vrnn_tf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from functools import reduce\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "GLOBAL_SEED=1\n",
    "VERBOSITY=1\n",
    "TESTING=0\n",
    "\n",
    "np.random.seed(GLOBAL_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# useful function for recovering functions in deleted cells\n",
    "def rescue_code(function):\n",
    "    import inspect\n",
    "    get_ipython().set_next_input(\"\".join(inspect.getsourcelines(function)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# wrapper around tf.get_variable that checks if the variable has already been defined.\n",
    "# useful for playing around in jupyter. allows you to call cells multiple times\n",
    "def get_variable_wrap(*args, **kwargs):\n",
    "    try:\n",
    "        return tf.get_variable(*args, **kwargs)\n",
    "    except ValueError:\n",
    "        tf.get_variable_scope().reuse_variables()\n",
    "        return tf.get_variable(*args, **kwargs)\n",
    "\n",
    "## do matrix multiplication with weights and add bias (as in fully connected layer)\n",
    "def fc_act(x, next_layer_size, act=None):\n",
    "    prev_layer_size = x.get_shape()[1]\n",
    "    w = get_variable_wrap(\"weights\", [prev_layer_size, next_layer_size], initializer=tf.random_normal_initializer())\n",
    "    b = get_variable_wrap(\"bias\", [next_layer_size], initializer=tf.constant_initializer(0.0))\n",
    "    o = tf.add(tf.matmul(x, w), b)\n",
    "    if act: return act(o)\n",
    "    else: return o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Data\n",
    "I want to create 100 bi-variate time-series sampled from the same distribution and split them into train/validation/test 70/20/10, i.e. split into groups of time-series size 14/4/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gen_time_series(n_points):\n",
    "    time_series = np.array([[np.random.normal(mu*i, sigma*i), \n",
    "                        np.random.normal(mu*i, sigma*i/4)] \n",
    "                       for i in range(n_points)])\n",
    "    return time_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100, 2)\n",
      "14000 data values\n"
     ]
    },
    {
     "data": {
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/+JF6E/vF+zlfqtYNkgKBv974UKx6GTHTIQvGF/UrdrsLXDx13J2SoyJ1hUKx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\nXSAQCPIIIeoCgUCQRwhRFwgEgjzi/wO/yIiif+zOAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d5082e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu=1\n",
    "sigma=1\n",
    "n_series=70\n",
    "n_points = 100\n",
    "n_features = 2\n",
    "# multi-variate time-series: \n",
    "# x_t ~ [Gaussian(mu*t,sigma*t), Gaussian(mu*t,sigma*t/4)]\n",
    "# Points X Time X Features\n",
    "x_data = np.zeros((n_series,n_points,n_features))\n",
    "for i in range(n_series):\n",
    "    x_data[i] = gen_time_series(n_points)\n",
    "\n",
    "if TESTING: x_data_test=x_data[:10]\n",
    "plt.title(\"Sample bi-variate time series\")\n",
    "for i in range(n_features):\n",
    "    plt.plot(x_data[0,:,i]);\n",
    "print(x_data.shape)\n",
    "print(reduce(lambda x,y: x*y, list(x_data.shape)), \"data values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Settings for VRNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nbatches 7\n",
      "input shape [10, 100, 2]\n"
     ]
    }
   ],
   "source": [
    "# test computing latent variable\n",
    "batch_size=10\n",
    "\n",
    "rnn_size=20\n",
    "x_size=x_data.shape[1]\n",
    "x_1_size=x_data.shape[1]\n",
    "\n",
    "z_size=15\n",
    "z_1_size=15\n",
    "\n",
    "phi_size = 25   # N ~ q(z|x)\n",
    "prior_size = 25 # N ~ p(z)\n",
    "theta_size = 25 # N ~ p(x|z)\n",
    "\n",
    "#get shape\n",
    "x_t_shape = list(x_data.shape)\n",
    "nbatches=max(x_t_shape[0]//batch_size,1)\n",
    "x_t_shape[0] = int(max(x_t_shape[0]/nbatches,1))\n",
    "print(\"nbatches\", nbatches)\n",
    "print(\"input shape\", x_t_shape)\n",
    "# input\n",
    "x_t = tf.placeholder(tf.float32, shape=x_t_shape, name=\"x_t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Variational RNN\n",
    "\n",
    "note that all gaussian distribtutions: p(z), q(z|x), p(x|z) should have the same dimensionality, i.e. share z_size\n",
    "\n",
    "## LSTM Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def init_lstm_cell(rnn_size, batch_size):\n",
    "    with tf.variable_scope(\"lstm\"):\n",
