inception cifar 100 semisup-sup-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import argparse\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "from common import find_mxnet, data, fit\n",
    "from common.util import download_file\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "from mxnet.symbol import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def download_cifar100():\n",
    "    data_dir=\"/efs/data/cifar-100-mxnet\"\n",
    "    #data_dir=\"/data/cifar-100-mxnet\"\n",
    "    fnames = (os.path.join(data_dir, \"train.rec\"),\n",
    "              os.path.join(data_dir, \"test.rec\"))\n",
    "    return fnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_fname, val_fname) = download_cifar100()\n",
    "\n",
    "# parse args\n",
    "parser = argparse.ArgumentParser(description=\"train cifar100\",\n",
    "                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n",
    "fit.add_fit_args(parser)\n",
    "data.add_data_args(parser)\n",
    "data.add_data_aug_args(parser)\n",
    "data.set_data_aug_level(parser, 2)\n",
    "\n",
    "num_unsup_examples = 50000\n",
    "subset_factor = 5\n",
    "\n",
    "parser.set_defaults(\n",
    "    # data\n",
    "    data_train     = train_fname,\n",
    "    data_val       = val_fname,\n",
    "    num_classes    = 100,\n",
    "    image_shape    = '3,28,28',\n",
    " #   model_prefix   = '/efs/checkpoints/inceptionStandard',\n",
    "    pad_size       = 4,\n",
    "    # train\n",
    "    batch_size     = 128,\n",
    "    num_epochs     = 120,\n",
    "    lr_step_epochs = '80,120',\n",
    "    gpus           = '7',\n",
    "    disp_batches   = 10,\n",
    "    num_examples   = num_unsup_examples / subset_factor, \n",
    "    wd             = 1e-4,\n",
    "    lr             = .002,\n",
    "    lr_factor      = .33,\n",
    "    optimizer      = 'adam'\n",
    ")\n",
    "\n",
    "args = parser.parse_args(\"\")\n",
    "unsup_multiplier = 1\n",
    "labeled_per_class = 500 / subset_factor\n",
    "sample_seed = 47\n",
    "val_interval = 1\n",
    "num_sup_examples = labeled_per_class * args.num_classes\n",
    "sup_batch_size = args.batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_rec_iter(args, get_val=True, devide_by=1, kv=None, shuffle=True):\n",
    "    image_shape = tuple([int(l) for l in args.image_shape.split(',')])\n",
    "    dtype = np.float32;\n",
    "    if kv:\n",
    "        (rank, nworker) = (kv.rank, kv.num_workers)\n",
    "    else:\n",
    "        (rank, nworker) = (0, 1)\n",
    "    rgb_mean = [float(i) for i in args.rgb_mean.split(',')]\n",
    "    train = mx.io.ImageRecordIter(\n",
    "        path_imgrec         = args.data_train,\n",
    "        label_width         = 1,\n",
    "        mean_r              = rgb_mean[0],\n",
    "        mean_g              = rgb_mean[1],\n",
    "        mean_b              = rgb_mean[2],\n",
    "        data_name           = 'dataSup',\n",
    "        label_name          = 'labelSup',\n",
    "        data_shape          = image_shape,\n",
    "        batch_size          = args.batch_size,\n",
    "        rand_crop           = args.random_crop,\n",
    "        max_random_scale    = args.max_random_scale,\n",
    "        pad                 = args.pad_size,\n",
    "        fill_value          = 127,\n",
    "        min_random_scale    = args.min_random_scale,\n",
    "        max_aspect_ratio    = args.max_random_aspect_ratio,\n",
    "        random_h            = args.max_random_h,\n",
    "        random_s            = args.max_random_s,\n",
    "        random_l            = args.max_random_l,\n",
    "        max_rotate_angle    = args.max_random_rotate_angle,\n",
    "        max_shear_ratio     = args.max_random_shear_ratio,\n",
    "        rand_mirror         = args.random_mirror,\n",
    "        preprocess_threads  = args.data_nthreads,\n",
    "        shuffle             = shuffle,\n",
    "        num_parts           = nworker * devide_by, #only take subset if we're in the supervised part\n",
