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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Diclaimer/Context:_ This is the material for a practical given at LSCP (ENS, Paris). Some stuff can be missing from my whiteboard explanations there. Tell me on Github, at https://github.com/SnippyHolloW/DL4H/, or at gabriel.synnaeve@gmail.com or @syhw on Twitter, I'll try and update it accordingly. Pull requests welcomed."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_How to read this (for students):_ go ahead and run things, change things, break things, and fix them!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "From Logistic Regression to Deep Nets, a Crash Course [Practical 1/2]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Important concepts (I'll explain them on demand during this practical):\n",
      "- [loss functions](http://en.wikipedia.org/wiki/Loss_function) (e.g. log-loss vs hinge loss)\n",
      "- [regularization](http://bit.ly/1A4HuLo) (e.g. L1 vs L2)\n",
      "- [purely stochastic gradient](http://en.wikipedia.org/wiki/Stochastic_gradient_descent) vs. mini-batches averaging\n",
      "- [early stopping](http://en.wikipedia.org/wiki/Early_stopping) (one part of the solution to [overfitting](http://en.wikipedia.org/wiki/Overfitting#Machine_learning))\n",
      "- [momentum-based SGD](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf)\n",
      "- [back-propagation](http://en.wikipedia.org/wiki/Backpropagation) (the chain rule with partial derivatives)\n",
      "\n",
      "Central concepts in machine learning that are not covered here:\n",
      "- ***[Cross-validation](http://scikit-learn.org/stable/modules/cross_validation.html)*** (for (hyper-)parameters optimization)\n",
      "- [Feature selection](http://scikit-learn.org/stable/modules/feature_selection.html)\n",
      "- ...\n",
      "\n",
      "_Practical 2/2_ will be more about how to work with Theano and how to extend my own code  \n",
      "(in particular https://github.com/SnippyHolloW/abnet or https://github.com/bootphon/crossitlearn)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In general:\n",
      "$X$ is for data/samples (n_samples, n_features), $x_i$ is one data point (n_features,),\n",
      "$y$ is for labels (n_samples,), $y_i$ is the label of $x_i$,\n",
      "$W$ is for weights,\n",
      "$\\alpha$ is for regularization,\n",
      "$\\lambda$ is for learning rates"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\n",
      "0. The Small Digits Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "import numpy as np\n",
      "from sklearn.datasets import load_digits\n",
      "\n",
      "digits = load_digits()\n",
      "import matplotlib.pyplot as plt\n",
      "plt.rcParams['figure.figsize'] = (8, 5)\n",
      "plt.gray()\n",
      "plt.figure(1, figsize=(3, 3))\n",
      "plt.imshow(digits.images[0], cmap=plt.cm.gray_r, interpolation='nearest')\n",
      "data = np.asarray(digits.data, dtype='float32')\n",
      "target = np.asarray(digits.target, dtype='int32')\n",
      "x = data\n",
      "y = target\n",
      "from sklearn import preprocessing\n",
      "x = preprocessing.scale(x)\n",
      "from sklearn import cross_validation\n",
      "x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.15, random_state=42)\n",
      "print(x_train.shape)\n",
      "print(y_train.shape)\n",
      "print(x_test.shape)\n",
      "print(y_test.shape)\n",
      "assert (len(set(y_train)) == len(set(y_test)))  # should work with random_state=42 ;-)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1527, 64)\n",
        "(1527,)\n",
        "(270, 64)\n",
        "(270,)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11256d4d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. Dumb-Simple Logistic Regression"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A minimal amount of theory"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Logistic regression](http://en.wikipedia.org/wiki/Logistic_regression) is linear regression ($y = X^T\\cdot W + \\epsilon$) to which we apply a logistic function ($\\frac{1}{1+e^{-z}}$). Why? To transform the $y$ into something more categorical (binary if only two classes). In particular, when we are doing logistic regression, we are making the assumption that the $\\log$ of the odds (of e.g. a $X \\sim Bernouilli(P)$) is a linear regression: \n",
      "\n",
      "$$\\log ( \\frac{P}{1-P} ) = X^T \\cdot W  \\Longleftrightarrow P = \\frac{\\exp(X^T \\cdot W)}{1 + \\exp(X^T \\cdot W)}$$\n",
      "\n",
      "We want to _maximize_ the likelihood of our model (actually, if we have a prior on the parameters, we want the maximum a posteriori, an in this case we need to add a term to the negative log-likelihood below, if that prior is Gaussian, that's like doing L2 regularization).\n",
      "\n",
      "If we think in term of loss function, we want to _minimize_ the _negative_ log-likelihood ($\\log$ is monotonically increasing). I'm writing it in the general case where $y$ is not just binary:\n",
      "\n",
      "$$NLL(W) = -\\frac{1}{n} \\sum_{i=1}^n \\sum_{j=1}^m \\mathbb{1}_{y_i=j} \\log P(y_i=j|x_i; W)$$\n",
      "with\n",
      "$$P(y_i=j|x_i; W) = softmax(W, x_i)[j]$$\n",
      "and\n",
      "$$softmax(W, x)[j] = \\frac{\\exp(x \\cdot W_j)}{\\sum_{k} \\exp(x \\cdot W_k)}$$\n",
      "We can derive the gradient:\n",
      "$$\\nabla_{W_j} = -\\frac{1}{n} \\sum_{i=1}^n x_i (\\mathbb{1}_{y_i=j} - P(y_i=j|x_i, W))$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A not-so-minimal amount of code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By no mean this is minimal nor \"smart\" nor efficient, this aims at being _easy_ to understand, it fills only a pedagogical role. That said, it can always be improved. All pull requests making it easier to grasp are welcomed!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def softmax(W, x):\n",
      "    \"\"\" P(y=j | X) = exp(<X,W_j>) / (sum_k(exp(<X,W_k>)))\n",
      "    W: (n_features, n_outputs)\n",
      "    X: (n_features,)\n",
      "    \"\"\"\n",
      "    tmp = np.exp(np.dot(x, W))\n",
      "    return tmp / np.sum(tmp)\n",
      "\n",
      "from scipy.optimize.optimize import check_grad\n",
      "from scipy.optimize import fmin_l_bfgs_b\n",
      "\n",
      "class LogisticRegression:\n",
      "    def __init__(self):\n",
      "        self.name = \"Logistic Regression\"\n",
      "        \n",
      "    def loss(self, W):\n",
      "        \"\"\" Negative log-likelihood as described above \"\"\"\n",
      "        l = 0.\n",
      "        tmp_W = W.reshape((self.x.shape[1], self.n_classes))\n",
      "        for i, xi in enumerate(self.x):\n",
      "            p_i = softmax(tmp_W, xi)\n",
      "            l += np.log(p_i[self.y[i]])\n",
      "        return - l / self.x.shape[0]\n",
      "                   \n",
      "    def loss_derivative(self, W):\n",
      "        \"\"\" flattened (1-D) gradient vector \"\"\"\n",
      "        d = np.zeros((self.x.shape[1], self.n_classes)) # that's the Jacobian\n",
      "        tmp_W = W.reshape((self.x.shape[1], self.n_classes))\n",
      "        for i, xi in enumerate(self.x):\n",
      "            p_i = softmax(tmp_W, xi)\n",
      "            for j, v in enumerate(p_i):\n",
      "                if j == self.y[i]:  # because we don't have a \"one-hot\" encoding\n",
      "                    d[:,j] += (1 - p_i[j]) * xi\n",
      "                else:\n",
      "                    d[:,j] += - p_i[j] * xi\n",
      "        d = d.reshape(W.shape) # and we flatten it\n",
      "        return - d / self.x.shape[0]\n",
      "    \n",
      "    def train(self, x_train, y_train, debug=False):\n",
      "        self.x = np.hstack((x_train, np.ones((x_train.shape[0], 1))))\n",
      "        self.y = y_train\n",
      "        self.n_classes = len(set(y_train))\n",
      "        self.W = np.ones((x_train.shape[1] + 1, self.n_classes))\n",
      "        print(\"W shape:\", self.W.shape)\n",
      "        print(\"x shape:\", self.x.shape)\n",
      "        print(\"loss before training:\", self.loss(self.W))\n",
      "        print(\"prediction on x[0] before training:\", softmax(self.W, self.x[0]))\n",
      "        if debug == True:\n",
      "            tmp = self.loss_derivative(self.W.reshape((self.W.shape[0]*self.W.shape[1],)))\n",
      "            print(\"loss derivative shape:\", tmp.shape)\n",
      "            print(\"loss derivative value:\", tmp)\n",