    "        try:\n",
    "            cell = tf.contrib.rnn.LSTMCell(rnn_size, state_is_tuple=True)\n",
    "        except:\n",
    "            cell = tf.contrib.rnn.LSTMCell(rnn_size, state_is_tuple=True, reuse=True)\n",
    "        # get initial_state of rnn\n",
    "        state = cell.zero_state(batch_size, tf.float32)\n",
    "    return cell, state\n",
    "\n",
    "# cell, state = init_lstm_cell(rnn_size, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder/Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def get_encoding(x_t, rnn_hidden, x_1_size, phi_size, z_size):\n",
    "    with tf.variable_scope(\"encoder\"):\n",
    "        with tf.variable_scope(\"x_1\"):\n",
    "            x_1 = fc_act(x_t, x_1_size, act=tf.nn.relu)\n",
    "        with tf.variable_scope(\"phi\"):\n",
    "            phi = fc_act(tf.concat([x_1, rnn_hidden], axis=1), z_size, act=tf.nn.relu)\n",
    "        with tf.variable_scope(\"phi_mu\"):\n",
    "            phi_mu = fc_act(phi, z_size)\n",
    "        with tf.variable_scope(\"phi_sigma\"):\n",
    "            phi_sigma = fc_act(phi, z_size, act=tf.nn.softplus)\n",
    "    epsilon = tf.random_normal(shape=[x_t.get_shape().as_list()[0], z_size], seed=GLOBAL_SEED)\n",
    "    # z = mu + epsilon*sigma\n",
    "    z_t = tf.add(phi_mu, tf.multiply(epsilon, phi_sigma))\n",
    "    return [x_1, \n",
    "            phi,\n",
    "            phi_mu,\n",
    "            phi_sigma,\n",
    "            z_t]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_prior(rnn_hidden, prior_size, z_size):\n",
    "    with tf.variable_scope(\"prior\"):\n",
    "        with tf.variable_scope(\"prior\"):\n",
    "            prior = fc_act(rnn_hidden, prior_size, act=tf.nn.relu)\n",
    "        with tf.variable_scope(\"prior_mu\"):\n",
    "            prior_mu = fc_act(prior, z_size)\n",
    "        with tf.variable_scope(\"prior_sigma\"):\n",
    "            prior_sigma = fc_act(prior, z_size, act=tf.nn.softplus)\n",
    "    return [prior, \n",
    "            prior_mu, \n",
    "            prior_sigma]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoder/Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_decoding(z_t, rnn_hidden, z_1_size, theta_size, x_size):\n",
    "    with tf.variable_scope(\"decoder\"):\n",
    "        with tf.variable_scope(\"z_1\"):\n",
    "            z_1 = fc_act(z_t, z_1_size, act=tf.nn.relu)\n",
    "        with tf.variable_scope(\"theta\"):\n",
    "            theta = fc_act(tf.concat([z_1, rnn_hidden],axis=1), theta_size, act=tf.nn.relu)\n",
    "        with tf.variable_scope(\"theta_mu\"):\n",
    "            theta_mu = fc_act(theta, x_size)\n",
    "        with tf.variable_scope(\"theta_sigma\"):\n",
    "            theta_sigma = fc_act(theta, x_size, act=tf.nn.softplus)\n",
    "    return [z_1,\n",
    "            theta,\n",
    "            theta_mu,\n",
    "            theta_sigma]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## vrnn recurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def vrnn_recurrence(rnn_cell, x_1, z_1, state):\n",
    "    try:\n",
    "        return rnn_cell(tf.concat([x_1, z_1], axis=1), state, \"rnn\")\n",
    "    except:\n",
    "        tf.get_variable_scope().reuse_variables()\n",
    "        return rnn_cell(tf.concat([x_1, z_1], axis=1), state, \"rnn\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## calculate values for objective function and define optimization op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "PI=np.pi\n",
    "def negative_log_likelihood_gaussian(y, mu, sigma):\n",
    "    \"\"\"\n",
    "                a                  b              c\n",
    "    (1/2)*[ ((y-mu)/sigma)^2 + 2*log(sigma) + log(2*pi) ]\n",
    "    learn more: https://www.statlect.com/fundamentals-of-statistics/normal-distribution-maximum-likelihood\n",
    "    \"\"\"\n",
    "#     sigma might be really small. in that case, a & b blow up\n",
    "    sigma=tf.clip_by_value(sigma, 1e-3,1e10)\n",
    "    a = tf.square(tf.div(tf.subtract(y,mu), sigma))\n",
    "    b = 2*tf.log(sigma)\n",
    "    c = tf.log(2*PI)\n",
    "    return tf.add(tf.add(a,b),c)\n",
    "\n",
    "def kl_gaussian_gaussian(mu1, sigma1, mu2, sigma2):\n",
    "    \"\"\"\n",
    "            a                  b         c           d\n",
    "    log(sigma2/sigma1) + (sigma1^2 + (mu1-mu2)^2)/2sigma2^2 - 1/2\n",
    "    \"\"\"\n",
