notebooks/neural_networks/cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten Digit Classfication using Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tutorial presented will again tackle the MNIST digit classification problem. You can find more information from the previous [MNIST introduction](introduction_to_mnist.ipynb) tutorial. This time, we will build a Convolutional Neural Network which is vastly used in many different applications. CNN are networks with loops in them, allowing information to persist. Take a look at [this great blog](https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/) to gain insights of CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "%pylab inline\n",
    "\n",
    "import pandas\n",
    "import datetime as dt\n",
    "\n",
    "from bigdl.nn.layer import *\n",
    "from bigdl.nn.criterion import *\n",
    "from bigdl.optim.optimizer import *\n",
    "from bigdl.util.common import *\n",
    "from bigdl.dataset.transformer import *\n",
    "from bigdl.dataset import mnist\n",
    "from utils import get_mnist\n",
    "from matplotlib.pyplot import imshow\n",
    "import matplotlib.pyplot as plt\n",
    "from pyspark import SparkContext\n",
    "sc=SparkContext.getOrCreate(conf=create_spark_conf().setMaster(\"local[4]\").set(\"spark.driver.memory\",\"2g\"))\n",
    "\n",
    "init_engine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Train the network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we should get and store MNIST into RDD of Sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: *edit the \"mnist_path\" accordingly. If the \"mnist_path\" directory does not consist of the mnist data, mnist.read_data_sets method will download the dataset directly to the directory*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Extracting', 'datasets/mnist/train-images-idx3-ubyte.gz')\n",
      "('Extracting', 'datasets/mnist/train-labels-idx1-ubyte.gz')\n",
      "('Extracting', 'datasets/mnist/t10k-images-idx3-ubyte.gz')\n",
      "('Extracting', 'datasets/mnist/t10k-labels-idx1-ubyte.gz')\n",
      "60000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "# Get and store MNIST into RDD of Sample, please edit the \"mnist_path\" accordingly.\n",
    "mnist_path = \"datasets/mnist\"\n",
    "(train_data, test_data) = get_mnist(sc, mnist_path)\n",
    "\n",
    "print(train_data.count())\n",
    "print(test_data.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Model creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the LeNet-5 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createSequential\n",
      "creating: createReshape\n",
      "creating: createSpatialConvolution\n",
      "creating: createTanh\n",
      "creating: createSpatialMaxPooling\n",
      "creating: createTanh\n",
      "creating: createSpatialConvolution\n",
      "creating: createSpatialMaxPooling\n",
      "creating: createReshape\n",
      "creating: createLinear\n",
      "creating: createTanh\n",
      "creating: createLinear\n",
      "creating: createLogSoftMax\n"
     ]
    }
   ],
   "source": [
    "# Create a LeNet model\n",
    "def build_model(class_num):\n",
    "    model = Sequential()\n",
    "    model.add(Reshape([1, 28, 28]))\n",
    "    model.add(SpatialConvolution(1, 6, 5, 5).set_name('conv1'))\n",
    "    model.add(Tanh())\n",
    "    model.add(SpatialMaxPooling(2, 2, 2, 2).set_name('pool1'))\n",
    "    model.add(Tanh())\n",
    "    model.add(SpatialConvolution(6, 12, 5, 5).set_name('conv2'))\n",
    "    model.add(SpatialMaxPooling(2, 2, 2, 2).set_name('pool2'))\n",
    "    model.add(Reshape([12 * 4 * 4]))\n",
    "    model.add(Linear(12 * 4 * 4, 100).set_name('fc1'))\n",
    "    model.add(Tanh())\n",
    "    model.add(Linear(100, class_num).set_name('score'))\n",
    "    model.add(LogSoftMax())\n",
    "    return model\n",
    "lenet_model = build_model(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Optimizer setup and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createClassNLLCriterion\n",
      "creating: createDefault\n",
      "creating: createSGD\n",
      "creating: createMaxEpoch\n",
      "creating: createOptimizer\n",
      "creating: createEveryEpoch\n",
      "creating: createTop1Accuracy\n",
      "creating: createTrainSummary\n",
      "creating: createSeveralIteration\n",
      "creating: createValidationSummary\n",
      "saving logs to  lenet-20180118-164519\n"
     ]
    }
   ],
   "source": [
    "# Create an Optimizer\n",
    "\n",
    "optimizer = Optimizer(\n",
    "    model=lenet_model,\n",
    "    training_rdd=train_data,\n",
    "    criterion=ClassNLLCriterion(),\n",
    "    optim_method=SGD(learningrate=0.4, learningrate_decay=0.0002),\n",
