MachineLearningCap12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#http://yann.lecun.com/exdb/mnist\n",
    "\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "\n",
    "def load_mnist(path, kind='train'):\n",
    "    \"\"\"Load MNIST data from 'path'\"\"\"\n",
    "    labels_path = os.path.join(path,'%s-labels.idx1-ubyte'%kind)\n",
    "    images_path = os.path.join(path,'%s-images.idx3-ubyte'%kind)\n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic,n = struct.unpack('>II',lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath,dtype=np.uint8)\n",
    "    with open(images_path,'rb') as imgpath:\n",
    "        masgic,num,rows,cols = struct.unpack(\">IIII\",imgpath.read(16))\n",
    "        images = np.fromfile(imgpath,dtype=np.uint8).reshape(len(labels),784)\n",
    "\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 60000, columns: 784\n"
     ]
    }
   ],
   "source": [
    "X_train,y_train = load_mnist('/Users/antelInvestigacion/IA/mnist',kind='train')\n",
    "print('Rows: %d, columns: %d'%(X_train.shape[0], X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 10000, columns: 784\n"
     ]
    }
   ],
   "source": [
    "X_test,y_test = load_mnist('/Users/antelInvestigacion/IA/mnist',kind='t10k')\n",
    "print('Rows: %d, columns: %d'%(X_test.shape[0], X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hw7ds+tPzC0xsf9PWW28d2mwKvxSrHsTO9pD5AtAvvfRSiG1KdqeddirshVWD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\n/wHTK4KPiGYqugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169eaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig,ax = plt.subplots(nrows=2,ncols=5,sharex=True,sharey=True)\n",
    "ax = ax.flatten()\n",
    "for i in range(10):\n",
    "    img = X_train[y_train==i][0].reshape(28,28)\n",
    "    ax[i].imshow(img,cmap='Greys',interpolation='nearest')\n",
    "ax[0].set_xticks([])\n",
    "ax[0].set_yticks([])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KHeGiShIiIhJVwnoPasyYMfTs2RPwpuoG4+px79699r6WGVv95ptvqFq16lm/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\nxEn/D4pZX87LFMIKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115994588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ejemplos distintos del mismo número\n",
    "fig, ax = plt.subplots(nrows=5,ncols=5,sharex=True,sharey=True)\n",
    "ax = ax.flatten()\n",
    "for i in range(25):\n",
    "    img = X_train[y_train==7][i].reshape(28,28)\n",
    "    ax[i].imshow(img,cmap='Greys',interpolation='nearest')\n",
    "ax[0].set_xticks([])\n",
    "ax[0].set_yticks([])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Si se quisiera guardar y después volver a cargar en formato CSV... (no lo voy a hacer porque ocupa mucho espacio;\n",
    "#lo dejo comentado)\n",
    "\n",
    "#np.savetxt('train_img.csv',X_train,fmt='%i',delimiter=',')\n",
    "#np.savetxt('train_labels.csv',y_train,fmt='%i',delimiter=',')\n",
    "#np.savetxt('test_img.csv',X_test,fmt='%i',delimiter=',')\n",
    "#np.savetxt('test_labels.csv',y_test,fmt='%i',delimiter=',')\n",
    "\n",
    "#X_train = np.genfromtxt('train_img.csv',dtype=int,delimiter=',')\n",
    "#y_train = np.genfromtxt('train_labels.csv',dtype=int,delimiter=',')\n",
    "#X_test = np.genfromtxt('test_img.csv',dtype=int,delimiter=',')\n",
    "#y_test = np.genfromtxt('test_labels.csv',dtype=int,delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.special import expit\n",
    "import sys\n",
    "\n",
    "class NeuralNetMLP(object):\n",
    "    def __init__(self, n_output, n_features, n_hidden=30, l1=0.0, l2=0.0, epochs=500, eta=0.001,\n",
    "                alpha=0.0, decrease_const=0.0, shuffle=True, minibatches=1, random_state=None):\n",
    "        np.random.seed(random_state)\n",
    "        self.n_output = n_output\n",
    "        self.n_features = n_features\n",
    "        self.n_hidden = n_hidden\n",
    "        self.w1, self.w2 = self._initialize_weights()\n",
    "        self.l1 = l1\n",
    "        self.l2 = l2\n",
    "        self.epochs = epochs\n",
    "        self.eta = eta\n",
    "        self.alpha = alpha\n",
    "        self.decrease_const = decrease_const\n",
    "        self.shuffle = shuffle\n",
    "        self.minibatches = minibatches\n",
    "        \n",
    "    def _encode_labels(self,y,k):\n",
    "        onehot = np.zeros((k,y.shape[0]))\n",
    "        for idx,val in enumerate(y):\n",
    "            onehot[val,idx] = 1.0\n",
    "        return onehot\n",
    "    \n",
    "    def _initialize_weights(self):\n",