    "    # Note: if sigma1 or sigma2 go to 0, this explodes\n",
    "    sigma1=tf.clip_by_value(sigma1, 1e-3,1e10)\n",
    "    sigma2=tf.clip_by_value(sigma2,1e-3,1e10)\n",
    "    a = tf.log(sigma2/sigma1)\n",
    "    b = tf.square(sigma1)\n",
    "    c = tf.square(tf.subtract(mu1,mu2))\n",
    "    d = 2*tf.square(sigma2)\n",
    "    e = tf.divide(tf.add(b,c), d)\n",
    "    return tf.add(a,e) - 0.5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# variable scope used as per: http://stackoverflow.com/questions/43183850/tensorflow-valueerror-variable-does-not-exist-or-was-not-created-with-tf-get-v\n",
    "def get_optimizer(objective_function):\n",
    "    train_op = tf.train.AdamOptimizer()\n",
    "    try:\n",
    "        return train_op.minimize(tf.reduce_mean(objective_function))\n",
    "    except ValueError:\n",
    "        with tf.variable_scope(tf.get_variable_scope(),reuse=False): \n",
    "            return train_op.minimize(tf.reduce_mean(objective_function))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## class for vrnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x12d6670b8>\n",
      "state LSTMStateTuple(c=<tf.Tensor 'lstm/LSTMCellZeroState/zeros:0' shape=(10, 20) dtype=float32>, h=<tf.Tensor 'lstm/LSTMCellZeroState/zeros_1:0' shape=(10, 20) dtype=float32>)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'state' 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-14-80f5839d92de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m vrnn = VRNN(x_t, batch_size, rnn_size, x_size, x_1_size, \n\u001b[0;32m--> 118\u001b[0;31m        z_size, z_1_size, phi_size, prior_size, theta_size)    \n\u001b[0m",
      "\u001b[0;32m<ipython-input-14-80f5839d92de>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x_t, batch_size, rnn_size, x_size, x_1_size, z_size, z_1_size, phi_size, prior_size, theta_size)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"cell\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"state\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreset_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-14-80f5839d92de>\u001b[0m in \u001b[0;36m_build\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     69\u001b[0m                )\n\u001b[1;32m     70\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scan_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnegative_log_likelihood_gaussian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         self.klgg = kl_gaussian_gaussian(output[4], output[5], \n",
      "\u001b[0;32m<ipython-input-14-80f5839d92de>\u001b[0m in \u001b[0;36m_scan_sequence\u001b[0;34m(self, sequence)\u001b[0m\n\u001b[1;32m     79\u001b[0m         return tf.scan(self._vrnn_step, sequence, initializer=(\n\u001b[1;32m     80\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# cell_state - 0,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# hidden_state - 1,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;31m#                 tf.Variable(np.zeros((self.batch_size, rnn_size))) # cell_output - 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;31m#                 tf.Variable(np.zeros((self.batch_size, x_size))), # x_1 - 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/roger/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/functional_ops.py\u001b[0m in \u001b[0;36mscan\u001b[0;34m(fn, elems, initializer, parallel_iterations, back_prop, swap_memory, infer_shape, name)\u001b[0m\n\u001b[1;32m    582\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma_flat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccs_ta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         \u001b[0mparallel_iterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparallel_iterations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m         back_prop=back_prop, swap_memory=swap_memory)\n\u001b[0m\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[0mresults_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr_a\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/roger/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[0;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name)\u001b[0m\n\u001b[1;32m   2814\u001b[0m     \u001b[0mloop_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWhileContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparallel_iterations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mback_prop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswap_memory\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=redefined-outer-name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2815\u001b[0m     \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_collection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWHILE_CONTEXT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloop_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2816\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloop_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBuildLoop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloop_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape_invariants\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2817\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/roger/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36mBuildLoop\u001b[0;34m(self, pred, body, loop_vars, shape_invariants)\u001b[0m\n\u001b[1;32m   2638\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEnter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2639\u001b[0m       original_body_result, exit_vars = self._BuildLoop(\n\u001b[0;32m-> 2640\u001b[0;31m           pred, body, original_loop_vars, loop_vars, shape_invariants)\n\u001b[0m\u001b[1;32m   2641\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/roger/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36m_BuildLoop\u001b[0;34m(self, pred, body, original_loop_vars, loop_vars, shape_invariants)\u001b[0m\n\u001b[1;32m   2588\u001b[0m         \u001b[0mstructure\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moriginal_loop_vars\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2589\u001b[0m         flat_sequence=vars_for_body_with_tensor_arrays)\n\u001b[0;32m-> 2590\u001b[0;31m     \u001b[0mbody_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpacked_vars_for_body\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2591\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2592\u001b[0m       \u001b[0mbody_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/roger/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/functional_ops.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(i, a_flat, tas)\u001b[0m\n\u001b[1;32m    572\u001b[0m       \u001b[0mpacked_elems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_pack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0melem_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melem_ta\u001b[0m \u001b[0;32min\u001b[0m \u001b[0melems_ta\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m       \u001b[0mpacked_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput_pack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_flat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m       \u001b[0ma_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpacked_elems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m       nest.assert_same_structure(\n\u001b[1;32m    576\u001b[0m           elems if initializer is None else initializer, a_out)\n",
      "\u001b[0;32m<ipython-input-14-80f5839d92de>\u001b[0m in \u001b[0;36m_vrnn_step\u001b[0;34m(self, vals, x_t)\u001b[0m\n\u001b[1;32m     50\u001b[0m                     \u001b[0mget_decoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprev_hidden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz_1_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m         \u001b[0mcell_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvrnn_recurrence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         return (cell_state[0],\n",
      "\u001b[0;31mNameError\u001b[0m: name 'state' is not defined"
     ]
    }
   ],
   "source": [
    "class VRNN(object):\n",
    "    \"\"\"docstring for VRNN\"\"\"\n",
    "    def __init__(self, x_t, batch_size, rnn_size, x_size, x_1_size, \n",
    "               z_size, z_1_size, phi_size, prior_size, theta_size):\n",
    "        self.x_t = x_t # place holder for time-series\n",
    "        self.batch_size = batch_size\n",
    "        self.rnn_size = rnn_size\n",
    "        self.x_size = x_size\n",
    "        self.x_1_size = x_1_size\n",
    "        self.z_size = z_size\n",
    "        self.z_1_size = z_1_size\n",
    "        self.phi_size = phi_size\n",
    "        self.prior_size = prior_size\n",
    "        self.theta_size = theta_size\n",
    "        \n",
    "        self.cell, self.state = init_lstm_cell(rnn_size, batch_size)\n",
    "        print (\"cell\", self.cell)\n",
