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      "# Reading group on Deep Learning\n",
      "\n",
      "LEAR - XRCE, 23 Nov. 2012\n",
      "\n",
      "Notes on this document:\n",
      "\n",
      "* this HTML page was generated from the [IPython](http://ipython.org/) notebook available [here](http://lear.inrialpes.fr/people/gaidon/lear_xrce_deep_learning_01.ipynb) (pure python version executable in any python interpreter: [here](http://lear.inrialpes.fr/people/gaidon/lear_xrce_deep_learning_01.py))\n",
      "\n",
      "* dependencies to run the code (only Open Source Software!):\n",
      "    * [Python](http://python.org/): the best programming language, ever.\n",
      "    * [IPython](http://ipython.org/): an enhanced python interpreter + great tools for scientific workflow\n",
      "    * [Numpy](http://numpy.scipy.org/): the Matlab library for Python (multi-dimensional arrays, linear algebra, ...)\n",
      "    * [Matplotlib](http://matplotlib.org/): the scientific plotting library for Python\n",
      "    * [Networkx](http://networkx.lanl.gov/): package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks\n",
      "    * [Theano](http://deeplearning.net/software/theano/): efficient tensor library, CPU + GPU expresion compiler, used for Deep Learning\n",
      "\n",
      "\n",
      "## Outline\n",
      "\n",
      "* Refresher on Neural Networks\n",
      "\n",
      "    * Neural Networks\n",
      "\n",
      "    * Back-Propagation\n",
      "\n",
      "* Deep Learning in practice\n",
      "\n",
      "    * What is Deep Learning?\n",
      "\n",
      "    * Overview of Theano\n",
      "\n",
      "    * Overview of EBLearn\n",
      "\n",
      "\n",
      "## Pointers\n",
      "\n",
      "\n",
      "### Reading material\n",
      "\n",
      "* [The Elements of Statistical Learning (5th ed.)](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) (T. Hastie, R. Tibshirani, & J. Friedman) : Chapter 11 (Neural Networks)\n",
      "* [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/239) (Y. Bengio, In Foundations & Trends in Machine Learning, 2009) : small book providing an introduction to deep learning and individual chapters on most of the main deep architectures\n",
      "* [A fast learning algorithm for deep belief nets](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf) (G. Hinton *et al.*, Neural Computation, 2006): fast algorithm for learning deep beliefs nets + justification of greedy layer-wise training and stacking\n",
      "* [Building High-level Features Using Large Scale Unsupervised Learning](http://research.google.com/archive/unsupervised_icml2012.html) (Quoc V. Le *et al.*, ICML'2012): Large scale deep learning (sparse auto-encoders) using 16000 cores; led to face and cat neurons from unlabeled data; state of the art on ImageNet from raw pixels; made the front page of NY Times.\n",
      "* [On Deep Generative Models with Applications to Recognition](http://www.cs.toronto.edu/~hinton/absps/ranzato_cvpr2011.pdf) (M.A. Ranzato *et al.*, CVPR 2011)\n",
      "* Y. LeCun's recent papers (cf. his [publication list](http://yann.lecun.com/exdb/publis/index.html))\n",
      "* [CVPR'2012 workshop on Deep Learning Methods for Vision](http://cs.nyu.edu/~fergus/tutorials/deep_learning_cvpr12/): overview of most common deep architectures, transfer learning, application to motion and video\n",
      "* [CVPR'2012 workshop on Multiview Feature Learning](http://learning.cs.toronto.edu/~rfm/multiview-feature-learning-cvpr/): recent workshop about deep unsupervised feature learning, includes application of deep learning techniques to video\n",
      "* [tutorial slides](http://deeplearningworkshopnips2010.files.wordpress.com/2010/09/nips10-workshop-tutorial-final.pdf) at the NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning\n",
      "* cf. the [deeplearning.net reading list](http://deeplearning.net/reading-list/) for additional pointers\n",
      "\n",
      "\n",
      "### Websites\n",
      "\n",
      "* [deeplearning.net](http://deeplearning.net) : Big hub with many links related to deep learning (software, publications, ...)\n",
      "* [Deep learning tutorials](http://deeplearning.net/tutorial/) : Set of tutorials on deep learning (RBM, ConvNets, ...) with theano\n",
      "* [Neural Nets F.A.Q.](ftp://ftp.sas.com/pub/neural/FAQ.html) : extremely comprehensive FAQ on neural nets (from stats to software)\n",
      "* [Homepage of G. Hinton](http://www.cs.toronto.edu/~hinton/)\n",
      "* [Homepage of Y. LeCun](http://yann.lecun.com/)\n",
      "* Mainstream media coverage of advances in deep learning:\n",
