PyTorch_nn_SGD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import TensorDataset\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Input examples of various LPs\n",
    "X_np = np.array([[1, 1, 2, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 1, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 1, 1, 1, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 2, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 2, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 2, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 2, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 2, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 2, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 2, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 1, 1, 1, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 1, 1, 1, 1, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 1, 1, 1, 1, 4, -1, -1, -1, -1],\n",
    "             [2, 1, 1, 2, 1, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 1, 2, 1, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 1, 2, 1, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 1, 2, 1, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 1, 2, 1, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 1, 1, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 1, 1, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 1, 1, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 1, 1, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 1, 1, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 1, 1, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 1, 2, 1, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 1, 2, 1, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 1, 2, 1, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 1, 1, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 1, 1, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 1, 1, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 1, 1, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 1, 1, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 1, 1, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 3, 2, 1, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 3, 2, 1, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 3, 2, 1, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 3, 1, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 3, 1, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 3, 1, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 1, 3, 1, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 1, 3, 1, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 1, 3, 1, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 2, 1, 1, 3, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 2, 1, 1, 3, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 2, 1, 1, 3, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 1, 2, 1, 3, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 1, 2, 1, 3, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 1, 2, 1, 3, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 1, 1, 2, 3, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 1, 1, 2, 3, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 1, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 3, 2, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 3, 2, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 3, 2, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [1, 2, 3, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [1, 2, 3, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [1, 2, 3, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [3, 1, 2, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 1, 2, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 1, 2, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 3, 1, 2, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 1, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [2, 3, 1, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [2, 3, 1, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 3, 1, 2, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 3, 1, 2, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 3, 1, 2, 3, -1, 0, -1, -1],\n",
    "             [2, 1, 3, 3, 2, 1, 4, -1, -1, 0, -1],\n",
    "             [2, 1, 3, 3, 2, 1, 4, 0, -1, -1, -1],\n",
    "             [2, 1, 3, 3, 2, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 2, 3, 1, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 2, 3, 1, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 2, 3, 1, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 2, 1, 3, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 2, 1, 3, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 2, 1, 3, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 1, 3, 2, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 1, 3, 2, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 1, 3, 2, 4, -1, 0, -1, -1],\n",
    "             [3, 2, 1, 1, 2, 3, 4, -1, -1, 0, -1],\n",