    "        part_index          = rank * devide_by)  # from every 'nworker' subset, take the first part\n",
    "    if args.data_val is None or not get_val:\n",
    "        return (train, None)\n",
    "    val = mx.io.ImageRecordIter(\n",
    "        path_imgrec         = args.data_val,\n",
    "        label_width         = 1,\n",
    "        mean_r              = rgb_mean[0],\n",
    "        mean_g              = rgb_mean[1],\n",
    "        mean_b              = rgb_mean[2],\n",
    "        data_name           = 'data',\n",
    "        label_name          = 'softmax_label',\n",
    "        batch_size          = args.batch_size,\n",
    "        data_shape          = image_shape,\n",
    "        preprocess_threads  = args.data_nthreads,\n",
    "        rand_crop           = False,\n",
    "        rand_mirror         = False,\n",
    "        num_parts           = nworker,\n",
    "        part_index          = rank)\n",
    "    return (train, val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5  times more unsup data than sup data\n"
     ]
    }
   ],
   "source": [
    "from common.multi_iterator import Multi_iterator\n",
    "from common.data import get_partial_rec_iter\n",
    "\n",
    "(train_sup, val) = get_partial_rec_iter(args, get_val=True, devide_by=5, shuffle=True)\n",
    "(train_unsup, _) = get_partial_rec_iter(args, get_val=False, devide_by=1, shuffle=True)\n",
    "    \n",
    "train = Multi_iterator(train_sup, train_unsup, unsup_multiplier, num_unsup_examples, num_sup_examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<NDArray 128x3x28x28 @cpu(0)>, <NDArray 128x3x28x28 @cpu(0)>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.next().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from symbols import inception_cifar as inception\n",
    "from common.lba import compute_semisup_loss, logit_loss\n",
    "\n",
    "t_nb = sup_batch_size * unsup_multiplier\n",
    "\n",
    "def build():\n",
    "    dataSup = mx.symbol.Variable(name=\"dataSup\")\n",
    "    labelSup = mx.symbol.Variable(name='labelSup')\n",
    "    overall_loss = []\n",
    "    \n",
    "    if unsup_multiplier >= 1:\n",
    "        dataUnsup = []\n",
    "        for i in range(unsup_multiplier):\n",
    "            dataUnsup.append(Variable(name=\"dataUnsup\"+str(i)))\n",
    "\n",
    "        # concat data, feed both through the network\n",
    "        # then split it up again\n",
    "        data = concat(dataSup, *dataUnsup, dim=0)\n",
    "\n",
    "        embeddings = inception.build_embeddings(data, nembeddings=256)\n",
    "        splitted = split(embeddings, num_outputs=(unsup_multiplier+1), axis=0, name='split')\n",
    "\n",
    "        supEmbeddings = splitted[0]\n",
    "        \n",
    "        unsupEmbeddings = []\n",
    "        for i in range(unsup_multiplier):\n",
    "            unsupEmbeddings.append(splitted[1+i])\n",
    "    \n",
    "        unsupEmbeddings = concat(*unsupEmbeddings, dim=0)\n",
    "            \n",
    "        (walker_loss, visit_loss) = compute_semisup_loss(supEmbeddings, unsupEmbeddings, labelSup, t_nb, \n",
    "                                                     walker_weight=1.0, visit_weight=0.7)\n",
    "        overall_loss = [walker_loss, visit_loss]\n",
    "        \n",
    "    else:\n",
    "        supEmbeddings = inception.build_embeddings(dataSup, nembeddings=256)\n",
    "        \n",
    "    overall_loss = [logit_loss(supEmbeddings, labelSup, 100)] + overall_loss\n",
    "    \n",
    "    return Group(overall_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Multi_Accuracy(mx.metric.EvalMetric):\n",
    "    \"\"\"Calculate accuracies of multi label\"\"\"\n",
    "\n",
    "    def __init__(self, num=None):\n",
    "        super(Multi_Accuracy, self).__init__('multi-accuracy', num)\n",
    "\n",
    "    def update(self, labels, preds):\n",
    "\n",
    "        #for i in range(len(preds)):\n",
    "        for i in range(1):\n",
    "            pred_label = mx.nd.argmax_channel(preds[i]).asnumpy().astype('int32')\n",
    "            label = labels[0].asnumpy().astype('int32')\n",