      "    * [NY Times front page article](http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html) on the [ICML'12 paper](http://research.google.com/archive/unsupervised_icml2012.html) of Andrew Ng's group (Ng's interview: [here](http://www.npr.org/2012/06/26/155792609/a-massive-google-network-learns-to-identify))\n",
      "    * [Another NY Times article](http://www.nytimes.com/2012/11/24/science/scientists-see-advances-in-deep-learning-a-part-of-artificial-intelligence.html?pagewanted=all&_r=1&&_r=0) about the recent progress in deep learning on a wide range of problems\n",
      "    * [Blog post](http://blogs.technet.com/b/next/archive/2012/11/08/microsoft-research-shows-a-promising-new-breakthrough-in-speech-translation-technology.aspx#.ULM2fZu9DSJ) of [Rick Rashid](http://research.microsoft.com/en-us/people/rashid/) Microsoft's CRO, with an interesting video explaining the progress made in Automatic Speech Recognition and Translation: from matching sound waves, to HMMs, to Deep Learning; R. Rashid makes a cool live demo (at the end of the video) where his speech gets translated on the fly in (spoken!) Mandarin\n",
      "\n",
      "\n",
      "### Code\n",
      "\n",
      "* [theano](http://deeplearning.net/software/theano/) : Python library for efficient computations on tensors (multi-dimensional arrays)\n",
      "* [EBLearn](http://eblearn.cs.nyu.edu:21991/doku.php) : C++ library developped @NYU (LeCun) for *Energy-Based Learning*\n",
      "* [cuda-convnet](http://code.google.com/p/cuda-convnet/) : fast implementation of ConvNet in C++ and Cuda (GPU), by winners of ImageNet LSVRC 2012 (Krizhevsky A. *et al.* NIPS'12 paper to come)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Refresher on Neural Networks\n",
      "\n",
      "\n",
      "## Central idea\n",
      "\n",
      "* extract *derived features* = linear combination of the inputs\n",
      "* predict target using a *non-linear* function of these features\n",
      "\n",
      "\n",
      "## Preliminary: Projection Pursuit Regression (PPR)\n",
      "\n",
      "* Supervised learning: input vector $X \\in \\mathbb{R}^p$, target $Y$\n",
      "* $V_m = w_m^T X$ : derived features, with $M$ unit vectors $w_m \\in \\mathbb{R}^p$\n",
      "* PPR prediction:\n",
      "\n",
      ">    $$f(X) = \\sum_{m=1}^{M} g_m(V_m)$$\n",
      "\n",
      "* Parameters:\n",
      "    * $\\\\{w_m\\\\}_m$ : projection directions\n",
      "    * $\\\\{g_m\\\\}_m$ : ridge functions (each varies only in the direction of $w_m$) "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot of two ridge function examples\n",
      "\n",
      "\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from matplotlib import cm\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "\n",
      "def g_left(X1, X2):\n",
      "    V = (X1 + X2) / np.sqrt(2)\n",
      "    return 1.0 / (1.0 + np.exp(-5 * (V - 0.5)))\n",
      "\n",
      "\n",
      "def g_right(X1, X2):\n",
      "    V = X1\n",
      "    return (V + 0.1) * np.sin(10 / (V / 3.0 + 0.1))\n",
      "\n",
      "\n",
      "def plot_surface_3d(X1, X2, func, fig=None, subplot=(1, 1, 1)):\n",
      "    if fig is None:\n",
      "        fig = plt.figure(figsize=(12, 5))\n",
      "    ax = fig.add_subplot(*subplot, projection='3d')\n",
      "    X1, X2 = np.meshgrid(X1, X2)\n",
      "    Z = func(X1, X2)\n",
      "    surf = ax.plot_surface(X1, X2, Z,\n",
      "        rstride=1, cstride=1, cmap=cm.coolwarm,\n",
      "        linewidth=0, antialiased=False)\n",
      "\n",
      "\n",
      "fig = plt.figure(figsize=(12, 5))\n",
      "X1, X2 = np.linspace(-1.5, 1.5, 150), np.linspace(-0.5, 1, 150)\n",
      "plot_surface_3d(X1, X2, g_left, fig, subplot=(1, 2, 1))\n",
      "X1, X2 =  np.linspace(-0.05, 0.05, 300), np.linspace(0, 1, 100)\n",
      "plot_surface_3d(X1, X2, g_right, fig, subplot=(1, 2, 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "output_type": "display_data",
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41lln4ciRI45tFhcX7X+feOKJ+MlPfsLcF+uxcSm8WCWLHpB+hCaLhiYyW1rX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\n51t3H3ZfQUI1SjTVzzs1am0qwBCqLrI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2TRlG67qOK664Aueee24pFlFOMGHWNq9oqlqZ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BzGpEmTMHHixJht9uzZg3vuuQcejwcNGjTAJ598ksSzslCTcfjwYUGGw3FclCabkDYjF9Bk6lMT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\nQmCC\n"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* PPR model is a *universal approximator*: can approximate any continuous function in $\\mathbb{R}^p$ (if M is taken arbitrarily large, for appropriate choices of $g_m$)\n",