    "        w1 = np.random.uniform(-1.0, 1.0, size=self.n_hidden*(self.n_features+1))\n",
    "        w1 = w1.reshape(self.n_hidden,self.n_features+1)\n",
    "        w2 = np.random.uniform(-1.0,1.0,size=self.n_output*(self.n_hidden+1))\n",
    "        w2 = w2.reshape(self.n_output,self.n_hidden+1)\n",
    "        return w1,w2\n",
    "    \n",
    "    def _sigmoid(self,z):\n",
    "        #expit es equivalente a 1.0/(1.0 + np.exp(-z))\n",
    "        return expit(z)\n",
    "    \n",
    "    def _sigmoid_gradient(self,z):\n",
    "        sg = self._sigmoid(z)\n",
    "        return sg * (1 - sg)\n",
    "    \n",
    "    def _add_bias_unit(self,X,how='column'):\n",
    "        if how == 'column':\n",
    "            X_new = np.ones((X.shape[0],X.shape[1]+1))\n",
    "            X_new[:,1:] = X\n",
    "        elif how == 'row':\n",
    "            X_new = np.ones((X.shape[0]+1,X.shape[1]))\n",
    "            X_new[1:,:] = X\n",
    "        return X_new\n",
    "    \n",
    "    def _feedforward(self,X,w1,w2):\n",
    "        a1 = self._add_bias_unit(X,how='column')\n",
    "        z2 = w1.dot(a1.T)\n",
    "        a2 = self._sigmoid(z2)\n",
    "        a2 = self._add_bias_unit(a2,how='row')\n",
    "        z3 = w2.dot(a2)\n",
    "        a3 = self._sigmoid(z3)\n",
    "\n",
    "        return a1,z2,a2,z3,a3\n",
    "    \n",
    "    def _L2_reg(self,lambda_,w1,w2):\n",
    "        return (lambda_/2.0) * (np.sum(w1[:,1:]**2) + np.sum(w2[:,1:]**2))\n",
    "    \n",
    "    def _L1_reg(self,lambda_,w1,w2):\n",
    "        return (lambda_/2.0) * (np.abs(w1[:,1:]).sum() + np.abs(w2[:,1:]).sum())\n",
    "    \n",
    "    def _get_cost(self,y_enc,output,w1,w2):\n",
    "        term1 = -y_enc * (np.log(output))\n",
    "        term2 = (1-y_enc)*np.log(1-output)\n",
    "        cost = np.sum(term1-term2)\n",
    "        L1_term = self._L1_reg(self.l1,w1,w2)\n",
    "        L2_term = self._L2_reg(self.l2,w1,w2)\n",
    "        cost = cost + L1_term + L2_term\n",
    "        return cost\n",
    "    \n",
    "    def _get_gradient(self, a1, a2, a3, z2, y_enc, w1, w2):\n",
    "        # backpropagation\n",
    "        sigma3 = a3 - y_enc\n",
    "        z2 = self._add_bias_unit(z2, how='row')\n",
    "        sigma2 = w2.T.dot(sigma3) * self._sigmoid_gradient(z2)\n",
    "        sigma2 = sigma2[1:, :]\n",
    "        grad1 = sigma2.dot(a1)\n",
    "        grad2 = sigma3.dot(a2.T)\n",
    "\n",
    "        # regularize\n",
    "        grad1[:, 1:] += (w1[:, 1:] * (self.l1 + self.l2))\n",
    "        grad2[:, 1:] += (w2[:, 1:] * (self.l1 + self.l2))\n",
    "\n",
    "        return grad1, grad2\n",
    "\n",
    "    def predict(self, X):\n",
    "        a1, z2, a2, z3, a3 = self._feedforward(X, self.w1, self.w2)\n",
    "        y_pred = np.argmax(z3, axis=0)\n",
    "        return y_pred\n",
    "\n",
    "    def fit(self, X, y, print_progress=False):\n",
    "        self.cost_ = []\n",
    "        X_data, y_data = X.copy(), y.copy()\n",
    "        y_enc = self._encode_labels(y, self.n_output)\n",
    "\n",
    "        delta_w1_prev = np.zeros(self.w1.shape)\n",
    "        delta_w2_prev = np.zeros(self.w2.shape)\n",
    "\n",
    "        for i in range(self.epochs):\n",
    "\n",
    "            # adaptive learning rate\n",
    "            self.eta /= (1 + self.decrease_const*i)\n",
    "\n",
    "            if print_progress:\n",
    "                sys.stderr.write('\\rEpoch: %d/%d' % (i+1, self.epochs))\n",
    "                sys.stderr.flush()\n",
    "\n",
    "            if self.shuffle:\n",
    "                idx = np.random.permutation(y_data.shape[0])\n",
    "                X_data, y_enc = X_data[idx], y_enc[:, idx]\n",
    "\n",
    "            mini = np.array_split(range(y_data.shape[0]), self.minibatches)\n",
    "            for idx in mini:\n",
    "\n",
    "                # feedforward\n",
    "                a1, z2, a2, z3, a3 = self._feedforward(X_data[idx],\n",
    "                                                       self.w1,\n",
    "                                                       self.w2)\n",
    "                cost = self._get_cost(y_enc=y_enc[:, idx],\n",
    "                                      output=a3,\n",
    "                                      w1=self.w1,\n",
    "                                      w2=self.w2)\n",
    "                self.cost_.append(cost)\n",
    "\n",
    "                # compute gradient via backpropagation\n",