      "            print(\"checking the gradient with finite differences, show be low (something E-6):\",\n",
      "                  check_grad(self.loss, self.loss_derivative, self.W.reshape((self.W.shape[0]*self.W.shape[1],))))\n",
      "            # that's nothing more than checking that f'(x) ~= (f(x+h)-f(x))/h with h->0\n",
      "        tmp = fmin_l_bfgs_b(self.loss, self.W.reshape((self.W.shape[0]*self.W.shape[1],)), fprime=self.loss_derivative)\n",
      "        self.W = tmp[0].reshape((self.x.shape[1], self.n_classes))\n",
      "        print(\"loss after training:\", self.loss(self.W))\n",
      "        print(\"prediction on x[0] after training:\", softmax(self.W, self.x[0]))\n",
      "        print(\"true x[0] label:\", self.y[0])\n",
      "        \n",
      "    def score(self, x_test, y_test):\n",
      "        x = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\n",
      "        y = y_test\n",
      "        c = 0\n",
      "        for i, xi in enumerate(x):\n",
      "            a = np.argmax(softmax(self.W, xi))\n",
      "            if a == y[i]:\n",
      "                c += 1\n",
      "        return c * 1. / y.shape[0]\n",
      "\n",
      "lr = LogisticRegression()\n",
      "lr.train(x_train, y_train)\n",
      "plt.imshow(lr.W.transpose(), interpolation='none')\n",
      "plt.show()\n",
      "print(\"weights for 0:\")\n",
      "plt.imshow(lr.W.transpose()[0][:64].reshape((8,8)), interpolation='none')\n",
      "plt.show()\n",
      "print(\"weights for 5:\")\n",
      "plt.imshow(lr.W.transpose()[5][:64].reshape((8,8)), interpolation='none')\n",
      "plt.show()\n",
      "print(\"weights for 8 minus weights for 3:\")\n",
      "plt.imshow(lr.W.transpose()[8][:64].reshape((8,8)) - lr.W.transpose()[3][:64].reshape((8,8)), interpolation='none')\n",
      "plt.show()\n",
      "print(\"training accuracy:\", lr.score(x_train, y_train))\n",
      "print(\"test accuracy:\", lr.score(x_test, y_test))\n",
      "print(\"We should overfit, i.e. test score << train score, you can add an L2 regularization\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "W shape: (65, 10)\n",
        "x shape: (1527, 65)\n",
        "loss before training: 2.30258509299\n",
        "prediction on x[0] before training: [ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n",
        "loss after training:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1.84669284921e-05\n",
        "prediction on x[0] after training: [  1.08822355e-31   9.98300553e-01   1.77810355e-28   3.23091472e-47\n",
        "   9.64927736e-20   2.79499404e-36   1.13639647e-16   6.26997256e-36\n",
        "   1.69944705e-03   1.81750303e-29]\n",
        "true x[0] label: 1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10f5f30d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weights for 0:\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11056ae10>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weights for 5:\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x110df80d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weights for 8 minus weights for 3:\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TD5/TcZ7x48enfA7P8xQKhVI+Tzpuzb579+6M3uI9z/M8L6UT5OWlawsAnGGsPKV8xZVi\n9wDgvPEcFwBzCBcAc5yHq62tTTNnztT06dPP+QlC2bRixQqVlpaqrq7O6Y6enh7NmzdPtbW1mjVr\nltNPDj9+/LiampoUDocVCoX0zDPPONty2sjIiCKRiBYvXux0R1VVla6//npFIhHdcMMNTrccPnxY\nra2tqqmpUSgU0rfffutkx+7dWfrke8+h4eFhr7q62tu/f78Xi8W8+vp67+eff3a2Z9u2bV40GvVm\nzZrlbIPneV5/f7/X1dXleZ7nHT161JsxY4bT/y6Dg4Oe53neyZMnvaamJq+jo8PZFs/zvJdfftm7\n7777vMWLFzvdUVVV5f31119ON5y2bNky76233vI879Tf0+HDhx0v8ryRkRGvrKzM+/3339N+bqdX\nXJ2dnZo2bZqqqqo0btw4LVmyROvXr3e2Z+7cuZo8ebKzP/+0srIyhcNhSdLEiRNVU1Pj9J0FEyZM\nkHTqxbAjIyOaMmWKsy0HDhzQxo0btWrVqpz4wVAubPj777/V0dGhFStWSJIKCgpUVFTkeFVmP/ne\nabh6e3vP+JcKBoMZf6uANd3d3erq6lJTU5OzDaOjowqHwyotLdW8efPS8lqhC/X000/rxRdfVH6+\n82c5lJeXpwULFqixsVFvvvmmsx379+9XcXGxli9frtmzZ+vhhx/W0NCQsz2nZfKT753+7fMasPiO\nHTum1tZWrVmzRhMnTnS2Iz8/X7t27dKBAwe0bds2tbe3O9nxySefqKSkRJFIJCeudLZv366uri5t\n2rRJb7zxhjo6OpzsGB4eVjQa1eOPP65oNKrCwkI999xzTracFoud+uT7e++9NyPndxquQCCgnp4e\n//uenh4Fg0GHi3LHyZMndc899+iBBx7QXXfd5XqOJKmoqEiLFi2K+wnDmfT1119rw4YNmjp1qpYu\nXaotW7Zo2bJlTrZIUnl5uSSpuLhYLS0t6uzsdLIjGAwqGAxqzpw5kqTW1lZFo1EnW07btGnsT75P\nB6fhamxs1N69e9Xd3a1YLKZ169bpzjvvdDkpJ3iep5UrVyoUCumpp55yuuXQoUP+m87/+ecfff75\n54pEIk62rF69Wj09Pdq/f78+/PBD3XLLLXrvvfecbBkaGtLRo0clnXpz82effebsp9FlZWWqrKzU\nnj17JJ16bqm2ttbJltMy/sn3aX+6/zxt3LjRmzFjhlddXe2tXr3a6ZYlS5Z45eXl3qWXXuoFg0Hv\n7bffdrKjo6PDy8vL8+rr671wOOyFw2Fv06ZNTrZ8//33XiQS8err6726ujrvhRdecLLj39rb253+\nVPG3337z6uvrvfr6eq+2ttb5/7u7du3yGhsbveuvv95raWlx+lPFY8eOeVdddZV35MiRjP0ZKb9X\nEQCyzf2PZgDgPBEuAOYQLgDmEC4A5hAuAOYQLgDmEC4A5hAuAOb8H/sO10rDKlnxAAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110e42710>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "training accuracy: 1.0\n",
        "test accuracy: 0.955555555556\n",
        "We should overfit, i.e. test score << train score, you can add an L2 regularization\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Bonus:_ you can add regularization (order to try: L2, L1, ElasticNet), start with adding the square of the weights magnitudes to the loss function (weight this sum by an $\\alpha$ as \"L2 regularization parameter\") http://en.wikipedia.org/wiki/Tikhonov_regularization // http://en.wikipedia.org/wiki/Least_squares#Lasso_method"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Reality check: linear and RBF SVMs from scikit-learn"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import svm\n",
      "clf = svm.SVC(kernel='linear')\n",
      "clf.fit(x_train, y_train)\n",
      "print(\"Linear SVM\")\n",
      "print(\"training accuracy:\", clf.score(x_train, y_train))\n",
      "print(\"test accuracy:\", clf.score(x_test, y_test))\n",
      "clf = svm.SVC(kernel='rbf')\n",
      "clf.fit(x_train, y_train)\n",
      "print(\"with and RBF kernel\")\n",
      "print(\"training accuracy:\", clf.score(x_train, y_train))\n",
      "print(\"test accuracy:\", clf.score(x_test, y_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Linear SVM\n",
        "training accuracy: 1.0\n",
        "test accuracy:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.97037037037\n",
        "with and RBF kernel"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training accuracy:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.996725605763\n",
        "test accuracy: 0.981481481481\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Bonus:_ change the loss of our logistic regression for a [hinge loss](http://en.wikipedia.org/wiki/Hinge_loss) and get a linear SVM. With regularization, you should approach scores above."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. Intro to SGD"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We used an [L-BFGS](http://en.wikipedia.org/wiki/Limited-memory_BFGS) optimization procedure, which is quasi-Newton (Newton algorithm to find the 0s of f: $x_{n+1} = x_n - \\frac{f(x_n)}{f'(x_n)}$, and we search the 0s of $\\nabla(loss)$). It uses an approximation of the Hessian matrix (matrix of second-order partial derivatives), but it's still more costly (in CPU time, but particularly in RAM) than just using the Jacobian (matrix of the first-order partial derivatives). Let's optimize this $loss$ by just taking small steps in the direction of the gradient, each datapoint at a time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def init(lr, x_train, y_train):\n",
      "    lr.x = np.hstack((x_train, np.ones((x_train.shape[0], 1))))\n",
      "    lr.y = y_train\n",
      "    lr.n_classes = len(set(y_train))\n",
      "    lr.W = np.zeros((x_train.shape[1] + 1, lr.n_classes))\n",
      "    \n",
      "    \n",
      "def one_epoch_of_SGD(lr, learning_rate):\n",
      "    \"\"\" an \"epoch\" is a full pass/iteration on the dataset \"\"\"\n",
      "    tmp_W = lr.W[:]\n",
      "    for i, xi in enumerate(lr.x):\n",
      "        p_i = softmax(tmp_W, xi)\n",
      "        for j, v in enumerate(p_i):\n",
      "            if j == lr.y[i]:\n",
      "                tmp_W[:,j] += learning_rate * (xi * (1 - v))\n",
      "            else:\n",
      "                tmp_W[:,j] += learning_rate * (- xi * v)\n",
      "    return tmp_W\n",
      "    \n",
      "    \n",
      "class LogisticRegression2:\n",
      "    def __init__(self):\n",
      "        self.name = \"Logistic Regression\"\n",
      "        \n",
      "    def loss(self):  # we redefine it so that we dont have to do all the reshaping, as we don't use scipy's L-BFGS anymore\n",