      "* but interpretation of the fitted model is difficult (mixing of the input features)\n",
      "* **Learning**: PPR model is fitted by minimizing the squared error in an alternating manner:\n",
      "    * Given $w_m$, estimate $g_m$: 1D smoothing problem (e.g. use splines)\n",
      "    * Given $g_m$, estimate $w_m$: non-linear least-squares (e.g. use Gauss-Newton)\n",
      "    * Iterate these two steps until convergence\n",
      "    * Greedily add a new pair $(w_{m+1}, g_{m+1})$\n",
      "* PPR not used much (originally for computational reasons)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Neural Networks (NN)\n",
      "\n",
      "\n",
      "### The model\n",
      "\n",
      "* NN = non-linear statistical model inspired by biological neural networks\n",
      "* Set of neurons (much like $g_m(w_m^T X)$ in PPR) interconnected in layers in a feed-forward fashion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How to plot a network diagram of a NN\n",
      "\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import networkx as nx\n",
      "\n",
      "\n",
      "class NeuralNetwork(object):\n",
      "    \"\"\" A simple neural network class for visualization purposes\n",
      "    \"\"\"\n",
      "    def __init__(self, n_nodes_per_layer):\n",
      "        \"\"\" Build the network layer by layer\n",
      "        \"\"\"\n",
      "        self.n_nodes_per_layer = n_nodes_per_layer\n",
      "        self.graph = nx.DiGraph()\n",
      "        self.nodes_pos = {}\n",
      "        self.nodes_label = {}\n",
      "        self.input_units = []\n",
      "        self.hidden_units = []\n",
      "        self.output_units = []\n",
      "\n",
      "        # add the units per layer\n",
      "        for layer_i, layer_size in enumerate(n_nodes_per_layer):\n",
      "\n",
      "            # add the nodes for this layer\n",
      "            for node_i in range(layer_size):\n",
      "\n",
      "                node = \"%d_%d\" % (layer_i, node_i)  # simple encoding of a node\n",
      "                self.graph.add_node(node)\n",
      "                self.nodes_pos[node] = (layer_i, layer_size / 2.0 - node_i)\n",
      "\n",
      "                if layer_i == 0:\n",
      "                    # label for input layer\n",
      "                    self.input_units.append(node)\n",
      "                    self.nodes_label[node] = r\"$X_%d$\" % node_i\n",
      "                elif layer_i == len(n_nodes_per_layer) - 1:\n",
      "                    # label for output layer\n",
      "                    self.output_units.append(node)\n",
      "                    self.nodes_label[node] = r\"$Y_%d$\" % node_i\n",
      "                else:\n",
      "                    # hidden layer\n",
      "                    self.hidden_units.append(node)\n",
      "                    self.nodes_label[node] = r\"$Z_{%d, %d}$\" % (layer_i, node_i)\n",
      "\n",
      "            # add the edges: full connection between layer_i -1 and layer_i\n",
      "            if layer_i > 0:\n",
      "                prev_layer_i = layer_i - 1\n",
      "                prev_layer_size = n_nodes_per_layer[prev_layer_i]\n",
      "                self.graph.add_edges_from([\n",
      "                    (\"%d_%d\" % (prev_layer_i, l), \"%d_%d\" % (layer_i, k))\n",
      "                    for k in range(layer_size) for l in range(prev_layer_size)\n",
      "                ])\n",
      "\n",
      "    def draw(self, ax=None):\n",
      "        \"\"\" Draw the neural network\n",
      "        \"\"\"\n",
      "        if ax is None:\n",
      "            ax = plt.figure(figsize=(10, 6)).add_subplot(1, 1, 1)\n",
      "        nx.draw_networkx_edges(self.graph, pos=self.nodes_pos, alpha=0.7, ax=ax)\n",
      "        nx.draw_networkx_nodes(self.graph, nodelist=self.input_units,\n",
      "                               pos=self.nodes_pos, ax=ax,\n",
      "                               node_color='#66FFFF', node_size=700)\n",
      "        nx.draw_networkx_nodes(self.graph, nodelist=self.hidden_units,\n",
      "                               pos=self.nodes_pos, ax=ax,\n",
      "                               node_color='#CCCCCC', node_size=900)\n",
      "        nx.draw_networkx_nodes(self.graph, nodelist=self.output_units,\n",
      "                               pos=self.nodes_pos, ax=ax,\n",
      "                               node_color='#FFFF99', node_size=700)\n",
      "        nx.draw_networkx_labels(self.graph, labels=self.nodes_label,\n",