    "\n",
    "            #mx.metric.check_label_shapes(label, pred_label)\n",
    "            \n",
    "            #print((pred_label.flat == label.flat).sum())\n",
    "            #print(len(pred_label.flat))\n",
    "\n",
    "            \n",
    "            self.sum_metric[i] += (pred_label.flat == label.flat).sum()\n",
    "            self.num_inst[i] += len(pred_label.flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluation metrices\n",
    "eval_metrics = Multi_Accuracy(num= 3 if unsup_multiplier >= 1 else 1)\n",
    "                    \n",
    "def fit_model(args, network, data, **kwargs):\n",
    "    \"\"\"\n",
    "    train a model\n",
    "    args : argparse returns\n",
    "    network : the symbol definition of the nerual network\n",
    "    data_loader : function that returns the train and val data iterators\n",
    "    \"\"\"\n",
    "    # kvstore\n",
    "    kv = mx.kvstore.create(args.kv_store)\n",
    "\n",
    "    # logging\n",
    "    head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'\n",
    "    logging.basicConfig(level=logging.DEBUG, format=head)\n",
    "    logging.info('start with arguments %s', args)\n",
    "    batch_size = sup_batch_size * (unsup_multiplier + 1)\n",
    "\n",
    "    # data iterators\n",
    "    (train, val) = data\n",
    "    if args.test_io:\n",
    "        tic = time.time()\n",
    "        for i, batch in enumerate(train):\n",
    "            for j in batch.data:\n",
    "                j.wait_to_read()\n",
    "            if (i+1) % args.disp_batches == 0:\n",
    "                logging.info('Batch [%d]\\tSpeed: %.2f samples/sec' % (\n",
    "                    i, args.disp_batches*batch_size/(time.time()-tic)))\n",
    "                tic = time.time()\n",
    "        return\n",
    "\n",
    "    # load model\n",
    "    if 'arg_params' in kwargs and 'aux_params' in kwargs:\n",
    "        arg_params = kwargs['arg_params']\n",
    "        aux_params = kwargs['aux_params']\n",
    "    else:\n",
    "        sym, arg_params, aux_params = fit._load_model(args, kv.rank)\n",
    "        if sym is not None:\n",
    "            assert sym.tojson() == network.tojson()\n",
    "\n",
    "    # save model\n",
    "    checkpoint = fit._save_model(args, kv.rank)\n",
    "\n",
    "    # devices for training\n",
    "    devs = mx.cpu() if args.gpus is None or args.gpus is '' else [\n",
    "        mx.gpu(int(i)) for i in args.gpus.split(',')]\n",
    "\n",
    "    # learning rate\n",
    "    lr, lr_scheduler = fit._get_lr_scheduler(args, kv)\n",
    "\n",
    "    data_names = ['dataSup'] + ['dataUnsup'+str(i) for i in range(unsup_multiplier)]\n",
    "        \n",
    "    # create model\n",
    "    model = mx.mod.Module(\n",
    "        context       = devs,\n",
    "        symbol        = network,\n",
    "        label_names   = ['labelSup'],\n",
    "        data_names   = data_names\n",
    "    )\n",
    "    \n",
    "    def validate_model(epoch, *args):\n",
    "        if epoch % val_interval != 0: \n",
    "            return\n",
    "        res = model.score(val, eval_metrics)\n",
    "        #TODO: pull this into default\n",
    "        for name, value in res:\n",
    "            print('Epoch[%d] Validation-%s=%f' % (epoch, name, value))\n",
    "\n",
    "    \n",
    "    #print(model.label_names)\n",
    "    lr_scheduler  = lr_scheduler\n",
    "    optimizer_params = {\n",
    "            'learning_rate': lr,\n",
    "            #'momentum' : args.mom,\n",
    "            'wd' : args.wd,\n",
    "            #'rescale_grad': 1,\n",
    "            'lr_scheduler': lr_scheduler}\n",
    "\n",
    "    #monitor = mx.mon.Monitor(interval=1000, pattern='.*aba_backward.*') \n",
    "    monitor = mx.mon.Monitor(interval=1000, pattern='.*') \n",
    "    \n",
    "    # callbacks that run after each batch\n",
    "    batch_end_callbacks = [mx.callback.Speedometer(batch_size, args.disp_batches)]\n",
    "    \n",
    "    epoch_end_callbacks = validate_model\n",
    "\n",
    "    # run\n",
