{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: Training convolutional neural networks with nolearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Author: [Benjamin Bossan](https://github.com/benjaminbossan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This notebook was updated on April 4, 2016, to reflect recent changes in nolearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This tutorial's goal is to teach you how to use [nolearn](https://github.com/dnouri/nolearn) to train convolutional neural networks (CNNs). The nolearn documentation can be found [here](https://pythonhosted.org/nolearn/). We assume that you have some general knowledge about machine learning in general or neural nets specifically, but want to learn more about convolutional neural networks and nolearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We well cover several points in this notebook.\n",
    "\n",
    "1. How to [load image data](#Loading-MNIST-data) such that we can use it for our purpose. For this tutorial, we will use the MNIST data set, which consists of images of the numbers from 0 to 9.\n",
    "2. How to [properly define layers](#Definition-of-the-layers) of the net. A good choice of layers, i.e. a good network architecture, is most important to get nice results out of a neural net.\n",
    "3. The definition of the [neural network](#Definition-of-the-neural-network) itself. Here we define important hyper-parameters.\n",
    "4. Next we will see how [visualizations](#Visualizations) may help us to further refine the network.\n",
    "5. Finally, we will show you how nolearn can help us find [better architectures](#Finding-a-good-architecture) for our neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from lasagne.layers import DenseLayer\n",
    "from lasagne.layers import InputLayer\n",
    "from lasagne.layers import DropoutLayer\n",
    "from lasagne.layers import Conv2DLayer\n",
    "from lasagne.layers import MaxPool2DLayer\n",
    "from lasagne.nonlinearities import softmax\n",
    "from lasagne.updates import adam\n",
    "from lasagne.layers import get_all_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nolearn.lasagne import NeuralNet\n",
    "from nolearn.lasagne import TrainSplit\n",
    "from nolearn.lasagne import objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MNIST data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This little helper function loads the MNIST data available [here](https://www.kaggle.com/c/digit-recognizer/data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_mnist(path):\n",
    "    X = []\n",
    "    y = []\n",
    "    with open(path, 'rb') as f:\n",
    "        next(f)  # skip header\n",
    "        for line in f:\n",
    "            yi, xi = line.split(',', 1)\n",
    "            y.append(yi)\n",
    "            X.append(xi.split(','))\n",
    "\n",
    "    # Theano works with fp32 precision\n",
    "    X = np.array(X).astype(np.float32)\n",
    "    y = np.array(y).astype(np.int32)\n",
    "\n",
    "    # apply some very simple normalization to the data\n",
    "    X -= X.mean()\n",
    "    X /= X.std()\n",
    "\n",
    "    # For convolutional layers, the default shape of data is bc01,\n",
    "    # i.e. batch size x color channels x image dimension 1 x image dimension 2.\n",
    "    # Therefore, we reshape the X data to -1, 1, 28, 28.\n",
    "    X = X.reshape(\n",
    "        -1,  # number of samples, -1 makes it so that this number is determined automatically\n",
    "        1,   # 1 color channel, since images are only black and white\n",
    "        28,  # first image dimension (vertical)\n",
    "        28,  # second image dimension (horizontal)\n",
    "    )\n",
    "\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# here you should enter the path to your MNIST data\n",
    "path = os.path.join(os.path.expanduser('~'), 'data/mnist/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X, y = load_mnist(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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9vDx+/HgvH3XUUUD99sIcoe1DrzaUHeUazGptOshKIk9XCCEiokZXCCEiUvbw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\nx4qCt0WEPlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3df5d13250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs, axes = plt.subplots(4, 4, figsize=(6, 6))\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        axes[i, j].imshow(-X[i + 4 * j].reshape(28, 28), cmap='gray', interpolation='none')\n",
    "        axes[i, j].set_xticks([])\n",
    "        axes[i, j].set_yticks([])\n",
    "        axes[i, j].set_title(\"Label: {}\".format(y[i + 4 * j]))\n",
    "        axes[i, j].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definition of the layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let us define the layers for the convolutional net. In general, layers are assembled in a list. Each element of the list is a tuple -- first a Lasagne layer, next a dictionary containing the arguments of the layer. We will explain the layer definitions in a moment, but in general, you should look them up in the [Lasagne documenation](http://lasagne.readthedocs.org/en/latest/modules/layers.html).\n",
    "\n",
    "Nolearn allows you to skip Lasagne's _incoming_ keyword, which specifies how the layers are connected. Instead, nolearn will automatically assume that layers are connected in the order they appear in the list.\n",
    "\n",