      "                                pos=self.nodes_pos, font_size=14, ax=ax)\n",
      "        ax.axis('off')\n",
      "\n",
      "\n",
      "# classifcal 3 layer neural network example\n",
      "n_layers = 3  # input layer | hidden layer | output layer\n",
      "n_input_dims = 5\n",
      "n_hidden_units = 3\n",
      "n_output_dims = 2\n",
      "n_nodes_per_layer = [n_input_dims, n_hidden_units, n_output_dims]\n",
      "nn1 = NeuralNetwork(n_nodes_per_layer)\n",
      "\n",
      "# another example\n",
      "nn2 = NeuralNetwork([10, 5, 10])\n",
      "\n",
      "# show the graphical representations\n",
      "fig = plt.figure(figsize=(12, 8))\n",
      "nn1.draw(fig.add_subplot(1, 2, 1))\n",
      "nn2.draw(fig.add_subplot(1, 2, 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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jIyMqV66Mo6MjOp2OmzdvYmlpWbA04t8MEpUrVyYuLo59+35h3773GTnS94my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\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+vsf+tSsHFfGfDYZ/dnUlulYtWjs71+i4ylpaKNeuzbFly5j+gWUXVeHgwYN069YNi/cs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\n/QkXL6JXuzYAOqamJN+8WWw76lpa5MlkFdt5QXiFZLJ8tLS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      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Formal definition of NN (assuming full connectivity between layers):\n",
      "\n",
      "* **Input layer**: one unit for each dimension of the input $X \\in \\mathbb{R}^p$\n",
      "* **Hidden layers**: *derived features* from the input of the previous layer\n",
      "    * Assuming one hidden layer with $M$ units\n",
      "    * Derived features: $Z_m = \\sigma (\\alpha_m^T X + \\alpha_{0, m}), \\ m = 1, \\cdots, M$\n",
      "    * Activation function $\\sigma$: typically a sigmoid $\\sigma(v) = (1 + e^{-v})^{-1}$\n",
      "* **Output layer**: one unit for each dimension of the output $Y$\n",
      "    * Output $Y_k$ again computed using a linear combination of the outputs $Z$ of the previous layer: $Y_k = f_k(X) = g_k(\\beta_m^T Z + \\beta_{0, m})$\n",
      "    * Output function $g_k$ chosen based on task:\n",
      "        * regression (e.g. denoising with auto-encoders, $p$ units to obtain $Y \\in \\mathbb{R}^p$ = denoised $X$): identity $g_k(V) = V$\n",
      "        * classification (e.g. for object recognition, $K$ units to output probability for each possible object category): softmax $g_k(V) = \\frac{e^{V_k}}{\\sum_{l=1}^K e^{V_k}}$\n",
      "* Parameters (*weights*, noted $\\theta$) to estimate from the data:\n",
      "    * the $\\alpha_m$'s and bias terms $\\alpha_{0, m}$ of each unit at each hidden layer\n",
      "    * the $\\beta_m$'s and bias terms $\\beta_{0, m}$ of each unit of the output layer\n",
      "* Remarks:\n",
      "    * linear $\\sigma \\Rightarrow$ linear model\n",
      "    * one hidden layer $\\Rightarrow$ similar to PPR (but PPR uses few non-parametric $g_m$ functions, whereas NN uses many units with a simple $\\sigma$ function)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## The Back-Propagation algorithm\n",
      "\n",
      "(Again assuming one hidden layer for brevity)\n",
      "\n",
      "* Algorithm used to learn the weights $\\theta$ in a NN\n",
      "* Minimization of a loss function $R(\\theta)$\n",
      "    * Regression: SSE\n",
      "\n",
      "        $$R(\\theta) = \\sum_{k=1}^K \\sum_{i=1}^N (y_{i,k} - f_k(x_i))^2$$\n",
      "\n",
      "    * Classification: (SSE or) Cross-Entropy\n",
      "\n",
      "        $$R(\\theta) = - \\sum_{i=1}^N \\sum_{k=1}^K y_{i,k} \\mathrm{log} f_k(x_i)$$\n",
      "\n",
      "* **Regularization**:\n",
      "    * by adding a penalty term (e.g. $\\Vert \\theta \\Vert_2^2$, called weight decay)\n",
      "    * or early stopping\n",
      "\n",
      "* **Back-Propagation**:\n",
      "    * minimization of $R(\\theta)$ by *gradient descent*:\n",
      "\n",
      "        $$\\beta_{k, m}^{(r+1)} = \\beta_{k, m}^{(r)} - \\gamma_r \\frac{\\partial R_i}{\\partial \\beta_{k, m}^{(r)}}$$\n",
      "\n",
      "        $$\\alpha_{m, l}^{(r+1)} = \\alpha_{m, l}^{(r)} - \\gamma_r \\frac{\\partial R_i}{\\partial \\alpha_{m, l}^{(r)}}$$\n",
      "\n",
      "\n",
      "    * Computing the gradient: using the chain rule, done in a forward and backward sweep over the network\n",
      "        * forward pass: fix the weights, get the predicted values $\\hat{f}_k(x_i)$\n",
      "        * backward pass: compute the errors and back-propagate (estimate weights from output $\\rightarrow$ input layers\n",
      "\n",
      "### Remarks\n",
      "\n",