    "                grad1, grad2 = self._get_gradient(a1=a1, a2=a2,\n",
    "                                                  a3=a3, z2=z2,\n",
    "                                                  y_enc=y_enc[:, idx],\n",
    "                                                  w1=self.w1,\n",
    "                                                  w2=self.w2)\n",
    "\n",
    "                #update weights\n",
    "                delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2\n",
    "                self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))\n",
    "                self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))\n",
    "                delta_w1_prev, delta_w2_prev = delta_w1, delta_w2\n",
    "\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nn = NeuralNetMLP(n_output=10,\n",
    "                 n_features=X_train.shape[1],\n",
    "                 n_hidden=50,\n",
    "                 l2=0.1,\n",
    "                 l1=0.0,\n",
    "                 epochs=1000,\n",
    "                 eta=0.001,\n",
    "                 alpha=0.001,\n",
    "                 decrease_const=0.00001,\n",
    "                 shuffle=True,\n",
    "                 minibatches=50,\n",
    "                 random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: 1000/1000"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<__main__.NeuralNetMLP at 0x1169d8748>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.fit(X_train,y_train,print_progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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9TMSbWofryCPD86/UYGQzuPDC7M/BMi1fHp6j9euXe75GjgzzTW65ZXi/YkUY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11699c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(nn.cost_)),nn.cost_)\n",
    "plt.ylim([0, 2000])\n",
    "plt.ylabel('Cost')\n",
    "plt.xlabel('Epochs * 50')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZbQqpGiikROQrvvwS3nkHli+P8Fq2DJYsgbVr4ZVXYr9ly2iZbdsWra9Ro+CA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\nCikRESlY/x8Zue88KbAoRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119afa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batches = np.array_split(range(len(nn.cost_)), 1000)\n",
    "cost_ary = np.array(nn.cost_)\n",
    "cost_avgs = [np.mean(cost_ary[i]) for i in batches]\n",
    "\n",
    "plt.plot(range(len(cost_avgs)), cost_avgs, color='red')\n",
    "plt.ylim([0, 2000])\n",
    "plt.ylabel('Cost')\n",
    "plt.xlabel('Epochs')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('./figures/cost2.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 97.59%\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = nn.predict(X_train)\n",
    "\n",
    "if sys.version_info < (3, 0):\n",
    "    acc = ((np.sum(y_train == y_train_pred, axis=0)).astype('float') /\n",
    "           X_train.shape[0])\n",
    "else:\n",
    "    acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]\n",
    "\n",
    "print('Training accuracy: %.2f%%' % (acc * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 95.62%\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = nn.predict(X_test)\n",
    "\n",
    "if sys.version_info < (3, 0):\n",
    "    acc = ((np.sum(y_test == y_test_pred, axis=0)).astype('float') /\n",
    "           X_test.shape[0])\n",
    "else:\n",
    "    acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]\n",
    "\n",
    "print('Test accuracy: %.2f%%' % (acc * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9bUYCq1V1ZwRlIkZVjwI7gbtEJMkOwLdgjdl7cVrPc0Ukza6fdbFGEH7CWkkV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\n0Wg0QcX/BxwZsXUhaiL1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b00e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "miscl_img = X_test[y_test != y_test_pred][:25]\n",
    "correct_lab = y_test[y_test != y_test_pred][:25]\n",
    "miscl_lab = y_test_pred[y_test != y_test_pred][:25]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n",
    "ax = ax.flatten()\n",
    "for i in range(25):\n",
    "    img = miscl_img[i].reshape(28, 28)\n",
    "    ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
    "    ax[i].set_title('%d) t: %d p: %d' % (i+1, correct_lab[i], miscl_lab[i]))\n",
    "\n",
    "ax[0].set_xticks([])\n",
    "ax[0].set_yticks([])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Se podría hacer \"Gradient Checking\" para comprobar que el cálculo del gradiente está bien,\n",
    "#pero no lo voy a hacer acá."
   ]
  }
 ],
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