    "        print (\"state\", self.state)\n",
    "        self._build()\n",
    "\n",
    "    def reset_cell(self): \n",
    "        self.state = self.cell.zero_state(self.batch_size, tf.float32)\n",
    "    \n",
    "    def _vrnn_step(self, vals, x_t):\n",
    "        # get latest values\n",
    "        # I chose to store them all but you don't HAVE to do this. \n",
    "        # This is a convenient thing to do however so you can check values later on\n",
    "        cell_state = vals[0]\n",
    "\n",
    "        prev_hidden = vals[1]\n",
    "        # mu_z, sigma_z = f(x, ht-1)\n",
    "        encoding_vals = [x_1, \n",
    "                phi,\n",
    "                phi_mu,\n",
    "                phi_sigma,\n",
    "                z_t] = \\\n",
    "                    get_encoding(x_t, prev_hidden, self.x_1_size, self.phi_size, self.z_size)\n",
    "\n",
    "        # mu_phi, sigma_phi = f(ht-1)\n",
    "        prior_vals = [prior, \n",
    "                prior_mu, \n",
    "                prior_sigma] = \\\n",
    "                    get_prior(prev_hidden, self.prior_size, self.z_size)\n",
    "\n",
    "        # mu_x , sigma_x = f(z, ht-1)\n",
    "        decoding_vals = [z_1,\n",
    "                theta,\n",
    "                theta_mu,\n",
    "                theta_sigma] = \\\n",
    "                    get_decoding(z_t, prev_hidden, self.z_1_size, self.theta_size, self.x_size)\n",
    "\n",
    "        cell_output, cell_state = vrnn_recurrence(self.cell, x_1, z_1, state)\n",
    "\n",
    "        return (cell_state[0],\n",
    "                cell_state[1],\n",
    "                cell_output\n",
    "#                 x_1,\n",
    "#                 phi,\n",
    "#                 phi_mu,\n",
    "#                 phi_sigma,\n",
    "#                 prior,\n",
    "#                 prior_mu,\n",
    "#                 prior_sigma,\n",
    "#                 z_t,\n",
    "#                 z_1,\n",
    "#                 theta,\n",
    "#                 theta_mu,\n",
    "#                 theta_sigma\n",
    "               )\n",
    "    def _build(self):\n",
    "        self.output = self._scan_sequence(tf.transpose(self.x_t, [1, 0, 2]))\n",
    "        self.nll = negative_log_likelihood_gaussian(self.x_t, output[-2], output[-1])\n",
    "        self.klgg = kl_gaussian_gaussian(output[4], output[5], \n",
    "                                    output[7], output[8])\n",
    "        self.objective = tf.add(tf.reduce_mean(nll),tf.reduce_mean(klgg))\n",
    "        self.train_op = get_optimizer(self.objective)\n",
    "    \n",
    "    def _scan_sequence(self, sequence):\n",
    "        return tf.scan(self._vrnn_step, sequence, initializer=(\n",
    "                self.state[0], # cell_state - 0,\n",
    "                self.state[1] # hidden_state - 1,\n",
    "#                 tf.Variable(np.zeros((self.batch_size, rnn_size))) # cell_output - 2\n",
    "#                 tf.Variable(np.zeros((self.batch_size, x_size))), # x_1 - 3\n",
    "#                 tf.Variable(np.zeros((self.batch_size, phi_size))), # phi - 4\n",
    "#                 tf.Variable(np.zeros((self.batch_size, phi_size))), # phi_mu - 5\n",
    "#                 tf.Variable(np.zeros((self.batch_size, phi_size))), # phi_sigma - 6\n",
    "#                 tf.Variable(np.zeros((self.batch_size, prior_size))), # prior - 7\n",
    "#                 tf.Variable(np.zeros((self.batch_size, prior_size))), # prior_mu - 8\n",
    "#                 tf.Variable(np.zeros((self.batch_size, prior_size))), # prior_sigma - 9\n",
    "#                 tf.Variable(np.zeros((self.batch_size, z_size))), # z_t - 10\n",
    "#                 tf.Variable(np.zeros((self.batch_size, z_size))), # z_1 - 11\n",
    "#                 tf.Variable(np.zeros((self.batch_size, theta_size))), # theta - 12\n",
    "#                 tf.Variable(np.zeros((self.batch_size, theta_size))), # theta_mu - 13\n",
    "#                 tf.Variable(np.zeros((self.batch_size, theta_size))) # theta_sigma - 14\n",
    "        ))\n",
    "    \n",
    "    @property\n",
    "    def reconstructions(self):\n",
    "        return self.output[10], self.output[11]\n",
    "    \n",
    "    @property\n",
    "    def cell_states(self):\n",
    "        return self.output[0]\n",
    "\n",
    "    @property\n",
    "    def cell_outputs(self):\n",
