multi-task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Multi-task Tutorial\n",
    "----\n",
    "\n",
    "In this example, I will show you the method how to do mutl-task(multi-label) training, after that I will show how to predict using saved model.\n",
    "\n",
    "This tutorial based on mxnet examples under \"/example/multi-task\" folder, most of the code I use based on it, but I will try to make clear some questions I met during try such example.\n",
    "\n",
    "let's get started!\n",
    "\n",
    "----\n",
    "\n",
    "At the very begining, I would like to make clear what kind of \"multi-task\" we will do. Basically, I will use MNIST dataset, try to do 10digits classification, but this time I added a completely new label to MNIST dataset, describe whether the digit is larger than 5. so our network do two tasks now, one is digit classification, the other is true/false classification.\n",
    "\n",
    "In order to realise this design, we need to modify the network definition and data iterator in \"multi-task\" example.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following code do some preparation, just same as \"muti-task\" example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, \"../../python/\")\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "import time\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Then I change the network definition for real multi-task case. Please take care of fc4,sm2 espacially, they are different from original example. fc4 num_hidden=2 is on the basis of requirement of our second task, a true/false classification. If you have a different task, please at least modify num_hidden to adapt your own's request."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_network():\n",
    "    data = mx.symbol.Variable('data')\n",
    "    fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)\n",
    "    act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type=\"relu\")\n",
    "    fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)\n",
    "    act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type=\"relu\")\n",
    "    fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)\n",
    "    sm1 = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax1')\n",
    "    fc4 = mx.symbol.FullyConnected(data = act2, name='fc4', num_hidden=2)\n",
    "    sm2 = mx.symbol.SoftmaxOutput(data = fc4, name = 'softmax2')\n",
    "\n",
    "    softmax = mx.symbol.Group([sm1, sm2])\n",
    "\n",
    "    return softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step, I modify the data iterator to support output two labels, this time I just try to do minimum modification on minist_iterator, in the end of this tutorial, I will show you how to generate and load data from .rec files through  ImageRecordIter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Multi_mnist_iterator(mx.io.DataIter):\n",
    "    '''multi label mnist iterator'''\n",
    "\n",
    "    def __init__(self, data_iter):\n",
    "        super(Multi_mnist_iterator, self).__init__()\n",
    "        self.data_iter = data_iter\n",
    "        self.batch_size = self.data_iter.batch_size\n",
    "\n",
    "    @property\n",
    "    def provide_data(self):\n",
    "        return self.data_iter.provide_data\n",
    "\n",
    "    @property\n",
    "    def provide_label(self):\n",
    "        provide_label = self.data_iter.provide_label[0]\n",
    "        # Different labels should be used here for actual application\n",
    "        return [('softmax1_label', provide_label[1]), \\\n",
    "                ('softmax2_label', provide_label[1])]\n",
    "\n",
    "    def hard_reset(self):\n",
    "        self.data_iter.hard_reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.data_iter.reset()\n",
    "\n",
    "    def next(self):\n",
    "        batch = self.data_iter.next()\n",
    "        label = batch.label[0] # the original label here\n",
    "        label2 = mx.nd.array(label.asnumpy()>5).astype('float32') # new label based on the original label, output 0 or 1 here\n",
    "        return mx.io.DataBatch(data=batch.data, label=[label, label2], \\\n",
    "                pad=batch.pad, index=batch.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy calculate code is exactly same as \"multi-task\" sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "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",
    "        mx.metric.check_label_shapes(labels, preds)\n",
    "\n",
    "        if self.num != None:\n",
    "            assert len(labels) == self.num\n",
    "\n",
    "        for i in range(len(labels)):\n",
    "            pred_label = mx.nd.argmax_channel(preds[i]).asnumpy().astype('int32')\n",
    "            label = labels[i].asnumpy().astype('int32')\n",
    "\n",
    "            mx.metric.check_label_shapes(label, pred_label)\n",
    "\n",
    "            if i == None:\n",
    "                self.sum_metric += (pred_label.flat == label.flat).sum()\n",
    "                self.num_inst += len(pred_label.flat)\n",
    "            else:\n",
    "                self.sum_metric[i] += (pred_label.flat == label.flat).sum()\n",
    "                self.num_inst[i] += len(pred_label.flat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data iterator is also exactly same as the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pylint: skip-file\n",
    "\"\"\" data iterator for mnist \"\"\"\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# code to automatically download dataset\n",
    "sys.path.append( \"../../tests/python/common\")\n",
    "import get_data\n",
    "import mxnet as mx\n",
    "\n",
    "def mnist_iterator(batch_size, input_shape):\n",
    "    \"\"\"return train and val iterators for mnist\"\"\"\n",
    "    # download data\n",
    "    get_data.GetMNIST_ubyte()\n",
    "    flat = False if len(input_shape) == 3 else True\n",
    "\n",
    "    train_dataiter = mx.io.MNISTIter(\n",
    "        image=\"data/train-images-idx3-ubyte\",\n",
    "        label=\"data/train-labels-idx1-ubyte\",\n",
    "        input_shape=input_shape,\n",
    "        batch_size=batch_size,\n",
    "        shuffle=True,\n",
    "        flat=flat)\n",
    "\n",
    "    val_dataiter = mx.io.MNISTIter(\n",
    "        image=\"data/t10k-images-idx3-ubyte\",\n",
    "        label=\"data/t10k-labels-idx1-ubyte\",\n",
    "        input_shape=input_shape,\n",
    "        batch_size=batch_size,\n",
    "        flat=flat)\n",
    "\n",
    "    return (train_dataiter, val_dataiter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, do some settings and load data, here I add some code to save trained model after each epoch. \n",
    "Also, please pay attation to the status of ipython notebook, make sure it finished and then continue. MNIST data will be downloaded if it is not exists. It can take several minutes due to bad network condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size=100\n",
    "num_epochs=100\n",
    "device = mx.gpu(0) # use mx.cpu(0) if you don't have a gpu card properly installed.\n",
    "lr = 0.01\n",
    "save_model_prefix = \"mnist_multitask\" # model save prefix\n",
    "\n",
    "network = build_network()\n",
    "\n",
    "train, val = mnist_iterator(batch_size=batch_size, input_shape = (784,))\n",
    "train = Multi_mnist_iterator(train)\n",
    "val = Multi_mnist_iterator(val)\n",
    "\n",
    "checkpoint = mx.callback.do_checkpoint(save_model_prefix) # checkpoint callback save trained parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next set the checkpoint to model.fit function and we can start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Start training with [gpu(0)]\n",
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      "INFO:root:Epoch[6] Validation-multi-accuracy_0=0.974000\n",
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      "INFO:root:Epoch[8] Validation-multi-accuracy_0=0.974400\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0010.params\"\n",
      "INFO:root:Epoch[9] Validation-multi-accuracy_0=0.975400\n",
      "INFO:root:Epoch[9] Validation-multi-accuracy_1=0.980700\n",
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      "INFO:root:Epoch[13] Validation-multi-accuracy_0=0.975200\n",
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      "INFO:root:Epoch[14] Validation-multi-accuracy_0=0.975500\n",
      "INFO:root:Epoch[14] Validation-multi-accuracy_1=0.982700\n",
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      "INFO:root:Epoch[15] Validation-multi-accuracy_0=0.975500\n",
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      "INFO:root:Epoch[18] Validation-multi-accuracy_0=0.975200\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0020.params\"\n",
      "INFO:root:Epoch[19] Validation-multi-accuracy_0=0.976300\n",
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      "INFO:root:Epoch[20] Validation-multi-accuracy_0=0.976800\n",
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      "INFO:root:Epoch[21] Validation-multi-accuracy_0=0.977300\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0023.params\"\n",
      "INFO:root:Epoch[22] Validation-multi-accuracy_0=0.978100\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0024.params\"\n",
      "INFO:root:Epoch[23] Validation-multi-accuracy_0=0.977400\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0025.params\"\n",
      "INFO:root:Epoch[24] Validation-multi-accuracy_0=0.977800\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0026.params\"\n",
      "INFO:root:Epoch[25] Validation-multi-accuracy_0=0.977600\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0027.params\"\n",
      "INFO:root:Epoch[26] Validation-multi-accuracy_0=0.977900\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0028.params\"\n",
      "INFO:root:Epoch[27] Validation-multi-accuracy_0=0.978000\n",
      "INFO:root:Epoch[27] Validation-multi-accuracy_1=0.984100\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0029.params\"\n",
      "INFO:root:Epoch[28] Validation-multi-accuracy_0=0.977400\n",
      "INFO:root:Epoch[28] Validation-multi-accuracy_1=0.984200\n",
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      "INFO:root:Epoch[29] Validation-multi-accuracy_0=0.978500\n",
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      "INFO:root:Epoch[30] Validation-multi-accuracy_0=0.977700\n",
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      "INFO:root:Epoch[31] Resetting Data Iterator\n",