    "             [3, 2, 1, 1, 2, 3, 4, 0, -1, -1, -1],\n",
    "             [3, 2, 1, 1, 2, 3, 4, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 2, 3, 4, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 2, 3, 4, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 2, 3, 4, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 2, 4, 3, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 2, 4, 3, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 2, 4, 3, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 2, 3, 4, 7, -1, -1, -1, -1],\n",
    "             [2, 3, 4, 2, 4, 3, 7, -1, -1, -1, -1],\n",
    "             [2, 3, 4, 3, 2, 4, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 3, 2, 4, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 3, 2, 4, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 3, 4, 2, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 3, 4, 2, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 3, 4, 2, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 3, 4, 2, 7, -1, -1, -1, -1],\n",
    "             [2, 3, 4, 3, 2, 4, 7, -1, -1, -1, -1],\n",
    "             [2, 3, 4, 4, 2, 3, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 4, 2, 3, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 4, 2, 3, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 4, 2, 3, 7, -1, -1, -1, -1],\n",
    "             [2, 3, 4, 4, 3, 2, 7, -1, -1, 0, -1],\n",
    "             [2, 3, 4, 4, 3, 2, 7, 0, -1, -1, -1],\n",
    "             [2, 3, 4, 4, 3, 2, 7, -1, 0, -1, -1],\n",
    "             [2, 3, 4, 4, 3, 2, 7, -1, -1, -1, -1],\n",
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    "             [8, 7, 6, 7, 6, 8, 15, -1, -1, -1, -1],\n",
    "             [8, 7, 6, 7, 8, 6, 15, -1, -1, 0, -1],\n",
    "             [8, 7, 6, 7, 8, 6, 15, 0, -1, -1, -1],\n",
    "             [8, 7, 6, 7, 8, 6, 15, -1, 0, -1, -1],\n",
    "             [8, 7, 6, 7, 8, 6, 15, -1, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 15, -1, -1, 0, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 15, 0, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 15, -1, 0, -1, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 15, -1, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 15, -1, -1, 0, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 15, 0, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 15, -1, 0, -1, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 15, -1, -1, -1, -1],\n",
    "             [7, 6, 8, 6, 7, 8, 21, 0, -1, -1, -1],\n",
    "             [7, 6, 8, 6, 8, 7, 21, -1, -1, 0, -1],\n",
    "             [7, 6, 8, 8, 6, 7, 6, -1, -1, -1, -1],\n",
    "             [7, 6, 8, 7, 8, 6, 7, -1, 0, -1, -1],\n",
    "             [6, 7, 8, 8, 7, 6, 8, -1, -1, -1, -1],\n",
    "             [6, 7, 8, 7, 6, 8, 8, -1, 0, -1, -1],\n",
    "             [6, 7, 8, 6, 8, 7, 7, -1, -1, 0, -1],\n",
    "             [6, 7, 8, 6, 7, 8, 7, 0, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 13, -1, 0, -1, -1],\n",
    "             [8, 7, 6, 8, 7, 6, 8, 0, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 8, -1, -1, -1, -1],\n",
    "             [8, 7, 6, 8, 6, 7, 7, -1, -1, 0, -1],\n",
    "             [8, 6, 7, 6, 8, 7, 21, 0, -1, -1, -1],\n",
    "             [8, 6, 7, 8, 7, 6, 21, -1, 0, -1, -1],\n",
    "             [8, 6, 7, 8, 7, 6, 25, -1, -1, -1, -1],\n",
    "             [8, 6, 7, 7, 8, 6, 28, -1, -1, -1, -1],\n",
    "             [7, 8, 6, 8, 7, 6, 21, -1, -1, 0, -1],\n",
    "             [7, 8, 6, 7, 6, 8, 21, -1, -1, -1, -1],\n",
    "             [7, 8, 6, 7, 8, 6, 21, -1, 0, -1, -1],\n",
    "             [7, 8, 6, 7, 8, 6, 21, 0, -1, -1, -1],\n",
    "             [6, 8, 7, 8, 6, 7, 7, 0, -1, -1, -1],\n",
    "             [6, 8, 7, 8, 7, 6, 8, -1, 0, -1, -1],\n",
    "             [6, 8, 7, 8, 7, 6, 6, -1, -1, -1, -1],\n",
    "             [6, 8, 7, 7, 8, 6, 8, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 8, 2, 9, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 8, 2, 9, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 8, 2, 9, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 8, 2, 9, 19, -1, -1, -1, -1],\n",
    "             [2, 8, 9, 2, 8, 9, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 2, 8, 9, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 2, 8, 9, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 2, 8, 9, 19, -1, -1, -1, -1],\n",
    "             [2, 8, 9, 9, 2, 8, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 9, 2, 8, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 9, 2, 8, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 9, 2, 8, 19, -1, -1, -1, -1],\n",
    "             [2, 8, 9, 2, 9, 8, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 2, 9, 8, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 2, 9, 8, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 2, 9, 8, 19, -1, -1, -1, -1],\n",
