X_zero, training_phase:False, kl_scalar:1.} \n",
    "    zero_, dropout_, rate_  = sess.run((model.zero_prob, model.dropout_prob, model.px_rate), \\\n",
    "                                       feed_dict=dic_zero)\n",
    "    return imputation_error(rate_, expression_train, X_zero, i, j, ix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Elapsed Time: 0:02:25\n",
      "170\n",
      "Train / Test performance: 1150.38 1177.09\n",
      "Elapsed Time: 0:02:26\n",
      "171\n",
      "Train / Test performance: 1156.0 1157.03\n",
      "Elapsed Time: 0:02:27\n",
      "172\n",
      "Train / Test performance: 1159.78 1228.14\n",
      "Elapsed Time: 0:02:27\n",
      "173\n",
      "Train / Test performance: 1148.63 1194.25\n",
      "Elapsed Time: 0:02:28\n",
      "174\n",
      "Train / Test performance: 1150.76 1196.15\n",
      "Elapsed Time: 0:02:29\n",
      "175\n",
      "Train / Test performance: 1153.09 1176.61\n",
      "Elapsed Time: 0:02:30\n",
      "176\n",
      "Train / Test performance: 1155.23 1134.97\n",
      "Elapsed Time: 0:02:31\n",
      "177\n",
      "Train / Test performance: 1148.38 1135.93\n",
      "Elapsed Time: 0:02:32\n",
      "178\n",
      "Train / Test performance: 1152.9 1193.92\n",
      "Elapsed Time: 0:02:32\n",
      "179\n",
      "Train / Test performance: 1150.51 1182.16\n",
      "Elapsed Time: 0:02:33\n",
      "180\n",
      "Train / Test performance: 1157.94 1178.3\n",
      "Elapsed Time: 0:02:34\n",
      "181\n",
      "Train / Test performance: 1152.77 1194.03\n",
      "Elapsed Time: 0:02:35\n",
      "182\n",
      "Train / Test performance: 1157.81 1143.33\n",
      "Elapsed Time: 0:02:36\n",
      "183\n",
      "Train / Test performance: 1155.09 1178.29\n",
      "Elapsed Time: 0:02:37\n",
      "184\n",
      "Train / Test performance: 1149.39 1207.37\n",
      "Elapsed Time: 0:02:37\n",
      "185\n",
      "Train / Test performance: 1156.0 1168.96\n",
      "Elapsed Time: 0:02:38\n",
      "186\n",
      "Train / Test performance: 1151.92 1139.76\n",
      "Elapsed Time: 0:02:39\n",
      "187\n",
      "Train / Test performance: 1150.63 1244.91\n",
      "Elapsed Time: 0:02:40\n",
      "188\n",
      "Train / Test performance: 1153.81 1184.75\n",
      "Elapsed Time: 0:02:41\n",
      "189\n",
      "Train / Test performance: 1145.82 1211.81\n",
      "Elapsed Time: 0:02:42\n",
      "190\n",
      "Train / Test performance: 1148.97 1196.56\n",
      "Elapsed Time: 0:02:43\n",
      "191\n",
      "Train / Test performance: 1147.89 1169.29\n",
      "Elapsed Time: 0:02:43\n",
      "192\n",
      "Train / Test performance: 1148.14 1154.16\n",
      "Elapsed Time: 0:02:44\n",
      "193\n",
      "Train / Test performance: 1145.57 1188.55\n",
      "Elapsed Time: 0:02:45\n",
      "194\n",
      "Train / Test performance: 1146.9 1179.76\n",
      "Elapsed Time: 0:02:46\n",
      "195\n",
      "Train / Test performance: 1146.09 1157.74\n",
      "Elapsed Time: 0:02:47\n",
      "196\n",
      "Train / Test performance: 1143.98 1203.88\n",
      "Elapsed Time: 0:02:48\n",
      "197\n",
      "Train / Test performance: 1150.58 1175.04\n",
      "Elapsed Time: 0:02:49\n",
      "198\n",
      "Train / Test performance: 1149.17 1151.12\n",
      "Elapsed Time: 0:02:49\n",
      "199\n",
      "Train / Test performance: 1148.31 1213.99\n",
      "Elapsed Time: 0:02:50\n"
     ]
    }
   ],
   "source": [
    "history_impute = train_model(200, X_zero, expression_test, model.train_step, batch_size, impute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RGZy9ENPPaomNaD6bFXY0uzIVjlJKZUzSpGCMuTH6u8gYU5zwU2SMKZ69ENMvVn0USkgK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\nBaWUUnGaFJRSSsVpUlBKKRX3/wGbITNKwzFgYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2b01a3a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history_impute[\"epoch\"][1:], history_impute[\"impute\"][1:],  marker='+', linestyle=\"--\", alpha=0.5)\n",
    "plt.ylabel(\"Imputation error\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.savefig(\"Imputation_stability.pdf\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation for same number of epochs, different latent dimension for 5 seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 2) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 2) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(2,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n",