      "INFO:root:Epoch[31] Time cost=0.653\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0032.params\"\n",
      "INFO:root:Epoch[31] Validation-multi-accuracy_0=0.978100\n",
      "INFO:root:Epoch[31] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[32] Resetting Data Iterator\n",
      "INFO:root:Epoch[32] Time cost=0.728\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0033.params\"\n",
      "INFO:root:Epoch[32] Validation-multi-accuracy_0=0.977400\n",
      "INFO:root:Epoch[32] Validation-multi-accuracy_1=0.985400\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0034.params\"\n",
      "INFO:root:Epoch[33] Validation-multi-accuracy_0=0.977400\n",
      "INFO:root:Epoch[33] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[34] Resetting Data Iterator\n",
      "INFO:root:Epoch[34] Time cost=0.746\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0035.params\"\n",
      "INFO:root:Epoch[34] Validation-multi-accuracy_0=0.977000\n",
      "INFO:root:Epoch[34] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[35] Resetting Data Iterator\n",
      "INFO:root:Epoch[35] Time cost=0.586\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0036.params\"\n",
      "INFO:root:Epoch[35] Validation-multi-accuracy_0=0.977900\n",
      "INFO:root:Epoch[35] Validation-multi-accuracy_1=0.984900\n",
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      "INFO:root:Epoch[36] Resetting Data Iterator\n",
      "INFO:root:Epoch[36] Time cost=0.740\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0037.params\"\n",
      "INFO:root:Epoch[36] Validation-multi-accuracy_0=0.977800\n",
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      "INFO:root:Epoch[37] Resetting Data Iterator\n",
      "INFO:root:Epoch[37] Time cost=0.728\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0038.params\"\n",
      "INFO:root:Epoch[37] Validation-multi-accuracy_0=0.978100\n",
      "INFO:root:Epoch[37] Validation-multi-accuracy_1=0.984900\n",
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      "INFO:root:Epoch[38] Resetting Data Iterator\n",
      "INFO:root:Epoch[38] Time cost=0.523\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0039.params\"\n",
      "INFO:root:Epoch[38] Validation-multi-accuracy_0=0.978200\n",
      "INFO:root:Epoch[38] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[39] Resetting Data Iterator\n",
      "INFO:root:Epoch[39] Time cost=0.534\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0040.params\"\n",
      "INFO:root:Epoch[39] Validation-multi-accuracy_0=0.978300\n",
      "INFO:root:Epoch[39] Validation-multi-accuracy_1=0.985400\n",
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      "INFO:root:Epoch[40] Resetting Data Iterator\n",
      "INFO:root:Epoch[40] Time cost=0.553\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0041.params\"\n",
      "INFO:root:Epoch[40] Validation-multi-accuracy_0=0.978600\n",
      "INFO:root:Epoch[40] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[41] Resetting Data Iterator\n",
      "INFO:root:Epoch[41] Time cost=0.576\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0042.params\"\n",
      "INFO:root:Epoch[41] Validation-multi-accuracy_0=0.978700\n",
      "INFO:root:Epoch[41] Validation-multi-accuracy_1=0.985300\n",
      "INFO:root:Epoch[42] Batch [50]\tSpeed: 83753.75 samples/sec\tTrain-multi-accuracy_0=1.000000\n",
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      "INFO:root:Epoch[42] Resetting Data Iterator\n",
      "INFO:root:Epoch[42] Time cost=0.744\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0043.params\"\n",
      "INFO:root:Epoch[42] Validation-multi-accuracy_0=0.978800\n",
      "INFO:root:Epoch[42] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[43] Resetting Data Iterator\n",
      "INFO:root:Epoch[43] Time cost=0.727\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0044.params\"\n",
      "INFO:root:Epoch[43] Validation-multi-accuracy_0=0.978800\n",
      "INFO:root:Epoch[43] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[44] Resetting Data Iterator\n",
      "INFO:root:Epoch[44] Time cost=0.528\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0045.params\"\n",
      "INFO:root:Epoch[44] Validation-multi-accuracy_0=0.978800\n",
      "INFO:root:Epoch[44] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[45] Resetting Data Iterator\n",
      "INFO:root:Epoch[45] Time cost=0.524\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0046.params\"\n",
      "INFO:root:Epoch[45] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[45] Validation-multi-accuracy_1=0.985300\n",
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      "INFO:root:Epoch[46] Resetting Data Iterator\n",
      "INFO:root:Epoch[46] Time cost=0.540\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0047.params\"\n",
      "INFO:root:Epoch[46] Validation-multi-accuracy_0=0.979000\n",
      "INFO:root:Epoch[46] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[47] Resetting Data Iterator\n",
      "INFO:root:Epoch[47] Time cost=0.710\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0048.params\"\n",
      "INFO:root:Epoch[47] Validation-multi-accuracy_0=0.978900\n",
      "INFO:root:Epoch[47] Validation-multi-accuracy_1=0.985400\n",
      "INFO:root:Epoch[48] Batch [50]\tSpeed: 118370.81 samples/sec\tTrain-multi-accuracy_0=1.000000\n",
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      "INFO:root:Epoch[48] Batch [500]\tSpeed: 109175.49 samples/sec\tTrain-multi-accuracy_0=1.000000\n",
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      "INFO:root:Epoch[48] Resetting Data Iterator\n",
      "INFO:root:Epoch[48] Time cost=0.544\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0049.params\"\n",
      "INFO:root:Epoch[48] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[48] Validation-multi-accuracy_1=0.985400\n",
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      "INFO:root:Epoch[49] Resetting Data Iterator\n",
      "INFO:root:Epoch[49] Time cost=0.528\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0050.params\"\n",
      "INFO:root:Epoch[49] Validation-multi-accuracy_0=0.978600\n",
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      "INFO:root:Epoch[50] Resetting Data Iterator\n",
      "INFO:root:Epoch[50] Time cost=0.603\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0051.params\"\n",
      "INFO:root:Epoch[50] Validation-multi-accuracy_0=0.979000\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0052.params\"\n",
      "INFO:root:Epoch[51] Validation-multi-accuracy_0=0.978900\n",
      "INFO:root:Epoch[51] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[52] Resetting Data Iterator\n",
      "INFO:root:Epoch[52] Time cost=0.524\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0053.params\"\n",
      "INFO:root:Epoch[52] Validation-multi-accuracy_0=0.978900\n",
      "INFO:root:Epoch[52] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[53] Resetting Data Iterator\n",
      "INFO:root:Epoch[53] Time cost=0.547\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0054.params\"\n",
      "INFO:root:Epoch[53] Validation-multi-accuracy_0=0.979000\n",
      "INFO:root:Epoch[53] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[54] Resetting Data Iterator\n",
      "INFO:root:Epoch[54] Time cost=0.527\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0055.params\"\n",
      "INFO:root:Epoch[54] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[54] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[55] Time cost=0.555\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0056.params\"\n",
      "INFO:root:Epoch[55] Validation-multi-accuracy_0=0.979100\n",
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      "INFO:root:Saved checkpoint to \"mnist_multitask-0057.params\"\n",
      "INFO:root:Epoch[56] Validation-multi-accuracy_0=0.979100\n",
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      "INFO:root:Epoch[57] Resetting Data Iterator\n",
      "INFO:root:Epoch[57] Time cost=0.623\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0058.params\"\n",
      "INFO:root:Epoch[57] Validation-multi-accuracy_0=0.979100\n",
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      "INFO:root:Epoch[58] Resetting Data Iterator\n",
      "INFO:root:Epoch[58] Time cost=0.560\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0059.params\"\n",
      "INFO:root:Epoch[58] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[58] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[59] Resetting Data Iterator\n",
      "INFO:root:Epoch[59] Time cost=0.542\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0060.params\"\n",
      "INFO:root:Epoch[59] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[59] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[60] Resetting Data Iterator\n",
      "INFO:root:Epoch[60] Time cost=0.589\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0061.params\"\n",
      "INFO:root:Epoch[60] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[60] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[61] Resetting Data Iterator\n",
      "INFO:root:Epoch[61] Time cost=0.536\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0062.params\"\n",
      "INFO:root:Epoch[61] Validation-multi-accuracy_0=0.979100\n",
      "INFO:root:Epoch[61] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[62] Resetting Data Iterator\n",
      "INFO:root:Epoch[62] Time cost=0.539\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0063.params\"\n",
      "INFO:root:Epoch[62] Validation-multi-accuracy_0=0.979200\n",
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      "INFO:root:Epoch[63] Time cost=0.539\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0064.params\"\n",
      "INFO:root:Epoch[63] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[63] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[64] Resetting Data Iterator\n",
      "INFO:root:Epoch[64] Time cost=0.525\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0065.params\"\n",
      "INFO:root:Epoch[64] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[64] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[65] Resetting Data Iterator\n",
      "INFO:root:Epoch[65] Time cost=0.527\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0066.params\"\n",
      "INFO:root:Epoch[65] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[65] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[66] Resetting Data Iterator\n",
      "INFO:root:Epoch[66] Time cost=0.532\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0067.params\"\n",