    "        return self.output[1]\n",
    "\n",
    "    @property\n",
    "    def inferences(self):\n",
    "        return self.output[3], self.output[4]\n",
    "\n",
    "    @property\n",
    "    def loss(self):\n",
    "        return self.objective\n",
    "    \n",
    "vrnn = VRNN(x_t, batch_size, rnn_size, x_size, x_1_size, \n",
    "       z_size, z_1_size, phi_size, prior_size, theta_size)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "NEPOCH = 1\n",
    "nbatches=max(x_t_shape[0]//batch_size,1)\n",
    "\n",
    "loss = np.zeros(NEPOCH) # store the training progress here.\n",
    "\n",
    "# saver = tf.train.Saver()\n",
    "# sess = tf.InteractiveSession()\n",
    "# sess.run(tf.global_variables_initializer())\n",
    "# nbatches=1\n",
    "# for i in range(NEPOCH):\n",
    "#     for j in range(nbatches):\n",
    "#         sess.run(vrnn.get_train_op(), \n",
    "#                  feed_dict={x_t: x_data[j*10:(j+1)*10]})\n",
    "# #             nll, klgg = sess.run([vrnn_vals[\"nll\"], vrnn_vals[\"klgg\"]], \n",
    "# #                      feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "# #             loss[i] += (nll.mean()+klgg.mean())/batch_size\n",
    "\n",
    "# # save_path = saver.save(sess, \"/tmp/vrnn_1000.ckpt\")\n",
    "# plt.plot(loss);\n",
    "# print (loss[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# functions to train model and plot results\n",
    "def run_model(NEPOCH, vrnn_vals, x_data):\n",
    "    loss = np.zeros(NEPOCH)\n",
    "    for i in range(NEPOCH):\n",
    "        for j in range(x_data.shape[0]//batch_size):\n",
    "            sess.run(vrnn_vals[\"train_op\"], \n",
    "                     feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "            nll, klgg = sess.run([vrnn_vals[\"nll\"], vrnn_vals[\"klgg\"]], \n",
    "                     feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "            loss[i] += (nll.mean()+klgg.mean())/batch_size\n",
    "    return loss\n",
    "\n",
    "def plot_results(x_data, vrnn_vals, indx):\n",
    "    reconstruction=np.zeros((x_data.shape))\n",
    "    for j in range(x_data.shape[0]//batch_size):\n",
    "            mu, sigma = sess.run([vrnn_vals[\"theta_mu\"], vrnn_vals[\"theta_sigma\"]], \n",
    "                     feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "            reconstruction[j*10:(j+1)*10] = np.random.normal(mu, sigma)\n",
    "    plt.title(\"After 1000 epochs\")\n",
    "    plt.plot(x_data[:,indx], label=\"original\");\n",
    "    plt.plot(reconstruction[:,indx], label=\"reconstruction\");\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (1, 100, 2) for Tensor 'x_t_1:0', which has shape '(10, 100)'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-eca40d494d43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbatches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         sess.run(vrnn_vals[\"train_op\"], \n\u001b[0;32m---> 15\u001b[0;31m                  feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;31m#             nll, klgg = sess.run([vrnn_vals[\"nll\"], vrnn_vals[\"klgg\"]],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m#                      feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/wilkacarvalho/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    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/Users/wilkacarvalho/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    942\u001b[0m                 \u001b[0;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m                 \u001b[0;34m'which has shape %r'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 944\u001b[0;31m                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\n\u001b[0m\u001b[1;32m    945\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tensor %s may not be fed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot feed value of shape (1, 100, 2) for Tensor 'x_t_1:0', which has shape '(10, 100)'"
     ]
    }
   ],
   "source": [
    "NEPOCH = 1\n",
    "nbatches=max(x_t_shape[0]//batch_size,1)\n",
    "\n",
    "loss = np.zeros(NEPOCH) # store the training progress here.\n",
    "vrnn_vals = build_vrnn(batch_size, rnn_size, x_size, x_1_size, \n",
    "           z_size, z_1_size, phi_size, prior_size, theta_size)\n",
    "\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "nbatches=1\n",
    "for i in range(NEPOCH):\n",
    "    for j in range(nbatches):\n",
