4.0_Recap_exercise.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrap up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%reset -f\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "tf.reset_default_graph() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data \n",
    "from sklearn.datasets import load_digits\n",
    "data = load_digits()\n",
    "\n",
    "idx = np.random.permutation(data.data.shape[0])\n",
    "idx_train = idx[:-100]\n",
    "idx_test = idx[-100:]\n",
    "\n",
    "x_train = data.data[idx_train,:]\n",
    "y_train = data.target[idx_train]\n",
    "x_test = data.data[idx_test,:]\n",
    "y_test = data.target[idx_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-14-db2ff49e774e>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-14-db2ff49e774e>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    ...\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Network Parameters\n",
    "n_input = XX\n",
    "n_hidden_1 = XX\n",
    "...\n",
    "\n",
    "# Parameters\n",
    "learning_rate = XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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