      "INFO:root:Epoch[66] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[66] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[67] Resetting Data Iterator\n",
      "INFO:root:Epoch[67] Time cost=0.642\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0068.params\"\n",
      "INFO:root:Epoch[67] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[67] Validation-multi-accuracy_1=0.985000\n",
      "INFO:root:Epoch[68] Batch [50]\tSpeed: 108035.46 samples/sec\tTrain-multi-accuracy_0=1.000000\n",
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      "INFO:root:Epoch[68] Batch [500]\tSpeed: 116855.11 samples/sec\tTrain-multi-accuracy_0=1.000000\n",
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      "INFO:root:Epoch[68] Resetting Data Iterator\n",
      "INFO:root:Epoch[68] Time cost=0.544\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0069.params\"\n",
      "INFO:root:Epoch[68] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[68] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[69] Resetting Data Iterator\n",
      "INFO:root:Epoch[69] Time cost=0.688\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0070.params\"\n",
      "INFO:root:Epoch[69] Validation-multi-accuracy_0=0.979300\n",
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      "INFO:root:Epoch[70] Time cost=0.535\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0071.params\"\n",
      "INFO:root:Epoch[70] Validation-multi-accuracy_0=0.979300\n",
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      "INFO:root:Epoch[71] Resetting Data Iterator\n",
      "INFO:root:Epoch[71] Time cost=0.525\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0072.params\"\n",
      "INFO:root:Epoch[71] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[71] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[72] Resetting Data Iterator\n",
      "INFO:root:Epoch[72] Time cost=0.561\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0073.params\"\n",
      "INFO:root:Epoch[72] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[72] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[73] Resetting Data Iterator\n",
      "INFO:root:Epoch[73] Time cost=0.724\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0074.params\"\n",
      "INFO:root:Epoch[73] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[73] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[74] Resetting Data Iterator\n",
      "INFO:root:Epoch[74] Time cost=0.529\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0075.params\"\n",
      "INFO:root:Epoch[74] Validation-multi-accuracy_0=0.979200\n",
      "INFO:root:Epoch[74] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[75] Time cost=0.555\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0076.params\"\n",
      "INFO:root:Epoch[75] Validation-multi-accuracy_0=0.979300\n",
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      "INFO:root:Epoch[76] Resetting Data Iterator\n",
      "INFO:root:Epoch[76] Time cost=0.655\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0077.params\"\n",
      "INFO:root:Epoch[76] Validation-multi-accuracy_0=0.979300\n",
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      "INFO:root:Epoch[77] Resetting Data Iterator\n",
      "INFO:root:Epoch[77] Time cost=0.533\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0078.params\"\n",
      "INFO:root:Epoch[77] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[77] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[78] Resetting Data Iterator\n",
      "INFO:root:Epoch[78] Time cost=0.541\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0079.params\"\n",
      "INFO:root:Epoch[78] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[78] Validation-multi-accuracy_1=0.985200\n",
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      "INFO:root:Epoch[79] Resetting Data Iterator\n",
      "INFO:root:Epoch[79] Time cost=0.533\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0080.params\"\n",
      "INFO:root:Epoch[79] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[79] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[80] Resetting Data Iterator\n",
      "INFO:root:Epoch[80] Time cost=0.679\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0081.params\"\n",
      "INFO:root:Epoch[80] Validation-multi-accuracy_0=0.979200\n",
      "INFO:root:Epoch[80] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[81] Resetting Data Iterator\n",
      "INFO:root:Epoch[81] Time cost=0.524\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0082.params\"\n",
      "INFO:root:Epoch[81] Validation-multi-accuracy_0=0.979200\n",
      "INFO:root:Epoch[81] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[82] Resetting Data Iterator\n",
      "INFO:root:Epoch[82] Time cost=0.522\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0083.params\"\n",
      "INFO:root:Epoch[82] Validation-multi-accuracy_0=0.979200\n",
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      "INFO:root:Epoch[83] Resetting Data Iterator\n",
      "INFO:root:Epoch[83] Time cost=0.579\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0084.params\"\n",
      "INFO:root:Epoch[83] Validation-multi-accuracy_0=0.979200\n",
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      "INFO:root:Epoch[84] Resetting Data Iterator\n",
      "INFO:root:Epoch[84] Time cost=0.568\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0085.params\"\n",
      "INFO:root:Epoch[84] Validation-multi-accuracy_0=0.979200\n",
      "INFO:root:Epoch[84] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[85] Resetting Data Iterator\n",
      "INFO:root:Epoch[85] Time cost=0.593\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0086.params\"\n",
      "INFO:root:Epoch[85] Validation-multi-accuracy_0=0.979200\n",
      "INFO:root:Epoch[85] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[86] Resetting Data Iterator\n",
      "INFO:root:Epoch[86] Time cost=0.599\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0087.params\"\n",
      "INFO:root:Epoch[86] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[86] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[87] Resetting Data Iterator\n",
      "INFO:root:Epoch[87] Time cost=0.548\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0088.params\"\n",
      "INFO:root:Epoch[87] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[87] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[88] Resetting Data Iterator\n",
      "INFO:root:Epoch[88] Time cost=0.531\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0089.params\"\n",
      "INFO:root:Epoch[88] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[88] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[89] Resetting Data Iterator\n",
      "INFO:root:Epoch[89] Time cost=0.532\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0090.params\"\n",
      "INFO:root:Epoch[89] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[89] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[90] Resetting Data Iterator\n",
      "INFO:root:Epoch[90] Time cost=0.521\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0091.params\"\n",
      "INFO:root:Epoch[90] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[90] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[91] Resetting Data Iterator\n",
      "INFO:root:Epoch[91] Time cost=0.748\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0092.params\"\n",
      "INFO:root:Epoch[91] Validation-multi-accuracy_0=0.979400\n",
      "INFO:root:Epoch[91] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[92] Resetting Data Iterator\n",
      "INFO:root:Epoch[92] Time cost=0.548\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0093.params\"\n",
      "INFO:root:Epoch[92] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[92] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[93] Resetting Data Iterator\n",
      "INFO:root:Epoch[93] Time cost=0.754\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0094.params\"\n",
      "INFO:root:Epoch[93] Validation-multi-accuracy_0=0.979500\n",
      "INFO:root:Epoch[93] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[94] Resetting Data Iterator\n",
      "INFO:root:Epoch[94] Time cost=0.525\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0095.params\"\n",
      "INFO:root:Epoch[94] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[94] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[95] Resetting Data Iterator\n",
      "INFO:root:Epoch[95] Time cost=0.684\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0096.params\"\n",
      "INFO:root:Epoch[95] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[95] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[96] Resetting Data Iterator\n",
      "INFO:root:Epoch[96] Time cost=0.532\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0097.params\"\n",
      "INFO:root:Epoch[96] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[96] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[97] Resetting Data Iterator\n",
      "INFO:root:Epoch[97] Time cost=0.531\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0098.params\"\n",
      "INFO:root:Epoch[97] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[97] Validation-multi-accuracy_1=0.985000\n",
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      "INFO:root:Epoch[98] Resetting Data Iterator\n",
      "INFO:root:Epoch[98] Time cost=0.636\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0099.params\"\n",
      "INFO:root:Epoch[98] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[98] Validation-multi-accuracy_1=0.985100\n",
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      "INFO:root:Epoch[99] Batch [600]\tSpeed: 106757.89 samples/sec\tTrain-multi-accuracy_1=1.000000\n",
      "INFO:root:Epoch[99] Resetting Data Iterator\n",
      "INFO:root:Epoch[99] Time cost=0.564\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0100.params\"\n",
      "INFO:root:Epoch[99] Validation-multi-accuracy_0=0.979300\n",
      "INFO:root:Epoch[99] Validation-multi-accuracy_1=0.985100\n"
     ]
    }
   ],
   "source": [
    "model = mx.model.FeedForward(\n",
    "    ctx                = device,\n",
    "    symbol             = network,\n",
    "    num_epoch          = num_epochs,\n",
    "    learning_rate      = lr,\n",
    "    momentum           = 0.9,\n",
    "    wd                 = 0.00001,\n",
    "    initializer        = mx.init.Xavier(factor_type=\"in\", magnitude=2.34))\n",
    "\n",
    "model.fit(\n",
    "    X                  = train,\n",
    "    eval_data          = val,\n",