    "*Note: Of course you can manually set the *incoming* parameter if your neural net's layers are connected differently. To do so, you have to give the corresponding layer a name (e.g. *'name': 'my layer'*) and use that name as a reference (*'incoming': 'my layer'*).*\n",
    "\n",
    "The layers we use are the following:\n",
    "\n",
    "* _InputLayer_: We have to specify the _shape_ of the data. For image data, it is batch size x color channels x image dimension 1 x image dimension 2 (aka bc01). Here you should generally just leave the batch size as _None_, so that it is taken care off automatically. The other dimensions are given by _X_.\n",
    "* _Conv2DLayer_: The most important keywords are _num_filters_ and _filter_size_. The former indicates the number of channels -- the more you choose, the more different filters can be learned by the CNN. Generally, the first convolutional layers will learn simple features, such as edges, while deeper layers can learn more abstract features. Therefore, you should increase the number of filters the deeper you go. The _filter_size_ is the size of the filter/kernel. The current consensus is to always use 3x3 filters, as these allow to cover the same number of image pixels with fewer parameters than larger filters do.\n",
    "* _MaxPool2DLayer_: This layer performs max pooling and hopefully provides translation invariance. We need to indicate the region over which it pools, with 2x2 being the default of most users.\n",
    "* _DenseLayer_: This is your vanilla fully-connected layer; you should indicate the number of 'neurons' with the _num_units_ argument. The very last layer is assumed to be the output layer. We thus set the number of units to be the number of classes, 10, and choose _softmax_ as the output nonlinearity, as we are dealing with a classification task.\n",
    "* _DropoutLayer_: Dropout is a common technique to regularize neural networks. It is almost always a good idea to include dropout between your dense layers.\n",
    "\n",
    "Apart from these arguments, the Lasagne layers have very reasonable defaults concerning weight initialization, nonlinearities (rectified linear units), etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers0 = [\n",
    "    # layer dealing with the input data\n",
    "    (InputLayer, {'shape': (None, X.shape[1], X.shape[2], X.shape[3])}),\n",
    "\n",
    "    # first stage of our convolutional layers\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': 5}),\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': 3}),\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': 3}),\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': 3}),\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': 3}),\n",
    "    (MaxPool2DLayer, {'pool_size': 2}),\n",
    "\n",
    "    # second stage of our convolutional layers\n",
    "    (Conv2DLayer, {'num_filters': 128, 'filter_size': 3}),\n",
    "    (Conv2DLayer, {'num_filters': 128, 'filter_size': 3}),\n",
    "    (Conv2DLayer, {'num_filters': 128, 'filter_size': 3}),\n",
    "    (MaxPool2DLayer, {'pool_size': 2}),\n",
    "\n",
    "    # two dense layers with dropout\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "    (DropoutLayer, {}),\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "\n",
    "    # the output layer\n",
    "    (DenseLayer, {'num_units': 10, 'nonlinearity': softmax}),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definition of the neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize nolearn's neural net itself. We will explain each argument shortly:\n",
    "* The most important argument is the _layers_ argument, which should be the list of layers defined above.\n",
    "* _max_epochs_ is simply the number of epochs the net learns with each call to _fit_ (an 'epoch' is a full training cycle using all training data). \n",
    "* As _update_, we choose _adam_, which for many problems is a good first choice as updateing rule.\n",
    "* The _objective_ of our net will be the _regularization_objective_ we just defined.\n",
    "* To change the magnitude of L2 regularization ([see here](http://cs231n.github.io/neural-networks-2/#reg)), we set the _objective_l2_ parameter. The NeuralNetwork class will then automatically pass this value when calling the objective. Usually, moderate L2 regularization is applied, whereas L1 regularization is less frequently used.\n",
    "* For 'adam', a small learning rate is best, so we set it with the _update_learning_rate_ argument (nolearn will automatically interpret this argument to mean the _learning_rate_ argument of the _update_ parameter, i.e. adam in our case).\n",
    "* The NeuralNet will hold out some of the training data for validation if we set the _eval_size_ of the _TrainSplit_ to a number greater than 0. This will allow us to monitor how well the net generalizes to yet unseen data. By setting this argument to 1/4, we tell the net to hold out 25% of the samples for validation.\n",
    "* Finally, we set _verbose_ to 1, which will result in the net giving us some useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "net0 = NeuralNet(\n",
    "    layers=layers0,\n",
    "    max_epochs=10,\n",
    "\n",
    "    update=adam,\n",
    "    update_learning_rate=0.0002,\n",
    "\n",
    "    objective_l2=0.0025,\n",
    "\n",
    "    train_split=TrainSplit(eval_size=0.25),\n",
    "    verbose=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the net, we call its _fit_ method with our X and y data, as we would with any [scikit learn](http://scikit-learn.org) classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 753610 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "  #  name         size\n",