    "    end_trigger=MaxEpoch(20),\n",
    "    batch_size=2048)\n",
    "\n",
    "# Set the validation logic\n",
    "optimizer.set_validation(\n",
    "    batch_size=2048,\n",
    "    val_rdd=test_data,\n",
    "    trigger=EveryEpoch(),\n",
    "    val_method=[Top1Accuracy()]\n",
    ")\n",
    "\n",
    "app_name='lenet-'+dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "train_summary = TrainSummary(log_dir='/tmp/bigdl_summaries',\n",
    "                                     app_name=app_name)\n",
    "train_summary.set_summary_trigger(\"Parameters\", SeveralIteration(50))\n",
    "val_summary = ValidationSummary(log_dir='/tmp/bigdl_summaries',\n",
    "                                        app_name=app_name)\n",
    "optimizer.set_train_summary(train_summary)\n",
    "optimizer.set_val_summary(val_summary)\n",
    "print(\"saving logs to \",app_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Train the network. Wait some time till it finished.. Voila! You've got a trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Done.\n",
      "CPU times: user 0 ns, sys: 20 ms, total: 20 ms\n",
      "Wall time: 1min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Boot training process\n",
    "trained_model = optimizer.optimize()\n",
    "print(\"Optimization Done.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Predication on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def map_predict_label(l):\n",
    "    return np.array(l).argmax()\n",
    "def map_groundtruth_label(l):\n",
    "    return l[0] - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a few traing samples and check the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth labels:\n",
      "5.0, 0.0, 4.0, 1.0, 9.0, 2.0, 1.0, 3.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 223.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f166dcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# label-1 to restore the original label.\n",
    "print(\"Ground Truth labels:\") \n",
    "print(', '.join([str(map_groundtruth_label(s.label.to_ndarray())) for s in train_data.take(8)]))\n",
    "imshow(np.column_stack([np.array(s.features[0].to_ndarray()).reshape(28,28) for s in train_data.take(8)]),cmap='gray'); plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's see the prediction results on test data by our trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth labels:\n",
      "7.0, 2.0, 1.0, 0.0, 4.0, 1.0, 4.0, 9.0\n",
      "Predicted labels:\n",
      "7, 2, 1, 0, 4, 1, 4, 9\n",
      "CPU times: user 52 ms, sys: 4 ms, total: 56 ms\n",
      "Wall time: 324 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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h4eHmJA1wvSJDD75aQS+B27t3b7MOz7Bhw276uOTkZGbOnBnQtt0qfQIEsK66RX823aty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\ngC6EEDYhAV0IIWxCAroQQtjE/wFIye0NfFiqDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f166f3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "predictions = trained_model.predict(test_data)\n",
    "imshow(np.column_stack([np.array(s.features[0].to_ndarray()).reshape(28,28) for s in test_data.take(8)]),cmap='gray'); plt.axis('off')\n",
    "print('Ground Truth labels:')\n",
    "print(', '.join(str(map_groundtruth_label(s.label.to_ndarray())) for s in test_data.take(8)))\n",
    "print('Predicted labels:')\n",
    "print(', '.join(str(map_predict_label(s)) for s in predictions.take(8)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Model inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now look at the parameter shapes. The parameters are exposed as a dict, and can be retrieved using model.parameters()."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The param shapes typically have the form (batch_number?, output_channels, input_channels, filter_height, filter_width) (for the weights) and the 1-dimensional shape (output_channels,) (for the biases)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv2 (1, 12, 6, 5, 5) (12,)\n",
      "conv1 (1, 6, 1, 5, 5) (6,)\n",
      "score (10, 100) (10,)\n",
      "fc1 (100, 192) (100,)\n"
     ]
    }
   ],
   "source": [
    "params = trained_model.parameters()\n",
    "\n",
    "#batch num, output_dim, input_dim, spacial_dim\n",
    "for layer_name, param in params.items():\n",
    "    print(layer_name,param['weight'].shape,param['bias'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Weight visualiztion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then let's demonstrate how to visualize the weights of convolutional layers in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#vis_square is borrowed from caffe example\n",
    "def vis_square(data):\n",