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    },
    {
     "name": "stdout",
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 5) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(5,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 5) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(5,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 5) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(5,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 5) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(5,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 7) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(7,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 7) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(7,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
    },
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 10) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(10,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 12) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(12,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 12) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(12,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
    },
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 12) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(12,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 12) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(12,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 15) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(15,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 15) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(15,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
    },
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 15) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(15,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 15) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(15,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 20) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(20,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 20) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(20,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 20) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(20,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 20) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(20,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 30) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(30,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 30) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(30,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
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      "[<tf.Variable 'variational_distribution/fully_connected/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/weights:0' shape=(128, 30) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_1/biases:0' shape=(30,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/weights:0' shape=(128, 30) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_2/biases:0' shape=(30,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/weights:0' shape=(720, 128) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_3/biases:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/batch_normalization_1/gamma:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_4/biases:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/weights:0' shape=(128, 1) dtype=float32_ref>, <tf.Variable 'variational_distribution/fully_connected_5/biases:0' shape=(1,) dtype=float32_ref>]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "learning_rate = 0.005\n",
    "epsilon = 0.01\n",
    "\n",
    "range_d = [2, 5, 7, 10, 12, 15, 20, 30]\n",
    "results = {}\n",
    "\n",
    "\n",
    "for d in range_d:\n",
    "    results[d] = []\n",
    "    for n in [0, 0, 0, 0]:\n",
    "        tf.reset_default_graph()\n",
    "        expression = tf.placeholder(tf.float32, (None, expression_train.shape[1]), name='x')\n",