      "* back-propagation can be done in batch or *online*, i.e. one observation at a time $\\Rightarrow$ (SGD: stochastic gradient descent)\n",
      "* learning rate $\\gamma_r$:\n",
      "    * usually constant in batch\n",
      "    * must verify $\\gamma_r \\rightarrow 0$, $\\sum_r \\gamma_r = \\infty$, and $\\sum_r \\gamma_r^2 \\lt \\infty$ (e.g. $\\gamma_r = 1/r$) in SGD\n",
      "* back-prop is very slow in practice: use conjugate gradient\n",
      "* scaling of inputs determines scaling of weights $\\Rightarrow$ standardize (0 mean, unit variance)\n",
      "* initialization:\n",
      "    * important in practice: loss is not convex (many local minima!)\n",
      "    * multiple re-starts with random near-zero weights (nearly linear regime for sigmoid)\n",
      "    * Combine the outputs of $L$ different learned NNs by averaing their predictions\n",
      "    \n",
      "        $$\\hat{f}(X_{\\mathrm{new}}) = \\sum_{l=1}^L w_l \\mathbb{E}(Y_{\\mathrm{new}} | X_{\\mathrm{new}}, \\hat{\\theta}_l)$$\n",
      "\n",
      "        * *bagging*: $w_l = 1/L$ and $\\hat{\\theta}_l$ parameters obtained by fitting on different random permutations of the training data\n",
      "        * *boosting*: $w_l = 1$ but $\\hat{\\theta}_l$ chosen in non-random sequential fashion to improve the fit\n",
      "        * *Bayesian inference*: $w_l = 1/L$ and sample $\\theta_l$ from the posterior using MCMC\n",
      "* number of hidden:\n",
      "    * units: the more the better + regularization\n",
      "    * layers: task-dependent (multiple layers $\\Rightarrow$ hierarchical features)\n",
      "\n",
      "    > $\\Rightarrow$ that is where hand-tuning comes in! no free lunch ;-)\n",
      "\n",
      "* difficult to interpret / visualize what is learned (is it still true?)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Deep Learning in practice"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## What is Deep Learning?\n",
      "\n",
      "* According to [deeplearning.net](http://deeplearning.net):\n",
      "\n",
      "    > \"Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.\"\n",
      "\n",
      "    > \"... moving beyond shallow learning since 2006!\"\n",
      "\n",
      "* Learning of probabilistic generative models with deep architectures, i.e. with many layers of non-linearities\n",
      "\n",
      "\n",
      "### Examples\n",
      "\n",
      "* Deep Multi-Layer Perceptrons (i.e. NN like before, but with many hidden layers)\n",
      "* Convolutional Networks\n",
      "* Stacked Auto-encoders\n",
      "* Restricted Boltzman Machines (RBM)\n",
      "* Deep Belief Networks (DBN)\n",
      "\n",
      "\n",
      "### Why going deep?\n",
      "    \n",
      "* Can trade-off number of units for layers (more efficient: less connections and therefore weights)\n",
      "* Hierarchical: useful for multi-scale analysis\n",
      "* \"Combinatorial sharing of statistical strength between multiple levels of latent variables\" (NIPS'10 workshop)... ?\n",
      "* it works (according to Hinton and Lecun... but starting to generalize more and more to other researchers)\n",
      "\n",
      "\n",
      "### How to learn deeply?\n",
      "\n",
      "* Back-prop has some severe limitations:\n",
      "    * Gradient gets progressively diluted across layers (very limited corrections in first layers)\n",
      "    * Local minima (very sensitive to initialization)\n",
      "    * Needs labeled data (in usual settings)\n",
      "\n",
      "* What works, e.g. on Deep Belief Networks (DBN):\n",
      "    * greedy layer-wise unsupervised learning (serves as initialization step):\n",
      "        * learn a RBM by maximizing lower bound on likelihood\n",
      "        * stack a new RBM on top, update the prior of the second layer with posterior of the first, learn\n",
      "        * iterate\n",
      "        * justified in theory (cf. [Hinton'2006](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf))\n",
      "    * top-down supervised training as final refinement step\n",
      "\n",
      "* Many tools to learn deep architectures:\n",
      "    * At the core: libraries to work on *tensors* (multi-dimensional arrays)\n",
      "    * We will review two recent, efficient, easy to use, and actively maintained librairies:\n",
      "        * Theano (python)\n",
      "        * EBLearn (C++)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Overview of Theano\n",
      "\n",
      "* [Theano](http://deeplearning.net/software/theano/):\n",
      "    * python Open Source library (BSD-like permissive license, cross-platform)\n",
