learningPoles_network-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# import cv2\n",
    "# import csv\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import struct\n",
    "import h5py\n",
    "import scipy.io as sio\n",
    "# from scipy import ndimage\n",
    "from numpy import linalg as LA\n",
    "from IPython.display import display, Image\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "import tensorflow as tf\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "# Load synthetic dataset\n",
    "num_classes = 3\n",
    "\n",
    "# 60 samples\n",
    "#X = h5py.File('/Users/angelsrates/Documents/PhD/Robust Systems Lab/Activity Recognition/Code/poles_data.mat')\n",
    "#y = h5py.File('/Users/angelsrates/Documents/PhD/Robust Systems Lab/Activity Recognition/Code/poles_y.mat')\n",
    "\n",
    "# 300 samples\n",
    "X = scipy.io.loadmat('/Users/angelsrates/Documents/activity-recog-synthetic/poles_data2.mat')\n",
    "y = scipy.io.loadmat('/Users/angelsrates/Documents/activity-recog-synthetic/poles_y2.mat')\n",
    "\n",
    "X = X['data']\n",
    "X = np.squeeze(np.transpose(X))\n",
    "y = y['label']\n",
    "y = y - 1\n",
    "y = np.squeeze(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(4294967295)\n",
    "permutation = np.random.permutation(len(X))\n",
    "X = [X[perm] for perm in permutation]\n",
    "y = [y[perm] for perm in permutation]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225, 50)\n",
      "(225,)\n",
      "(75, 50)\n",
      "(75,)\n"
     ]
    }
   ],
   "source": [
    "#Select training and testing (75% and 25%)\n",
    "y = [int(i) for i in y]\n",
    "X_train = np.asarray(X[:225])\n",
    "y_train = np.asarray(y[:225])\n",
    "X_test = np.asarray(X[225:])\n",
    "y_test = np.asarray(y[225:])\n",
    "\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sys_par = np.array([[-1, 0.823676337910219, -1], [-1,-1.93592782488463,-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_batch_size(_train, step, batch_size):\n",
    "    # Function to fetch a \"batch_size\" amount of data from \"(X|y)_train\" data. \n",
    "    \n",
    "    shape = list(_train.shape)\n",
    "    #shape = list((batch_size, 1843200))\n",
    "    shape[0] = batch_size\n",
    "    #shape[1] = 1843200\n",
    "    batch_s = np.empty(shape)\n",
    "\n",
    "    for i in range(batch_size):\n",
    "        # Loop index\n",
    "        index = ((step-1)*batch_size + i) % len(_train)\n",
    "        batch_s[i] = _train[index]\n",
    "        #batch_s[i] = np.reshape(load_video(_train[index]), (1,1843200))\n",
    "\n",
    "    return batch_s\n",
    "\n",
    "def one_hot(y_):\n",
    "    # Function to encode output labels from number indexes \n",
    "    # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]\n",
    "    \n",
    "    y_ = y_.reshape(len(y_))\n",
    "    n_values = np.max(y_) + 1\n",
    "    return np.eye(n_values)[np.array(y_, dtype=np.int32)]  # Returns FLOATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "#alpha = 0.1\n",
    "def np_sparseLoss(y,p,alpha):\n",
    "    #Assume p is real\n",
    "    N = y.shape[0]\n",
    "    k = p.shape[1]\n",
    "    print(p)\n",
    "    W = np.zeros((N,k))\n",
    "    pw_idx = np.arange(1, N+1, 1)\n",
    "    #print(pw_idx.shape)\n",
    "    # Define vocabulary on set of poles\n",
    "    for i in range(k):\n",
    "        W[:,i] = np.power(np.squeeze(np.full((1, N), np.squeeze(p[0,i]))), pw_idx)\n",
    "    # ADMM - Lasso\n",
    "    clf = linear_model.Lasso(alpha=alpha)\n",
    "    clf.fit(W, y)\n",
    "    linear_model.Lasso(alpha=alpha, copy_X=True, fit_intercept=True, max_iter=1000,\n",
    "       normalize=True, positive=False, precompute=False, random_state=None,\n",
    "       selection='cyclic', tol=0.0001, warm_start=False)\n",
    "    coeff = clf.coef_\n",
    "    coeff = np.reshape(coeff, [k,1])\n",
    "    print(coeff)\n",
    "    return coeff\n",
    "\n",
    "#s = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])\n",
    "#c = np_sparseLoss(s,p,alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.framework import ops\n",
    "from tensorflow.python.ops import array_ops\n",
    "from tensorflow.python.ops import sparse_ops\n",
    "\n",
    "def coeff_grad(y,p,c, grad):\n",
    "    #y = op.inputs[0] \n",
    "    #p = op.inputs[1]\n",