      "---  -----------  --------\n",
      "  0  input0       1x28x28\n",
      "  1  conv2d1      96x24x24\n",
      "  2  conv2d2      96x22x22\n",
      "  3  conv2d3      96x20x20\n",
      "  4  conv2d4      96x18x18\n",
      "  5  conv2d5      96x16x16\n",
      "  6  maxpool2d6   96x8x8\n",
      "  7  conv2d7      128x6x6\n",
      "  8  conv2d8      128x4x4\n",
      "  9  conv2d9      128x2x2\n",
      " 10  maxpool2d10  128x1x1\n",
      " 11  dense11      64\n",
      " 12  dropout12    64\n",
      " 13  dense13      64\n",
      " 14  dense14      10\n",
      "\n",
      "  epoch    train loss    valid loss    train/val    valid acc  dur\n",
      "-------  ------------  ------------  -----------  -----------  ------\n",
      "      1       \u001b[36m2.49039\u001b[0m       \u001b[32m1.47864\u001b[0m      1.68424      0.93246  21.84s\n",
      "      2       \u001b[36m1.46182\u001b[0m       \u001b[32m1.20667\u001b[0m      1.21145      0.96527  21.43s\n",
      "      3       \u001b[36m1.21271\u001b[0m       \u001b[32m1.08095\u001b[0m      1.12190      0.96913  21.34s\n",
      "      4       \u001b[36m1.06744\u001b[0m       \u001b[32m0.96083\u001b[0m      1.11096      0.97421  21.34s\n",
      "      5       \u001b[36m0.94933\u001b[0m       \u001b[32m0.87513\u001b[0m      1.08479      0.97675  21.37s\n",
      "      6       \u001b[36m0.86063\u001b[0m       \u001b[32m0.79308\u001b[0m      1.08517      0.97967  21.36s\n",
      "      7       \u001b[36m0.78233\u001b[0m       \u001b[32m0.72516\u001b[0m      1.07885      0.97986  21.48s\n",
      "      8       \u001b[36m0.71531\u001b[0m       \u001b[32m0.66827\u001b[0m      1.07039      0.97939  21.59s\n",
      "      9       \u001b[36m0.65571\u001b[0m       \u001b[32m0.61533\u001b[0m      1.06562      0.98136  21.37s\n",
      "     10       \u001b[36m0.60635\u001b[0m       \u001b[32m0.57576\u001b[0m      1.05313      0.98033  21.35s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NeuralNet(X_tensor_type=None,\n",
       "     batch_iterator_test=<nolearn.lasagne.base.BatchIterator object at 0x7f3de3115910>,\n",
       "     batch_iterator_train=<nolearn.lasagne.base.BatchIterator object at 0x7f3de3115790>,\n",
       "     check_input=True, custom_scores=None,\n",
       "     layers=[(<class 'lasagne.layers.input.InputLayer'>, {'shape': (None, 1, 28, 28)}), (<class 'lasagne.layers.conv.Conv2DLayer'>, {'filter_size': 5, 'num_filters': 96}), (<class 'lasagne.layers.conv.Conv2DLayer'>, {'filter_size': 3, 'num_filters': 96}), (<class 'lasagne.layers.conv.Conv2DLayer'>, {'fil...layers.dense.DenseLayer'>, {'num_units': 10, 'nonlinearity': <function softmax at 0x7f3dec0f9848>})],\n",
       "     loss=None, max_epochs=10, more_params={},\n",
       "     objective=<function objective at 0x7f3de311ade8>, objective_l2=0.0025,\n",
       "     objective_loss_function=<function categorical_crossentropy at 0x7f3dec0d15f0>,\n",
       "     on_batch_finished=[],\n",
       "     on_epoch_finished=[<nolearn.lasagne.handlers.PrintLog instance at 0x7f3dde0997a0>],\n",
       "     on_training_finished=[],\n",
       "     on_training_started=[<nolearn.lasagne.handlers.PrintLayerInfo instance at 0x7f3dde0998c0>],\n",
       "     regression=False,\n",
       "     train_split=<nolearn.lasagne.base.TrainSplit object at 0x7f3dec5aae10>,\n",
       "     update=<function adam at 0x7f3dec0d7140>, update_learning_rate=0.0002,\n",
       "     use_label_encoder=False, verbose=1,\n",
       "     y_tensor_type=TensorType(int32, vector))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net0.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we set the verbosity to 1, nolearn will print some useful information for us:\n",
    "\n",
    "* First of all, some _general information_ about the net and its layers is printed. Then, during training, the progress will be printed after each epoch. \n",
    "* The _train loss_ is the loss/cost that the net tries to minimize. For this example, this is the _log loss_ (_cross entropy_).\n",
    "* The _valid loss_ is the loss for the hold out validation set. You should expect this value to indicate how well your model generalizes to yet unseen data.\n",
    "* _train/val_ is simply the ratio of train loss to valid loss. If this value is very low, i.e. if the train loss is much better than your valid loss, it means that the net has probably [overfitted](https://en.wikipedia.org/wiki/Overfitting) the train data.\n",
    "* When we are dealing with a classification task, the accuracy score of the valdation set, _valid acc_, is also printed.\n",
    "* _dur_ is simply the duration it took to process the given epoch.\n",
    "\n",
    "In addition to this, nolearn will color the as of yet best train and valid loss, so that it is easy to spot whether the net makes progress."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diagnosing what's wrong with your neural network if the results are unsatisfying can sometimes be difficult, something closer to an art than a science. But with nolearn's visualization tools, we should be able to get some insights that help us diagnose if something is wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nolearn.lasagne.visualize import draw_to_notebook\n",
    "from nolearn.lasagne.visualize import plot_loss\n",
