Part2_ppl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from my_batch import MnistBatch\n",
    "from dataset import DatasetIndex, Dataset\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=[]\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=[]\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import blosc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src = '/notebooks/Koryagin/TfCourse/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(os.path.join(src, 'mnist_pics.blk'), 'rb') as file:\n",
    "    full_imgs = blosc.unpack_array(file.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(os.path.join(src, 'mnist_labels.blk'), 'rb') as file:\n",
    "    full_labs = blosc.unpack_array(file.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src = (full_imgs, full_labs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up mnist-dataset and pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LEN_MNIST = 65000\n",
    "indy = DatasetIndex(np.arange(LEN_MNIST))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnistset = Dataset(indy, batch_class=MnistBatch)\n",
    "mnistset.cv_split([0.9, 0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bch = MnistBatch(indy.create_subset(indy.indices[1:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stats = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pipeline for train\n",
    "ppl = (mnistset.train.pipeline().\n",
    "       load(src=src, fmt='ndarray').\n",
    "       train_convy(sess))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pipeline for test\n",
    "ppl_test = (mnistset.test.pipeline().\n",
    "            load(src=src, fmt='ndarray').\n",
    "            update_stats(sess, stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 400/400 [00:23<00:00, 17.30it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(400)):\n",
    "    ppl.next_batch(100, n_epochs=None)\n",
    "    ppl_test.next_batch(100, n_epochs=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZvE0fUbQfQCPnwqK6gK1HRLHHbk3hFwAaALgHwABoE+Nd71RQVDewT4Eo9gSt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\nbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fcb9d6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stats)\n",
    "plt.xlabel('iter')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}