    "    #c = op.outputs[0]\n",
    "    #W_shape = W.get_shape().as_list()\n",
    "    y_shape = y.get_shape().as_list()\n",
    "    p_shape = p.get_shape().as_list()\n",
    "    N = y_shape[0]\n",
    "    K = p_shape[1]\n",
    "    # W Calculation\n",
    "    impulse = []\n",
    "    idx = tf.cast(tf.stack(np.arange(1, N+1, 1)), tf.float64)\n",
    "    for cc in range(K):\n",
    "        impulse.append(tf.pow(tf.tile(p[:,cc], [50], name=None), idx , name=None))\n",
    "    W = tf.cast(tf.reshape(tf.stack(impulse, axis=1), (N,K)), tf.float64)\n",
    "    WW = tf.matrix_inverse(tf.matmul(tf.transpose(W), W))\n",
    "    Wty = tf.matmul(tf.transpose(W), y)\n",
    "    WWc = tf.matmul(WW, c)\n",
    "    output_dW = []\n",
    "    # Grad wrt W\n",
    "    for i in range(N):\n",
    "        for j in range(K):\n",
    "            output_dWty = []\n",
    "            output_dWWc = []\n",
    "            for n in range(K):\n",
    "                gr1 = tf.gradients(Wty[n,:], [W[i,j]])\n",
    "                gr1 = [tf.constant(0, dtype=tf.float64) if t == None else t for t in gr1]\n",
    "                gr2 = tf.gradients(WWc[n,:], [W[i,j]])\n",
    "                gr2 = [tf.constant(0, dtype=tf.float64) if t == None else t for t in gr2]\n",
    "                output_dWty.append(gr1)\n",
    "                output_dWWc.append(gr2)\n",
    "            gr = tf.matmul(WW, tf.subtract(tf.stack(output_dWty), tf.stack(output_dWWc)))\n",
    "            output_dW.append(gr)\n",
    "    dW = tf.reshape(tf.squeeze(tf.stack(output_dW)), [N, K, K])\n",
    "    \n",
    "    # Grad wrt p\n",
    "    grp = []\n",
    "    for k in range(K):\n",
    "        output_dp = []\n",
    "        for i in range(N):\n",
    "            output_dp.append(tf.multiply(tf.reshape(tf.multiply(tf.cast(i, tf.float64), tf.pow(p[0, k],tf.cast(i-1, tf.float64))), [1]), tf.reshape(dW[i,k,:], [K,1])))\n",
    "        grp.append(tf.add_n(output_dp))\n",
    "    dp = tf.stack(grp)\n",
    "    dp_list = []\n",
    "    for j in range(K):\n",
    "        dp_list.append(tf.reduce_sum(tf.multiply(dp[j,:,:], grad)))\n",
    "    dp = tf.reshape(tf.stack(dp_list), [1, K])\n",
    "    print('dc/dp size:', dp.get_shape())\n",
    "    \n",
    "    #dW = tf.reshape(dW, [N*K,K,1])\n",
    "    #dW_list = []\n",
    "    #for j in range(N*K):\n",
    "    #    dW_list.append(tf.reduce_sum(tf.multiply(dW[j,:,:], grad)))\n",
    "    #dW = tf.reshape(tf.stack(dW_list), [N, K])\n",
    "    #print('dc/dW size:', dW.get_shape())\n",
    "    \n",
    "    # Grad wrt y\n",
    "    dy = tf.matmul(WW, tf.transpose(W))\n",
    "    dy_list = []\n",
    "    for j in range(N):\n",
    "        dy_list.append(tf.reduce_sum(tf.multiply(dy[:,j], grad)))\n",
    "    dy = tf.reshape(tf.stack(dy_list), [N, 1])\n",
    "    print('dc/dy size:', dy.get_shape())\n",
    "    \n",
    "    # Grad wrt alpha   \n",
    "    dalpha = tf.matmul(tf.scalar_mul(tf.constant(-1, dtype=tf.float64), WW), tf.sign(c))\n",
    "    dalpha = tf.reduce_sum(tf.multiply(dalpha, grad))\n",
    "    print('dc/dalpha size:', dalpha.get_shape())\n",
    "    \n",
    "    return dy, dp, dalpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def py_func(func, inp, Tout, stateful=True, name=None, grad=None):\n",
    "\n",
    "    # Need to generate a unique name to avoid duplicates:\n",
    "    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1000000000000000))\n",
    "\n",
    "    tf.RegisterGradient(rnd_name)(grad)\n",
    "    g = tf.get_default_graph()\n",
    "    with g.gradient_override_map({\"PyFunc\": rnd_name}):\n",
    "        return tf.py_func(func, inp, Tout, stateful=stateful, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.framework import ops\n",
    "\n",
    "def tf_coeff_grad(y,p,alpha, name=None):\n",
    "\n",
    "    with ops.op_scope([y,p,alpha], name, \"CoeffGrad\") as name:\n",
    "        z = py_func(np_sparseLoss,\n",
    "                        [y,p,alpha],\n",
    "                        [tf.double],\n",
    "                        name=name,\n",
    "                        grad=coeff_grad)  # <-- here's the call to the gradient\n",
    "        return z[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)\n",
      "Tensor(\"CoeffGrad_10:0\", dtype=float64)\n",