      "        \"\"\" Negative log-likelihood as described above \"\"\"\n",
      "        l = 0.\n",
      "        for i, xi in enumerate(self.x):\n",
      "            p_i = softmax(self.W, xi)\n",
      "            l += np.log(p_i[self.y[i]])\n",
      "        return - l / self.x.shape[0]\n",
      "    \n",
      "    def score(self, x_test, y_test):\n",
      "        x = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\n",
      "        y = y_test\n",
      "        c = 0\n",
      "        for i, xi in enumerate(x):\n",
      "            a = np.argmax(softmax(self.W, xi))\n",
      "            if a == y[i]:\n",
      "                c += 1\n",
      "        return c * 1. / y.shape[0]\n",
      "\n",
      "    \n",
      "def stupid_train_with_SGD(lr, x_train, y_train):\n",
      "    init(lr, x_train, y_train)\n",
      "    print(\"loss before training:\", lr.loss())\n",
      "    lr.W = one_epoch_of_SGD(lr, learning_rate=0.01)\n",
      "    old_loss = 1.E31\n",
      "    new_loss = lr.loss()\n",
      "    losses = [new_loss]\n",
      "    print(\"loss after one epoch of training:\", new_loss)\n",
      "    lr.W = one_epoch_of_SGD(lr, learning_rate=0.01)\n",
      "    new_loss = lr.loss()\n",
      "    losses.append(new_loss)\n",
      "    print(\"loss after two epochs of training:\", new_loss)\n",
      "    while old_loss - new_loss > 1.E-5:  # we want to optimize up to decreasing in the loss of 1.E-5 ==> STUPIDELY BAD\n",
      "        old_loss = new_loss\n",
      "        lr.W = one_epoch_of_SGD(lr, learning_rate=0.01)\n",
      "        new_loss = lr.loss()\n",
      "        losses.append(new_loss)\n",
      "    print(\"best loss:\", new_loss)\n",
      "    plt.plot(np.arange(len(losses)), np.log(losses), label=\"stupid\", linewidth=2, linestyle=\"--\")\n",
      "    \n",
      "    \n",
      "def slightly_less_stupid_train_with_SGD(lr, x_train, y_train):\n",
      "    init(lr, x_train, y_train)\n",
      "    print(\"loss before training:\", lr.loss())\n",
      "    old_loss = 1.E31\n",
      "    losses = []\n",
      "    new_loss = 1.E30\n",
      "    while abs(old_loss - new_loss) > 1.E-5:  # we want to optimize up to changes in the loss of 1.E-5 ==> BAD\n",
      "        old_loss = new_loss\n",
      "        lr.W = one_epoch_of_SGD(lr, learning_rate=0.01)\n",
      "        new_loss = lr.loss()\n",
      "        losses.append(new_loss)\n",
      "    print(\"best loss:\", new_loss)\n",
      "    plt.plot(np.arange(len(losses)), np.log(losses), label=\"slightly less stupid\", linewidth=2, linestyle=\":\")\n",
      "    \n",
      "    \n",
      "def early_stopping_train_with_SGD(lr, x_train, y_train):\n",
      "    \"\"\" using the training set as validation set, \n",
      "    DO NOT DO THIS IN PRACTICE! USE A SEPARATE VALIDATION SET \"\"\"\n",
      "    init(lr, x_train, y_train)\n",
      "    print(\"loss before training:\", lr.loss())\n",
      "    old_score = -1.\n",
      "    new_score = lr.score(x_train, y_train)\n",
      "    losses = []\n",
      "    patience = 3  # number of epochs of patience\n",
      "    while new_score > old_score or patience:\n",
      "        if new_score > old_score:\n",
      "            patience = 3\n",
      "        else:\n",
      "            patience -= 1\n",
      "        old_score = new_score\n",
      "        lr.W = one_epoch_of_SGD(lr, learning_rate=0.01)\n",
      "        new_score = lr.score(x_train, y_train)\n",
      "        losses.append(lr.loss())\n",
      "    print(\"best loss:\", losses[-1])\n",
      "    plt.plot(np.arange(len(losses)), np.log(losses), label=\"crude early stopping\\n3 epochs of patience\", linewidth=1.5)\n",
      "\n",
      "    \n",
      "lr = LogisticRegression2()\n",
      "    \n",
      "print(\"stupid stopping condition [will stop at the first bump in the loss function]\")\n",
      "stupid_train_with_SGD(lr, x_train, y_train)\n",
      "print(\"training accuracy:\", lr.score(x_train, y_train))\n",
      "print(\"test accuracy:\", lr.score(x_test, y_test))\n",
      "print()\n",
      "\n",
      "print(\"slightly less stupid stopping condition [will stop when the loss function is flat]\")\n",
      "slightly_less_stupid_train_with_SGD(lr, x_train, y_train)\n",
      "print(\"training accuracy:\", lr.score(x_train, y_train))\n",
      "print(\"test accuracy:\", lr.score(x_test, y_test))\n",
      "print()\n",
      "\n",
      "print(\"(very) early stopping (crude, with 3 epochs of patience)\")\n",
      "print(\"[optimizes for the right thing, but do it on a _separate_ validation set!]\")\n",
      "early_stopping_train_with_SGD(lr, x_train, y_train)  # using the training set as validation set, \n",
      "                                                     # DO NOT DO THIS IN PRACTICE!\n",
      "                                                     # USE A SEPARATE VALIDATION SET\n",
      "print(\"training accuracy:\", lr.score(x_train, y_train))\n",
      "print(\"test accuracy:\", lr.score(x_test, y_test))\n",
      "plt.ylabel(\"log of the loss function\")\n",
      "plt.xlabel(\"#epochs\")\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "stupid stopping condition [will stop at the first bump in the loss function]\n",
        "loss before training: 2.30258509299\n",
        "loss after one epoch of training:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.229635078515\n",
        "loss after two epochs of training:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.157913366763\n",
        "best loss:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.00421204401221\n",
        "training accuracy:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1.0\n",
        "test accuracy: 0.962962962963\n",
        "\n",
        "slightly less stupid stopping condition [will stop when the loss function is flat]\n",
        "loss before training: 2.30258509299\n",
        "best loss:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.00421204401221\n",
        "training accuracy: 1.0\n",
        "test accuracy: 0.962962962963\n",
        "\n",
        "(very) early stopping (crude, with 3 epochs of patience)\n",
        "[optimizes for the right thing, but do it on a _separate_ validation set!]\n",
        "loss before training: 2.30258509299\n",
        "best loss:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.029649373213\n",
        "training accuracy: 0.998035363458\n",
        "test accuracy: 0.966666666667\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.legend.Legend at 0x110dffa10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vfR4BakrTmkqBmvt9qLHDEUIUI6ae/JVSKi0tTe3du1fZ29urw4cPZ1mmY8eO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\nz9SpU1VAQIAKCQlRmZmZVhz57/ttbIsWLVJDhw5V4eHhymA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       "text": [
        "<matplotlib.figure.Figure at 0x110889250>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Bonus:_ plot the train set and test set accuracies (scores) over epochs, see overfitting."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Learning rate schemes and \"pure\" SGD"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Can we decrease the learning rate smartly? Why, yes! Check out below with $\\lambda_t = \\frac{\\lambda_0}{1 + \\lambda_0 \\sqrt t}$, where we also get rid of epochs to do pure SGD. Read [SGD tricks](http://research.microsoft.com/pubs/192769/tricks-2012.pdf) for more good stuff (e.g. you should try $\\lambda_t = \\frac{\\lambda_0}{(1 + \\lambda_0 \\alpha t)^p}$ with powers $p=0.5$ and $p=1$). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from random import randint\n",
      "\n",
      "def train_with_pure_SGD(lr, x_train, y_train, init_learning_rate=0.1):\n",
      "    init(lr, x_train, y_train)\n",
      "    old_loss = 1.E31\n",
      "    new_loss = lr.loss()\n",
      "    print(\"loss before training:\", new_loss)\n",
      "    def get_learning_rate(t):\n",
      "        return init_learning_rate / (1 + init_learning_rate*np.sqrt(t))\n",
      "        #return init_learning_rate / (1 + init_learning_rate*t)\n",
      "    learning_rate = get_learning_rate(0)\n",
      "    losses = [new_loss]\n",
      "    gradients = []\n",
      "    # here we do purely stochastic gradient descent\n",
      "    t = 0\n",
      "    while t < 1000:  # here we see a fixed number of samples\n",
      "        t += 1\n",
      "        old_loss = new_loss\n",
      "        # pick a random sample\n",
      "        i = randint(0, lr.x.shape[0]-1)\n",