    "    \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
    "       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
    "    \n",
    "    # normalize data for display\n",
    "    data = (data - data.min()) / (data.max() - data.min())\n",
    "    # force the number of filters to be square\n",
    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
    "    padding = (((0, n ** 2 - data.shape[0]),\n",
    "               (0, 1), (0, 1))                 # add some space between filters\n",
    "               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\n",
    "    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)\n",
    "    \n",
    "    # tile the filters into an image\n",
    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
    "  \n",
    "    plt.imshow(data,cmap='gray'); plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAABr9JREFUeJzt3d9rzv8fx/H3+ERsk1p+bE2ppUUoP8rY0bTjOVCKoogi\n/gKlHDhyJiUHlJQzihOTpOTAgR/LgQOlpUlZKW0ZDdnnL/jWnvu263PwuN2OH13vq9a918lr17tt\nbm6uAfIs+a+/APDfED+EEj+EEj+EEj+EEj+EEj+EEj+EEj+E+qeVDxscHCxdJ+zt7S19fmdnZ2lf\n/fzDhw+X9k3TNP39/aX9iRMnSvtv376V9t3d3aX948ePS/s1a9aU9i9evCjt7969W9o/e/astG+a\nphkaGirtf/z4Udpv2bKltN+5c2dp3zRN23xGTn4IJX4IJX4IJX4IJX4IJX4IJX4IJX4IJX4IJX4I\nJX4I1dK7/Tdv3iztX758Wdo/f/68tB8YGCjtq/f0F2L16tWlffXXlycmJkr7kZGR0v7Lly+lfdXS\npUtL++r3b5qmefv2bWl/8uTJ0n5ycrK0XyxOfgglfgglfgglfgglfgglfgglfgglfgglfgglfggl\nfgjV0rv9T548Ke0/fPhQ2k9NTZX2o6Ojpf2qVatK+6Zpmn379pX2O3bsKO3HxsZK+9nZ2dK++pv0\nR44cKe2rLl68WNpfunSp/IxDhw6V9rdu3Srtu7q6SvvF+p8SJz+EEj+EEj+EEj+EEj+EEj+EEj+E\nEj+EEj+EEj+EEj+Eaund/itXrpT258+fL+2XLVtW2g8NDZX269atK+0XYnx8vLTv7Ows7X/9+lXa\nb926tbT//ft3aV9VfS9AR0dH+RnXr18v7avfqfrugaNHj5b28+Xkh1Dih1Dih1Dih1Dih1Dih1Di\nh1Dih1Dih1Dih1Dih1AtvdtfvWf94MGD0v7AgQOl/f79+0v7hw8flvZN0zR9fX2lfXd3d2k/PDxc\n2n/8+LG0X7t2bWn/7t270r6q+jc7d+5c+Rl79uwp7Tds2FDa3759u7RfLE5+CCV+CCV+CCV+CCV+\nCCV+CCV+CCV+CCV+CCV+CCV+CCV+CNU2NzfXyue19GEQqm0+Iyc/hBI/hBI/hBI/hBI/hBI/hBI/\nhBI/hBI/hBI/hBI/hGrpSzsuXLhQ2j969Ki07+3tLe0/ffpU2v/8+bO0b5r6SyxOnz5d2ldfVPL1\n69fS/saNG6V9e3t7aV99Ecr9+/dL++XLl5f2TVP/O09PTy/q5585c6a0ny8nP4QSP4QSP4QSP4QS\nP4QSP4QSP4QSP4QSP4QSP4QSP4Rq6d3+qamp0v7s2bOlfU9PT2n/+vXr0v7OnTul/UIcPHiwtN+w\nYUNpPzMzU9oPDg6W9v39/aV91fj4eGk/MTFRfsafP39K+/Xr15f2AwMDpf1icfJDKPFDKPFDKPFD\nKPFDKPFDKPFDKPFDKPFDKPFDKPFDqJbe7R8ZGSntq/fEv3//Xtpfu3attN+4cWNpvxCbN28u7Wdn\nZ0v76u/kHzt2rLTv7Ows7avu3btX2p86dar8jBUrVpT2o6Ojpf2bN29K++Hh4dJ+vpz8EEr8EEr8\nEEr8EEr8EEr8EEr8EEr8EEr8EEr8EEr8EKqld/tfvXpV2nd1dZX2HR0dpf3KlStL+02bNpX2CzE2\nNlbat7e3l/bV35i/fPlyab9t27bSfteuXaX99PR0aX/8+PHSvmmaZsmS2pm4e/fu0n5ycrK0XyxO\nfgglfgglfgglfgglfgglfgglfgglfgglfgglfgglfgjV0rv9PT09pf27d+9K+8+fP5f2e/fuLe3f\nv39f2i/E1atXS/u/f/+W9tu3by/t5+bmSvunT5+W9lV9fX2l/UJ+835mZqa0r/6PSHW/WJz8EEr8\nEEr8EEr8EEr8EEr8EEr8EEr8EEr8EEr8EEr8EKqtenf7/9TSh0GotvmMnPwQSvwQSvwQSvwQSvwQ\nSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQ\nSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQ\nSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQ\nSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQSvwQ\nSvwQSvwQSvwQ6p8WP6+txc8D/gcnP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QS\nP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4QSP4T6F2cq89hTiZA2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f2a74e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filters_conv1 = params['conv1']['weight']\n",
    "\n",
    "filters_conv1[0,0,0]\n",
    "\n",
    "vis_square(np.squeeze(filters_conv1, axis=(0,)).reshape(1*6,5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZPW/ePKOPHj1qNGv32e/APPmCBQuMnjJlitGslWd/fZkyZYzm3H7uBejSpYvR9erVM5p1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1328a110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the parameters are a list of [weights, biases]\n",
    "filters_conv2 = params['conv2']['weight']\n",
    "\n",
    "vis_square(np.squeeze(filters_conv2, axis=(0,)).reshape(12*6,5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Loss visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pMIuIiIhI/7JlYI73ugBo7ghEuBIRERERGexsGpjdADR3+CNciYiIiIgMdvYMzNHdgbmp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f1670a310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = np.array(train_summary.read_scalar(\"Loss\"))\n",
    "top1 = np.array(val_summary.read_scalar(\"Top1Accuracy\"))\n",
    "\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(loss[:,0],loss[:,1],label='loss')\n",
    "plt.xlim(0,loss.shape[0]+10)\n",
    "plt.grid(True)\n",
    "plt.title(\"loss\")\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(top1[:,0],top1[:,1],label='top1')\n",
    "plt.xlim(0,loss.shape[0]+10)\n",
    "plt.title(\"top1 accuracy\")\n",
    "plt.grid(True)"
   ]
  }
 ],
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