    "from nolearn.lasagne.visualize import plot_conv_weights\n",
    "from nolearn.lasagne.visualize import plot_conv_activity\n",
    "from nolearn.lasagne.visualize import plot_occlusion\n",
    "from nolearn.lasagne.visualize import plot_saliency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the network architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we may be interested in simply visualizing the architecture. When using an IPython/Jupyter notebook, this is achieved best by calling the _draw_to_notebook_ function, passing the net as the first argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IO5bB09P+aH9erStcyUV6ucbtac9cpEql0uXX+Ab48OtPfoah1sDL9zlexVLW9zoLLhY7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\n7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WeWDCAlxs8xQRmk4XBWtWtfa3lDPW1PW3lDv013fClunBBFxfvTmTc+qs227D1bRWiA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D1Ovp+/+AY5ZjBxzAI12s6ZvHYERA5UYLwABJ3+Bf03/gB5QhzxAhfYkcrdudaN7mRMa1/Umr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\nEUKIIBRECCGCSAC8Ge1KEELi1/8Dv74H2SlCfTYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_to_notebook(net0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have accidentally made an error during the construction of the architecture, you should be able to spot it easily now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and validation loss progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "With nolearn's visualization tools, it is possible to get some further insights into the working of the CNN. Below, we will simply plot the log loss of the training and validation data over each epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/vinh/anaconda/envs/nolearn/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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usBh1Zw6kcYdCJuJZsRwGZl0sT4TbbsBtGfqHwwzg0VYHRB53mERfOOxCmv9w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\nmZnhMDAzM+D/A0Bx3qu8b8XGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dd062d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(net0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This kind of visualization can be helpful in determining whether we want to continue training or not. For instance, here we see that both loss functions still are still decreasing and that more training will pay off. This graph can also help determine if we are overfitting: If the train loss is much lower than the validation loss, we should probably do something to regularize the net."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing layer weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further have a look at the weights learned by the net. The first argument of the function should be the layer we want to visualize. The layers can be accessed through the *layers_* attribute and then by name (e.g. 'conv2dcc1') or by index, as below. (Obviously, visualizing the weights only makes sense for convolutional layers.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/vinh/anaconda/envs/nolearn/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KyorGY8aMMXPMRaYEyuzZs42tJVUotcnvhTms2pfiMdPP7N69u7HZzkRD33Hk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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3de294d890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(net0.layers_[1], figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen above, in our case, the results are not too interesting. If the weights just look like noise, we might have to do something (e.g. use more filters so that each can specialize better)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the layers' activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To see through the \"eyes\" of the net, we can plot the activities produced by different layers. The *plot_conv_activity* function is made for that. The first argument, again, is a layer, the second argument an image in the bc01 format (which is why we use _X[0:1]_ instead of just _X[0]_)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = X[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/vinh/anaconda/envs/nolearn/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1IIpMWSwWxo8fL3et9gkJRqBpmlyvZWVlTJ8+XR56d32fEYbBt1K67e3tjBo1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P5ULcYUdLiSkhLy8vL44IMPUBSF3bt3A/Zuuba2Nju31+3IpJrSFYvItjOuULhnz57lzJkzODs7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3db8b73690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_activity(net0.layers_[1], x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that depending on the learned filters, the neural net represents the image in different ways, which is what we should expect. If, e.g., some images were completely black, that could indicate that the corresponding filters have not learned anything useful. When you find yourself in such a situation, training longer or initializing the weights differently might do the trick."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot occlusion images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A possibility to check if the net, for instance, overfits or learns important features is to occlude part of the image. Then we can check whether the net still makes correct predictions. The idea behind that is the following: If the most critical part of an image is something like the head of a person, that is probably right. If it is instead a random part of the background, the net probably overfits (see [here](http://www.matthewzeiler.com/pubs/arxive2013/arxive2013.pdf) for more).\n",