      "    * Active project maintained by many people, including members of Yoshua Bengio's [LISA](http://www.iro.umontreal.ca/rubrique.php3?id_rubrique=27) group at U. of Montreal\n",
      "\n",
      "* Main purpose of Theano (from their [website](http://deeplearning.net/software/theano/)):\n",
      "\n",
      "> Theano is a Python library that allows you to define, optimize, and\n",
      "> evaluate mathematical expressions involving multi-dimensional\n",
      "> arrays efficiently. Theano features:\n",
      ">\n",
      "> * **tight integration with numpy** -- Use `numpy.ndarray` in Theano-compiled functions.\n",
      "> * **transparent use of a GPU** -- Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)\n",
      "> * **efficient symbolic differentiation** -- Theano does your derivatives for function with one or many inputs.\n",
      "> * **speed and stability optimizations** -- Get the right answer for ``log(1+x)`` even when ``x`` is really tiny.\n",
      "> * **dynamic C code generation** -- Evaluate expressions faster.\n",
      "> * **extensive unit-testing and self-verification** -- Detect and diagnose many types of mistake.\n",
      "\n",
      "* Sort of mix between [Numpy](http://numpy.scipy.org/) (Python's Matlab) and [Sympy](http://sympy.org/en/index.html) (Python's Mathematica) focusing on tensor operations\n",
      "\n",
      ">    * *execution speed optimizations*: Theano can use `g++` or `nvcc` to compile\n",
      ">      parts your expression graph into CPU or GPU instructions, which run\n",
      ">      much faster than pure Python.\n",
      ">\n",
      ">    * *symbolic differentiation*: Theano can automatically build symbolic graphs\n",
      ">      for computing gradients.\n",
      ">\n",
      ">    * *stability optimizations*: Theano can recognize [some] numerically unstable\n",
      ">      expressions and compute them with more stable algorithms.\n",
      "\n",
      "* Ambitious goals (and they're making progress!)\n",
      "\n",
      "> * Support tensor and sparse operations\n",
      "> * Support linear algebra operations\n",
      "> * Graph Transformations\n",
      ">     * Differentiation/higher order differentiation\n",
      ">     * 'R' and 'L' differential operators\n",
      ">     * Speed/memory optimizations\n",
      ">     * Numerical stability optimizations\n",
      "> * Can use many compiled languages, instructions sets: C/C++, CUDA, OpenCL, PTX, CAL, AVX, ...\n",
      "> * Lazy evaluation\n",
      "> * Loop\n",
      "> * Parallel execution (SIMD, multi-core, multi-node on cluster,\n",
      ">   multi-node distributed)\n",
      "> * Support all NumPy/basic SciPy functionality\n",
      "> * Easy wrapping of library functions in Theano\n",
      "\n",
      "* Easy to install (just do: ``pip install Theano``)\n",
      "* Extensively documented ([docs](http://deeplearning.net/software/theano/library/index.html#libdoc))\n",
      "* Great tutorial ([tuto](http://deeplearning.net/software/theano/tutorial/index.html#tutorial))\n",
      "* Great [tutorials on deep learning](http://deeplearning.net/tutorial/) written by members of the LISA lab"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sneak peek\n",
      "\n",
      "import theano\n",
      "from theano import tensor\n",
      "\n",
      "# declare two symbolic floating-point scalars\n",
      "a = tensor.dscalar()\n",
      "b = tensor.dscalar()\n",
      "\n",
      "# create a simple expression\n",
      "c = a + b\n",
      "\n",
      "# convert the expression into a callable object that takes (a,b)\n",
      "# values as input and computes a value for c\n",
      "f = theano.function([a,b], c)\n",
      "\n",
      "# bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c'\n",
      "assert 4.0 == f(1.5, 2.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Logistic Regression simple example\n",
      "\n",
      "import numpy\n",
      "import theano\n",
      "import theano.tensor as T\n",
      "rng = numpy.random\n",
      "\n",
      "N = 400\n",
      "feats = 784\n",
      "D = (rng.randn(N, feats), rng.randint(size=N,low=0, high=2))\n",
      "training_steps = 10000\n",
      "\n",
      "# Declare Theano symbolic variables\n",
      "x = T.matrix(\"x\")\n",
      "y = T.vector(\"y\")\n",
      "w = theano.shared(rng.randn(feats), name=\"w\")\n",
      "b = theano.shared(0., name=\"b\")\n",
      "#print \"Initial model:\"\n",
      "#print w.get_value(), b.get_value()\n",
      "\n",
      "# Construct Theano expression graph\n",
      "p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))       # Probability that target = 1\n",
      "prediction = p_1 > 0.5                        # The prediction thresholded\n",