    "             [2, 8, 9, 8, 9, 2, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 8, 9, 2, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 8, 9, 2, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 8, 9, 2, 19, -1, -1, -1, -1],\n",
    "             [2, 8, 9, 9, 8, 2, 19, -1, -1, 0, -1],\n",
    "             [2, 8, 9, 9, 8, 2, 19, 0, -1, -1, -1],\n",
    "             [2, 8, 9, 9, 8, 2, 19, -1, 0, -1, -1],\n",
    "             [2, 8, 9, 9, 8, 2, 19, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 3, 8, 9, 8, -1, -1, 0, -1],\n",
    "             [9, 8, 3, 3, 8, 9, 8, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 3, 8, 9, 9, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 3, 8, 9, 9, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 3, 9, 8, 8, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 3, 9, 8, 8, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 3, 9, 8, 8, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 3, 9, 8, 8, -1, -1, 0, -1],\n",
    "             [9, 8, 3, 8, 3, 9, 9, -1, -1, 0, -1],\n",
    "             [9, 8, 3, 8, 3, 9, 9, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 8, 3, 9, 8, -1, -1, 0, -1],\n",
    "             [9, 8, 3, 8, 3, 9, 8, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 8, 9, 3, 8, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 8, 9, 3, 9, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 8, 9, 3, 3, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 8, 9, 3, 8, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 3, 8, 9, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 3, 8, 3, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 9, 3, 8, 8, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 3, 8, 8, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 8, 3, 3, -1, 0, -1, -1],\n",
    "             [9, 8, 3, 9, 8, 3, 3, -1, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 8, 3, 3, 0, -1, -1, -1],\n",
    "             [9, 8, 3, 9, 8, 3, 9, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 5, 7, 9, 7, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 5, 7, 9, 7, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 5, 7, 9, 7, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 5, 7, 9, 9, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 5, 9, 7, 5, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 5, 9, 7, 8, -1, -1, -1, -1],\n",
    "             [9, 5, 7, 5, 9, 7, 7, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 5, 9, 7, 9, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 7, 5, 9, 9, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 7, 5, 9, 7, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 7, 5, 9, 5, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 7, 5, 9, 7, -1, -1, -1, -1],\n",
    "             [9, 5, 7, 7, 9, 5, 7, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 7, 9, 5, 7, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 7, 9, 5, 9, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 7, 9, 5, 7, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 9, 5, 7, 9, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 9, 5, 7, 9, -1, 0, -1, -1],\n",
    "             [9, 5, 7, 9, 5, 7, 7, 0, -1, -1, -1],\n",
    "             [9, 5, 7, 9, 5, 7, 7, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 9, 7, 5, 9, -1, -1, -1, -1],\n",
    "             [9, 5, 7, 9, 7, 5, 7, -1, -1, -1, -1],\n",
    "             [9, 5, 7, 9, 7, 5, 7, -1, -1, 0, -1],\n",
    "             [9, 5, 7, 9, 7, 5, 5, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 6, 7, 9, 22, -1, -1, 0, -1],\n",
    "             [7, 6, 9, 6, 7, 9, 22, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 6, 7, 9, 22, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 6, 7, 9, 22, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 6, 9, 7, 9, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 6, 9, 7, 24, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 6, 9, 7, 7, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 6, 9, 7, 7, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 7, 6, 9, 9, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 7, 6, 9, 6, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 7, 6, 9, 7, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 7, 6, 9, 22, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 7, 9, 6, 22, -1, -1, 0, -1],\n",
    "             [7, 6, 9, 7, 9, 6, 9, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 7, 9, 6, 7, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 7, 9, 6, 6, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 6, 7, 9, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 6, 7, 22, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 9, 6, 7, 22, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 6, 7, 22, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 7, 6, 9, 0, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 7, 6, 9, -1, 0, -1, -1],\n",
    "             [7, 6, 9, 9, 7, 6, 7, -1, -1, -1, -1],\n",