      "        xi = lr.x[i]\n",
      "        p_i = softmax(lr.W, xi)\n",
      "        error = np.zeros(p_i.shape)\n",
      "        error[lr.y[i]] = 1\n",
      "        error = error - p_i\n",
      "        grad = np.outer(xi, error)\n",
      "        lr.W += get_learning_rate(t) * grad\n",
      "        new_loss = lr.loss()\n",
      "        losses.append(new_loss)\n",
      "        gradients.append(grad)\n",
      "    plt.plot(np.arange(len(losses)), np.log(losses))\n",
      "    plt.ylabel(\"log of the loss function\")\n",
      "    plt.xlabel(\"#samples seen\")\n",
      "    plt.show()\n",
      "    from sklearn.decomposition import PCA\n",
      "    pca = PCA(n_components=3)\n",
      "    gradients = np.array(gradients)\n",
      "    #gradients = np.log(1 + np.abs(gradients.reshape(gradients.shape[0], gradients.shape[1]*gradients.shape[2])))\n",
      "    gradients = gradients.reshape(gradients.shape[0], gradients.shape[1]*gradients.shape[2])\n",
      "    grad = pca.fit_transform(gradients).transpose()\n",
      "    #grad = np.log(1 + np.abs(grad))\n",
      "    plt.plot(np.arange(grad.shape[1]), grad[0], label=\"#1 direction of the gradient\")\n",
      "    plt.plot(np.arange(grad.shape[1]), grad[1], label=\"#2 direction of the gradient\")\n",
      "    plt.plot(np.arange(grad.shape[1]), grad[2], label=\"#3 direction of the gradient\", linestyle=\":\")\n",
      "    m = np.mean(np.abs(gradients), axis=-1)\n",
      "    plt.plot(np.arange(grad.shape[1]-50), [10*np.sum(m[i:i+50]) for i in xrange(grad.shape[1]-50)],\n",
      "             label=\"smoothed $\\propto$ mean(abs(gradients))\", linestyle=\"--\")\n",
      "    #plt.ylabel(\"gradient log-magnitude\")\n",
      "    plt.ylabel(\"gradient values\")\n",
      "    plt.xlabel(\"#samples seen\")\n",
      "    plt.legend(loc=4)\n",
      "\n",
      "train_with_pure_SGD(lr, x_train, y_train)\n",
      "print(\"training accuracy:\", lr.score(x_train, y_train))\n",
      "print(\"test accuracy:\", lr.score(x_test, y_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "loss before training: 2.30258509299\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DQN++ffHTTz/h7NmzWLZsmSFrUtxTTwG7dwNXrgB9+ihdDRERkf5UGwQyMjJw+PBh7Nix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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11034aed0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "training accuracy: 0.956777996071\n",
        "test accuracy: 0.933333333333\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RcPApL2A0TeO2U41K6cDqa/p7jYWkpZW3OKrFlezA9PBwHouJwfsM+uKc0yBx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\n+U95IU9d6kf+A2VccVMQKI1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Gz69du7bdghIdn6y4K4QQnZPTxC/t+kIIIUTX4TTxv/ba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\nP2OMMdaPcOFnjDHG+hEu/Iwxxlg/8v84WswBa9qJnAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1104d6490>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Can you think about an even more \"stochastic\" gradient descent? (yes, 2 answers at least: 1) just care about the sign, not the magnitude, 2) just update one of the weights per datapoint)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "_Bonus:_ Adding momentum"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can accumulate gradient in directions that are consistent thorough samples by using a momentum update. Instead of doing: $$W_{t+1} = W_t + \\lambda \\nabla f(W_t)$$ add a momentum $v$ (with a parameter $\\mu$ and $v_0=0$, $\\mu$ being a \"friction\", it makes sense to take it $\\in [0\\dots1]$, I would advise trying $0.9$) s.t.: $$v_{t+1} = \\mu v_t + \\lambda \\nabla f(W_t)$$ $$W_{t+1} = W_t + v_{t+1}$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. Adding hidden layers"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Some common non-linearities"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(-3, 3, 0.01)\n",
      "plt.plot(a, a, label=\"linear\", linewidth=2, linestyle='--')\n",
      "plt.plot(a, 1./(1+np.exp(-4*a)), label=\"sigmoid\")  # I multiply by 4 so that's it's less flat\n",
      "plt.plot(a, np.tanh(4*a), label=\"tanh\")            # I multiply by 4 so that's it's less flat\n",
      "plt.plot(a, (a+abs(a))/2, label=\"ReLU\", linewidth=1.5)\n",
      "plt.legend(loc=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.legend.Legend at 0x11036a610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HOHQI2rSBdu0gJgbCw6FZM2jeHMLC4KKLzERahtz8XNJy0tiTs4ffjv7G/tz9HMg7UOox\nvzCf4IbBBDUIKnNrUC+IrwtDWXq6IeRBx031mDEggJbNGtHItxENfRtSz6ueCqykRhxyn++gQYNY\nvXo1R44cITg4mMmTJ3P33XdXKQgRqSUKC2H5cli4EJYsMVuvCQnQrRtcfjm0bg1eXuUebjWs/Hb0\nN7b8voUth7awPXs7e3L2sOePPeQV5BEVGEV0UDQtA1rSrHEzmjVuRljjMPOxURhN6jcpN3Euyc5m\ndOq5gqqJwVG0CFRBldieJtkQEcc4eBBefx3efRdatIDbboO+fc1kWw7DMEjLSWN95nq+z/yenw/8\nzK+//4p/PX86hXSiU3An2l/cntZBrYkOjCa0UWiVW6SGARt+y+f5PHPJv/Z+frzVtq0KqsSutKqR\niNjXoUMwZQp89BEMHmy2ejt0KHNXq2El5WAK/0v7H2v3ruX7zO/x9fKlW0Q3uoV347YOt9EpuBOB\nDQJtElrqHoN+c7LY2SOdeg0NprZqxaMREfh6aqIMcT61fEWk6goL4c034ZlnYOhQePxxCA09b7fD\nJw6zZNcSlqctZ2XaSgIbBJIYlciVkVfSLaIb4f7hNg/NYoFHZ+byer1dGG1z8U4J5MP/i+H2KzQZ\nsziGup1FxPbS02HQILMC+Y03IDa21MdZx7P4YvsXfL79czYd3ETv6N5cG30tiVGJRDaJtGtoq38s\nZMBXGRxJyIRjPsSntGbBqGBCQ1VAJY6j5CsitrVwIdx/P4wbBw8/DGfGYI/nH2f+r/OZu3ku2w5v\no29MX/rH9icxKpEGPo5pcS7JzuberakcIh//1WG82z2K/okqqBLHU/IVEdv5179g+nT44gv4y18w\nDIPkjGTeTXmXxTsX06tVL+6Ou5vrW1+Pj5fjkl5Wfj4PpZ4rqBp8oC2P9NaSf+I8Sr4iUnOGYbZ0\nv/oKli3jZNhFfPjLh7z6w6t44MG9l93LkE5DuLjhxQ4Nq8gwmJmVxYT0dCyGwaTISBVUiUtQ8hWR\nmps4Eb7+mv0L5vLa7o94e9PbdAvvxpjLx9CrZS+HT0hxtqBqQeQuDgTkkhgYyMyYGKLV1BUXoVuN\nRKRmpk/HMn8eE5Ou4O15PbnjkjtYP3w9rYPKv3/Xnv73XSEDl50pqDruw6sXxfJgBy35J+5HyVdE\nynTovTfxmTaJhHu96RMcwo6bdhDcMNgpseTkwODXslkWkwpX59M42SyoGtBRBVXintTtLCKl/H7i\nd2Z/9AgjH/mY+S/ezW13vkBTv6ZOiycrP5+en6WSFpENGX4M/b0tsx9UQZW4Lo35ikilnSg4wb++\n/xf/Xv0vvn8bGj3+TxqPfthp8ZQqqLIaRK6O5NP+EcR1VEGVuDYlXxGpUJG1iHdT3uWp5Ke4osUV\nvLXYwN+zgTlPs5PGUlNycxmxaxc/5aqgStyPCq5E5IJ+yvqJB/7zAH4+fiy8fSF//fUorL4Ptmxx\nSuJdua6QD70y+OBUJk19fPg4NpaBwSqoktpHyVekDso5lcPEVRP5YvsXvJD4AkMvGYpHbi7c1wn+\n/W9zAXsHOnoUBs/IZmnbVAjJ577QMJ6PjiKwnLV9RdydBk9E6hDDMHh/8/u0f7M9hmGwffR2hl06\nzGxZjhsHvXubm8PigZnz8wmdvZWlPbfCKS+G/tiZ16PaKvFKraaWr0gdse3wNkb9ZxS5BbksGriI\nvzb/67kPN2+Gzz+HHTscFk+RYfDXKVlsvCwd4gwil7fiy8EqqJK6QQVXIrXciYITPLPmGeZsmsNT\nVz7FyC4j8fL0OreDYcBVV8Ftt8HIkQ6JqWRBlXdKIElNYhg/rAGaGVJqAxVcidRxi3Ys4qFlDxHf\nIp4tI7cQ2uj8NXf54gvIzob77rN7PHmFhSRlZPBKpllQ9WHbWK6JDSYkRAVVUrco+YrUQhlHM/jH\n0n+w68gu3rnpHa5qdVXZOxYUwOOPw+zZ4G2/n4Pjx2F1QTZ/T01lb34+I8LCeC5KBVVSdyn5itQi\nBUUFvLTuJaavn84j3R5h/q3zqeddr/wD3n0XoqPhmmvsEo9hwKwF+Tyclkp+V3PJv7WdOxMfEGCX\n64m4C435itQSq9JXMfq/o4kOjOa161+jVWCrCx+Qnw9t2sBnn8Hll9s8nt1pBv3mZLG9ezp4G8T+\nFEnKBC35J7WfxnxF6oADuQcYu2Is3+79lleve5Wb2t5UuUkp3n4bOnWyeeK1WOCxWbm85rsLI7FE\nQdUEFVSJnKXkK+KmLEUWXv/xdZ799lnuvexeto3aRkPfhpU7+PRpmDoVFi2yaUx5hYVMyshgRvtM\nOOZD/KpYFowKJjRUBVUiJSn5irihNb+tYfR/RxPWKIzv7vmOthe1rdoJ3nsPOneGLl1sFtOS7GxG\nnymo+lv9MIYURTFgsgqqRMqi5CviRs52Ma/5bQ3Tr53OgNgBVZ/32GqFl182p5G0gaz8fB5KTeXz\nbBVUiVSWkq+IGzhRcILp66fzyg+vMOKyEWwfvb3yXcx/9tVX0KQJ9OxZo5hS9xgM+jSLnVekU4jB\n1FateDRCBVUilaHkK+LCiqxFvLf5PSZ9M4keLXrw030/ERUYVbOTvvgiPPZYtVctsljg0Zm5vF5v\nF0Z8LhEHAvmmn5b8E6kKJV8RF2QYBsv3LGfsirH41/NnwW0LuDzcBlXJ69dDVhb071+tw1euK+T2\npRkcSfhTQVUDFVSJVIXu8xVxIYZhsCp9FUmrk/j9xO9Mu3oaf2v3N9utZztgACQkwIMPVvnQt3/N\n5r6t5pJ//qvDeLd7FP0TVVAl8meVyXtKviIu4M9J98meTzKw40C8PW3YOZWWBn/9K2RkQKNGlT6s\nZEFV4DE/+u5qy6y/B6BeZpGyaZINERdXaC3ki+1fMH39dP449QeTrpxk+6R71qxZcPfdlU68RYbB\nzKwsJqSnYzHMgqpHwiOo56WCKpGaUstXxAmOnT7GnE1zePWHV4kMiOSRbo/QN6Zv6aX+bOnUKWjR\nAr7/3pzL+QIsFpiTnMs7QeaSf4mBgcyMUUGVSGWp5SviQgzDYH3meuZsnMMXO76gT5s+fH7b53Rp\nZruJLso1f745oUYFibdkQVVgrg8fx8YyMDjYdmPOIgIo+YrY3YHcA3y85WPmbJpDkVHE8M7D2T56\ne9lr69rLm2/CxInlfnz0KAyekc3SmFS4Op/GyWF8dHUU14eooErEHtTtLGIHWcez+Hz758zfNp+t\nv2/lprY3MbzzcHq06OH4VuTPP5u3FqWlgdf53dqL1+Vz27fmkn9k+DH097bMflAFVSLVpW5nEQcp\nshax8cBGlu1exrI9y9h+eDt92/blifgnSIxKvPCauvY2cyY88MB5ifdsQdX4wnTy4wwil7fiy8ER\nxHVUQZWIvanlK1INVsPK9sPb+W7fd3yT8Q0r9qwgpFEI10Zfy7XR15LQMsG5CfesnByIioKdOyE4\nuPjtlNxcRuw6V1D1hG8MvdpqyT8RW1DLV8QGDMPgQN4BUg6msOnAJtZnrmfdvnUENQgivkU8V7e6\nmheueYGIgAhnh3q+996DPn2KE++Rk4VM25/BK5mZNPVRQZWIs9S45bts2TLGjBlDUVER9957L088\n8UTpC6jlK27ibJLddWQXqUdS2XVkF5sPbSblYAoGBp1DOxMXGkfX5l2JbxHv2IKp6jAMaNcO5swh\np0MPBr+WzapOqRQE5jMiLIznoqII9FFBlYit2X2Gq6KiItq2bcv//vc/mjdvzl/+8hc++eQTYmNj\nqxSEiCPkF+azP3c/mcczycrNMh+PZ7Hv+D52/7Gb3X/spqFvQ9oEtaFN0zbEBMVwScglxIXG0axx\nM/drHa5ahfHQQ8z85088krG7uKBqVpu23B+vJf9E7MXu3c4//vgjrVu3pmXLlgAMHDiQRYsWlUq+\nIrZiNaycKDhBbkEueQV55OafeTzz+nj+cY6cPMKRU+aWfTKbIyfPPJ46Qm5+LmGNwwj3D6d54+aE\n+4cT7h/O5eGXEx0UTZugNgTUrz1J6dj0t7gvfjzzG/0EKqgScSk1Sr5ZWVlERJwb5woPD+eHH344\nb78xW9eVebxB6X8ZVPQvhT/vf97nDj7+vPOd97KKn1dwvYqOv9D5qvPdGIaB1bBiNawYhoFBideU\n/qzke8XHYcWwGlgpvV+htZBCayFFRtG559ZzzwuNQgqLzjye+aygqICCogJ8vXyp51UPX2/zsb53\nffM973rU86pHQ59A/Oo3p6F/Qy72aUikjx8NfRvi5+NHQ5+GZbZeM4HMIlh9+Dhw/ILfk7swcnOZ\n1/dqNrRthneKP083iWHcFBVUibiKGiXfynbDvfrSW+dexMWZm1v685/XzbohncHGP/YFZ7YLsgKn\nz2xYgdwzW90S0iKc/ttjeWNgMKGh+rsqYi/JyckkJydX6ZgaJd/mzZuzb9++4tf79u0jPDz8vP2O\nv/12TS4jIlVhtUJcHA3mzsX7+hBnRyNS6yUkJJCQkFD8+umnn67wmBol3y5dupCamkpGRgbNmjVj\n3rx5fPLJJ+ft19hbdzSJ2INhwLx5Zr4dPPjMm19/DfXrw1/+4tTYRKR8NcqK3t7evP7661x77bUU\nFRUxfPhwFVuJOEhaGowaZebaJk0gMREuvhiYPduc0crdqrNF6hDNcCXiZiwWePllePppOH3aTLwv\nvADDh4Pnwf3QsSP89hs0buzsUEXqJM1wJVILDR8OH3xgPh88GKZPh5CzQ7tz5sDttyvxirg4tXxF\n3MzGjTBoELz2GvTuXeKDoiJo1QoWL3bjOwpE3J9aviK10GWXwbZtZawOuHQpNGumxCviBnTLvYiL\nSkuD7OyyPytjWV6YNQvuv9+uMYmIbSj5irgYiwWeew46dICxYyt5UEYGrF9vjveKiMtTt7OIC1m/\nHkaMgK1bzdcWizmUW2ZLt6Q33oC77wY/P7vHKCI1p4IrERdgGPDgg/Dmm+bz6GiYOdO8d7dCJ05A\nZCRs2ABnFjkREeepTN5Tt7OICzg7H4aXF0yYAFu2VDLxArz/PlxxhRKviBtRy1fERRw7BpmZ5lhv\npVmt5gEzZ0KJuWVFxHl0q5GIC7JaKXNpv4AAc6uSFSvA1xeuvNImsYmIY6jbWcSB1q+Hzp1hXdlL\nXFfdjBnw0EOax1nEzSj5ijjA0aMwciR07w6//GLeSlRj27aZRVaDBtngZCLiSEq+InZ0dsm/du3M\nOTC8vc2CqnnzbHDy554zW70NGtjgZCLiSCq4ErGj48ehdWs4fBji483V/qpUUFWe9HTo0gX27DGX\nNRIRl1GZvKfkK2JnCxZATs6ZJf9s1dc0ejT4+8O0aTY6oYjYipKvSG108CC0bw/bt5dYS1BEXIUm\n2RBxkKNHYcoUKCx0wMWmToVhw5R4RdyY7vMVqQHDgM8+M+ueDh0yp1Z+5BE7XjAjAz76yGz1iojb\nUvIVqaa0NBg1Cr7+2nwdHw/XXmvniz71lDneGxxs5wuJiD0p+YpUw5Yt0LUrnDplFhu/+CLcc48N\nC6rK8uuvsHQppKba8SIi4ggquBKpBsOAXr0gPBxeftkBw6+GAX36mKst2LVfW0RqStXOInZ0+jTU\nr++giy1cCP/8J6SkgI+Pgy4qItWhameRGjIMcx6Lsjgs8Z44AWPGwOuvK/GK1BJKviLlSEuD6683\nF0LYv9+JgTz7rFnN1auXE4MQEVtSwZXIn1gs5jju00+bXctNmphrGDRr5oRgfvgB5swxu5tFpNZQ\n8hUpYeNGuPNO2LrVfD1kiIMKqspy4gQMHQpvvAFhYU4IQETsRclXpARvb9ixA6KjYeZMs7jYaR57\nDC6/HG65xYlBiIg9KPmKlHDJJbBkCfTs6eSV+t5/H5YvN5viIlLr6FYjEVfz/ffQrx98842N1h8U\nEUfSrUYiZbBY4Pnn4b77nB1JGX77zexmnjNHiVekFlPLV+qU9ethxIhzBVW//AKdOjk3pmL79kFC\ngnlP74MPOjsaEakmtXxFzsjJgQcegO7dzcQbHW0OqbpM4s3KgquuMldqUOIVqfWUfKVOeOklmD3b\nrGaeONFcGMGplcwlbdoE3brB/ffDo486OxoRcQB1O0udkJsL994Lkya52FDq55+bTfKZM3VLkUgt\noYUVRFxjTHsLAAAYW0lEQVTV8ePm2O7q1TBvHnTp4uyIRMRGNOYrdc769eadOi7LMOCTT8zBZh8f\n2LxZiVekDtIkG1Ir5OTA+PHmuG7btmZOq1fP2VGVYLXCf/8LU6aY9zq9/z5ceaWzoxIRJ6l2y3f+\n/Pl06NABLy8vNmoWHnESw4BPP4XY2HMFVbfcYr7vEn7/HV591RxonjTJ7Gr+6SclXpE6rtot306d\nOrFw4ULuv/9+W8YjUiV33w3vvWc+j483E7BTC6oMA7Zvh2XLzO3HH6FvX3jzTfMeXg8PJwYnIq6i\n2sm3Xbt2toxDpFr69IFFi+CFF2D4cPB0ZBXD6dOwZw+kppr93D/9ZG4NGsB118HIkfDFF9CokQOD\nEhF3oDFfcWu33grXXANBQTU4SVERFBTAqVPmPUnHj597PLsdPgwHDsDBg+bjvn3m88hIiImBjh3h\nnnvMW4bCw9XCFZELumDyTUxM5ODBg+e9P3XqVPr27Vv5q9x4Y+nX5Q3I6X3XisWF3i8sMlu1npR+\n3wMIutA5iorMAqeCgvIfAXx9oX598PcvvTVubG7BwRAVZfZth4aaCTYy0hxkFhGpogv+cqxYscIm\nF0ny9y9+ntCpEwmdOpXfMtD75b/vSrE46H3DMKeBnD4dBg82F7qv0nm8vMzE6uNT/qOXV9nnExGp\nhOTkZJKTk6t0TI0n2ejVqxcvvfQS//d//1f2BTTJhlRTero51fGyZebrxET4+mv16IqIa7PrJBsL\nFy4kIiKC77//nhtuuIHrr7++uqcSKaWoyFzyr0MHM/E2aQJvvWU+V+IVkdpA00uKyzEMs5W7cqXZ\n1Tx9OoSEODsqEZHK0dzO4rZ274a0NOjd29mRiIhUjZKviIiIg2lhBXFp6ekwYADs3evsSEREHEs3\nKYrDWSzmOO7TT5vzWjRoAB9+6OyoRGofD1Uo2l11e3aVfMWh1q+HESNg61bz9eDB8PLLzo1JpDbT\nsJ/91OQfNxrzFYf5/Xdo0QLy883JombOVEGViD3p99e+yvt+VXAlLmfyZDP5/vOfZneziNiPfn/t\nS8lXRETOo99f+6pJ8lW1s9icxQILFzo7ChFxVS1btmTlypVMmzaN++67z9nhOIUKrsSmShZULVkC\nN9zg7IhExNV4eHjg4eHB+PHjnR2K06jlKzZx9Ki5dnx8vJl4o6KgYUNnRyUicj6r1ersEJR8peY2\nbYJ27WDWLHN1vgkTzASckODsyETEVRmGQVJSEkOHDgUgIyMDT09P3n//fSIjI7n44ouZOnVqqf2f\ne+45WrduzUUXXcTtt99OTk5O8ee33norYWFhNGnShCuvvJJt27YVf3bXXXcxcuRI+vTpQ6NGjaq8\n/J89KPlKjcXEmOvQx8dDSgo8+6wqmUWkYmXdJ/vdd9+xa9cuVq5cyeTJk9m5cycAM2bMYPHixaxZ\ns4YDBw4QGBjI6NGji4+74YYb2L17N4cPH+ayyy5jyJAhpc77ySef8OSTT5KXl0d8fLx9/2CVoGpn\nsYm9eyE8HDz1zzkRl1HR7295c0SUd0hZ+1fn571Vq1a8/fbbrF27lt27d/PBBx+QkZFBVFQUmZmZ\nNGvWDICuXbvy6KOPcttttxEbG8sbb7zBVVddBcCBAweIjIzk9OnTeP7ph+fo0aMEBQVx7NgxGjdu\nzF133QXA3Llzqx7sBdSk2lkFV1IlJ0+Cn9/577do4fhYRKT2CQ0NLX7u5+dHXl4eAL/99ht/+9vf\nSiVab29vDh06RHBwMBMnTmTBggUcPny4eJ/s7GwaN26Mh4cH4eHhjv2DVEDtFKmUswVVnTub8zGL\niPszjLK3quzvKC1atGDZsmXk5OQUbydPniQsLIyPP/6YxYsXs3LlSo4dO0Z6evqZeF2311XJVy7I\nMGDevHMFVWlpsHats6MSkdqgKsnxgQceYMKECew9swza4cOHWbx4MQB5eXnUq1ePoKAgTpw4wYQJ\nE6p9HUdR8pVypaVBnz4wcCAcOnSuoCox0dmRiYi7O3uvb8miqwstVPDQQw/Rr18/evfujb+/P926\ndePHH38EYNiwYURGRtK8eXM6duxIt27dzjuvq63wpIIrKdeXX8Lf/gZNmsCLL8I996igSsSd6PfX\nvjS3s9jNK6/AoEEQEuLsSESkqvT7a19KviIich79/tqXFlaQajtbUPXaa86ORESk7lDLtw5LS4NR\no+Drr6FePdi+HVq1cnZUImIr+v21L7V8pUosFnjuOejQwUy8TZrA669DZKSzIxMRqRs0w1Ud9PDD\n8MYb5vMhQ+Dll1VQJSLiSOp2roPS0+Gmm8ykq3t2RWov/f7al6qdpcoMo/xJ1UWkdtDvr31pzFfK\nlJZmrjZUFiVeEXE106ZN47777nO567Zs2ZKVK1fa9Jpq+dZCFovZpfz00+aC9v/9r5KtSF2k31/b\naNWqFXPmzClezvAsLSkoxdavhxEjYOtW83XTppCfby52LyIirkHdzrXIww9D9+5m4o2OhuXL4cMP\nlXhFxPU8//zzhIeH4+/vT7t27Vi1ahVJSUkMHTq0eJ/333+fyMhILrroIqZMmULLli1ZtWoVAElJ\nSdx6660MHToUf39/LrnkElJTU5k2bRohISFERkayYsWK4nPt37+ffv360bRpU9q0acPbb79d/Nmf\nr/vBBx8UX3fq1Kl2+fMr+dYiQUHg4wMTJ8KWLapkFhHXtHPnTt544w02bNjA8ePHWb58OS1btiy1\n8tC2bdsYPXo0n3zyCQcOHODYsWPs37+/1HmWLFnCsGHDyMnJoXPnziSe+dHbv38/Tz75JPfff3/x\nvgMHDqRFixYcOHCABQsWMGHCBL755huA8647atQoPvroI/bv38+RI0fIzMy0+Xeg5FuLPP44bN4M\nU6ZAgwbOjkZEpGxeXl7k5+fz66+/YrFYaNGiBVFRUaXGSRcsWEC/fv3o3r07Pj4+TJ48+bxlAXv2\n7EliYiJeXl7ccsstHDlyhHHjxuHl5cXtt99ORkYGx48fZ9++faxbt47nn38eX19fLr30Uu69917e\nf/99gPOu27dvX3r06IGvry/PPPMMnnZYzk1jvm6osBC8y/h/rl49iI11fDwi4p48nrZNJabxVNWK\nulq3bs0rr7xCUlISv/76K9deey3Tp08vtc/+/fsJDw8vft2gQQOaNm1aap/g4OBSn1900UXFCbrB\nmRZIXl4e+/fvJygoiIYNGxbv36JFCzZs2HBebH++rp+f33nXtQUlXzezbh3cfz888wzcfLOzoxER\nd1bVpGlLgwYNYtCgQeTm5nL//ffzxBNPEB0dXfx5s2bN2LlzZ/HrU6dOceTIkWpdq1mzZvzxxx/k\n5eXRqFEjAPbu3VsqyZbcd/v27cWvT548We3rXoi6nd1ETg488ADEx5sFVa+84uyIRESqZ9euXaxa\ntYr8/Hzq1atH/fr18fLyKrXPgAED+Oqrr1i/fj0FBQUkJSVV+7apiIgIunfvzvjx48nPz+eXX37h\nnXfe4Y477jhv3wEDBrBkyRK+++47CgoKmDRpElartVrXvRAlXxdnGPDpp2Z38uzZZnfzhAmwdKmz\nIxMRqZ78/HzGjx/PxRdfTFhYGNnZ2UybNg04V/zUoUMHXnvtNQYOHEizZs1o3LgxwcHB1KtXr3i/\nP48BX+j1J598QkZGBs2aNaN///5Mnjy5+L7dkufq0KEDb7zxBoMHD6ZZs2YEBQURERFh8+9Ak2y4\nuPx8c/WhPXvMVu/s2eZrEZGK1Kbf37y8PAIDA9m9ezeRLrIEm1Omlxw7diyxsbFceuml9O/fn2PH\njlX3VHIB9erBW2+Z25o1SrwiUnd89dVXnDx5khMnTvDYY49xySWXuEziralqJ9/evXvz66+/snnz\nZmJiYoq7DMT2rroK7rsP7FDtLiLishYvXkzz5s1p3rw5e/bs4dNPP3V2SDZjk27nhQsX8vnnn/Ph\nhx+ef4Fa1O1hT0ePwgsvwD//CX5+zo5GRGoD/f7al9Pndn7nnXcYNGiQLU5V5xgGfPYZPPQQHDoE\nVis895yzoxIREXu6YPJNTEzk4MGD570/depU+vbtC8Czzz6Lr68vgwcPLvc8SUlJxc8TEhJISEio\nXrS1THo6jBoFy5aZr+PjocT0oiIi4gaSk5NJTk6u0jE16naeO3cu//73v1m5ciX1y5m9X90eZcvI\ngPbt4dQpaNLE7HIePlzjuiJiO/r9tS+ndDsvW7aMF198kdWrV5ebeKV8LVvCDTeY1cwvvwwhIc6O\nSEREHKXaLd82bdpQUFBAUFAQAN26dePNN988/wL6l1e5LBZzFSIREXvQ76991aTlq0k27MwwYNs2\n3Z8rIo5XV35/PT092b17N1FRUQ69rlMm2ZCKpadDnz7QuTOUmKdbRKTOa9myJatWrXJ2GE6j5GsH\nFgs8/7zZ2l22DBo2NKeHFBERU11plZdHydfGtmyByy6DcePMSuYhQ2DHDrjxRmdHJiLiGoYOHcre\nvXvp27cvjRs35sUXX+TWW28lLCyMJk2acOWVV7Jt27bi/e+66y5Gjx7NjTfeiL+/P5dffjlpaWml\nzrlixQpiYmIIDAzk73//u6P/SFWm5GtjAQFmd3N0NCxfDh9+qEpmEZGSPvjgA1q0aMGSJUvIzc1l\n7Nix3HDDDezevZvDhw9z2WWXMWTIkFLHzJs3j6SkJHJycmjdujUTJ04s9fl//vMfNmzYwC+//MJn\nn33G119/7cg/UpUp+dpYixZmV/OWLZCY6OxoRETcw1133UXDhg3x8fHhqaeeYvPmzeTm5gJmF3X/\n/v3p0qULXl5eDBkyhJSUlFLHjxs3Dn9/fyIiIujVq9d5n7saJd8aKG+4okcPaNDAsbGIiFSZh4dt\nthoqKipi3LhxtG7dmoCAAFq1agVAdnZ28T4hJboQGzRoQF5eXqlzhIaGFj/38/M773NXo+RbDRaL\nOf/yzTeXn4BFRFyeYdhmq4aSC91//PHHLF68mJUrV3Ls2DHS09PPhFd7f2CVfKto/XqzoGr8eFi8\nGNaudXZEIiLuJyQkhD1nbgPJzc2lXr16BAUFceLECSZMmFBq36omYXdI2kq+lXT0KIwcCd27w9at\n5wqqrrjC2ZGJiLif8ePHM2XKFAIDA8nJySEyMpLmzZvTsWNHunXrVqpl7OHhUer12ffKel7e/q5G\nM1xV0vTp8Oij4O0Njz9urrurcV0RcWW15ffXVWl6SQewWOCBB+CRRzRVpIi4h9ry++uqlHxFROQ8\n+v21L83tbEPr14OL35stIiJuTsn3jJIFVXfdBceOOTsiERGprbydHYCzGQZ89hk89BAcOmQWVN1z\nD/j6OjsyERGprep88n3wQXjjDfN5fDzMnq2CKhERsa863+18yy0QGAj//jesWaPEKyIi9qdqZyA3\nFxo3dnYUIiK25Q6/v+5M1c6VcPQolDfPthKviIg4Uq1PvoYBn34K7drBpEnOjkZERFq2bImfnx+N\nGzcmNDSUoUOHcvz48QqPS0hIYM6cOWWeb+XKlaXemzt3Lle48Py/tTr5pqXB9dfDoEFmJfPGjVBY\n6OyoRETqNg8PD5YsWUJubi6bN29my5YtTJkypVLHlTVnszvM5fxntTL5GgY8/zx07GhOmNGkiVlQ\ntWqVeSuRiIi4hpCQEHr37s2vv/4KwPfff0/37t0JDAwkLi6O1atXOzlC+6iVydfDA375BU6dgiFD\nYMcOuPde8KyVf1oREfdztiApMzOTZcuW0bVrV7KysrjxxhuZNGkSOTk5vPTSSwwYMIAjR444OVrb\nq7XtwOnTzZmqEhOdHYmIiOsZk5pKSnlVqFUQ16gRr7RpU6VjDMPg5ptvxsPDg7y8PG666SYmTpzI\nSy+9RJ8+fbjuuusAuOaaa+jSpQv/+c9/GDZsWI1jdSW1ti0YEqLEKyLiijw8PFi0aBHHjx8nOTmZ\nVatW8fPPP/Pbb78xf/58AgMDi7fvvvuOgwcPXvB83t7eWCyWUu9ZLBZ8fHzs+ceoEbdu+aalmdNC\nTp0KnTo5OxoREfdR1daqvfTs2ZMHH3yQJ554gmuvvZahQ4fy1ltvVekcLVq0ID09vdR76enptGzZ\n0oaR2pZbtnwtFnjuOXM2qiVLzMXtRUTEPY0ZM4Yff/yRHj168NVXX7F8+XKKioo4ffo0ycnJZGVl\nFe9rsVg4ffp08WaxWLj99tt55ZVX2LlzJ4ZhsGHDBt59910GDhzoxD/Vhbldy3fdOrj/fti61Xw9\nZAi8/LJzYxIRkeq76KKLuPPOO5k+fTqLFy9m7NixDBo0CC8vL7p27crMmTOL9x05ciQjR44sfn3H\nHXfw3nvvkZOTQ9++fTl06BDh4eFMnTqV3r17O+OPUyluNb1kbi5ERJjL/UVHw8yZGtcVESmPppe0\nr5pML+lWyRdg1izYtw/++U9o0MBmpxURqXWUfO2rTiVfERGpHP3+2letW1jBYoH33zdnqhIREalt\nXC75rlsHl10Gd94Jc+c6OxoRERHbc5nke/QojBwJ8fFmJXNUFLRo4eyoREREbM8lbjXasQMSEsyV\nh7y9zft2VVAlIiK1lUsUXBUWQpcu0KgRzJ5tTp4hIiI1o4Ir+6oV1c6HDsHFF2vlIRERW3G3NW7d\nkcOT75NPPsnixYvx8PCgadOmzJ07l4iIiAqDOHYMAgKqc0URERHXZ9dbjR5//HE2b95MSkoKN998\nM08//fQF9z9bUNWuHfzxR3WvWnslJyc7OwS3oe+qcvQ9VZ6+q8rR92Q71U6+jRs3Ln6el5fHRRdd\nVO6+8+aZSXfWLMjOhjVrqnvV2kt/qStP31Xl6HuqPH1XlaPvyXZqVO08ceJEPvjgA/z8/Pj+++/L\n3e/swhLx8SqoEhERuWDLNzExkU6dOp23ffXVVwA8++yz7N27l7vuuouHH3643PM0aQL//rfZ4lXi\nFRGRus4m1c579+6lT58+bD27zl/JC6jaTkRE6piKUmu1u51TU1Np06YNAIsWLaJz587VCkBERKSu\nqXbL95ZbbmHnzp14eXkRHR3NzJkzCQ4OtnV8IiIitY7dJ9kQERGR0hwyn9STTz7JpZdeSlxcHFdf\nfTX79u1zxGXdztixY4mNjeXSSy+lf//+HDt2zNkhuaT58+fToUMHvLy82Lhxo7PDcUnLli2jXbt2\ntGnThueff97Z4bike+65h5CQEDp16uTsUFzevn376NWrFx06dKBjx47MmDHD2SG5pNOnT9O1a1fi\n4uJo374948ePL39nwwGOHz9e/HzGjBnG8OHDHXFZt7N8+XKjqKjIMAzDeOKJJ4wnnnjCyRG5pu3b\ntxs7d+40EhISjJ9//tnZ4bicwsJCIzo62khPTzcKCgqMSy+91Ni2bZuzw3I5a9asMTZu3Gh07NjR\n2aG4vAMHDhibNm0yDMMwcnNzjZiYGP2dKseJEycMwzAMi8VidO3a1fj222/L3M8hLd+qTMhRlyUm\nJuJ5ZnLrrl27kpmZ6eSIXFO7du2IiYlxdhgu68cff6R169a0bNkSHx8fBg4cyKJFi5wdlsu54oor\nCAwMdHYYbiE0NJS4uDgAGjVqRGxsLPv373dyVK7Jz88PgIKCAoqKiggKCipzP4ctYzBx4kRatGjB\ne++9x7hx4xx1Wbf1zjvv0KdPH2eHIW4oKyur1Dzr4eHhZGVlOTEiqU0yMjLYtGkTXbt2dXYoLslq\ntRIXF0dISAi9evWiffv2Ze5ns+Rrqwk5aruKvicwvytfX18GDx7sxEidqzLfk5RN99aLveTl5XHL\nLbfw6quv0qhRI2eH45I8PT1JSUkhMzOTNWvWlDslZ42mlyxpxYoVldpv8ODBdbpFV9H3NHfuXP77\n3/+ycuVKB0Xkmir790nO17x581JFjfv27SM8PNyJEUltYLFYGDBgAHfccQc333yzs8NxeQEBAdxw\nww1s2LCBhISE8z53SLdzampq8fMLTchR1y1btowXX3yRRYsWUb9+fWeH4xYM3Sl3ni5dupCamkpG\nRgYFBQXMmzePfv36OTsscWOGYTB8+HDat2/PmDFjnB2Oy8rOzubo0aMAnDp1ihUrVpSb7xxyn68m\n5KicNm3aUFBQUDxA361bN958800nR+V6Fi5cyD/+8Q+ys7MJCAigc+fOLF261NlhuZSlS5cyZswY\nioqKGD58+IVveaijBg0axOrVqzly5AjBwcFMnjyZu+++29lhuaS1a9fSs2dPLrnkkuJhjWnTpnHd\nddc5OTLXsmXLFu68806sVitWq5WhQ4cyduzYMvfVJBsiIiIO5rBqZxERETEp+YqIiDiYkq+IiIiD\nKfmKiIg4mJKviIiIgyn5ioiIOJiSr4iIiIMp+YqIiDjY/wMIMr2/EHRuRAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110f2f910>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.random.standard_normal((20,20))\n",
      "def non_linearities_imshows(a):\n",
      "    plt.imshow(a, label=\"linear\", cmap=plt.cm.gray_r, interpolation=\"none\")\n",
      "    print(\"linear\")\n",
      "    plt.colorbar()\n",
      "    plt.show()\n",
      "    plt.imshow(1./(1+np.exp(-4*a)), label=\"sigmoid\", cmap=plt.cm.gray_r, interpolation=\"none\") # /!\\ mult by 4\n",
      "    print(\"sigmoid\")\n",
      "    plt.colorbar()\n",
      "    plt.show()\n",
      "    plt.imshow(np.tanh(4*a), label=\"tanh\", cmap=plt.cm.gray_r, interpolation=\"none\") # /!\\ mult by 4\n",
      "    print(\"tanh\")\n",
      "    plt.colorbar()\n",
      "    plt.show()\n",
      "    plt.imshow((a+abs(a))/2, label=\"ReLU\", cmap=plt.cm.gray_r, interpolation=\"none\")\n",
      "    print(\"Rectified Linear Unit\")\n",
      "    plt.colorbar()\n",
      "    plt.show()\n",
      "\n",
      "non_linearities_imshows(a)\n",
      "#non_linearities_imshows(digits.images[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "linear\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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boqOjERMTg9mzZ3d5Q1lVVRVmzZqFiIgITJo0Ca+++qpF+3ZImefn5zti2AGv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\nBVjmREQqwDInIlKB/wdOdonD6tcfvAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11056fc90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sigmoid\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IaCzpyocHDx4U5d5++21RTvpuTv379xflysrKRLlLL71UlPv2228Rj9rSaDSo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\nWNZERCrwf/ABtLhejQ12AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11054cdd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "tanh\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5q6sLer0esbGxSE1NRXd395Db/f73v8eMGTOQkJCAe+65x66LFBnmREQKd4e5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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11109e410>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Rectified Linear Unit\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1135d1550>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Backpropagation"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Intro to backprop"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's say we want to maximize $f = g*z$ and we also have $g = x + y$  \n",
      "Previously, we saw the case for one layer (logistic regression), that we can minimize $f$ by following the gradient in both these directions $\\frac{\\partial f(q,z)}{\\partial g} = z$ and $\\frac{\\partial f(g,z)}{\\partial z} = g$   \n",
      "Now, what does it mean to follow $g$? We have $\\frac{\\partial g(x,y)}{\\partial x} = 1$ and $\\frac{\\partial g(x,y)}{\\partial y} = 1$  \n",
      "We want to know how we have to change $x, y, z$ so that $f$ increases. Just as previously, we will take a gradient step $x = x + \\lambda \\frac{\\partial f}{\\partial x}$; $y = y + \\lambda  \\frac{\\partial f}{\\partial y}$; $z = z + \\lambda \\frac{\\partial f}{\\partial z}$. For $z$, we already know the gradient, it's $g$, so it's $x+y$. So we update $z$ with $z = z + \\lambda \\times (x+y)$. What about for $x$ and for $y$? We just apply the chain rule: $\\frac{\\partial f(g,z)}{\\partial x} = \\frac{\\partial f(g,z)}{\\partial g}\\frac{\\partial g(x,y)}{\\partial x}$, so for $x$ we update it with $x = x + \\lambda \\times \\frac{\\partial f(g,z)}{\\partial g} \\times \\frac{\\partial g(x,y)}{\\partial x} = x + z \\times 1$, and for $y$ we update it with $y = y + \\lambda \\times z \\times 1)$ too.\n",
      "\n",
      "So if you have not understood the above, you can try reading more details about backprop [here](http://karpathy.github.io/neuralnets/). Otherwise, let's apply backprop to our logistic regression to which we had a layer:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Backprop on our logistic regression, with one additional hidden layer"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The forward pass of our logistic regression is currently: $$X \\rightarrow W_{logistic} \\cdot X \\rightarrow softmax$$\n",
      "\n",
      "Let's consider that we add a hidden layer with a non-linearity $\\sigma$ to our logistic regression, it becomes: \n",
      "$$X \\rightarrow W_{hidden} \\cdot X \\rightarrow \\sigma(W_{hidden} \\cdot X) \\rightarrow W_{logistic} \\cdot \\sigma(W_{hidden} \\cdot X) \\rightarrow softmax$$\n",
      "\n",
      "The intuition behind back-propagation is that we keep doing what we did for optimising our previous loss function but for all the layers. The cool thing is that thanks to the chain rule of derivatives, this is an efficient process! Previously our loss (error) was the sum of the errors for all points, and it's a function of our weights: $$E(W) = \\sum_{n=1}^N E_n(W)$$\n",
      "\n",
      "Now if we add a hidden layer with a non-linearity $\\sigma$ our predictions become $$P(y_i=j|x_n; W) = softmax(W_{logistic}, \\sigma(W_{hidden}, x_n))[j]$$\n",
      "\n",
      "Our loss function code changes to take this formula into account. And that's it for the forward pass. Let's note $a_j = \\sum_i w_{ji}z_i$ for the pre-activation units, also called pre-synaptic, and $z_i = \\sigma(a_i)$ are the activations (or the layer below in the previous $a_j$ formula), also called post-synaptic. Now if we keep using the gradient updates that we saw before, we can only apply them to the activated hidden units, and we don't know how we should change $W_{hidden}$. We do that through the chain rule: $$\\frac{\\partial E_n}{\\partial w_{ji}} = \\frac{\\partial E_n}{\\partial a_j}\\frac{\\partial a_j}{\\partial w_{ji}}$$\n",
      "\n",
      "That means, we can back-propagate the errors through the partial derivatives: we can compute how much each of the hidden units contributes to the error, and have a gradient w.r.t. its weights. In the code, I called *err_h* the errors at the hidden units, and *err_o* the errors are the output, *act_xi* the activations of the hidden units, *act_d* the activation derivatives, and *backprop_o* the backpropagation of the output error. The algorithm looks like:\n",
      "1. Do a forward pass to get the output errors $\\delta_k$ (*err_o*)\n",