    "    eval_metric        = Multi_Accuracy(num=2),\n",
    "    batch_end_callback = mx.callback.Speedometer(batch_size, 50),\n",
    "    epoch_end_callback = checkpoint # here we set call checkpoint method after each epoch ended.\n",
    "    ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, after 100 epoch, the validation accuracy of task1 is around 0.979300, the validation accuracy of task2 is around 0.985100. (the result on your environment may different with this, but should be very close.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we got the trained prameters, let us have a test. I will show you how to predict a test image using trained parameters.\n",
    "Let us display the test image firstly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAgAAAQABAAD//gAQTGF2YzU2LjYwLjEwMAD/2wBDAAgEBAQEBAUFBQUFBQYG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\nsGigDQuNEgit5Jo9a0a5CKW8uKS7SV/ZEubOElj6VQpKKAP/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='2.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predict method as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sys.path.insert(0, \"../../python/\")\n",
    "sys.path.append(\"../../amalgamation/python/\")\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "from mxnet_predict import Predictor\n",
    "from skimage import io\n",
    "from skimage.color import rgb2gray\n",
    "\n",
    "def predict(params, symbol, image):\n",
    "    predictor = Predictor(open(symbol).read(), open(params).read(), {'data':(1 , 1, 28, 28)},'gpu',0) #use cpu if you don't have a gpu card\n",
    "    img = io.imread(image)\n",
    "    img = rgb2gray(img)\n",
    "    \n",
    "    predictor.forward(data=img)\n",
    "    # for first task\n",
    "    prob = predictor.get_output(0)\n",
    "    pred = np.argsort(prob[0])[::-1]\n",
    "    # label_id (same as label if MNIST), score\n",
    "    print pred[0], prob[0][pred[0]]\n",
    "    # for second task\n",
    "    prob2 = predictor.get_output(1)\n",
    "    pred2 = np.argsort(prob2[0])[::-1]\n",
    "    # label_id (same as label if MNIST), score\n",
    "    print pred2[0],  prob2[0][pred2[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 0.535227\n",
      "0 0.999994\n"
     ]
    }
   ],
   "source": [
    "symbol_file = \"./mnist_multitask-symbol.json\"\n",
    "params_file = \"./mnist_multitask-0100.params\"\n",
    "image_file = \"2.jpg\"\n",
    "predict(params_file, symbol_file, image_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, for the task one, predict output 2 0.535227 which means the predict result is 2 and confidence is 0.535227\n",
    "for the task two, predict output 0 0.999994 which means the predict result is \"not >5\" and confidence is 0.999994"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Somebody may have question about data iterator, since in my previous tutorials, I did't load data from scratch.\n",
    "Now I will show you the method to load data from scratch.\n",
    "\n",
    "My dataset located under mxnet/example/notebooks folder and looks like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/haria/mxnet/example/notebooks/dataset/female/PersonA/00e93901213fb80e4c0c11df34d12f2eb83894dc_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/female/PersonA/00e93901213fb80e747739df34d12f2eb9389417_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/female/PersonA/0b46f21fbe096b63a2019b510e338744ebf8ac93_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/female/PersonA/0b46f21fbe096b63c7c9785a0e338744eaf8ace3_ok.jpg',\n",
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       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/4e4a20a4462309f74f55c213710e0cf3d7cad6fd_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/50da81cb39dbb6fd2df889270a24ab18972b370b_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/50da81cb39dbb6fd77e363090a24ab18972b3760_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/54fbb2fb43166d22944c7a95452309f79052d2fc_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/622762d0f703918f71fe52cb523d269759eec480_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/63d9f2d3572c11dfdbbc390c602762d0f703c2b4_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/6609c93d70cf3bc7fbd4a1edd200baa1cc112ade_ok.jpg',\n",
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       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/6f061d950a7b02085268fcc561d9f2d3572cc875_ok.jpg',\n",
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       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a2cc7cd98d1001e9fc359220bb0e7bec54e79790_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a5c27d1ed21b0ef405fb1e71dec451da81cb3e89_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a686c9177f3e6709a89e29fe38c79f3df8dc5510_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a71ea8d3fd1f4134c01e6a7f261f95cad1c85e81_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a8773912b31bb051e985700c357adab44aede075_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/a9d3fd1f4134970a69a57c2e96cad1c8a6865dd8_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/b58f8c5494eef01fe5189eeae3fe9925bc317d95_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/b8389b504fc2d562ac3cbfece41190ef76c66cfe_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/c2fdfc039245d688a6d6dea5a7c27d1ed31b24de_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/ca1349540923dd541346c869d209b3de9c8248fd_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/cefc1e178a82b901ed78af32708da9773812efde_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d009b3de9c82d1583e3cdf9e830a19d8bc3e4267_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d1160924ab18972b571f6fcbe5cd7b899e510a8d_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d1160924ab18972bfe7586e5e5cd7b899e510a55_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d4628535e5dde7111ee88ed2a4efce1b9d1661d8_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d52a2834349b033bc4c47af516ce36d3d539bd55_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/d8f9d72a6059252d21887105379b033b5bb5b910_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/dc54564e9258d109fbd9c6b3d258ccbf6c814dfc_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/f636afc379310a552e0bb1f5b44543a982261087_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/faedab64034f78f06d93f6f27a310a55b3191c12_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/fc1f4134970a304e7fb0ccfbd2c8a786c9175c24_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonG/fd039245d688d43ff431fdf37e1ed21b0ef43b12_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0824ab18972bd4074d8b81e479899e510fb3095e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/09fa513d269759ee367873c1b0fb43166d22df8b_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0b7b02087bf40ad1d97295fa552c11dfa9ecce11_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0b7b02087bf40ad1d9aa95fa552c11dfa9ecce59_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0df3d7ca7bcb0a4647ab32306963f6246b60af2a_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0e2442a7d933c895748aee62d31373f082020036_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0eb30f2442a7d93320aa2e7eaf4bd11373f001b9_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/0ff41bd5ad6eddc4b45271f93bdbb6fd5266337f_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/1ad5ad6eddc451dac623b6e2b4fd5266d11632e5_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/1c950a7b02087bf42b49630bf3d3572c10dfcfae_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/1e30e924b899a90114b0aa9a1c950a7b0308f591_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/1f178a82b9014a90eeb699b4ab773912b31bee2b_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/203fb80e7bec54e76a280d1cbb389b504ec26aef_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/2cf5e0fe9925bc311b1266b25fdf8db1cb137048_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/2f738bd4b31c87016fcbc4ff257f9e2f0708ffae_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/314e251f95cad1c8602a392a7d3e6709c93d512e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/34fae6cd7b899e517ee37d1940a7d933c8950d49_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/34fae6cd7b899e51aff34c0d40a7d933c8950dad_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/35a85edf8db1cb13db010322de54564e92584b92_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/3bf33a87e950352ae8f53f065143fbf2b2118b23_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/5d6034a85edf8db1fe622e7d0b23dd54564e745e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/5d6034a85edf8db1febf2e7d0b23dd54564e7411_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/622762d0f703918f9c6bf8c8533d269759eec47e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/63d0f703918fa0ec5dec890a249759ee3d6ddb8a_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/79f0f736afc37931f33d4d24e9c4b74543a91162_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/7e3e6709c93d70cf6db1f814fadcd100baa12b5b_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/8601a18b87d6277f87f9fc392a381f30e824fccc_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/8644ebf81a4c510f774c49d16159252dd52aa575_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/9345d688d43f879468da7f20d01b0ef41bd53a92_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/95eef01f3a292df5b2a4da0cbe315c6034a87347_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/96dda144ad345982a673889f0ef431adcaef84c5_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/9e3df8dcd100baa1fdaee0b54510b912c8fc2e39_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/a8014c086e061d95a636703579f40ad162d9ca4f_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/ac345982b2b7d0a20b585684c9ef76094b369a11_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/ae51f3deb48f8c54be42973638292df5e0fe7f11_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/b3b7d0a20cf431ad21c4f0384936acaf2edd9899_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/b58f8c5494eef01f5e8102cbe2fe9925bc317d22_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/b90e7bec54e736d16ddd840999504fc2d46269e0_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/b999a9014c086e063027fa4600087bf40ad1cb34_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/bf096b63f6246b604d538196eaf81a4c500fa2f6_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/c83d70cf3bc79f3d65d6f33eb8a1cd11728b298c_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/cefc1e178a82b901fb1ea719728da9773912ef6d_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/d043ad4bd11373f080b7b096a60f4bfbfbed042a_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/d1a20cf431adcbef7c782c1faeaf2edda3cc9f5e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/d31b0ef41bd5ad6eb0b536f383cb39dbb6fd3c5e_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/dcc451da81cb39db57f9a557d2160924ab1830f2_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/e61190ef76c6a7ef40f96127fffaaf51f2de66d0_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/e824b899a9014c08e7e98eb8087b02087af4f4d9_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/f9198618367adab45a3bd24289d4b31c8601e4cc_ok.jpg',\n",