      "dc/dp size: (1, 3)\n",
      "dc/dy size: (50, 1)\n",
      "dc/dalpha size: ()\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "coeff_grad() takes exactly 4 arguments (2 given)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-f81b37c8c9fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;31m#cost = tf.subtract(pred, labels)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGradientDescentOptimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;31m# Evaluate model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/angelsrates/miniconda3/envs/deepvision/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(self, loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss)\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0maggregation_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation_method\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0mcolocate_gradients_with_ops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolocate_gradients_with_ops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m         grad_loss=grad_loss)\n\u001b[0m\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m     \u001b[0mvars_with_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgrads_and_vars\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/angelsrates/miniconda3/envs/deepvision/lib/python2.7/site-packages/tensorflow/python/training/optimizer.pyc\u001b[0m in \u001b[0;36mcompute_gradients\u001b[0;34m(self, loss, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, grad_loss)\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0mgate_gradients\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgate_gradients\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mOptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGATE_OP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m         \u001b[0maggregation_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregation_method\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m         colocate_gradients_with_ops=colocate_gradients_with_ops)\n\u001b[0m\u001b[1;32m    355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgate_gradients\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mOptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGATE_GRAPH\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m       \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontrol_flow_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/angelsrates/miniconda3/envs/deepvision/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.pyc\u001b[0m in \u001b[0;36mgradients\u001b[0;34m(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method)\u001b[0m\n\u001b[1;32m    480\u001b[0m                 \u001b[0;31m# If grad_fn was found, do not use SymbolicGradient even for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m                 \u001b[0;31m# functions.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m                 \u001b[0min_grads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mout_grads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m               \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m                 \u001b[0;31m# For function call ops, we add a 'SymbolicGradient'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: coeff_grad() takes exactly 4 arguments (2 given)"
     ]
    }
   ],
   "source": [
    "from scipy import signal\n",
    "#import control\n",
    "from scipy.signal import step2\n",
    "import math\n",
    "\n",
    "# Parameters\n",
    "learning_rate = 0.0015\n",
    "#training_iters = 45000\n",
    "batch_size = 1\n",
    "#display_step = 10\n",
    "\n",
    "# Network Parameters\n",
    "n_input = 50\n",
    "n_classes = 3\n",
    "N = 50\n",
    "#dropout = 0.75 # Dropout, probability to keep units\n",
    "\n",
    "# tf Graph input\n",
    "x = tf.placeholder(tf.float64, [n_input, 1])\n",
    "y = tf.placeholder(tf.float64, [1, n_classes])\n",
    "grad = tf.constant(0, dtype=tf.float64)\n",
    "#y = tf.placeholder(tf.int32, [1,1])\n",
    "\n",
    "def index_along_every_row(array, index):\n",
    "    N,_ = array.shape \n",
    "    return array[np.arange(N), index]\n",
    "\n",
    "def build_hankel_tensor(x, nr, nc, N, dim):\n",
    "    cidx = np.arange(0, nc, 1)\n",
    "    ridx = np.transpose(np.arange(1, nr+1, 1))\n",