    "        sess.run(vrnn_vals[\"train_op\"], \n",
    "                 feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "#             nll, klgg = sess.run([vrnn_vals[\"nll\"], vrnn_vals[\"klgg\"]], \n",
    "#                      feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "#             loss[i] += (nll.mean()+klgg.mean())/batch_size\n",
    "\n",
    "# save_path = saver.save(sess, \"/tmp/vrnn_1000.ckpt\")\n",
    "plt.plot(loss);\n",
    "print (loss[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b7e5630>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SMA3kgaj4JLevDtr/7GwGXR89ImZhQTiN9IS4qceOTJNPiW0zK+JhzeT08Hu7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lAAAD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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e68f748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reconstruction=np.zeros((x_data.shape))\n",
    "for j in range(x_data.shape[0]//batch_size):\n",
    "        mu, sigma = sess.run([vrnn_vals[\"theta_mu\"], vrnn_vals[\"theta_sigma\"]], \n",
    "                 feed_dict={vrnn_vals[\"x_t\"]: x_data[j*10:(j+1)*10]})\n",
    "#         print (np.random.normal(mu, sigma))\n",
    "        reconstruction[j*10:(j+1)*10] = np.random.normal(mu, sigma)\n",
    "plt.title(\"After 1000 epochs\")\n",
    "plt.plot(x_data[:,0], label=\"original\");\n",
    "plt.plot(reconstruction[:,0], label=\"reconstruction\");\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7IG/UOw0HGcnXimLFA5OFZu/7zCCpWNDXnrqXeW7r3UHjvO3aZ9oW0BncdA/+C/f/9+68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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ee47400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = run_model(1000, vrnn_vals, x_data)\n",
    "plt.plot(loss);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XNZP16LtTvTIpJh6aqBN9EAZ84Kcf47rdn+jo/laC2joqUzxbdgnCkC29okDZ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139955cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(x_data, vrnn_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.03561348915\n"
     ]
    },
    {
     "data": {
      "image/png": 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JaYOT/r4rgvp9hySyKPb4LgANjfHNRFlT18jvRoU2NZRU1AZq9P7kHv7z/KMZ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\nBWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130c2b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = run_model(8000, vrnn_vals, x_data)\n",
    "plt.plot(loss);\n",
    "print(loss[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E6iW/vCTnrAY1Mnaaai0kl7ZJWZF8JSZK428IjuVgC0e/F0XqvvJRoYUQgvFi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1318baf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(x_data, vrnn_vals)\n",
    "# officially overfit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Resources\n",
    "another link to useful tensorflow example but with RNN\n",
    "1. [Useful example](https://rdipietro.github.io/tensorflow-scan-examples/#some-final-thoughts) for using scan with a vanilla RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thrown out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# old vrnn_call\n",
    "def vrnn_call(x_t, rnn_cell, z_size, \n",
    "              x_1_size, z_1_size, phi_size, theta_size, vals):\n",
    "    state=vals[\"state\"]\n",
    "    encoding_vals = \\\n",
    "                get_encoding(x_t, state[1], x_1_size, phi_size, z_size)\n",
    "    vals.update(encoding_vals)\n",
    "\n",
    "    prior_vals = \\\n",
    "                get_prior(state[1], prior_size, z_size)\n",
    "    vals.update(prior_vals)   \n",
    "\n",
    "    decoding_vals = \\\n",
    "                get_decoding(vals[\"z_t\"], state[1], z_1_size, theta_size, x_size)\n",
    "    vals.update(decoding_vals)\n",
    "\n",
    "    output, state = vrnn_recurrence(rnn_cell, vals[\"x_1\"], vals[\"z_1\"], state)\n",
    "    vals[\"state\"] = state\n",
    "    vals[\"output\"] = output \n",
    "# testing function \n",
    "if TESTING:\n",
    "    vals={\"state\": state}\n",
    "    vrnn_call(x_t, cell, z_size, \n",
    "                  x_1_size, z_1_size, phi_size, theta_size, vals)\n",
    "    sess = tf.InteractiveSession()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    o = sess.run([vals[\"state\"]], feed_dict={x_t:x_data_test})\n",
    "#     print(o)\n",
    "    sess.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}