    "        kl_scalar = tf.placeholder(tf.float32, (), name='kl_scalar')\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, epsilon=epsilon)\n",
    "        training_phase = tf.placeholder(tf.bool, (), name='training_phase')\n",
    "\n",
    "        model = scVI.scVIModel(expression=expression, kl_scale=kl_scalar, \\\n",
    "                                 optimize_algo=optimizer, phase=training_phase, \\\n",
    "                                  library_size_mean=mean, library_size_var=var, \\\n",
    "                                  n_layers=1, n_hidden=128, n_latent=d)\n",
    "\n",
    "        # Session creation\n",
    "        sess = tf.Session()\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\n",
    "        train_model(120, X_zero, expression_test, model.train_step, batch_size)\n",
    "        results[d].append(eval_imputed_data(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5, 7, 10, 12, 15, 20, 30]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a02rCiAgvrWVmZh/LM1utmZmZE4aZmeXjhGFmZrk4YZiZWS5OGGZmlosThpmZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa2e403d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 8\n",
    "means = np.mean(results.values(), axis=1)\n",
    "std = np.std(results.values(), axis=1)\n",
    "\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.35       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.errorbar(results.keys(), means, color='r', yerr=std[0])\n",
    "\n",
    "# add some text for labels, title and axes ticks\n",
    "ax.set_ylabel('Imputation score for 120 epochs')\n",
    "ax.set_xticks(results.keys())\n",
    "ax.set_xlabel(\"Dimension of latent variables\")\n",
    "\n",
    "plt.savefig(\"imputation_dimension_stability.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NB VS ZINB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(num_epochs, expression_train, expression_test, step, batch_size, kl=1):\n",
    "    iterep = int(expression_train.shape[0]/float(batch_size))-1\n",
    "    training_history = {\"t_loss\":[], \"v_loss\":[], \"time\":[], \"epoch\":[]}\n",
    "    training_history[\"n_hidden\"] = model.n_hidden\n",
    "    training_history[\"model\"] = model.__class__.__name__\n",
    "    training_history[\"n_input\"] = model.n_input\n",
    "    training_history[\"dropout_nn\"] = model.dropout_rate\n",
    "    training_history[\"learning_rate\"] = learning_rate\n",
    "    training_history[\"dispersion\"] = model.dispersion\n",
    "    training_history[\"epsilon\"] = epsilon\n",
    "    training_history[\"n_layers\"] = model.n_layers\n",
    "    begin = time.time()\n",
    "    \n",
    "    \n",
    "    for t in range(iterep * num_epochs):\n",
    "    \n",
    "        # arange data in batches\n",
    "        #x_train, b_train = next_batch(expression_train, batch_train, batch_size)\n",
    "        #x_test, b_test = next_batch(expression_test, batch_test, batch_size)\n",
    "\n",
    "        #prepare data dictionaries\n",
    "        #dic_train = {expression: x_train, batch:b_train.A, training_phase:True, kl_scalar:0}\n",
    "        #dic_test = {expression: x_test, batch:b_test.A, training_phase:False, kl_scalar:0}\n",
    "        \n",
    "        # arange data in batches\n",
    "        #x_train = next_batch(expression_train, batch_size).astype(np.float32)\n",
    "        x_train = expression_train[batch_size * (t%iterep) :batch_size * (t%iterep + 1) ].astype(np.float32)\n",
    "        x_test = next_batch(expression_test, batch_size).astype(np.float32)\n",
    "\n",
    "        #prepare data dictionaries\n",
    "        dic_train = {expression: x_train, training_phase:True, kl_scalar:kl}\n",
    "        dic_test = {expression: x_test,  training_phase:False, kl_scalar:kl} \n",
    "        \n",
    "        # run an optimization set\n",
    "        _, l_tr = sess.run([step, model.loss], feed_dict=dic_train)\n",
    "        end_epoch, epoch = t % iterep == 0, t / iterep\n",
    "\n",
    "        if end_epoch:\n",
    "            now = time.time()\n",
    "            print epoch\n",
    "\n",
    "            l_t = sess.run((model.loss), feed_dict=dic_test)\n",
    "\n",