    "             [7, 6, 9, 9, 7, 6, 22, -1, -1, -1, -1]])\n",
    "\n",
    "y_np = np.array([[5],\n",
    "             [6],\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# N is batch size; D_in is input dimension;\n",
    "# H_{i} is hidden dimension of ith layer; D_out is output dimension.\n",
    "N, D_in, H_1, H_2, H_3, D_out = 1102, 11, 128, 256, 256, 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5, 6, 4, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2, 4, 4, 4, 2, 2, 2, 6, 6, 6, 6, 6]\n"
     ]
    }
   ],
   "source": [
    "def multi_class_format(numpy_array):\n",
    "    new = []\n",
    "    for item in y_np:\n",
    "        for i in item:\n",
    "            new.append(i)\n",
    "    \n",
    "    class_labels = []\n",
    "    \n",
    "    for i in new:\n",
    "        identifier = i-1\n",
    "        class_labels.append(identifier)\n",
    "        \n",
    "    return class_labels\n",
    "\n",
    "Y_array = multi_class_format(y_np)\n",
    "print Y_array[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_classes = 7\n",
    "def get_one_hot(targets, nb_classes):\n",
    "    return np.eye(nb_classes)[np.array(targets).reshape(-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape: (1102, 7) \n",
      "[[ 0.  0.  0. ...,  1.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  1.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  1.]\n",
      " ..., \n",
      " [ 1.  0.  0. ...,  0.  0.  0.]\n",
      " [ 1.  0.  0. ...,  0.  0.  0.]\n",
      " [ 0.  0.  0. ...,  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "y_one_hot = get_one_hot(Y_array, 7)\n",
    "print \"Output shape: {} \".format(y_one_hot.shape)\n",
    "print y_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: 881, y_train: 881 , X_test: 221 , y_test: 221 \n"
     ]
    }
   ],
   "source": [
    "# Make a train/test split using 20% test size\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_np, y_one_hot, test_size=0.20, shuffle = True)\n",
    "print \"X_train: {}, y_train: {} , X_test: {} , y_test: {} \".format(X_train.shape[0], y_train.shape[0], X_test.shape[0], y_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape: torch.Size([881, 11]) \n",
      "Training labels shape: torch.Size([881, 7]) \n",
      "Test set shape: torch.Size([221, 11]) \n",
      "Test labels shape: torch.Size([221, 7]) \n"
     ]
    }
   ],
   "source": [
    "X_train = torch.from_numpy(X_train).type(torch.FloatTensor)\n",
    "y_train = torch.from_numpy(y_train).type(torch.LongTensor)\n",
    "\n",
    "X_test = torch.from_numpy(X_test).type(torch.FloatTensor)\n",
    "y_test = torch.from_numpy(y_test).type(torch.LongTensor)\n",
    "\n",
    "print \"Training set shape: {} \".format(X_train.size())\n",
    "print \"Training labels shape: {} \".format(y_train.size())\n",
    "\n",
    "print \"Test set shape: {} \".format(X_test.size())\n",
    "print \"Test labels shape: {} \".format(y_test.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Linear(in_features=11, out_features=128)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=128, out_features=256)\n",
      "  (3): ReLU()\n",
      "  (4): Linear(in_features=256, out_features=256)\n",
      "  (5): ReLU()\n",
      "  (6): Linear(in_features=256, out_features=7)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#Instantiating a network model\n",
    "net = torch.nn.Sequential(\n",
    "    torch.nn.Linear(D_in, H_1),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(H_1, H_2),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(H_2, H_3),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(H_3, D_out)\n",
    "    )\n",
    "\n",
    "print net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Converting the training one hot vector labels into indices because PyTorch does not take one-hot vectors\n",
    "targetnp = y_train.numpy()\n",
    "idxs = np.where(targetnp > 0)[1]\n",
    "new_targets = torch.LongTensor(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Converting the test one hot vector labels into indices\n",
    "test_targetnp = y_test.numpy()\n",
    "test_idxs = np.where(test_targetnp > 0)[1]\n",
    "new_test_targets = torch.LongTensor(test_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a Stochastic gradient Descent optimizer\n",
    "learning_rate = 1e-3\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate, momentum = 0, weight_decay = 1e-4)\n",
    "# create a loss function\n",
    "loss_func = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training Epoch: 0 Average loss: 1.9299, Training Accuracy: 42/881 (4.7673%)\n",
      "\n",
      "\n",
      "Training Epoch: 1000 Average loss: 1.5344, Training Accuracy: 670/881 (76.0499%)\n",
      "\n",
      "\n",
      "Training Epoch: 2000 Average loss: 1.3385, Training Accuracy: 678/881 (76.9580%)\n",
      "\n",
      "\n",
      "Training Epoch: 3000 Average loss: 1.0088, Training Accuracy: 739/881 (83.8820%)\n",
      "\n",
      "\n",