    "    Hidx = np.transpose(np.tile(ridx, (nc,1))) + dim*np.tile(cidx, (nr,1))\n",
    "    Hidx = Hidx - 1\n",
    "    arr = tf.reshape(x[:], (1,N))\n",
    "    return tf.py_func(index_along_every_row, [arr, Hidx], [tf.float64])[0]\n",
    "\n",
    "def build_hankel(x, nr, nc, N, dim):\n",
    "    cidx = np.arange(0, nc, 1)\n",
    "    ridx = np.transpose(np.arange(1, nr+1, 1))\n",
    "    Hidx = np.transpose(np.tile(ridx, (nc,1))) + dim*np.tile(cidx, (nr,1))\n",
    "    Hidx = Hidx - 1\n",
    "    arr = x[:]\n",
    "    return arr[Hidx]\n",
    "\n",
    "# Create model\n",
    "def poles_net(x,grad):\n",
    "    # Reshape input picture\n",
    "    #x = tf.reshape(x, shape=[-1, , 50, 1])\n",
    "    # Change accordingly\n",
    "    dim = 1\n",
    "    N = 50\n",
    "    num_poles = 2\n",
    "    # Complex poles\n",
    "    #idx = tf.cast(tf.stack(np.arange(1, N+1, 1)), tf.complex128)\n",
    "    #p11 = tf.multiply(tf.cast(tf.sqrt(weights['r11']), tf.complex128), tf.exp(tf.complex(tf.constant(0, tf.float64), weights['theta11'])))\n",
    "    #p12 = tf.multiply(tf.cast(tf.sqrt(weights['r12']), tf.complex128), tf.exp(tf.complex(tf.constant(0, tf.float64), -weights['theta12'])))\n",
    "    #p21 = tf.multiply(tf.cast(tf.sqrt(weights['r21']), tf.complex128), tf.exp(tf.complex(tf.constant(0, tf.float64), weights['theta21'])))\n",
    "    #p22 = tf.multiply(tf.cast(tf.sqrt(weights['r22']), tf.complex128), tf.exp(tf.complex(tf.constant(0, tf.float64), -weights['theta22'])))   \n",
    "    #y11 = tf.pow(tf.tile(p11, [50], name=None), idx , name=None)\n",
    "    #y12 = tf.pow(tf.tile(p12, [50], name=None), idx, name=None)\n",
    "    #y21 = tf.pow(tf.tile(p21, [50], name=None), idx, name=None)\n",
    "    #y22 = tf.pow(tf.tile(p22, [50], name=None), idx, name=None)\n",
    "    #W = tf.cast(tf.reshape(tf.stack([y11, y21, y12, y22], 1), (N,4)), tf.float64)\n",
    "    \n",
    "    # Real poles\n",
    "    idx = tf.cast(tf.stack(np.arange(1, N+1, 1)), tf.float64)\n",
    "    p1 = weights['real1']\n",
    "    p2 = weights['real2']\n",
    "    p3 = weights['real3']\n",
    "    p = tf.stack([p1, p2, p3], 1)\n",
    "    \n",
    "    #alpha = tf.constant([0.1])\n",
    "    alpha = weights['alpha']\n",
    "    #a = tf.matmul(W, W, adjoint_a=True)\n",
    "    #c = tf.matrix_inverse(tf.cast(a, tf.float64), adjoint=False, name=None)\n",
    "    #coeff = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(tf.transpose(W), W)), tf.transpose(W)), tf.reshape(x, (N,1)))\n",
    "    #alpha_ind = tf.reshape(tf.stack([alpha_ind1, alpha_ind2]), (1,2))\n",
    "    #coeff = tf.matrix_solve_ls(W, tf.reshape(x, (N,1)), fast=False, l2_regularizer=0.002, name=None)\n",
    "    #x = tf.cast(tf.reshape(x, (N,1)), tf.complex128)\n",
    "    #coeff = tf.transpose(coeff)\n",
    "    coeff = tf_coeff_grad(x,p,alpha)\n",
    "    print(coeff)\n",
    "    out = tf.add(tf.matmul(tf.transpose(tf.cast(coeff, tf.float32)), weights['out']), biases['out'])\n",
    "    diff = tf.subtract(tf.cast(out, tf.float64), y)\n",
    "    grad = coeff_grad(x,p,coeff,grad)\n",
    "    return [coeff, out]\n",
    "\n",
    "weights = {\n",
    "    'r11': tf.Variable(tf.random_uniform([1], minval=(0.02)**2, maxval=(1)**2, dtype=tf.float64)), # Complex poles\n",
    "    'r12': tf.Variable(tf.random_uniform([1], minval=(0.02)**2, maxval=(1)**2, dtype=tf.float64)),\n",
    "    'theta11': tf.Variable(tf.random_uniform([1], minval=0, maxval=math.pi, dtype=tf.float64)),\n",
    "    'theta12': tf.Variable(tf.random_uniform([1], minval=0, maxval=math.pi, dtype=tf.float64)),\n",
    "    'r21': tf.Variable(tf.random_uniform([1], minval=(0.02)**2, maxval=(1)**2, dtype=tf.float64)),\n",
    "    'r22': tf.Variable(tf.random_uniform([1], minval=(0.02)**2, maxval=(1)**2, dtype=tf.float64)),\n",
    "    'theta21': tf.Variable(tf.random_uniform([1], minval=0, maxval=math.pi, dtype=tf.float64)),\n",
    "    'theta22': tf.Variable(tf.random_uniform([1], minval=0, maxval=math.pi, dtype=tf.float64)),\n",
    "    'real1': tf.Variable(tf.random_uniform([1], minval=-1, maxval=1, dtype=tf.float64)), # Real poles\n",
    "    'real2': tf.Variable(tf.random_uniform([1], minval=-1, maxval=1, dtype=tf.float64)),\n",
    "    'real3': tf.Variable(tf.random_uniform([1], minval=-1, maxval=1, dtype=tf.float64)),\n",