      "2. Back-propagate the errors with $\\sum_k w_{kj} \\delta_k$ (*backprop_o*) and multiply these backpropagated errors by the derivative of the activations $\\sigma'(a_j)$ (*act_d*) to get the error at this level: $\\delta_j = \\sigma'(a_j)\\sum_k w_{kj} \\delta_k$ (*err_h*)\n",
      "3. Multiply by the activations to get the local gradient: $\\frac{\\partial E_n}{\\partial w_{ji}} = \\delta_j z_i$\n",
      "\n",
      "Everything I told you about back-prop now generalizes from 1 to K hidden layers, because the chain rule $\\frac{\\partial E}{\\partial W} = \\frac{\\partial E}{\\partial a_j}\\frac{\\partial a_j}{\\dots }\\frac{\\dots }{\\partial W}$ stays true. So if we had more hidden layers, we would repeat steps 2 and 3 as much as needed to get back to the input features ($z_i == x_{ni}$).\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Changing our Logistic-regression into a \"Neural\" Net"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def relu_u(W, x, clip=6):\n",
      "    \"\"\"\n",
      "    ReLU unit function: max(0, W.x)\n",
      "    In [1]: a = np.random.standard_normal((1000,1000))\n",
      "    In [2]: %timeit (a+abs(a))/2\n",
      "    100 loops, best of 3: 4.27 ms per loop\n",
      "    In [3]: %timeit a[a<0]=0\n",
      "    1000 loops, best of 3: 736 \u00b5s per loop\n",
      "    \"\"\"\n",
      "    #tmp = np.dot(x, W)\n",
      "    #tmp[tmp < 0] = 0\n",
      "    #return np.append(tmp, 1)  # for the biases of the next layer\n",
      "    return np.append(np.clip(np.dot(x, W), 0, clip), 1)\n",
      "\n",
      "def relu_d(W, x, clip=4):\n",
      "    #return np.asarray(np.dot(x, W) >= 0, dtype='float')\n",
      "    tmp = np.dot(x, W)\n",
      "    return np.asarray((tmp >= 0) & (tmp < clip), dtype='float')\n",
      "\n",
      "def tanh_u(W, x):\n",
      "    tmp = np.dot(x, W)\n",
      "    return np.append(np.tanh(tmp), 1)  # for the biases of the next layer\n",
      "\n",
      "def tanh_d(W, x):\n",
      "    tmp = tanh_u(W, x)[:-1]\n",
      "    return 1 - tmp**2\n",
      "\n",
      "\n",
      "class NeuralNet:\n",
      "    def __init__(self):\n",
      "        self.name = \"Neural Net\"\n",
      "        #self.act_u = relu_u  # try ReLUs, but with a small learning rate\n",
      "        #self.act_d = relu_d  # (think about why)\n",
      "        self.act_u = tanh_u\n",
      "        self.act_d = tanh_d\n",
      "        \n",
      "    def loss(self):\n",
      "        \"\"\" Negative log-likelihood of logistic regression using the hidden layer \"\"\"\n",
      "        l = 0.\n",
      "        for i, xi in enumerate(self.x):\n",
      "            p_i = self.pred(xi)\n",
      "            l += np.log(p_i[self.y[i]])\n",
      "        return - l / self.x.shape[0]\n",
      "            \n",
      "    def one_epoch_of_backprop_SGD(self, learning_rate=0.01):\n",
      "        \"\"\" applying the backprop equations explained above \"\"\"\n",
      "        for i, xi in enumerate(self.x):\n",
      "            act_xi = self.act_u(self.W_hid, xi)\n",
      "            p_i = softmax(self.W_log, act_xi)\n",
      "            err_o = np.zeros(p_i.shape)\n",
      "            err_o[self.y[i]] = 1\n",
      "            err_o = err_o - p_i\n",
      "            backprop_o = np.dot(err_o, self.W_log[:-1,:].T)\n",
      "            act_d = self.act_d(self.W_hid, xi)\n",
      "            err_h = act_d * backprop_o\n",
      "            self.W_hid += learning_rate * np.outer(xi, err_h)\n",
      "            self.W_log += learning_rate * np.outer(act_xi, err_o)\n",
      "    \n",
      "    def pred(self, xi):\n",
      "        return softmax(self.W_log, self.act_u(self.W_hid, xi))\n",
      "                    \n",
      "    def train(self, x_train, y_train, n_hidden=42):  # try different numbers of hidden units, e.g. 4, 64, 128, 200\n",
      "        self.x = np.hstack((x_train, np.ones((x_train.shape[0], 1))))\n",
      "        self.y = y_train\n",
      "        self.n_classes = len(set(y_train))\n",
      "        self.n_hidden = n_hidden\n",
      "        self.W_hid = np.random.random((x_train.shape[1] + 1, self.n_hidden)) - 0.5\n",
      "        self.W_hid *= 2 * 6. / (x_train.shape[1] + self.n_hidden)\n",
      "        self.W_log = np.zeros((self.n_hidden + 1, self.n_classes))  # +1 for biases\n",
      "        print(\"x shape:\", self.x.shape)\n",
      "        print(\"W_hid shape:\", self.W_hid.shape)\n",
      "        print(\"W_log shape:\", self.W_log.shape)\n",
      "        tmp_loss = self.loss()\n",
      "        print(\"training loss before training:\", tmp_loss)\n",
      "        print(\"prediction on x[0] before training:\", self.pred(self.x[0]))\n",
      "        # training with SGD\n",
      "        losses = [tmp_loss]\n",
      "        l_r = 0.01\n",
      "        for _ in xrange(10):  # 10 epochs\n",
      "            self.one_epoch_of_backprop_SGD(learning_rate=l_r)\n",
      "            losses.append(self.loss())\n",
      "            # play with the learning rate schemes\n",
      "        print(\"best loss:\", losses[-1])\n",
      "        print(\"training loss after training:\", self.loss())\n",
      "        print(\"prediction on x[0] after training:\", self.pred(self.x[0]))\n",
      "        print(\"true x[0] label:\", self.y[0])\n",
      "        plt.plot(np.arange(len(losses)), np.log(losses), linewidth=1)\n",
      "        plt.ylabel(\"log of the loss function\")\n",
      "        plt.xlabel(\"#epochs\")\n",
      "        plt.show()\n",
      "        plt.imshow(self.W_hid, interpolation='none')\n",
      "        plt.colorbar()\n",
      "        plt.show()\n",
      "        #plt.imshow(self.W_log, interpolation='none')\n",
      "        #plt.colorbar()\n",
      "        #plt.show()\n",
      "        \n",
      "    def score(self, x_test, y_test):\n",
      "        x = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\n",
      "        y = y_test\n",
      "        c = 0\n",
      "        for i, xi in enumerate(x):\n",
      "            a = np.argmax(self.pred(xi))\n",
      "            if a == y[i]:\n",
      "                c += 1\n",
      "        return c * 1. / y.shape[0]\n",
      "    \n",
      "nn = NeuralNet()\n",
      "nn.train(x_train, y_train)\n",
      "print(\"Train set accuracy:\", nn.score(x_train, y_train))\n",
      "print(\"Test set accuracy:\", nn.score(x_test, y_test))\n",
      "#nn.train(x_train, y_train, n_hidden=10)\n",
      "#print \"Train set accuracy:\", nn.score(x_train, y_train)\n",
      "#print \"Test set accuracy:\", nn.score(x_test, y_test)\n",
      "print(\"you can also regularize a neural net, just change the loss function appropriately\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x shape: (1527, 65)\n",
        "W_hid shape: (65, 42)\n",
        "W_log shape: (43, 10)\n",
        "training loss before training: 2.30258509299\n",
        "prediction on x[0] before training: [ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n",
        "best loss:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.0253889805665\n",
        "training loss after training: 0.0253889805665\n",
        "prediction on x[0] after training: [  3.83913679e-05   8.68270986e-01   3.32674656e-04   1.51426304e-06\n",
        "   5.77964865e-03   1.86531340e-05   2.65499370e-03   2.51086136e-05\n",
        "   1.22848931e-01   2.90992410e-05]\n",
        "true x[0] label: 1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WIyNZsw74K4ocQCG//GJvQJM7Wg8NzS/1bt2k8uVNJwSQiyIH8JssS9q+Pb/U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\nZFmWDhw4oIMHDyoyMlJdu3b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       "text": [
        "<matplotlib.figure.Figure at 0x110f38b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BwoULMX36dCxcuDBkQ9vgXh+aQJTaRMuWLY2lb0lJiWvHiiSJHDlyRPTvVBLp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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110ef2550>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Train set accuracy: 0.998035363458\n",
        "Test set accuracy: 0.97037037037\n",