    "\n",
    "With the *plot_occlusion* function, we can check this. The approach is to occlude parts of the image and check how strongly this affects the power of our net to predict the correct label. The first argument to the function is the neural net, the second the X data, the third the y data. Be warned that this function can be quite slow for larger images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/vinh/anaconda/envs/nolearn/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nYWp3taSfqnPTkn/tV0heV6oY6zKz88O06qrwfBjZoe0tPiZ3b5R0hqR/krTE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\nAACZoggAACBTFAEAAGTq/wPaw+INlFLHnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3db99eb2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_occlusion(net0, X[:5], y[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see which parts of the number are most important for correct classification. We see that the critical parts are all directly above the numbers, so this seems to work out. For more complex images with different objects in the scene, this function should be more useful, though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salience plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to plotting the occlusion images, we may also backpropagate the error onto the image parts to see which ones matter to the net. The idea here is similar but the outcome differs, as a quick comparison shows. The advantage of using the gradient is that the computation is much quicker but the critical parts are more distributed across the image, making interpretation more difficult."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LQAC8HcbeGSJylIgsyrDtKSLypIjUhdvuFJFBGY/3NhH5DxF5LOz3aRGZmHP7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\nQKZIAgAAyBRJAAAAmfr/01ntJsKJhQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dd0579a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_saliency(net0, X[:5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding a good architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section tries to help you go deep with your convolutional neural net."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is more than one way to go deep with CNNs. A possibility is to try a [residual net architecture](http://arxiv.org/abs/1512.03385), which won several tasks of the 2015 imagenet competition. Here we will try instead a more \"traditional\" approach using blocks of convolutional layers separated by pooling layers. If we want to increase the number of convolutional layers, we cannot simply do so at will. It is important that the layers have a sufficiently high learning capacity while they should cover approximately 100% of the incoming image ([Xudong Cao, 2015](https://www.kaggle.com/c/datasciencebowl/forums/t/13166/happy-lantern-festival-report-and-code))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual approach is to try to go deep with convolutional layers. If you chain too many convolutional layers, though, the learning capacity of the layers falls too low. At this point, you have to add a max pooling layer. Use too many max pooling layers, and your image coverage grows larger than the image, which is clearly pointless. Striking the right balance while maximizing the depth of your layer is the final goal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is generally a good idea to use small filter sizes for your convolutional layers, generally <b>3x3</b>. The reason for this is that this allows to cover the same receptive field of the image while using less parameters that would be required if a larger filter size were used. Moreover, deeper stacks of convolutional layers are more expressive (see [here](http://cs231n.github.io/convolutional-networks) for more)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nolearn.lasagne import PrintLayerInfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A shallow net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try out a simple architecture and see how we fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers1 = [\n",
    "    (InputLayer, {'shape': (None, X.shape[1], X.shape[2], X.shape[3])}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 96, 'filter_size': (3, 3)}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "    (DropoutLayer, {}),\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 10, 'nonlinearity': softmax}),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net1 = NeuralNet(\n",
    "    layers=layers1,\n",
    "    update_learning_rate=0.01,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see information about the _capacity_ and _coverage_ of each layer, we need to set the verbosity of the net to a value of 2 and then _initialize_ the net. We next pass the initialized net to _PrintLayerInfo_ to see some useful information. By the way, we could also just call the _fit_ method of the net to get the same outcome, but since we don't want to fit just now, we proceed as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "net1.initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layer_info = PrintLayerInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 122154 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "name        size        total    cap.Y    cap.X    cov.Y    cov.X\n",
      "----------  --------  -------  -------  -------  -------  -------\n",
      "input0      1x28x28       784   100.00   100.00   100.00   100.00\n",