    "    model.fit(train,\n",
    "        begin_epoch        = args.load_epoch if args.load_epoch else 0,\n",
    "        num_epoch          = args.num_epochs,\n",
    "        #eval_data          = val,\n",
    "        eval_metric        = eval_metrics,\n",
    "        kvstore            = kv,\n",
    "        optimizer          = args.optimizer,\n",
    "        optimizer_params   = optimizer_params,\n",
    "        #initializer        = initializer,\n",
    "        arg_params         = arg_params,\n",
    "        aux_params         = aux_params,\n",
    "        batch_end_callback = batch_end_callbacks,\n",
    "        epoch_end_callback = epoch_end_callbacks,\n",
    "        allow_missing      = True\n",
    "        #monitor            = monitor\n",
    "             )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:start with arguments Namespace(batch_size=128, benchmark=0, data_nthreads=4, data_train='/efs/data/cifar-100-mxnet/train.rec', data_val='/efs/data/cifar-100-mxnet/test.rec', disp_batches=10, dtype='float32', gpus='7', image_shape='3,28,28', kv_store='device', load_epoch=None, lr=0.002, lr_factor=0.33, lr_step_epochs='80,120', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network=None, num_classes=100, num_epochs=120, num_examples=10000.0, num_layers=None, optimizer='adam', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6250.0, 9375.0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[0] Batch [10]\tSpeed: 123.42 samples/sec\tTrain-multi-accuracy_0=0.008523\n",
      "INFO:root:Epoch[0] Batch [10]\tSpeed: 123.42 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [10]\tSpeed: 123.42 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [20]\tSpeed: 123.13 samples/sec\tTrain-multi-accuracy_0=0.010156\n",
      "INFO:root:Epoch[0] Batch [20]\tSpeed: 123.13 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [20]\tSpeed: 123.13 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [30]\tSpeed: 122.70 samples/sec\tTrain-multi-accuracy_0=0.013281\n",
      "INFO:root:Epoch[0] Batch [30]\tSpeed: 122.70 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [30]\tSpeed: 122.70 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [40]\tSpeed: 122.35 samples/sec\tTrain-multi-accuracy_0=0.012500\n",
      "INFO:root:Epoch[0] Batch [40]\tSpeed: 122.35 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [40]\tSpeed: 122.35 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [50]\tSpeed: 122.15 samples/sec\tTrain-multi-accuracy_0=0.009375\n",
      "INFO:root:Epoch[0] Batch [50]\tSpeed: 122.15 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [50]\tSpeed: 122.15 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [60]\tSpeed: 122.24 samples/sec\tTrain-multi-accuracy_0=0.017969\n",
      "INFO:root:Epoch[0] Batch [60]\tSpeed: 122.24 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [60]\tSpeed: 122.24 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Batch [70]\tSpeed: 121.88 samples/sec\tTrain-multi-accuracy_0=0.009375\n",
      "INFO:root:Epoch[0] Batch [70]\tSpeed: 121.88 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Batch [70]\tSpeed: 121.88 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Train-multi-accuracy_0=0.017578\n",
      "INFO:root:Epoch[0] Train-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[0] Train-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[0] Time cost=164.573\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[0] Validation-multi-accuracy_0=0.011472\n",
      "Epoch[0] Validation-multi-accuracy_1=nan\n",
      "Epoch[0] Validation-multi-accuracy_2=nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[1] Batch [10]\tSpeed: 121.70 samples/sec\tTrain-multi-accuracy_0=0.021307\n",
      "INFO:root:Epoch[1] Batch [10]\tSpeed: 121.70 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [10]\tSpeed: 121.70 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [20]\tSpeed: 121.78 samples/sec\tTrain-multi-accuracy_0=0.012500\n",