    "            training_history[\"t_loss\"].append(l_tr)\n",
    "            training_history[\"v_loss\"].append(l_t)\n",
    "            training_history[\"time\"].append(format_time(int(now-begin)))\n",
    "            training_history[\"epoch\"].append(epoch)\n",
    "            \n",
    "            print 'Train / Test performance:', l_tr, l_t\n",
    "            print \"Elapsed Time:\", training_history[\"time\"][-1]\n",
    "            \n",
    "            #print eval_likelihood(expression_test, sess, kl=0)\n",
    "            if np.isnan(l_tr):\n",
    "                break\n",
    "    return training_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
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    },
    {
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     "output_type": "stream",
     "text": [
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
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     "output_type": "stream",
     "text": [
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
      "Will apply zero inflation\n",
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
      "Will apply zero inflation\n",
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
      "Will apply zero inflation\n",
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      "Running scVI on 720 genes\n",
      "scVI will run without batch correction\n",
      "Will work on mode numeric for incorporating library size\n",
      "Will work on mode gene for modeling inverse dispersion param\n",
      "Will apply zero inflation\n",
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      "Elapsed Time: 0:00:53\n",
      "76\n",
      "Train / Test performance: 1182.83 1168.69\n",
      "Elapsed Time: 0:00:53\n",
      "77\n",
      "Train / Test performance: 1181.43 1165.99\n",
      "Elapsed Time: 0:00:54\n",
      "78\n",
      "Train / Test performance: 1178.23 1150.07\n",
      "Elapsed Time: 0:00:55\n",
      "79\n",
      "Train / Test performance: 1181.99 1179.69\n",
      "Elapsed Time: 0:00:55\n",
      "80\n",
      "Train / Test performance: 1180.41 1195.27\n",
      "Elapsed Time: 0:00:56\n",
      "81\n",
      "Train / Test performance: 1175.83 1174.17\n",
      "Elapsed Time: 0:00:57\n",
      "82\n",
      "Train / Test performance: 1180.69 1200.33\n",
      "Elapsed Time: 0:00:57\n",
      "83\n",
      "Train / Test performance: 1177.21 1159.23\n",
      "Elapsed Time: 0:00:58\n",
      "84\n",
      "Train / Test performance: 1183.73 1146.3\n",
      "Elapsed Time: 0:00:59\n",
      "85\n",
      "Train / Test performance: 1177.76 1207.11\n",
      "Elapsed Time: 0:01:00\n",
      "86\n",
      "Train / Test performance: 1183.58 1205.33\n",
      "Elapsed Time: 0:01:00\n",
      "87\n",
      "Train / Test performance: 1185.13 1173.81\n",
      "Elapsed Time: 0:01:01\n",
      "88\n",
      "Train / Test performance: 1184.44 1233.35\n",
      "Elapsed Time: 0:01:02\n",
      "89\n",
      "Train / Test performance: 1176.66 1174.6\n",
      "Elapsed Time: 0:01:02\n",
      "90\n",
      "Train / Test performance: 1177.53 1182.33\n",
      "Elapsed Time: 0:01:03\n",
      "91\n",
      "Train / Test performance: 1176.64 1162.5\n",
      "Elapsed Time: 0:01:04\n",
      "92\n",
      "Train / Test performance: 1177.26 1188.56\n",
      "Elapsed Time: 0:01:05\n",
      "93\n",
      "Train / Test performance: 1177.06 1168.27\n",
      "Elapsed Time: 0:01:05\n",
      "94\n",
      "Train / Test performance: 1176.97 1111.06\n",
      "Elapsed Time: 0:01:06\n",
      "95\n",
      "Train / Test performance: 1177.37 1180.19\n",
      "Elapsed Time: 0:01:07\n",
      "96\n",
      "Train / Test performance: 1177.57 1190.55\n",
      "Elapsed Time: 0:01:07\n",
      "97\n",
      "Train / Test performance: 1177.12 1175.64\n",
      "Elapsed Time: 0:01:08\n",
      "98\n",
      "Train / Test performance: 1176.06 1169.05\n",
      "Elapsed Time: 0:01:09\n",
      "99\n",
      "Train / Test performance: 1178.83 1176.49\n",
      "Elapsed Time: 0:01:09\n",
      "100\n",
      "Train / Test performance: 1176.03 1183.05\n",
      "Elapsed Time: 0:01:10\n",
      "101\n",
      "Train / Test performance: 1183.28 1161.22\n",