       " '/home/haria/mxnet/example/notebooks/dataset/male/PersonH/fd039245d688d43f5a1354f07f1ed21b0ef43b7e_ok.jpg']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out =!tree -i -f `pwd`/dataset|grep .jpg\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I generate train.txt and val.txt base on previous command output. following two command show the content of train.txt and val.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       " '2 0 1 /female/PersonA/00e93901213fb80e747739df34d12f2eb9389417_ok.jpg',\n",
       " '3 0 1 /female/PersonA/0b46f21fbe096b63a2019b510e338744ebf8ac93_ok.jpg',\n",
       " '4 0 1 /female/PersonA/0b46f21fbe096b63c7c9785a0e338744eaf8ace3_ok.jpg',\n",
       " '5 0 1 /female/PersonA/0b55b319ebc4b745938fa91ccdfc1e178a8215a4_ok.jpg',\n",
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       " '9 0 1 /female/PersonA/0dd7912397dda1445f1b21bab0b7d0a20cf486ac_ok.jpg',\n",
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       " '377 7 0 /male/PersonH/8601a18b87d6277f87f9fc392a381f30e824fccc_ok.jpg',\n",
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       " '381 7 0 /male/PersonH/96dda144ad345982a673889f0ef431adcaef84c5_ok.jpg',\n",
       " '382 7 0 /male/PersonH/9e3df8dcd100baa1fdaee0b54510b912c8fc2e39_ok.jpg',\n",
       " '383 7 0 /male/PersonH/a8014c086e061d95a636703579f40ad162d9ca4f_ok.jpg',\n",
       " '384 7 0 /male/PersonH/ac345982b2b7d0a20b585684c9ef76094b369a11_ok.jpg',\n",
       " '385 7 0 /male/PersonH/ae51f3deb48f8c54be42973638292df5e0fe7f11_ok.jpg',\n",
       " '386 7 0 /male/PersonH/b3b7d0a20cf431ad21c4f0384936acaf2edd9899_ok.jpg',\n",
       " '387 7 0 /male/PersonH/b58f8c5494eef01f5e8102cbe2fe9925bc317d22_ok.jpg',\n",
       " '388 7 0 /male/PersonH/b90e7bec54e736d16ddd840999504fc2d46269e0_ok.jpg',\n",
       " '389 7 0 /male/PersonH/b999a9014c086e063027fa4600087bf40ad1cb34_ok.jpg',\n",
       " '390 7 0 /male/PersonH/bf096b63f6246b604d538196eaf81a4c500fa2f6_ok.jpg']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = !cat train.txt\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out =!cat val.txt\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we convert image list file to .rec file using im2rec tools. \n",
    "\n",
    "the command is: ~/mxnet/bin/im2rec  train.txt ./dataset train.rec label_width=2 resize=224\n",
    "\n",
    "the options described as below:\n",
    "\n",
    "~/mxnet/bin/im2rec    binary tool, if you can't find such tool, please check the compilation of mxnet.\n",
    "\n",
    " train.txt    image list file, format as I list above\n",
    " \n",
    " ./dataset    image base folder, ex, for ./dataset/female/PersonA/00e93901213fb80e4c0c11df34d12f2eb83894dc_ok.jpg the base folder is ./dataset\n",
    " \n",
    " train.rec    the output .rec file\n",
    " \n",
    " label_width=2     the label_width, since we have two labels, use 2 override the deafult 1.\n",
    " \n",
    " resize=224    the output shape of im2rec\n",
    "\n",
    "Now we convert train.txt to train.rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[13:38:17] tools/im2rec.cc:94: New Image Size: Short Edge 224',\n",
       " '[13:38:17] tools/im2rec.cc:107: Encoding is .jpg',\n",
       " '[13:38:17] tools/im2rec.cc:153: Write to output: train.rec',\n",
       " '[13:38:17] tools/im2rec.cc:155: Output: train.rec',\n",
       " '[13:38:17] tools/im2rec.cc:168: JPEG encoding quality: 80',\n",
       " '[13:38:17] tools/im2rec.cc:254: Total: 320 images processed, 0.288873 sec elapsed']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = !~/mxnet/bin/im2rec  train.txt ./dataset train.rec label_width=2 resize=224\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we convert val.txt to val.rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[13:38:18] tools/im2rec.cc:94: New Image Size: Short Edge 224',\n",
       " '[13:38:18] tools/im2rec.cc:107: Encoding is .jpg',\n",
       " '[13:38:18] tools/im2rec.cc:153: Write to output: val.rec',\n",
       " '[13:38:18] tools/im2rec.cc:155: Output: val.rec',\n",
       " '[13:38:18] tools/im2rec.cc:168: JPEG encoding quality: 80',\n",
       " '[13:38:18] tools/im2rec.cc:254: Total: 80 images processed, 0.0746553 sec elapsed']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = !~/mxnet/bin/im2rec  val.txt ./dataset val.rec label_width=2 resize=224\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is ready to train using generated train.rec and val.rec. I update data iterator as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mnist_iterator(batch_size, input_shape):\n",
    "    data_shape = input_shape\n",
    "    train = mx.io.ImageRecordIter(\n",
    "        path_imglist= \"./train.txt\",  # you have to specify path_imglist when label_width larger than 2.\n",
    "        path_imgrec = \"./train.rec\",\n",
    "        mean_img    = \"./mean.bin\", # it is OK no such file exists before train, the mxnet will generated it if not exists\n",
    "        data_shape  = data_shape,\n",
    "        batch_size  = batch_size,\n",
    "        rand_crop   = True,\n",
    "        rand_mirror = True,\n",
    "        label_width = 2          # specify label_width = 2 here\n",
    "        )\n",
    "\n",
    "    val = mx.io.ImageRecordIter(\n",
    "        path_imglist= \"./val.txt\", \n",
    "        path_imgrec = \"./val.rec\",\n",
    "        mean_img    = \"./mean.bin\",\n",
    "        rand_crop   = False,\n",
    "        rand_mirror = False,\n",
    "        data_shape  = data_shape,\n",
    "        batch_size  = batch_size,\n",
    "        label_width = 2\n",
    "        )\n",
    "\n",
    "    return (train, val)\n",
    "\n",
    "class Multi_mnist_iterator(mx.io.DataIter):\n",
    "    '''multi label mnist iterator'''\n",
    "\n",
    "    def __init__(self, data_iter):\n",
    "        super(Multi_mnist_iterator, self).__init__()\n",
    "        self.data_iter = data_iter\n",
    "        self.batch_size = self.data_iter.batch_size\n",
    "\n",
    "    @property\n",
    "    def provide_data(self):\n",
    "        return self.data_iter.provide_data\n",
    "\n",
    "    @property\n",
    "    def provide_label(self):\n",
    "        provide_label = self.data_iter.provide_label[0]\n",
    "        # Different labels should be used here for actual application\n",
    "        return [('softmax1_label', (self.batch_size,)), \\\n",
    "                ('softmax2_label', (self.batch_size,))]\n",
    "\n",
    "    def hard_reset(self):\n",
    "        self.data_iter.hard_reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.data_iter.reset()\n",
    "\n",
    "    def next(self):\n",
    "        batch = self.data_iter.next()\n",
    "        label = batch.label[0]\n",
    "        label1,label2 = label.T.asnumpy() \n",
    "        label1=mx.nd.array(label1)\n",
    "        label2=mx.nd.array(label2)\n",
    "        return mx.io.DataBatch(data=batch.data, label=[label1, label2], \\\n",
    "                pad=batch.pad, index=batch.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_network(): # I use vgg network architecture, I just prepared 400 samples, if use default network, not converge at all.\n",
    "    data = mx.symbol.Variable(name=\"data\")\n",
    "    # group 1\n",
    "    conv1_1 = mx.symbol.Convolution(data=data, kernel=(3, 3), pad=(1, 1), num_filter=64, name=\"conv1_1\")\n",
    "    relu1_1 = mx.symbol.Activation(data=conv1_1, act_type=\"relu\", name=\"relu1_1\")\n",
    "    pool1 = mx.symbol.Pooling(data=relu1_1, pool_type=\"max\", kernel=(2, 2), stride=(2,2), name=\"pool1\")\n",
    "    # group 2\n",
    "    conv2_1 = mx.symbol.Convolution( data=pool1, kernel=(3, 3), pad=(1, 1), num_filter=128, name=\"conv2_1\")\n",
    "    relu2_1 = mx.symbol.Activation(data=conv2_1, act_type=\"relu\", name=\"relu2_1\")\n",
    "    pool2 = mx.symbol.Pooling( data=relu2_1, pool_type=\"max\", kernel=(2, 2), stride=(2,2), name=\"pool2\")\n",
    "    # group 3\n",
    "    conv3_1 = mx.symbol.Convolution( data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=256, name=\"conv3_1\")\n",
    "    relu3_1 = mx.symbol.Activation(data=conv3_1, act_type=\"relu\", name=\"relu3_1\")\n",
    "    conv3_2 = mx.symbol.Convolution( data=relu3_1, kernel=(3, 3), pad=(1, 1), num_filter=256, name=\"conv3_2\")\n",
    "    relu3_2 = mx.symbol.Activation(data=conv3_2, act_type=\"relu\", name=\"relu3_2\")\n",
    "    pool3 = mx.symbol.Pooling( data=relu3_2, pool_type=\"max\", kernel=(2, 2), stride=(2,2), name=\"pool3\")\n",
    "    # group 4\n",
    "    conv4_1 = mx.symbol.Convolution( data=pool3, kernel=(3, 3), pad=(1, 1), num_filter=512, name=\"conv4_1\")\n",
    "    relu4_1 = mx.symbol.Activation(data=conv4_1, act_type=\"relu\", name=\"relu4_1\")\n",
    "    conv4_2 = mx.symbol.Convolution( data=relu4_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name=\"conv4_2\")\n",
    "    relu4_2 = mx.symbol.Activation(data=conv4_2, act_type=\"relu\", name=\"relu4_2\")\n",
    "    pool4 = mx.symbol.Pooling( data=relu4_2, pool_type=\"max\", kernel=(2, 2), stride=(2,2), name=\"pool4\")\n",
    "    # group 5\n",
    "    conv5_1 = mx.symbol.Convolution( data=pool4, kernel=(3, 3), pad=(1, 1), num_filter=512, name=\"conv5_1\")\n",
    "    relu5_1 = mx.symbol.Activation(data=conv5_1, act_type=\"relu\", name=\"relu5_1\")\n",
    "    conv5_2 = mx.symbol.Convolution( data=relu5_1, kernel=(3, 3), pad=(1, 1), num_filter=512, name=\"conv5_2\")\n",
    "    relu5_2 = mx.symbol.Activation(data=conv5_2, act_type=\"relu\", name=\"conv1_2\")\n",
    "    pool5 = mx.symbol.Pooling(  data=relu5_2, pool_type=\"max\", kernel=(2, 2), stride=(2,2), name=\"pool5\")\n",
    "    # group 6\n",
    "    flatten = mx.symbol.Flatten(data=pool5, name=\"flatten\")\n",
    "    fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name=\"fc6\")\n",
    "    relu6 = mx.symbol.Activation(data=fc6, act_type=\"relu\", name=\"relu6\")\n",