    "    'alpha' : tf.Variable(tf.constant(0.1, dtype=tf.float64)),\n",
    "    #'sys_par1': tf.Variable(tf.random_normal([1], dtype=tf.float64)),\n",
    "    #'sys_par2': tf.Variable(tf.random_normal([1], dtype=tf.float64)),\n",
    "    'out': tf.Variable(tf.random_normal([2, n_classes]))\n",
    "}\n",
    "    \n",
    "biases = {\n",
    "    'out': tf.Variable(tf.random_normal([1, n_classes]))\n",
    "}\n",
    "\n",
    "[coeff, pred]= poles_net(x,grad)\n",
    "\n",
    "# Define loss and optimizer\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
    "#cost = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=pred, onehot_labels=y))\n",
    "#cost = tf.reduce_mean(tf.losses.mean_squared_error(predictions=pred, labels=y))\n",
    "#cost = tf.subtract(pred, labels)\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0).minimize(cost)\n",
    "\n",
    "# Evaluate model\n",
    "correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_test = one_hot(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 14 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 15 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 16 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 17 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 18 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 19 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 20 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 21 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 22 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 23 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 24 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 25 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 26 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 27 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
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      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 28 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 29 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 30 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 31 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 32 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 33 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 34 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 35 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 36 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 37 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 38 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 39 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 40 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 41 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
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      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 42 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 43 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 44 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 45 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 46 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 47 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 48 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 49 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 50 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 51 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 52 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 53 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 54 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 55 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 56 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 57 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 58 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 59 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 60 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 61 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 62 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 63 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Trajectory: 64 [array([[ 0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 65 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 66 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 67 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Trajectory: 68 [array([[-0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 69 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 70 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Trajectory: 71 [array([[ 0.],\n",