      "conv2d1     32x26x26    21632   100.00   100.00    10.71    10.71\n",
      "maxpool2d2  32x13x13     5408   100.00   100.00    10.71    10.71\n",
      "conv2d3     64x11x11     7744    85.71    85.71    25.00    25.00\n",
      "conv2d4     64x9x9       5184    54.55    54.55    39.29    39.29\n",
      "maxpool2d5  64x4x4       1024    54.55    54.55    39.29    39.29\n",
      "conv2d6     96x2x2        384    63.16    63.16    67.86    67.86\n",
      "maxpool2d7  96x1x1         96    63.16    63.16    67.86    67.86\n",
      "dense8      64             64   100.00   100.00   100.00   100.00\n",
      "dropout9    64             64   100.00   100.00   100.00   100.00\n",
      "dense10     64             64   100.00   100.00   100.00   100.00\n",
      "dense11     10             10   100.00   100.00   100.00   100.00\n",
      "\n",
      "Explanation\n",
      "    X, Y:    image dimensions\n",
      "    cap.:    learning capacity\n",
      "    cov.:    coverage of image\n",
      "    \u001b[35mmagenta\u001b[0m: capacity too low (<1/6)\n",
      "    \u001b[36mcyan\u001b[0m:    image coverage too high (>100%)\n",
      "    \u001b[31mred\u001b[0m:     capacity too low and coverage too high\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "layer_info(net1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This net is fine. The capacity never falls below 1/6, which would be 16.7%, and the coverage of the image never exceeds 100%. However, with only 4 convolutional layers, this net is not very deep and will properly not achieve the best possible results.\n",
    "\n",
    "What we also see is the role of max pooling. If we look at 'maxpool2d1', after this layer, the capacity of the net is increased. Max pooling thus helps to increase capacity should it dip too low. However, max pooling also significantly increases the coverage of the image. So if we use max pooling too often, the coverage will quickly exceed 100% and we cannot go sufficiently deep."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too little maxpooling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us try an architecture that uses a lot of convolutional layers but only one maxpooling layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers2 = [\n",
    "    (InputLayer, {'shape': (None, X.shape[1], X.shape[2], X.shape[3])}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3)}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "    (DropoutLayer, {}),\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 10, 'nonlinearity': softmax}),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net2 = NeuralNet(\n",
    "    layers=layers2,\n",
    "    update_learning_rate=0.01,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net2.initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 273930 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "name         size        total    cap.Y    cap.X    cov.Y    cov.X\n",
      "-----------  --------  -------  -------  -------  -------  -------\n",
      "input0       1x28x28       784   100.00   100.00   100.00   100.00\n",
      "conv2d1      32x26x26    21632   100.00   100.00    10.71    10.71\n",
      "conv2d2      32x24x24    18432    60.00    60.00    17.86    17.86\n",
      "conv2d3      32x22x22    15488    42.86    42.86    25.00    25.00\n",
      "conv2d4      32x20x20    12800    33.33    33.33    32.14    32.14\n",
      "conv2d5      32x18x18    10368    27.27    27.27    39.29    39.29\n",
      "conv2d6      64x16x16    16384    23.08    23.08    46.43    46.43\n",
      "conv2d7      64x14x14    12544    20.00    20.00    53.57    53.57\n",
      "conv2d8      64x12x12     9216    17.65    17.65    60.71    60.71\n",
      "\u001b[35mconv2d9\u001b[0m      64x10x10     6400    15.79    15.79    67.86    67.86\n",
      "\u001b[35mconv2d10\u001b[0m     64x8x8       4096    14.29    14.29    75.00    75.00\n",
      "\u001b[35mmaxpool2d11\u001b[0m  64x4x4       1024    14.29    14.29    75.00    75.00\n",
      "dense12      64             64   100.00   100.00   100.00   100.00\n",
      "dropout13    64             64   100.00   100.00   100.00   100.00\n",
      "dense14      64             64   100.00   100.00   100.00   100.00\n",
      "dense15      10             10   100.00   100.00   100.00   100.00\n",
      "\n",
      "Explanation\n",
      "    X, Y:    image dimensions\n",
      "    cap.:    learning capacity\n",
      "    cov.:    coverage of image\n",
      "    \u001b[35mmagenta\u001b[0m: capacity too low (<1/6)\n",
      "    \u001b[36mcyan\u001b[0m:    image coverage too high (>100%)\n",
      "    \u001b[31mred\u001b[0m:     capacity too low and coverage too high\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "layer_info(net2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have a very deep net but we have a problem: The lack of max pooling layers means that the capacity of the net dips below 16.7%. The corresponding layers are shown in magenta. We need to find a better solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too much maxpooling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an architecture with too mach maxpooling. For illustrative purposes, we set the _pad_ parameter to 1; without it, the image size would shrink below 0, at which point the code will raise an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "layers3 = [\n",