    "    drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name=\"drop6\")\n",
    "    # group 7\n",
    "    fc7 = mx.symbol.FullyConnected(data=drop6, num_hidden=4096, name=\"fc7\")\n",
    "    relu7 = mx.symbol.Activation(data=fc7, act_type=\"relu\", name=\"relu7\")\n",
    "    drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name=\"drop7\")\n",
    "    # output\n",
    "    fc8a = mx.symbol.FullyConnected(data=drop7, num_hidden=8, name=\"fc8a\")\n",
    "    sm1 = mx.symbol.SoftmaxOutput(data = fc8a, name = 'softmax1')\n",
    "    fc8b = mx.symbol.FullyConnected(data = drop7, name='fc8b', num_hidden=2)\n",
    "    sm2 = mx.symbol.SoftmaxOutput(data = fc8b, name = 'softmax2')\n",
    "\n",
    "    softmax = mx.symbol.Group([sm1, sm2])\n",
    "\n",
    "    return softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next it's ready to training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Start training with [gpu(0)]\n",
      "INFO:root:Epoch[0] Resetting Data Iterator\n",
      "INFO:root:Epoch[0] Time cost=4.290\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0001.params\"\n",
      "INFO:root:Epoch[0] Validation-multi-accuracy_0=0.125000\n",
      "INFO:root:Epoch[0] Validation-multi-accuracy_1=0.500000\n",
      "INFO:root:Epoch[1] Resetting Data Iterator\n",
      "INFO:root:Epoch[1] Time cost=3.741\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0002.params\"\n",
      "INFO:root:Epoch[1] Validation-multi-accuracy_0=0.100000\n",
      "INFO:root:Epoch[1] Validation-multi-accuracy_1=0.487500\n",
      "INFO:root:Epoch[2] Resetting Data Iterator\n",
      "INFO:root:Epoch[2] Time cost=3.751\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0003.params\"\n",
      "INFO:root:Epoch[2] Validation-multi-accuracy_0=0.137500\n",
      "INFO:root:Epoch[2] Validation-multi-accuracy_1=0.575000\n",
      "INFO:root:Epoch[3] Resetting Data Iterator\n",
      "INFO:root:Epoch[3] Time cost=3.752\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0004.params\"\n",
      "INFO:root:Epoch[3] Validation-multi-accuracy_0=0.137500\n",
      "INFO:root:Epoch[3] Validation-multi-accuracy_1=0.662500\n",
      "INFO:root:Epoch[4] Resetting Data Iterator\n",
      "INFO:root:Epoch[4] Time cost=3.759\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0005.params\"\n",
      "INFO:root:Epoch[4] Validation-multi-accuracy_0=0.150000\n",
      "INFO:root:Epoch[4] Validation-multi-accuracy_1=0.700000\n",
      "INFO:root:Epoch[5] Resetting Data Iterator\n",
      "INFO:root:Epoch[5] Time cost=3.764\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0006.params\"\n",
      "INFO:root:Epoch[5] Validation-multi-accuracy_0=0.125000\n",
      "INFO:root:Epoch[5] Validation-multi-accuracy_1=0.700000\n",
      "INFO:root:Epoch[6] Resetting Data Iterator\n",
      "INFO:root:Epoch[6] Time cost=3.802\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0007.params\"\n",
      "INFO:root:Epoch[6] Validation-multi-accuracy_0=0.200000\n",
      "INFO:root:Epoch[6] Validation-multi-accuracy_1=0.737500\n",
      "INFO:root:Epoch[7] Resetting Data Iterator\n",
      "INFO:root:Epoch[7] Time cost=3.806\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0008.params\"\n",
      "INFO:root:Epoch[7] Validation-multi-accuracy_0=0.237500\n",
      "INFO:root:Epoch[7] Validation-multi-accuracy_1=0.750000\n",
      "INFO:root:Epoch[8] Resetting Data Iterator\n",
      "INFO:root:Epoch[8] Time cost=3.825\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0009.params\"\n",
      "INFO:root:Epoch[8] Validation-multi-accuracy_0=0.237500\n",
      "INFO:root:Epoch[8] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[9] Resetting Data Iterator\n",
      "INFO:root:Epoch[9] Time cost=3.794\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0010.params\"\n",
      "INFO:root:Epoch[9] Validation-multi-accuracy_0=0.312500\n",
      "INFO:root:Epoch[9] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[10] Resetting Data Iterator\n",
      "INFO:root:Epoch[10] Time cost=3.815\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0011.params\"\n",
      "INFO:root:Epoch[10] Validation-multi-accuracy_0=0.300000\n",
      "INFO:root:Epoch[10] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[11] Resetting Data Iterator\n",
      "INFO:root:Epoch[11] Time cost=3.797\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0012.params\"\n",
      "INFO:root:Epoch[11] Validation-multi-accuracy_0=0.287500\n",
      "INFO:root:Epoch[11] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[12] Resetting Data Iterator\n",
      "INFO:root:Epoch[12] Time cost=3.787\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0013.params\"\n",
      "INFO:root:Epoch[12] Validation-multi-accuracy_0=0.262500\n",
      "INFO:root:Epoch[12] Validation-multi-accuracy_1=0.800000\n",
      "INFO:root:Epoch[13] Resetting Data Iterator\n",
      "INFO:root:Epoch[13] Time cost=3.852\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0014.params\"\n",
      "INFO:root:Epoch[13] Validation-multi-accuracy_0=0.275000\n",
      "INFO:root:Epoch[13] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[14] Resetting Data Iterator\n",
      "INFO:root:Epoch[14] Time cost=3.770\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0015.params\"\n",
      "INFO:root:Epoch[14] Validation-multi-accuracy_0=0.325000\n",
      "INFO:root:Epoch[14] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[15] Resetting Data Iterator\n",
      "INFO:root:Epoch[15] Time cost=3.832\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0016.params\"\n",
      "INFO:root:Epoch[15] Validation-multi-accuracy_0=0.375000\n",
      "INFO:root:Epoch[15] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[16] Resetting Data Iterator\n",
      "INFO:root:Epoch[16] Time cost=3.780\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0017.params\"\n",
      "INFO:root:Epoch[16] Validation-multi-accuracy_0=0.300000\n",
      "INFO:root:Epoch[16] Validation-multi-accuracy_1=0.750000\n",
      "INFO:root:Epoch[17] Resetting Data Iterator\n",
      "INFO:root:Epoch[17] Time cost=3.818\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0018.params\"\n",
      "INFO:root:Epoch[17] Validation-multi-accuracy_0=0.262500\n",
      "INFO:root:Epoch[17] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[18] Resetting Data Iterator\n",
      "INFO:root:Epoch[18] Time cost=3.761\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0019.params\"\n",
      "INFO:root:Epoch[18] Validation-multi-accuracy_0=0.325000\n",
      "INFO:root:Epoch[18] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[19] Resetting Data Iterator\n",
      "INFO:root:Epoch[19] Time cost=3.796\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0020.params\"\n",
      "INFO:root:Epoch[19] Validation-multi-accuracy_0=0.337500\n",
      "INFO:root:Epoch[19] Validation-multi-accuracy_1=0.737500\n",
      "INFO:root:Epoch[20] Resetting Data Iterator\n",
      "INFO:root:Epoch[20] Time cost=3.839\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0021.params\"\n",
      "INFO:root:Epoch[20] Validation-multi-accuracy_0=0.337500\n",
      "INFO:root:Epoch[20] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[21] Resetting Data Iterator\n",
      "INFO:root:Epoch[21] Time cost=3.773\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0022.params\"\n",
      "INFO:root:Epoch[21] Validation-multi-accuracy_0=0.312500\n",
      "INFO:root:Epoch[21] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[22] Resetting Data Iterator\n",
      "INFO:root:Epoch[22] Time cost=3.834\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0023.params\"\n",
      "INFO:root:Epoch[22] Validation-multi-accuracy_0=0.350000\n",
      "INFO:root:Epoch[22] Validation-multi-accuracy_1=0.750000\n",
      "INFO:root:Epoch[23] Resetting Data Iterator\n",
      "INFO:root:Epoch[23] Time cost=3.788\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0024.params\"\n",
      "INFO:root:Epoch[23] Validation-multi-accuracy_0=0.375000\n",
      "INFO:root:Epoch[23] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[24] Resetting Data Iterator\n",
      "INFO:root:Epoch[24] Time cost=3.768\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0025.params\"\n",
      "INFO:root:Epoch[24] Validation-multi-accuracy_0=0.350000\n",
      "INFO:root:Epoch[24] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[25] Resetting Data Iterator\n",
      "INFO:root:Epoch[25] Time cost=3.892\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0026.params\"\n",
      "INFO:root:Epoch[25] Validation-multi-accuracy_0=0.375000\n",
      "INFO:root:Epoch[25] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[26] Resetting Data Iterator\n",
      "INFO:root:Epoch[26] Time cost=3.843\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0027.params\"\n",
      "INFO:root:Epoch[26] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[26] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[27] Resetting Data Iterator\n",
      "INFO:root:Epoch[27] Time cost=3.790\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0028.params\"\n",
      "INFO:root:Epoch[27] Validation-multi-accuracy_0=0.375000\n",
      "INFO:root:Epoch[27] Validation-multi-accuracy_1=0.750000\n",
      "INFO:root:Epoch[28] Resetting Data Iterator\n",
      "INFO:root:Epoch[28] Time cost=3.833\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0029.params\"\n",
      "INFO:root:Epoch[28] Validation-multi-accuracy_0=0.387500\n",
      "INFO:root:Epoch[28] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[29] Resetting Data Iterator\n",
      "INFO:root:Epoch[29] Time cost=3.776\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0030.params\"\n",
      "INFO:root:Epoch[29] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[29] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[30] Resetting Data Iterator\n",
      "INFO:root:Epoch[30] Time cost=3.795\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0031.params\"\n",
      "INFO:root:Epoch[30] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[30] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[31] Resetting Data Iterator\n",
      "INFO:root:Epoch[31] Time cost=3.806\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0032.params\"\n",
      "INFO:root:Epoch[31] Validation-multi-accuracy_0=0.350000\n",
      "INFO:root:Epoch[31] Validation-multi-accuracy_1=0.750000\n",
      "INFO:root:Epoch[32] Resetting Data Iterator\n",