      "       [-0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[ 0.]\n",
      " [-0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 72 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 73 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 0.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "(50, 1)\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Trajectory: 74 [array([[-0.],\n",
      "       [ 0.]])]\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Testing Accuracy: 1.0\n",
      "[[-0.66370539  0.23084568]]\n",
      "[[-0.]\n",
      " [ 0.]]\n",
      "Final Testing Accuracy: 0.413333333333\n"
     ]
    }
   ],
   "source": [
    "# Launch the graph\n",
    "training_iters = X_train.shape[0]*10\n",
    "display_step = 1\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    step = 1\n",
    "    # Keep training until reach max iterations\n",
    "    train_acc = 0\n",
    "    while step * batch_size <= training_iters:\n",
    "        batch_x = np.reshape(extract_batch_size(X_train,step,batch_size), [50, 1])\n",
    "        batch_y = extract_batch_size(one_hot(y_train),step,batch_size)\n",
    "        #batch_y = np.reshape(extract_batch_size(y_train,step,batch_size), (1,1))\n",
    "        # Run optimization op (backprop)\n",
    "        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n",
    "        if step % display_step == 0:\n",
    "            # Calculate batch loss and accuracy\n",
    "            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n",
    "                                                              y: batch_y})\n",
    "            train_acc += acc \n",
    "            print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n",
    "                  \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n",
    "                  \"{:.5f}\".format(acc))\n",
    "        step += 1\n",
    "    print('Final Training Accuracy:', train_acc/(X_train.shape[0]*10))\n",
    "    print(\"Optimization Finished!\")\n",
    "    \n",
    "    acc = 0\n",
    "    for i in range(X_test.shape[0]):\n",
    "        test = np.reshape(X_test[i,:], [50,1])\n",
    "        print(test.shape)\n",
    "        label = np.reshape(y_test[i,:], (1,3))\n",
    "        #label = np.reshape(y_test[i], (1,1))\n",
    "        print(\"Trajectory:\", i, \\\n",
    "            sess.run([coeff], feed_dict={x: test, y: label}))\n",
    "        print(\"Testing Accuracy:\", \\\n",
    "            sess.run(accuracy, feed_dict={x: test, y: label}))\n",
    "        acc += sess.run(accuracy, feed_dict={x: test, y: label})\n",
    "    print('Final Testing Accuracy:', acc/X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Confusion matrix code\n",
    "\n",
    "pred = multilayer_perceptron(x, weights, biases)\n",
    "    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "    init = tf.initialize_all_variables()\n",
    "    sess.run(init)\n",
    "    for epoch in xrange(150):\n",
    "            for i in xrange(total_batch):\n",
    "                    train_step.run(feed_dict = {x: train_arrays, y: train_labels})\n",
    "                    avg_cost += sess.run(cost, feed_dict={x: train_arrays, y: train_labels})/total_batch         \n",
    "            if epoch % display_step == 0:\n",
    "                    print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n",
    "\n",
    "    #metrics\n",
    "    y_p = tf.argmax(pred, 1)\n",
    "    val_accuracy, y_pred = sess.run([accuracy, y_p], feed_dict={x:test_arrays, y:test_label})\n",
    "\n",
    "    print \"validation accuracy:\", val_accuracy\n",
    "    y_true = np.argmax(test_label,1)\n",
    "    print \"Precision\", sk.metrics.precision_score(y_true, y_pred)\n",
    "    print \"Recall\", sk.metrics.recall_score(y_true, y_pred)\n",
    "    print \"f1_score\", sk.metrics.f1_score(y_true, y_pred)\n",
    "    print \"confusion_matrix\"\n",
    "    print sk.metrics.confusion_matrix(y_true, y_pred)\n",
    "    fpr, tpr, tresholds = sk.metrics.roc_curve(y_true, y_pred)"
   ]
  }
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