    "    (InputLayer, {'shape': (None, X.shape[1], X.shape[2], X.shape[3])}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "    (DropoutLayer, {}),\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 10, 'nonlinearity': softmax}),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net3 = NeuralNet(\n",
    "    layers=layers3,\n",
    "    update_learning_rate=0.01,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "net3.initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 166314 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "name         size        total    cap.Y    cap.X    cov.Y    cov.X\n",
      "-----------  --------  -------  -------  -------  -------  -------\n",
      "input0       1x28x28       784   100.00   100.00   100.00   100.00\n",
      "conv2d1      32x28x28    25088   100.00   100.00    10.71    10.71\n",
      "conv2d2      32x28x28    25088    60.00    60.00    17.86    17.86\n",
      "maxpool2d3   32x14x14     6272    60.00    60.00    17.86    17.86\n",
      "conv2d4      32x14x14     6272    66.67    66.67    32.14    32.14\n",
      "conv2d5      32x14x14     6272    46.15    46.15    46.43    46.43\n",
      "maxpool2d6   32x7x7       1568    46.15    46.15    46.43    46.43\n",
      "conv2d7      64x7x7       3136    57.14    57.14    75.00    75.00\n",
      "\u001b[36mconv2d8\u001b[0m      64x7x7       3136    41.38    41.38   103.57   103.57\n",
      "\u001b[36mmaxpool2d9\u001b[0m   64x3x3        576    41.38    41.38   103.57   103.57\n",
      "\u001b[36mconv2d10\u001b[0m     64x3x3        576    53.33    53.33   160.71   160.71\n",
      "\u001b[36mconv2d11\u001b[0m     64x3x3        576    39.34    39.34   217.86   217.86\n",
      "\u001b[36mmaxpool2d12\u001b[0m  64x1x1         64    39.34    39.34   217.86   217.86\n",
      "dense13      64             64   100.00   100.00   100.00   100.00\n",
      "dropout14    64             64   100.00   100.00   100.00   100.00\n",
      "dense15      64             64   100.00   100.00   100.00   100.00\n",
      "dense16      10             10   100.00   100.00   100.00   100.00\n",
      "\n",
      "Explanation\n",
      "    X, Y:    image dimensions\n",
      "    cap.:    learning capacity\n",
      "    cov.:    coverage of image\n",
      "    \u001b[35mmagenta\u001b[0m: capacity too low (<1/6)\n",
      "    \u001b[36mcyan\u001b[0m:    image coverage too high (>100%)\n",
      "    \u001b[31mred\u001b[0m:     capacity too low and coverage too high\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "layer_info(net3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This net uses too much maxpooling for too small an image. The later layers, colored in cyan, would cover more than 100% of the image. So this network is clearly also suboptimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A good compromise "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us have a look at a reasonably deep architecture that satisfies the criteria we set out to meet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layers4 = [\n",
    "    (InputLayer, {'shape': (None, X.shape[1], X.shape[2], X.shape[3])}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 32, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (Conv2DLayer, {'num_filters': 64, 'filter_size': (3, 3), 'pad': 1}),\n",
    "    (MaxPool2DLayer, {'pool_size': (2, 2)}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "    (DropoutLayer, {}),\n",
    "    (DenseLayer, {'num_units': 64}),\n",
    "\n",
    "    (DenseLayer, {'num_units': 10, 'nonlinearity': softmax}),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net4 = NeuralNet(\n",
    "    layers=layers4,\n",
    "    update_learning_rate=0.01,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net4.initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 353738 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "name         size        total    cap.Y    cap.X    cov.Y    cov.X\n",
      "-----------  --------  -------  -------  -------  -------  -------\n",
      "input0       1x28x28       784   100.00   100.00   100.00   100.00\n",
      "conv2d1      32x28x28    25088   100.00   100.00    10.71    10.71\n",
      "conv2d2      32x28x28    25088    60.00    60.00    17.86    17.86\n",
      "conv2d3      32x28x28    25088    42.86    42.86    25.00    25.00\n",
      "conv2d4      32x28x28    25088    33.33    33.33    32.14    32.14\n",
      "conv2d5      32x28x28    25088    27.27    27.27    39.29    39.29\n",
      "conv2d6      32x28x28    25088    23.08    23.08    46.43    46.43\n",
      "conv2d7      32x28x28    25088    20.00    20.00    53.57    53.57\n",
      "maxpool2d8   32x14x14     6272    20.00    20.00    53.57    53.57\n",
      "conv2d9      64x14x14    12544    31.58    31.58    67.86    67.86\n",
      "conv2d10     64x14x14    12544    26.09    26.09    82.14    82.14\n",
      "conv2d11     64x14x14    12544    22.22    22.22    96.43    96.43\n",
      "maxpool2d12  64x7x7       3136    22.22    22.22    96.43    96.43\n",
      "dense13      64             64   100.00   100.00   100.00   100.00\n",
      "dropout14    64             64   100.00   100.00   100.00   100.00\n",
      "dense15      64             64   100.00   100.00   100.00   100.00\n",
      "dense16      10             10   100.00   100.00   100.00   100.00\n",
      "\n",
      "Explanation\n",
      "    X, Y:    image dimensions\n",
      "    cap.:    learning capacity\n",
      "    cov.:    coverage of image\n",
      "    \u001b[35mmagenta\u001b[0m: capacity too low (<1/6)\n",