      "Training Epoch: 4000 Average loss: 0.6576, Training Accuracy: 771/881 (87.5142%)\n",
      "\n",
      "\n",
      "Training Epoch: 5000 Average loss: 0.4430, Training Accuracy: 788/881 (89.4438%)\n",
      "\n",
      "\n",
      "Training Epoch: 6000 Average loss: 0.3449, Training Accuracy: 799/881 (90.6924%)\n",
      "\n",
      "\n",
      "Training Epoch: 7000 Average loss: 0.2964, Training Accuracy: 801/881 (90.9194%)\n",
      "\n",
      "\n",
      "Training Epoch: 8000 Average loss: 0.2672, Training Accuracy: 803/881 (91.1464%)\n",
      "\n",
      "\n",
      "Training Epoch: 9000 Average loss: 0.2467, Training Accuracy: 807/881 (91.6005%)\n",
      "\n",
      "\n",
      "Training Epoch: 10000 Average loss: 0.2312, Training Accuracy: 812/881 (92.1680%)\n",
      "\n",
      "\n",
      "Training Epoch: 11000 Average loss: 0.2192, Training Accuracy: 816/881 (92.6220%)\n",
      "\n",
      "\n",
      "Training Epoch: 12000 Average loss: 0.2097, Training Accuracy: 818/881 (92.8490%)\n",
      "\n",
      "\n",
      "Training Epoch: 13000 Average loss: 0.2019, Training Accuracy: 817/881 (92.7355%)\n",
      "\n",
      "\n",
      "Training Epoch: 14000 Average loss: 0.1954, Training Accuracy: 818/881 (92.8490%)\n",
      "\n",
      "\n",
      "Training Epoch: 15000 Average loss: 0.1896, Training Accuracy: 818/881 (92.8490%)\n",
      "\n",
      "\n",
      "Training Epoch: 16000 Average loss: 0.1847, Training Accuracy: 820/881 (93.0760%)\n",
      "\n",
      "\n",
      "Training Epoch: 17000 Average loss: 0.1803, Training Accuracy: 822/881 (93.3031%)\n",
      "\n",
      "\n",
      "Training Epoch: 18000 Average loss: 0.1761, Training Accuracy: 825/881 (93.6436%)\n",
      "\n",
      "\n",
      "Training Epoch: 19000 Average loss: 0.1723, Training Accuracy: 825/881 (93.6436%)\n",
      "\n",
      "\n",
      "Training Epoch: 20000 Average loss: 0.1688, Training Accuracy: 824/881 (93.5301%)\n",
      "\n",
      "\n",
      "Training Epoch: 21000 Average loss: 0.1656, Training Accuracy: 825/881 (93.6436%)\n",
      "\n",
      "\n",
      "Training Epoch: 22000 Average loss: 0.1625, Training Accuracy: 825/881 (93.6436%)\n",
      "\n",
      "\n",
      "Training Epoch: 23000 Average loss: 0.1595, Training Accuracy: 826/881 (93.7571%)\n",
      "\n",
      "\n",
      "Training Epoch: 24000 Average loss: 0.1566, Training Accuracy: 826/881 (93.7571%)\n",
      "\n",
      "\n",
      "Training Epoch: 25000 Average loss: 0.1538, Training Accuracy: 827/881 (93.8706%)\n",
      "\n",
      "\n",
      "Training Epoch: 26000 Average loss: 0.1511, Training Accuracy: 827/881 (93.8706%)\n",
      "\n",
      "\n",
      "Training Epoch: 27000 Average loss: 0.1485, Training Accuracy: 827/881 (93.8706%)\n",
      "\n",
      "\n",
      "Training Epoch: 28000 Average loss: 0.1460, Training Accuracy: 829/881 (94.0976%)\n",
      "\n",
      "\n",
      "Training Epoch: 29000 Average loss: 0.1435, Training Accuracy: 830/881 (94.2111%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# run the main training loop\n",
    "epochs = 30000\n",
    "train_loss = 0\n",
    "train_correct = 0\n",
    "test_correct = 0\n",
    "for epoch in range(epochs):\n",
    "        data, target = Variable(X_train), Variable(new_targets)\n",
    "        optimizer.zero_grad()\n",
    "        net_out = net(data)\n",
    "        #print net_out\n",
    "        #print target.data\n",
    "        \n",
    "        #sum up the loss\n",
    "        train_loss = loss_func(net_out, target)\n",
    "        pred = net_out.data.max(1)[1] #get the index of the max log probabitlity\n",
    "        train_correct = pred.eq(target.data).sum()\n",
    "        \n",
    "        train_loss.backward()\n",
    "        optimizer.step()\n",
    "        if epoch % 1000 == 0:\n",
    "            print('\\nTraining Epoch: {} Average loss: {:.4f}, Training Accuracy: {}/{} ({:.4f}%)\\n'.format(\n",
    "        epoch, train_loss.data[0], train_correct, len(target),\n",
    "        100. * train_correct / len(target)))\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test loss: 0.1870, Test accuracy: 206/221 (93.2127%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_loss = 0\n",
    "test_correct = 0\n",
    "\n",
    "#Converting test data and target labels into PyTorch variables\n",
    "test_data, test_target = Variable(X_test), Variable(new_test_targets)\n",
    "\n",
    "#Test set output\n",
    "net_out = net(test_data)\n",
    "\n",
    "test_loss = loss_func(net_out, test_target)\n",
    "test_pred = net_out.data.max(1)[1] #get the index of max log probability\n",
    "test_correct = test_pred.eq(test_target.data).sum()\n",
    "\n",
    "print('\\nTest loss: {:.4f}, Test accuracy: {}/{} ({:.4f}%)\\n'.format(\n",
    "    test_loss.data[0], test_correct, len(test_target), 100. * test_correct / len(test_target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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