      "INFO:root:Epoch[1] Batch [20]\tSpeed: 121.78 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [20]\tSpeed: 121.78 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [30]\tSpeed: 121.62 samples/sec\tTrain-multi-accuracy_0=0.012500\n",
      "INFO:root:Epoch[1] Batch [30]\tSpeed: 121.62 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [30]\tSpeed: 121.62 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [40]\tSpeed: 121.76 samples/sec\tTrain-multi-accuracy_0=0.010156\n",
      "INFO:root:Epoch[1] Batch [40]\tSpeed: 121.76 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [40]\tSpeed: 121.76 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [50]\tSpeed: 121.85 samples/sec\tTrain-multi-accuracy_0=0.017188\n",
      "INFO:root:Epoch[1] Batch [50]\tSpeed: 121.85 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [50]\tSpeed: 121.85 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [60]\tSpeed: 121.61 samples/sec\tTrain-multi-accuracy_0=0.013281\n",
      "INFO:root:Epoch[1] Batch [60]\tSpeed: 121.61 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [60]\tSpeed: 121.61 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Batch [70]\tSpeed: 121.56 samples/sec\tTrain-multi-accuracy_0=0.013281\n",
      "INFO:root:Epoch[1] Batch [70]\tSpeed: 121.56 samples/sec\tTrain-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Batch [70]\tSpeed: 121.56 samples/sec\tTrain-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Train-multi-accuracy_0=0.015625\n",
      "INFO:root:Epoch[1] Train-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[1] Train-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[1] Time cost=162.708\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
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      "INFO:root:Epoch[58] Train-multi-accuracy_0=0.032366\n",
      "INFO:root:Epoch[58] Train-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[58] Train-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[58] Time cost=165.321\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[58] Validation-multi-accuracy_0=0.038962\n",
      "Epoch[58] Validation-multi-accuracy_1=nan\n",
      "Epoch[58] Validation-multi-accuracy_2=nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "INFO:root:Epoch[60] Train-multi-accuracy_0=0.046875\n",
      "INFO:root:Epoch[60] Train-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[60] Train-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[60] Time cost=164.059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[60] Validation-multi-accuracy_0=0.039864\n",
      "Epoch[60] Validation-multi-accuracy_1=nan\n",
      "Epoch[60] Validation-multi-accuracy_2=nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[61] Batch [10]\tSpeed: 120.13 samples/sec\tTrain-multi-accuracy_0=0.034091\n",
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      "INFO:root:Epoch[61] Train-multi-accuracy_0=0.043527\n",
      "INFO:root:Epoch[61] Train-multi-accuracy_1=nan\n",
      "INFO:root:Epoch[61] Train-multi-accuracy_2=nan\n",
      "INFO:root:Epoch[61] Time cost=177.201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[61] Validation-multi-accuracy_0=0.045873\n",
      "Epoch[61] Validation-multi-accuracy_1=nan\n",
      "Epoch[61] Validation-multi-accuracy_2=nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Epoch[62] Batch [10]\tSpeed: 120.24 samples/sec\tTrain-multi-accuracy_0=0.036932\n",
      "INFO:root:Epoch[62] Batch [10]\tSpeed: 120.24 samples/sec\tTrain-multi-accuracy_1=nan\n",
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      "INFO:root:Epoch[62] Batch [20]\tSpeed: 120.53 samples/sec\tTrain-multi-accuracy_0=0.028125\n",
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      "INFO:root:Epoch[62] Batch [30]\tSpeed: 120.54 samples/sec\tTrain-multi-accuracy_2=nan\n"
     ]
    }
   ],
   "source": [
    "train_sup.reset()\n",
    "train_unsup.reset()\n",
    "\n",
    "net = build()\n",
    "\n",
    "model = fit_model(args, net, (train, val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# batch 10 train 0.389, valid: 0.40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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