      "Elapsed Time: 0:01:11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n",
      "Train / Test performance: 1178.96 1169.83\n",
      "Elapsed Time: 0:01:12\n",
      "103\n",
      "Train / Test performance: 1178.06 1182.75\n",
      "Elapsed Time: 0:01:12\n",
      "104\n",
      "Train / Test performance: 1176.43 1177.86\n",
      "Elapsed Time: 0:01:13\n",
      "105\n",
      "Train / Test performance: 1179.83 1175.81\n",
      "Elapsed Time: 0:01:14\n",
      "106\n",
      "Train / Test performance: 1177.74 1173.84\n",
      "Elapsed Time: 0:01:14\n",
      "107\n",
      "Train / Test performance: 1179.04 1219.94\n",
      "Elapsed Time: 0:01:15\n",
      "108\n",
      "Train / Test performance: 1175.32 1152.91\n",
      "Elapsed Time: 0:01:16\n",
      "109\n",
      "Train / Test performance: 1176.04 1165.61\n",
      "Elapsed Time: 0:01:17\n",
      "110\n",
      "Train / Test performance: 1178.88 1180.31\n",
      "Elapsed Time: 0:01:17\n",
      "111\n",
      "Train / Test performance: 1174.49 1191.16\n",
      "Elapsed Time: 0:01:18\n",
      "112\n",
      "Train / Test performance: 1175.05 1182.39\n",
      "Elapsed Time: 0:01:19\n",
      "113\n",
      "Train / Test performance: 1176.66 1145.84\n",
      "Elapsed Time: 0:01:19\n",
      "114\n",
      "Train / Test performance: 1177.93 1132.35\n",
      "Elapsed Time: 0:01:20\n",
      "115\n",
      "Train / Test performance: 1182.91 1172.56\n",
      "Elapsed Time: 0:01:21\n",
      "116\n",
      "Train / Test performance: 1176.78 1177.26\n",
      "Elapsed Time: 0:01:21\n",
      "117\n",
      "Train / Test performance: 1174.15 1161.27\n",
      "Elapsed Time: 0:01:22\n",
      "118\n",
      "Train / Test performance: 1176.64 1163.16\n",
      "Elapsed Time: 0:01:23\n",
      "119\n",
      "Train / Test performance: 1175.18 1172.58\n",
      "Elapsed Time: 0:01:24\n"
     ]
    }
   ],
   "source": [
    "n_cells = 10000\n",
    "expression_train = f[\"data_train\"][:n_cells]\n",
    "#expression_train = f[\"data_train\"]\n",
    "batch_size = 128\n",
    "learning_rate = 0.005\n",
    "epsilon = 0.01\n",
    "\n",
    "res = {True:[], False:[]}\n",
    "\n",
    "for i in range(20):\n",
    "    if i < 10:\n",
    "        zi = False\n",
    "    else:\n",
    "        zi = True\n",
    "        \n",
    "    tf.reset_default_graph()\n",
    "    expression = tf.placeholder(tf.float32, (None, expression_train.shape[1]), name='x')\n",
    "    kl_scalar = tf.placeholder(tf.float32, (), name='kl_scalar')\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, epsilon=epsilon)\n",
    "    training_phase = tf.placeholder(tf.bool, (), name='training_phase')\n",
    "\n",
    "    model = scVI.scVIModel(expression=expression, kl_scale=kl_scalar, \\\n",
    "                             optimize_algo=optimizer, phase=training_phase, \\\n",
    "                              library_size_mean=mean, library_size_var=var, \\\n",
    "                              n_layers=1, n_hidden=128, zi=zi)\n",
    "\n",
    "    # Session creation\n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    train_model(120, expression_train, expression_test, model.train_step, batch_size, kl=1)\n",
    "    res[zi].append(eval_likelihood(expression_test, sess, kl=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S+Pa3U11DQ/l+8BMmeHqw9RjVJI+zgOOAOdn5fwI/jrQRyNtrFZjZdmHAgDT+MXs2fPrT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\nzHJz8jAzs9z+P/cN8oRByHY6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2de2a289d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 2\n",
    "means = np.mean(res.values(), axis=1)\n",
    "std = np.std(res.values(), axis=1)\n",
    "\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.35       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.errorbar(ind, means, color='r', yerr=std)\n",
    "\n",
    "# add some text for labels, title and axes ticks\n",
    "ax.set_ylabel('Negative log-likelihood')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels([\"NB\", \"ZINB\"])\n",
    "ax.set_xlabel(\"Conditional $p(x | l, z, s)$\")\n",
    "\n",
    "plt.savefig(\"ZINB_vs_NB.pdf\", dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}