      "xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function\n",
      "cost = xent.mean() + 0.01 * (w ** 2).sum()    # The cost to minimize\n",
      "gw, gb = T.grad(cost, [w, b])                 # Compute the gradient of the cost\n",
      "\n",
      "# Compile\n",
      "train = theano.function(\n",
      "          inputs=[x,y],\n",
      "          outputs=[prediction, xent],\n",
      "          updates={w: w - 0.1 * gw, b: b - 0.1 * gb})\n",
      "predict = theano.function(inputs=[x], outputs=prediction)\n",
      "\n",
      "# Train\n",
      "for i in range(training_steps):\n",
      "    pred, err = train(D[0], D[1])\n",
      "\n",
      "# To display the model, uncomment below:\n",
      "#print \"Final model:\"\n",
      "#print w.get_value(), b.get_value()\n",
      "#print \"target values for D:\", D[1]\n",
      "#print \"prediction on D:\", predict(D[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Deep Learning in Theano\n",
      "\n",
      "Many implementations already available and documented in the [deep learning tutorials]((http://deeplearning.net/tutorial/)) (cf. [the code on github](https://github.com/lisa-lab/DeepLearningTutorials))\n",
      "\n",
      "\n",
      "### Supervised learning\n",
      "\n",
      "* [Introduction](http://deeplearning.net/tutorial/gettingstarted.html): notations, datasets, extensive primer on supervised deep learning\n",
      "* [Logistic Regression (LR) + application on MNIST](http://deeplearning.net/tutorial/logreg.html) (both implemented using [conjugate gradient](https://github.com/lisa-lab/DeepLearningTutorials/blob/master/code/logistic_cg.py) and [SGD](https://github.com/lisa-lab/DeepLearningTutorials/blob/master/code/logistic_sgd.py)): often used as the last layer of classification architectures\n",
      "\n",
      ">   $$\n",
      ">   P(Y=i|x, W,b) = softmax_i(W x + b) \\\\\n",
      ">                   = \\frac {e^{W_i x + b_i}} {\\sum_j e^{W_j x + b_j}}\n",
      ">  $$\n",
      "\n",
      ">  $$y_{pred} = {\\rm argmax}_i P(Y=i|x,W,b)$$\n",
      "\n",
      "* [Multilayer Perceptron (MLP)](http://deeplearning.net/tutorial/mlp.html): going from LR to MLP, train with SGD + tricks of the trade (activation functions, regularization, initialization, learning rate, ...)\n",
      "\n",
      "<img src=\"http://deeplearning.net/tutorial/_images/mlp.png\" align=center>\n",
      "\n",
      "* [Deep Convolutional Neural Networks (CNN)](http://deeplearning.net/tutorial/lenet.html): variants of MLP with local connectivity (to mimic biological receptive fields), weight sharing (detect features regardless of position + reduces parameters / capacity control + still trainable with SGD), and many layers, succession of pooling and subsampling operations, example CNN used: LeNet (cf. [\"Gradient-based learning applied to document recognition\", LeCun *et al.*, 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf))\n",
      "\n",
      "<img src=\"http://deeplearning.net/tutorial/_images/mylenet.png\" align=center>\n",
      "\n",
      "\n",
      "### Unsupervised learning\n",
      "\n",
      "* [Auto-Encoders](http://deeplearning.net/tutorial/dA.html#daa): building a latent representation (*coding*), mapped back (*decoding*) into a reconstruction of the input by minimzing the reconstruction error, application to image denoising\n",
      "\n",
      "* [Stacked  Denoising Auto-Encoders](http://deeplearning.net/tutorial/SdA.html#sda): unsupervised pre-training one layer at a time for deep nets, final stage: task-specific fine tuning using a supervised MLP\n",
      "\n",
      "* [Restricted Boltzmann Machines (RBM)](http://deeplearning.net/tutorial/rbm.html#rbm):\n",
      "    * single layer generative Energy-Based Model (EBM, cf. EBLearn overview below)\n",
      "    * energy: $E(v, h) = -b' v - c'h - h'Wv$, where $v$ is the observed input units, $h$ the hidden units, $W$ are the connection weights, $b, c$ are offset parameters\n",
      "\n",
      "    <img src=\"http://deeplearning.net/tutorial/_images/rbm.png\" align=center>\n",
      "\n",
      "    * a particular form of log-linear Markov Random Field (MRF), i.e., for which the energy function is linear in its free parameters\n",
      "    * learned by SGD with MCMC sampling to approximate the gradient\n",
      "\n",
      "* [Deep Belief Networks (DBN)](http://deeplearning.net/tutorial/DBN.html#dbn): stacked RBMs, trained in a greedy layer-wise manner\n",
      "\n",
      "<img src=\"http://deeplearning.net/tutorial/_images/DBN3.png\" align=center>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Overview of EBLearn\n",
