{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NasNet Dogs v Cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fastai.conv_learner import *\n",
    "PATH = \"data/dogscats/\"\n",
    "sz=224; bs=48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nasnet(pre): return nasnetalarge(pretrained = 'imagenet' if pre else None)\n",
    "model_features[nasnet]=4032*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stats = ([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "tfms = tfms_from_stats(stats, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn = ConvLearner.pretrained(nasnet, data, precompute=True, xtra_fc=[], ps=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time learn.fit(1e-2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn.precompute=False\n",
    "learn.bn_freeze=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time learn.fit(1e-2, 1, cycle_len=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn.save('nas_pre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def freeze_to(m, n):\n",
    "    c=children(m[0])\n",
    "    for l in c:     set_trainable(l, False)\n",
    "    for l in c[n:]: set_trainable(l, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "freeze_to(learn.model, 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fit([1e-5,1e-4,1e-2], 1, cycle_len=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn.save('nas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "toc_section_display": "block",
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