      "INFO:root:Epoch[32] Time cost=3.847\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0033.params\"\n",
      "INFO:root:Epoch[32] Validation-multi-accuracy_0=0.362500\n",
      "INFO:root:Epoch[32] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[33] Resetting Data Iterator\n",
      "INFO:root:Epoch[33] Time cost=3.884\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0034.params\"\n",
      "INFO:root:Epoch[33] Validation-multi-accuracy_0=0.362500\n",
      "INFO:root:Epoch[33] Validation-multi-accuracy_1=0.762500\n",
      "INFO:root:Epoch[34] Resetting Data Iterator\n",
      "INFO:root:Epoch[34] Time cost=3.835\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0035.params\"\n",
      "INFO:root:Epoch[34] Validation-multi-accuracy_0=0.375000\n",
      "INFO:root:Epoch[34] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[35] Resetting Data Iterator\n",
      "INFO:root:Epoch[35] Time cost=3.909\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0036.params\"\n",
      "INFO:root:Epoch[35] Validation-multi-accuracy_0=0.362500\n",
      "INFO:root:Epoch[35] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[36] Resetting Data Iterator\n",
      "INFO:root:Epoch[36] Time cost=3.821\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0037.params\"\n",
      "INFO:root:Epoch[36] Validation-multi-accuracy_0=0.350000\n",
      "INFO:root:Epoch[36] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[37] Resetting Data Iterator\n",
      "INFO:root:Epoch[37] Time cost=3.793\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0038.params\"\n",
      "INFO:root:Epoch[37] Validation-multi-accuracy_0=0.362500\n",
      "INFO:root:Epoch[37] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[38] Resetting Data Iterator\n",
      "INFO:root:Epoch[38] Time cost=3.792\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0039.params\"\n",
      "INFO:root:Epoch[38] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[38] Validation-multi-accuracy_1=0.775000\n",
      "INFO:root:Epoch[39] Resetting Data Iterator\n",
      "INFO:root:Epoch[39] Time cost=3.842\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0040.params\"\n",
      "INFO:root:Epoch[39] Validation-multi-accuracy_0=0.387500\n",
      "INFO:root:Epoch[39] Validation-multi-accuracy_1=0.812500\n",
      "INFO:root:Epoch[40] Resetting Data Iterator\n",
      "INFO:root:Epoch[40] Time cost=3.878\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0041.params\"\n",
      "INFO:root:Epoch[40] Validation-multi-accuracy_0=0.412500\n",
      "INFO:root:Epoch[40] Validation-multi-accuracy_1=0.787500\n",
      "INFO:root:Epoch[41] Resetting Data Iterator\n",
      "INFO:root:Epoch[41] Time cost=3.810\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0042.params\"\n",
      "INFO:root:Epoch[41] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[41] Validation-multi-accuracy_1=0.800000\n",
      "INFO:root:Epoch[42] Resetting Data Iterator\n",
      "INFO:root:Epoch[42] Time cost=3.796\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0043.params\"\n",
      "INFO:root:Epoch[42] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[42] Validation-multi-accuracy_1=0.812500\n",
      "INFO:root:Epoch[43] Resetting Data Iterator\n",
      "INFO:root:Epoch[43] Time cost=3.792\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0044.params\"\n",
      "INFO:root:Epoch[43] Validation-multi-accuracy_0=0.412500\n",
      "INFO:root:Epoch[43] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[44] Resetting Data Iterator\n",
      "INFO:root:Epoch[44] Time cost=3.832\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0045.params\"\n",
      "INFO:root:Epoch[44] Validation-multi-accuracy_0=0.462500\n",
      "INFO:root:Epoch[44] Validation-multi-accuracy_1=0.812500\n",
      "INFO:root:Epoch[45] Resetting Data Iterator\n",
      "INFO:root:Epoch[45] Time cost=3.830\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0046.params\"\n",
      "INFO:root:Epoch[45] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[45] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[46] Resetting Data Iterator\n",
      "INFO:root:Epoch[46] Time cost=3.828\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0047.params\"\n",
      "INFO:root:Epoch[46] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[46] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[47] Resetting Data Iterator\n",
      "INFO:root:Epoch[47] Time cost=3.817\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0048.params\"\n",
      "INFO:root:Epoch[47] Validation-multi-accuracy_0=0.412500\n",
      "INFO:root:Epoch[47] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[48] Resetting Data Iterator\n",
      "INFO:root:Epoch[48] Time cost=3.798\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0049.params\"\n",
      "INFO:root:Epoch[48] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[48] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[49] Resetting Data Iterator\n",
      "INFO:root:Epoch[49] Time cost=3.818\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0050.params\"\n",
      "INFO:root:Epoch[49] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[49] Validation-multi-accuracy_1=0.837500\n",
      "INFO:root:Epoch[50] Resetting Data Iterator\n",
      "INFO:root:Epoch[50] Time cost=3.845\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0051.params\"\n",
      "INFO:root:Epoch[50] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[50] Validation-multi-accuracy_1=0.825000\n",
      "INFO:root:Epoch[51] Resetting Data Iterator\n",
      "INFO:root:Epoch[51] Time cost=3.850\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0052.params\"\n",
      "INFO:root:Epoch[51] Validation-multi-accuracy_0=0.412500\n",
      "INFO:root:Epoch[51] Validation-multi-accuracy_1=0.837500\n",
      "INFO:root:Epoch[52] Resetting Data Iterator\n",
      "INFO:root:Epoch[52] Time cost=3.794\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0053.params\"\n",
      "INFO:root:Epoch[52] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[52] Validation-multi-accuracy_1=0.837500\n",
      "INFO:root:Epoch[53] Resetting Data Iterator\n",
      "INFO:root:Epoch[53] Time cost=3.792\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0054.params\"\n",
      "INFO:root:Epoch[53] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[53] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[54] Resetting Data Iterator\n",
      "INFO:root:Epoch[54] Time cost=3.797\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0055.params\"\n",
      "INFO:root:Epoch[54] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[54] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[55] Resetting Data Iterator\n",
      "INFO:root:Epoch[55] Time cost=3.836\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0056.params\"\n",
      "INFO:root:Epoch[55] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[55] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[56] Resetting Data Iterator\n",
      "INFO:root:Epoch[56] Time cost=3.842\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0057.params\"\n",
      "INFO:root:Epoch[56] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[56] Validation-multi-accuracy_1=0.837500\n",
      "INFO:root:Epoch[57] Resetting Data Iterator\n",
      "INFO:root:Epoch[57] Time cost=3.837\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0058.params\"\n",
      "INFO:root:Epoch[57] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[57] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[58] Resetting Data Iterator\n",
      "INFO:root:Epoch[58] Time cost=3.805\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0059.params\"\n",
      "INFO:root:Epoch[58] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[58] Validation-multi-accuracy_1=0.837500\n",
      "INFO:root:Epoch[59] Resetting Data Iterator\n",
      "INFO:root:Epoch[59] Time cost=3.793\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0060.params\"\n",
      "INFO:root:Epoch[59] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[59] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[60] Resetting Data Iterator\n",
      "INFO:root:Epoch[60] Time cost=3.848\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0061.params\"\n",
      "INFO:root:Epoch[60] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[60] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[61] Resetting Data Iterator\n",
      "INFO:root:Epoch[61] Time cost=3.859\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0062.params\"\n",
      "INFO:root:Epoch[61] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[61] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[62] Resetting Data Iterator\n",
      "INFO:root:Epoch[62] Time cost=3.834\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0063.params\"\n",
      "INFO:root:Epoch[62] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[62] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[63] Resetting Data Iterator\n",
      "INFO:root:Epoch[63] Time cost=3.813\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0064.params\"\n",
      "INFO:root:Epoch[63] Validation-multi-accuracy_0=0.462500\n",
      "INFO:root:Epoch[63] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[64] Resetting Data Iterator\n",
      "INFO:root:Epoch[64] Time cost=3.794\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0065.params\"\n",
      "INFO:root:Epoch[64] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[64] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[65] Resetting Data Iterator\n",
      "INFO:root:Epoch[65] Time cost=3.796\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0066.params\"\n",
      "INFO:root:Epoch[65] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[65] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[66] Resetting Data Iterator\n",
      "INFO:root:Epoch[66] Time cost=3.805\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0067.params\"\n",
      "INFO:root:Epoch[66] Validation-multi-accuracy_0=0.525000\n",
      "INFO:root:Epoch[66] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[67] Resetting Data Iterator\n",
      "INFO:root:Epoch[67] Time cost=3.833\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0068.params\"\n",
      "INFO:root:Epoch[67] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[67] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[68] Resetting Data Iterator\n",
      "INFO:root:Epoch[68] Time cost=3.801\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0069.params\"\n",
      "INFO:root:Epoch[68] Validation-multi-accuracy_0=0.575000\n",