      "\n",
      "* [EBLearn](http://eblearn.cs.nyu.edu:21991/doku.php):\n",
      "    * well-architectured C++ library for Energy-Based Learning, maintained by Yann LeCun's group @NYU (in particular [Pierre Sermanet](http://cs.nyu.edu/~sermanet/))\n",
      "    \n",
      "    <img src=\"http://eblearn.cs.nyu.edu:21991/lib/exe/fetch.php?cache=&media=eblearn.png\" align=center>\n",
      "\n",
      "    * used in robotics (c.f the [Learning Applied to Ground Robots (LAGR)](http://www.cs.nyu.edu/~yann/research/lagr/) DARPA project)\n",
      "    * particular focus on supervised training of convolutional neural networks (especially LeNet)\n",
      "\n",
      "* Enery-Based learning (overview from [here](http://deeplearning.net/tutorial/rbm.html)):\n",
      "    * associate a scalar energy to each configuration of the variables of interest\n",
      "    * learning corresponds to modifying that energy function so that its shape has desirable properties:\n",
      "        * plausible configurations $\\Rightarrow$ low energy\n",
      "        * bad configurations $\\Rightarrow$ high energy\n",
      "    * probability distribution: $Pr(x) = e^{-E(x)} / Z$, where $Z=\\sum_x e^{-E(x)}$ is the *partition function*\n",
      "    * model learnt by (stochastic) gradient descent on the empirical negative log-likelihood of the training data\n",
      "\n",
      "        $$\n",
      "        l(\\theta, \\mathcal{D}) = - \\frac{1}{N} \\sum_{x^{(i)} \\in \\mathcal{D}} \\mathrm{log} Pr(x^{(i)})\n",
      "        $$\n",
      "\n",
      "* with latent variables $h$, the model becomes\n",
      "\n",
      "    $$\n",
      "    Pr(x) = \\sum_h Pr(x, h) = \\sum_h \\frac{e^{-E(x,h)}}{Z} = \\frac{e^{-\\mathcal{F}(x)}}{Z}\n",
      "    $$\n",
      "\n",
      "    where $\\mathcal{F}(x) = - \\mathrm{log} \\sum_h e^{-E(x, h)}$ is called the **free energy** and $Z = \\sum_x e^{-E(x,h)}$\n",
      "\n",
      "* in this case, the gradient of the data's negative log-likelihood decomposes over a *positive* and *negative* phase:\n",
      "\n",
      "    $$\n",
      "    \\frac{\\partial  \\log p(x)}{\\partial \\theta}\n",
      "    = \\frac{\\partial \\mathcal{F}(x)}{\\partial \\theta} -\n",
      "      \\sum_{\\tilde{x}} p(\\tilde{x})\n",
      "      \\frac{\\partial \\mathcal{F}(\\tilde{x})}{\\partial \\theta}\n",
      "    $$\n",
      "\n",
      "* approximate estimation of the *negative* phase is obtained by averaging over *negative particles* $\\mathcal{N}$ sampled using MCMC\n",
      "\n",
      "    $$\n",
      "    \\frac{\\partial  \\log p(x)}{\\partial \\theta}\n",
      "    \\approx \\frac{\\partial \\mathcal{F}(x)}{\\partial \\theta} -\n",
      "    \\frac{1}{|\\mathcal{N}|}\\sum_{\\tilde{x} \\in \\mathcal{N}}\n",
      "    \\frac{\\partial \\mathcal{F}(\\tilde{x})}{\\partial \\theta}\n",
      "    $$\n",
      "\n",
      "\n",
      "### EBLearn usage\n",
      "\n",
      "* Starting point: [paper](http://cs.nyu.edu/~koray/publis/sermanet-ictai-09.pdf), [tutorials](http://eblearn.cs.nyu.edu:21991/doku.php?id=start), [demos](http://eblearn.cs.nyu.edu:21991/doku.php?id=all_demos), and [documentation](http://eblearn.cs.nyu.edu:21991/doku.php?id=all_docs)\n",
      "\n",
      "* Installation: using CMake, cross-platform (even Android and iOS!), only few dependencies\n",
      "\n",
      "* Beginner tutorial ([part 1](http://eblearn.cs.nyu.edu:21991/doku.php?id=beginner_tutorial1_dscompile), [part 2](http://eblearn.cs.nyu.edu:21991/doku.php?id=beginner_tutorial2_train)):\n",
      "    * build a LeNet5 digit regognizer on MNIST\n",
      "\n",
      "    <img src=\"http://eblearn.cs.nyu.edu:21991/lib/exe/fetch.php?cache=&media=lenet5.png\" align=center>\n",
      "\n",
      "    * no C++ coding required, just write EBLearn configuration files and use EBLearn's command-line tools (e.g. `train`)\n",
      "\n",
      "* Set of more advanced tutorials (cf. [here](http://eblearn.cs.nyu.edu:21991/doku.php?id=start)) decribe how to use EBLearn's C++ API, resting on its powerful tensor library `libidx` ([tuto](http://eblearn.cs.nyu.edu:21991/doku.php?id=libidx)) and energy-based learning library `libeblearn` ([tuto](http://eblearn.sourceforge.net/old/tutorials/libeblearn/index.shtml))\n",
      "\n",
      "* [Face demo](http://eblearn.cs.nyu.edu:21991/doku.php?id=face_detector) on the \"Labeled Faces in the Wild\" dataset: just type `make detect` :-)\n",
      "\n",
      "<img src=\"http://eblearn.cs.nyu.edu:21991/lib/exe/fetch.php?media=face_detection_details.png\" align=center>\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}