python machine learning book.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "from theano import tensor as T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theano.config.floatX = 'float32'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_train = np.asarray([[0.0], [1.0], [2.0], [3.0], [4.0],\n",
    "                      [5.0], [6.0], [7.0], [8.0], [9.0]], \n",
    "                     dtype=theano.config.floatX)\n",
    "\n",
    "y_train = np.asarray([1.0, 1.3, 3.1, 2.0, 5.0, \n",
    "                      6.3, 6.6, 7.4, 8.0, 9.0], \n",
    "                     dtype=theano.config.floatX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "from theano import tensor as T\n",
    "import numpy as np\n",
    "\n",
    "def train_linreg(X_train, y_train, eta, epochs):\n",
    "\n",
    "    costs = []\n",
    "    # Initialize arrays\n",
    "    eta0 = T.fscalar('eta0')\n",
    "    y = T.fvector(name='y') \n",
    "    X = T.fmatrix(name='X')   \n",
    "    w = theano.shared(np.zeros(\n",
    "                      shape=(X_train.shape[1] + 1),\n",
    "                      dtype=theano.config.floatX),\n",
    "                      name='w')\n",
    "    \n",
    "    # calculate cost\n",
    "    net_input = T.dot(X, w[1:]) + w[0]\n",
    "    errors = y - net_input\n",
    "    cost = T.sum(T.pow(errors, 2)) \n",
    "\n",
    "    # perform gradient update\n",
    "    gradient = T.grad(cost, wrt=w)\n",
    "    update = [(w, w - eta0 * gradient)]\n",
    "\n",
    "    # compile model\n",
    "    train = theano.function(inputs=[eta0],\n",
    "                            outputs=cost,\n",
    "                            updates=update,\n",
    "                            givens={X: X_train,\n",
    "                                    y: y_train})      \n",
    "    \n",
    "    for _ in range(epochs):\n",
    "        costs.append(train(eta))\n",
    "    \n",
    "    return costs, w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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22rIrkSRJrWRwaoPZs2H6dIfrJEmqGoNTG0yYAAsWOEFckqSqMTi1Sb0OS5YUQ3aSJKka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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb57c383198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "costs, w = train_linreg(X_train, y_train, eta=0.001, epochs=10)\n",
    "   \n",
    "plt.plot(range(1, len(costs)+1), costs)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Cost')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/cost_convergence.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHDkia63XfNSoUcrNzdX06dMdSAdgsDG+vhA98ZOMGSfpP0v6XUmzJO2Q9P9I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\nTgAAAH6iOAEAAPjp/wfIh31aRy4oLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb53f5654e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predict_linreg(X, w):\n",
    "    Xt = T.matrix(name='X')\n",
    "    net_input = T.dot(Xt, w[1:]) + w[0]\n",
    "    predict = theano.function(inputs=[Xt], givens={w: w}, outputs=net_input)\n",
    "    return predict(X)\n",
    "\n",
    "plt.scatter(X_train, y_train, marker='s', s=50)\n",
    "plt.plot(range(X_train.shape[0]), \n",
    "         predict_linreg(X_train, w), \n",
    "         color='gray', \n",
    "         marker='o', \n",
    "         markersize=4, \n",
    "         linewidth=3)\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/linreg.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(y=1|x) = 0.707\n"
     ]
    }
   ],
   "source": [
    "# note that first element (X[0] = 1) to denote bias unit\n",
    "\n",
    "X = np.array([[1, 1.4, 1.5]])\n",
    "w = np.array([0.0, 0.2, 0.4])\n",
    "\n",
    "def net_input(X, w):\n",
    "    z = X.dot(w)\n",
    "    return z\n",
    "\n",
    "def logistic(z):\n",
    "    return 1.0 / (1.0 + np.exp(-z))\n",
    "\n",
    "def logistic_activation(X, w):\n",
    "    z = net_input(X, w)\n",
    "    return logistic(z)\n",
    "\n",
    "print('P(y=1|x) = %.3f' % logistic_activation(X, w)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities:\n",
      " [[ 0.87653295]\n",
      " [ 0.57688526]\n",
      " [ 0.90114393]]\n"
     ]
    }
   ],
   "source": [
    "# W : array, shape = [n_output_units, n_hidden_units+1]\n",
    "#          Weight matrix for hidden layer -> output layer.\n",
    "# note that first column (W[:][0]) contains the bias units\n",
    "W = np.array([[1.1, 1.2, 1.3, 0.5],\n",
    "              [0.1, 0.2, 0.4, 0.1],\n",
    "              [0.2, 0.5, 2.1, 1.9]])\n",
    "\n",
    "# A : array, shape = [n_hidden+1, n_samples]\n",
    "#          Activation of hidden layer.\n",