      "INFO:root:Epoch[68] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[69] Resetting Data Iterator\n",
      "INFO:root:Epoch[69] Time cost=3.824\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0070.params\"\n",
      "INFO:root:Epoch[69] Validation-multi-accuracy_0=0.462500\n",
      "INFO:root:Epoch[69] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[70] Resetting Data Iterator\n",
      "INFO:root:Epoch[70] Time cost=3.841\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0071.params\"\n",
      "INFO:root:Epoch[70] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[70] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[71] Resetting Data Iterator\n",
      "INFO:root:Epoch[71] Time cost=3.874\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0072.params\"\n",
      "INFO:root:Epoch[71] Validation-multi-accuracy_0=0.512500\n",
      "INFO:root:Epoch[71] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[72] Resetting Data Iterator\n",
      "INFO:root:Epoch[72] Time cost=3.811\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0073.params\"\n",
      "INFO:root:Epoch[72] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[72] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[73] Resetting Data Iterator\n",
      "INFO:root:Epoch[73] Time cost=3.793\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0074.params\"\n",
      "INFO:root:Epoch[73] Validation-multi-accuracy_0=0.525000\n",
      "INFO:root:Epoch[73] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[74] Resetting Data Iterator\n",
      "INFO:root:Epoch[74] Time cost=3.810\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0075.params\"\n",
      "INFO:root:Epoch[74] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[74] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[75] Resetting Data Iterator\n",
      "INFO:root:Epoch[75] Time cost=3.835\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0076.params\"\n",
      "INFO:root:Epoch[75] Validation-multi-accuracy_0=0.500000\n",
      "INFO:root:Epoch[75] Validation-multi-accuracy_1=0.850000\n",
      "INFO:root:Epoch[76] Resetting Data Iterator\n",
      "INFO:root:Epoch[76] Time cost=3.858\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0077.params\"\n",
      "INFO:root:Epoch[76] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[76] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[77] Resetting Data Iterator\n",
      "INFO:root:Epoch[77] Time cost=3.811\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0078.params\"\n",
      "INFO:root:Epoch[77] Validation-multi-accuracy_0=0.512500\n",
      "INFO:root:Epoch[77] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[78] Resetting Data Iterator\n",
      "INFO:root:Epoch[78] Time cost=3.792\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0079.params\"\n",
      "INFO:root:Epoch[78] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[78] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[79] Resetting Data Iterator\n",
      "INFO:root:Epoch[79] Time cost=3.797\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0080.params\"\n",
      "INFO:root:Epoch[79] Validation-multi-accuracy_0=0.525000\n",
      "INFO:root:Epoch[79] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[80] Resetting Data Iterator\n",
      "INFO:root:Epoch[80] Time cost=3.797\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0081.params\"\n",
      "INFO:root:Epoch[80] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[80] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[81] Resetting Data Iterator\n",
      "INFO:root:Epoch[81] Time cost=3.796\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0082.params\"\n",
      "INFO:root:Epoch[81] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[81] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[82] Resetting Data Iterator\n",
      "INFO:root:Epoch[82] Time cost=3.803\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0083.params\"\n",
      "INFO:root:Epoch[82] Validation-multi-accuracy_0=0.500000\n",
      "INFO:root:Epoch[82] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[83] Resetting Data Iterator\n",
      "INFO:root:Epoch[83] Time cost=3.803\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0084.params\"\n",
      "INFO:root:Epoch[83] Validation-multi-accuracy_0=0.462500\n",
      "INFO:root:Epoch[83] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[84] Resetting Data Iterator\n",
      "INFO:root:Epoch[84] Time cost=3.856\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0085.params\"\n",
      "INFO:root:Epoch[84] Validation-multi-accuracy_0=0.462500\n",
      "INFO:root:Epoch[84] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[85] Resetting Data Iterator\n",
      "INFO:root:Epoch[85] Time cost=3.802\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0086.params\"\n",
      "INFO:root:Epoch[85] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[85] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[86] Resetting Data Iterator\n",
      "INFO:root:Epoch[86] Time cost=3.798\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0087.params\"\n",
      "INFO:root:Epoch[86] Validation-multi-accuracy_0=0.412500\n",
      "INFO:root:Epoch[86] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[87] Resetting Data Iterator\n",
      "INFO:root:Epoch[87] Time cost=3.798\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0088.params\"\n",
      "INFO:root:Epoch[87] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[87] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[88] Resetting Data Iterator\n",
      "INFO:root:Epoch[88] Time cost=3.796\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0089.params\"\n",
      "INFO:root:Epoch[88] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[88] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[89] Resetting Data Iterator\n",
      "INFO:root:Epoch[89] Time cost=3.800\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0090.params\"\n",
      "INFO:root:Epoch[89] Validation-multi-accuracy_0=0.400000\n",
      "INFO:root:Epoch[89] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[90] Resetting Data Iterator\n",
      "INFO:root:Epoch[90] Time cost=3.794\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0091.params\"\n",
      "INFO:root:Epoch[90] Validation-multi-accuracy_0=0.487500\n",
      "INFO:root:Epoch[90] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[91] Resetting Data Iterator\n",
      "INFO:root:Epoch[91] Time cost=3.794\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0092.params\"\n",
      "INFO:root:Epoch[91] Validation-multi-accuracy_0=0.425000\n",
      "INFO:root:Epoch[91] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[92] Resetting Data Iterator\n",
      "INFO:root:Epoch[92] Time cost=3.793\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0093.params\"\n",
      "INFO:root:Epoch[92] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[92] Validation-multi-accuracy_1=0.887500\n",
      "INFO:root:Epoch[93] Resetting Data Iterator\n",
      "INFO:root:Epoch[93] Time cost=3.795\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0094.params\"\n",
      "INFO:root:Epoch[93] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[93] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[94] Resetting Data Iterator\n",
      "INFO:root:Epoch[94] Time cost=3.799\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0095.params\"\n",
      "INFO:root:Epoch[94] Validation-multi-accuracy_0=0.500000\n",
      "INFO:root:Epoch[94] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[95] Resetting Data Iterator\n",
      "INFO:root:Epoch[95] Time cost=3.799\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0096.params\"\n",
      "INFO:root:Epoch[95] Validation-multi-accuracy_0=0.450000\n",
      "INFO:root:Epoch[95] Validation-multi-accuracy_1=0.875000\n",
      "INFO:root:Epoch[96] Resetting Data Iterator\n",
      "INFO:root:Epoch[96] Time cost=3.797\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0097.params\"\n",
      "INFO:root:Epoch[96] Validation-multi-accuracy_0=0.475000\n",
      "INFO:root:Epoch[96] Validation-multi-accuracy_1=0.887500\n",
      "INFO:root:Epoch[97] Resetting Data Iterator\n",
      "INFO:root:Epoch[97] Time cost=3.890\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0098.params\"\n",
      "INFO:root:Epoch[97] Validation-multi-accuracy_0=0.500000\n",
      "INFO:root:Epoch[97] Validation-multi-accuracy_1=0.862500\n",
      "INFO:root:Epoch[98] Resetting Data Iterator\n",
      "INFO:root:Epoch[98] Time cost=3.827\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0099.params\"\n",
      "INFO:root:Epoch[98] Validation-multi-accuracy_0=0.437500\n",
      "INFO:root:Epoch[98] Validation-multi-accuracy_1=0.887500\n",
      "INFO:root:Epoch[99] Resetting Data Iterator\n",
      "INFO:root:Epoch[99] Time cost=3.880\n",
      "INFO:root:Saved checkpoint to \"mnist_multitask-0100.params\"\n",
      "INFO:root:Epoch[99] Validation-multi-accuracy_0=0.512500\n",
      "INFO:root:Epoch[99] Validation-multi-accuracy_1=0.887500\n"
     ]
    }
   ],
   "source": [
    "batch_size=20\n",
    "num_epochs=100\n",
    "device = mx.gpu(0)\n",
    "lr = 0.0001\n",
    "\n",
    "network = build_network()\n",
    "train, val = mnist_iterator(batch_size=batch_size, input_shape = (3,224,224))\n",
    "train = Multi_mnist_iterator(train)\n",
    "val = Multi_mnist_iterator(val)\n",
    "\n",
    "save_model_prefix = \"mnist_multitask\"\n",
    "checkpoint = mx.callback.do_checkpoint(save_model_prefix)\n",
    "\n",
    "model = mx.model.FeedForward(\n",
    "    ctx                = device,\n",
    "    symbol             = network,\n",
    "    num_epoch          = num_epochs,\n",
    "    learning_rate      = lr,\n",
    "    momentum           = 0.9,\n",
    "    wd                 = 0.00001,\n",
    "    initializer        = mx.init.Xavier(factor_type=\"in\", magnitude=2.34))\n",
    "\n",
    "model.fit(\n",
    "    X                  = train,\n",
    "    eval_data          = val,\n",
    "    eval_metric        = Multi_Accuracy(num=2),\n",
    "    batch_end_callback = mx.callback.Speedometer(batch_size, 50),\n",
    "    epoch_end_callback = checkpoint\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Since I just prepare 400 samples, the task one is not converge in a well manner, but as you can see, it works! if you have more data samples, you should get a much better output.\n",
    "\n",
    "That is all!"
   ]
  }
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