        "you can also regularize a neural net, just change the loss function appropriately\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Bonus:_ With more weights, this neural net is even more prone to overfitting. So you could regularize it."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "_Bonus:_ You can also add another hidden layer."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4. Graduate to MNIST and Theano"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Intro to Theano"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Theano works by compiling symbolic expressions into native code (on CPUs or GPUs). There is a nice \"getting started\" [Theano tutorial](http://nbviewer.ipython.org/github/craffel/theano-tutorial/blob/master/Theano%20Tutorial.ipynb) and a more thorough tutorial here http://deeplearning.net/software/theano/tutorial/ Everything you should need for the moment is here http://deeplearning.net/software/theano/library/tensor/basic.html\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from theano import tensor as T\n",
      "A = T.fmatrix()\n",
      "B = T.fmatrix()\n",
      "C = T.dot(A, B)\n",
      "C"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Using gpu device 0: GeForce GT 650M\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "dot.0"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.random.random((10,10))\n",
      "B = np.random.random((10,10))\n",
      "C = T.dot(A, B)\n",
      "C.eval()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "array([[ 2.49987768,  3.46830327,  3.07533679,  3.28263693,  3.52006918,\n",
        "         2.81629526,  3.50309921,  2.48141154,  4.19651885,  3.27780272],\n",
        "       [ 1.70199903,  2.00788938,  1.74255066,  1.61437016,  1.83195921,\n",
        "         1.17366047,  1.54721088,  1.0543538 ,  1.85787448,  2.13366925],\n",
        "       [ 2.57912841,  2.53388569,  2.52165203,  3.13951882,  2.59604248,\n",
        "         2.49420427,  2.86811637,  1.95585705,  3.64772433,  2.71894285],\n",
        "       [ 1.65627613,  1.69303027,  1.29031643,  1.75713726,  1.45599642,\n",
        "         1.6585249 ,  1.23043086,  1.07044738,  1.84521083,  1.72626196],\n",
        "       [ 1.61517609,  2.49009226,  2.00571427,  2.26128717,  2.07083152,\n",
        "         1.6539112 ,  1.97799148,  1.57657089,  2.51517353,  2.21145721],\n",
        "       [ 1.65610118,  1.61789874,  1.7242132 ,  1.90119529,  1.96550927,\n",
        "         1.46273452,  1.55532116,  0.9811569 ,  2.17289324,  1.58394601],\n",
        "       [ 1.94265607,  3.00453935,  1.91916286,  2.93620479,  2.81513856,\n",
        "         2.02763359,  2.90217799,  2.03910296,  2.92258548,  3.04995599],\n",
        "       [ 1.32328403,  2.54435891,  1.09419405,  1.52412963,  1.90384414,\n",
        "         1.41099655,  1.27805053,  1.15345173,  1.67611008,  2.30179732],\n",
        "       [ 1.74330342,  3.39876145,  2.35130411,  2.57335844,  2.4635886 ,\n",
        "         2.55897828,  2.49926516,  2.11455371,  3.71868673,  2.75911756],\n",
        "       [ 2.13621873,  2.85752605,  2.03129205,  2.87908882,  2.70363845,\n",
        "         2.1858482 ,  2.81973067,  2.07834169,  2.94183379,  2.96044698]])"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from theano import shared\n",
      "A = shared(A)\n",
      "B = shared(B)\n",
      "A"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "<TensorType(float64, matrix)>"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "C = T.dot(A, B)\n",
      "C.eval()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "array([[ 2.49987768,  3.46830327,  3.07533679,  3.28263693,  3.52006918,\n",
        "         2.81629526,  3.50309921,  2.48141154,  4.19651885,  3.27780272],\n",
        "       [ 1.70199903,  2.00788938,  1.74255066,  1.61437016,  1.83195921,\n",
        "         1.17366047,  1.54721088,  1.0543538 ,  1.85787448,  2.13366925],\n",
        "       [ 2.57912841,  2.53388569,  2.52165203,  3.13951882,  2.59604248,\n",
        "         2.49420427,  2.86811637,  1.95585705,  3.64772433,  2.71894285],\n",
        "       [ 1.65627613,  1.69303027,  1.29031643,  1.75713726,  1.45599642,\n",
        "         1.6585249 ,  1.23043086,  1.07044738,  1.84521083,  1.72626196],\n",
        "       [ 1.61517609,  2.49009226,  2.00571427,  2.26128717,  2.07083152,\n",
        "         1.6539112 ,  1.97799148,  1.57657089,  2.51517353,  2.21145721],\n",
        "       [ 1.65610118,  1.61789874,  1.7242132 ,  1.90119529,  1.96550927,\n",
        "         1.46273452,  1.55532116,  0.9811569 ,  2.17289324,  1.58394601],\n",
        "       [ 1.94265607,  3.00453935,  1.91916286,  2.93620479,  2.81513856,\n",
        "         2.02763359,  2.90217799,  2.03910296,  2.92258548,  3.04995599],\n",
        "       [ 1.32328403,  2.54435891,  1.09419405,  1.52412963,  1.90384414,\n",
        "         1.41099655,  1.27805053,  1.15345173,  1.67611008,  2.30179732],\n",
        "       [ 1.74330342,  3.39876145,  2.35130411,  2.57335844,  2.4635886 ,\n",
        "         2.55897828,  2.49926516,  2.11455371,  3.71868673,  2.75911756],\n",
        "       [ 2.13621873,  2.85752605,  2.03129205,  2.87908882,  2.70363845,\n",
        "         2.1858482 ,  2.81973067,  2.07834169,  2.94183379,  2.96044698]])"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you want to know more about \"shared-correctness\", you should read: http://deeplearning.net/software/theano/tutorial/aliasing.html"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "MNIST deep learning in one file"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use `python dnn.py` from this repo https://github.com/SnippyHolloW/DL4H"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5. LUCID words classification"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use `python lucid.py` from this repo https://github.com/SnippyHolloW/DL4H"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "6. Some of the \"bonus\" questions solutions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# solution for the first bonus question\n",
      "alpha = 0.001    \n",
      "    def loss_l2(self, W):\n",
      "        tmp_W = W.reshape((self.x.shape[1], self.n_classes))\n",
      "        return self.loss(W) + alpha*np.sum([np.linalg.norm(tmp)**2 for tmp in tmp_W])\n",
      "    \n",
      "    def loss_derivative_l2(self, W):\n",
      "        return self.loss_derivative(W) + 2*alpha*W    \n",
      "    \n",
      "    # in train:\n",
      "        tmp = fmin_l_bfgs_b(self.loss_l2, self.W.reshape((self.W.shape[0]*self.W.shape[1],)), fprime=self.loss_derivative_l2)\n",
      "        \n",
      "    \n",
      "# solution for the second bonus question\n",
      "    def loss_hinge_l2(self, W):\n",
      "        l = 0.\n",
      "        tmp_W = W.reshape((self.x.shape[1], self.n_classes))\n",
      "        for i, xi in enumerate(self.x):\n",
      "            tmp_max = -1.\n",
      "            good = np.dot(tmp_W[:,self.y[i]], xi)\n",
      "            for j in xrange(self.n_classes):\n",
      "                if j == self.y[i]:\n",
      "                    continue\n",
      "                delta = good - np.dot(tmp_W[:,j], xi)\n",
      "                if delta > tmp_max:\n",
      "                    tmp_max = delta\n",
      "            l += max(0, 1 + tmp_max)\n",
      "        return - l / self.x.shape[0] + alpha*np.sum([np.linalg.norm(tmp)**2 for tmp in tmp_W])\n",
      "\n",
      "    def loss_derivative_hinge_l2(self, W):\n",
      "        d = np.zeros((self.x.shape[1], self.n_classes)) # that's the Jacobian\n",
      "        tmp_W = W.reshape((self.x.shape[1], self.n_classes))\n",
      "        for i, xi in enumerate(self.x):\n",
      "            for j in xrange(self.n_classes):\n",
      "                if j == self.y[i]:\n",
      "                    pass\n",
      "                else:\n",
      "                    d[:,j] += -np.dot(tmp_W[:,j], xi) * xi               \n",
      "        d = d.reshape(W.shape) # and we flatten it\n",
      "        return - d / self.x.shape[0] + alpha*np.sum([np.linalg.norm(tmp)**2 for tmp in tmp_W])\n",
      "    \n",
      "    def score(self, x_test, y_test):\n",
      "        x = np.hstack((x_test, np.ones((x_test.shape[0], 1))))\n",
      "        y = y_test\n",
      "        c = 0\n",
      "        for i, xi in enumerate(x):\n",
      "            #a = np.argmax(softmax(self.W, xi))\n",
      "            a = np.argmax(np.dot(self.W.transpose(), xi))\n",
      "            if a == y[i]:\n",
      "                c += 1\n",
      "        return c * 1. / y.shape[0]\n",
      "    \n",
      "    # in train:\n",
      "        tmp = fmin_l_bfgs_b(self.loss_hinge_l2, self.W.reshape((self.W.shape[0]*self.W.shape[1],)),\n",
      "                            fprime=self.loss_derivative_hinge_l2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}