    "# note that first element (A[0][0] = 1) is for the bias units\n",
    "\n",
    "A = np.array([[1.0], \n",
    "              [0.1], \n",
    "              [0.3], \n",
    "              [0.7]])\n",
    "\n",
    "# Z : array, shape = [n_output_units, n_samples]\n",
    "#          Net input of output layer.\n",
    "\n",
    "Z = W.dot(A) \n",
    "y_probas = logistic(Z)\n",
    "print('Probabilities:\\n', y_probas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted class label: 2\n"
     ]
    }
   ],
   "source": [
    "y_class = np.argmax(Z, axis=0)\n",
    "print('predicted class label: %d' % y_class[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def softmax(z): \n",
    "    return np.exp(z) / np.sum(np.exp(z))\n",
    "\n",
    "def softmax_activation(X, w):\n",
    "    z = net_input(X, w)\n",
    "    return softmax(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities:\n",
      " [[ 0.40386493]\n",
      " [ 0.07756222]\n",
      " [ 0.51857284]]\n"
     ]
    }
   ],
   "source": [
    "y_probas = softmax(Z)\n",
    "print('Probabilities:\\n', y_probas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_probas.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_class = np.argmax(Z, axis=0)\n",
    "y_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tanh(z):\n",
    "    e_p = np.exp(z) \n",
    "    e_m = np.exp(-z)\n",
    "    return (e_p - e_m) / (e_p + e_m)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EpM3bFNjEnNI5tR45AtDB04ExaWPIS8nD4zxxiq7p7r///vC6ziaJtH1NPaP0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\nZmaWhBMlMzMzsyT+H9ymLSwKtRgdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb53f5abe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "z = np.arange(-5, 5, 0.005)\n",
    "log_act = logistic(z)\n",
    "tanh_act = tanh(z)\n",
    "\n",
    "# alternatives:\n",
    "# from scipy.special import expit\n",
    "# log_act = expit(z)\n",
    "# tanh_act = np.tanh(z)\n",
    "\n",
    "plt.ylim([-1.5, 1.5])\n",
    "plt.xlabel('net input $z$')\n",
    "plt.ylabel('activation $\\phi(z)$')\n",
    "plt.axhline(1, color='black', linestyle='--')\n",
    "plt.axhline(0.5, color='black', linestyle='--')\n",
    "plt.axhline(0, color='black', linestyle='--')\n",
    "plt.axhline(-1, color='black', linestyle='--')\n",
    "\n",
    "plt.plot(z, tanh_act, \n",
    "         linewidth=2, \n",
    "         color='black', \n",
    "         label='tanh')\n",
    "plt.plot(z, log_act, \n",
    "         linewidth=2, \n",
    "         color='lightgreen', \n",
    "         label='logistic')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('./figures/activation.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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, \n",
    "                               '%s-labels-idx1-ubyte' % kind)\n",
    "    images_path = os.path.join(path, \n",
    "                               '%s-images-idx3-ubyte' % kind)\n",
    "        \n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic, n = struct.unpack('>II', \n",
    "                                 lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath, \n",
    "                             dtype=np.uint8)\n",
    "\n",
    "    with open(images_path, 'rb') as imgpath:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\", \n",
    "                                               imgpath.read(16))\n",
    "        images = np.fromfile(imgpath, \n",
    "                             dtype=np.uint8).reshape(len(labels), 784)\n",
    " \n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 60000, columns: 784\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = load_mnist('mnist', kind='train')\n",
    "print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 10000, columns: 784\n"
     ]
    }
   ],
   "source": [
    "X_test, y_test = load_mnist('mnist', kind='t10k')\n",
    "print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import theano \n",
    "\n",
    "theano.config.floatX = 'float32'\n",
    "X_train = X_train.astype(theano.config.floatX)\n",
    "X_test = X_test.astype(theano.config.floatX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 3 labels:  [5 0 4]\n",
      "\n",
      "First 3 labels (one-hot):\n",
      " [[ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]\n",
      " [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "print('First 3 labels: ', y_train[:3])\n",
    "\n",
    "y_train_ohe = np_utils.to_categorical(y_train) \n",
    "print('\\nFirst 3 labels (one-hot):\\n', y_train_ohe[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
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