data_loader.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "with h5py.File('data/gaussian_test.h5', 'r') as hf:\n",
    "    data = hf['data'][:]\n",
    "    labels = hf['labels'][:]\n",
    "    \n",
    "data = np.squeeze(data)\n",
    "labels = np.squeeze(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = np.squeeze(data)\n",
    "labels = np.squeeze(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def next_batch(n_batch, data, labels):\n",
    "    '''\n",
    "    Return a total of `num` random samples and labels. \n",
    "    '''\n",
    "    idx = np.arange(0 , len(data))\n",
    "    np.random.shuffle(idx)\n",
    "    idx = idx[:num]\n",
    "    data_shuffle = [data[ i] for i in idx]\n",
    "    labels_shuffle = [labels[ i] for i in idx]\n",
    "\n",
    "    return np.asarray(data_shuffle), np.asarray(labels_shuffle)\n",
    "\n",
    "Xtr, Ytr = next_batch(100, data, labels)\n",
    "print(Xtr.shape)\n",
    "print(Ytr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "object, n_batch, ind, dataset"
   ]
  }
 ],
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