      "    \u001b[36mcyan\u001b[0m:    image coverage too high (>100%)\n",
      "    \u001b[31mred\u001b[0m:     capacity too low and coverage too high\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "layer_info(net4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With 10 convolutional layers, this network is rather deep, given the small image size. Yet the learning capacity is always suffiently large and never are is than 100% of the image covered. This could just be a good solution. Maybe you would like to give this architecture a spin?\n",
    "\n",
    "Note 1: The MNIST images typically don't cover the whole of the 28x28 image size. Therefore, an image coverage of less than 100% is probably very acceptable. For other image data sets such as CIFAR or ImageNet, it is recommended to cover the whole image.\n",
    "\n",
    "Note 2: This analysis does not tell us how many feature maps (i.e. number of filters per convolutional layer) to use. Here we have to experiment with different values. Larger values mean that the network should learn more types of features but also increase the risk of overfitting (and may exceed the available memory). In general though, deeper layers (those farther down) are supposed to learn more complex features and should thus have more feature maps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even more information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to get more information by increasing the verbosity level beyond 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net4.verbose = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 353738 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "name         size        total    cap.Y    cap.X    cov.Y    cov.X    filter Y    filter X    field Y    field X\n",
      "-----------  --------  -------  -------  -------  -------  -------  ----------  ----------  ---------  ---------\n",
      "input0       1x28x28       784   100.00   100.00   100.00   100.00          28          28         28         28\n",
      "conv2d1      32x28x28    25088   100.00   100.00    10.71    10.71           3           3          3          3\n",
      "conv2d2      32x28x28    25088    60.00    60.00    17.86    17.86           3           3          5          5\n",
      "conv2d3      32x28x28    25088    42.86    42.86    25.00    25.00           3           3          7          7\n",
      "conv2d4      32x28x28    25088    33.33    33.33    32.14    32.14           3           3          9          9\n",
      "conv2d5      32x28x28    25088    27.27    27.27    39.29    39.29           3           3         11         11\n",
      "conv2d6      32x28x28    25088    23.08    23.08    46.43    46.43           3           3         13         13\n",
      "conv2d7      32x28x28    25088    20.00    20.00    53.57    53.57           3           3         15         15\n",
      "maxpool2d8   32x14x14     6272    20.00    20.00    53.57    53.57           3           3         15         15\n",
      "conv2d9      64x14x14    12544    31.58    31.58    67.86    67.86           6           6         19         19\n",
      "conv2d10     64x14x14    12544    26.09    26.09    82.14    82.14           6           6         23         23\n",
      "conv2d11     64x14x14    12544    22.22    22.22    96.43    96.43           6           6         27         27\n",
      "maxpool2d12  64x7x7       3136    22.22    22.22    96.43    96.43           6           6         27         27\n",
      "dense13      64             64   100.00   100.00   100.00   100.00          28          28         28         28\n",
      "dropout14    64             64   100.00   100.00   100.00   100.00          28          28         28         28\n",
      "dense15      64             64   100.00   100.00   100.00   100.00          28          28         28         28\n",
      "dense16      10             10   100.00   100.00   100.00   100.00          28          28         28         28\n",
      "\n",
      "Explanation\n",
      "    X, Y:    image dimensions\n",
      "    cap.:    learning capacity\n",
      "    cov.:    coverage of image\n",
      "    \u001b[35mmagenta\u001b[0m: capacity too low (<1/6)\n",
      "    \u001b[36mcyan\u001b[0m:    image coverage too high (>100%)\n",
      "    \u001b[31mred\u001b[0m:     capacity too low and coverage too high\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "layer_info(net4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we get additional information about the real filter size of the convolutional layers, as well as their receptive field sizes. If the receptive field size grows too large compared to the real filter size, capacity dips too low. As receptive field size grows larger, more and more of the image is covered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caveat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A caveat to the findings presented here is that capacity and coverage may not be calculated correctly if you use padding or strides other than 1 for the convolutional layers. Including this would make the calculation much more complicated. However, even if you want to use these parameters, the calculations shown here should not deviate too much and the results may still serve as a rough guideline.\n",
    "\n",
    "Furthermore, to our knowledge, there is no publicly available paper on this topic, so all results should be taken with care."
   ]
  }
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