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@jtokoph
Forked from fuglede/tensorflow101.ipynb
Created March 14, 2017 01:44
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Revisions

  1. Søren Fuglede Jørgensen revised this gist Mar 3, 2017. 1 changed file with 104 additions and 17 deletions.
    121 changes: 104 additions & 17 deletions tensorflow101.ipynb
    104 additions, 17 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  2. Søren Fuglede Jørgensen revised this gist Mar 3, 2017. 1 changed file with 21 additions and 108 deletions.
    129 changes: 21 additions & 108 deletions tensorflow101.ipynb
    Original file line number Diff line number Diff line change
    @@ -2,7 +2,7 @@
    "cells": [
    {
    "cell_type": "code",
    "execution_count": 1,
    "execution_count": null,
    "metadata": {
    "collapsed": true
    },
    @@ -28,19 +28,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 2,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "4\n"
    ]
    }
    ],
    "outputs": [],
    "source": [
    "a = tf.placeholder('int64') # a will be an integer\n",
    "b = tf.placeholder('int64') # b will also be an integer\n",
    @@ -64,29 +56,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 3,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "Number of samples: 15\n"
    ]
    },
    {
    "data": {
    "image/png": 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5HRFDwH3ATXVnAsjMoxFxN/A3wJdrjkNEXAU8l5nfAAZqjnO8I8AtmXkJ8KfAl7vgukPD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\nkgpiqUtSQSx1SSrI/wNo3zTRfcwsbgAAAABJRU5ErkJggg==\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x8861fa4898>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "outputs": [],
    "source": [
    "train_X = np.array([7.33892413, 6.09065249, 3.6782235 , 1.5484478 , 5.2024495 ,\n",
    " 1.55973193, 1.90577005, 3.52333413, 1.89705273, 7.16303144,\n",
    @@ -110,18 +84,18 @@
    },
    {
    "cell_type": "code",
    "execution_count": 4,
    "execution_count": null,
    "metadata": {
    "collapsed": true
    "collapsed": false
    },
    "outputs": [],
    "source": [
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "# Set model weights; initialize all weights as random numbers\n",
    "W = tf.Variable(np.random.randn(), name=\"weight\")\n",
    "b = tf.Variable(np.random.randn(), name=\"bias\")\n",
    "# Set model weights; initialize all weights as zero\n",
    "W = tf.Variable(tf.zeros([1], \"float\"), name=\"weight\")\n",
    "b = tf.Variable(tf.zeros([1], \"float\"), name=\"bias\")\n",
    "\n",
    "# Construct a linear model\n",
    "pred = tf.add(tf.mul(X, W), b)"
    @@ -136,7 +110,7 @@
    },
    {
    "cell_type": "code",
    "execution_count": 5,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    @@ -162,30 +136,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 6,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "W = 0.906972\n",
    "b = 0.582044\n"
    ]
    },
    {
    "data": {
    "image/png": 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FfjfMWSJGIBjg+NV3ONz2FhOBCZam5rPHqGBxSu4D72O6i3RKnsrTuxARmTG6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\nAAAAAElFTkSuQmCC\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x8861f95cc0>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "outputs": [],
    "source": [
    "with tf.Session() as sess:\n",
    " sess.run(init)\n",
    @@ -223,22 +178,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 7,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "data": {
    "image/png": 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LZ3s+zubLNlIwRmf0qloEcIfJ5yR7lWfxfslq697DnrP6MBf+TuMasnG95uTavxeTA1it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\n3P8DJp9hEVmYwuoAAAAASUVORK5CYII=\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x886488afd0>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "outputs": [],
    "source": [
    "group1 = np.random.multivariate_normal([-4, -4], 20*np.identity(2), size=40)\n",
    "group2 = np.random.multivariate_normal([4, 4], 20*np.identity(2), size=40)\n",
    @@ -262,22 +206,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 8,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "data": {
    "image/png": 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4iIi0sWAUeildC3N54tVlvL5gbdgl7bWssAtoipkVAY8BBcBO4Hx3Xx9uVSIi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\nm8JDRETi9v8BeCkbE1SYRtEAAAAASUVORK5CYII=\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x886488a9e8>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "outputs": [],
    "source": [
    "x = np.arange(-10, 11)\n",
    "plt.title('The logarithm of the logistic function')\n",
    @@ -288,7 +221,7 @@
    },
    {
    "cell_type": "code",
    "execution_count": 9,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    @@ -315,7 +248,7 @@
    },
    {
    "cell_type": "code",
    "execution_count": 10,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    @@ -341,31 +274,11 @@
    },
    {
    "cell_type": "code",
    "execution_count": 11,
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "W = [[ 0.93485063]\n",
    " [ 0.66295463]]\n",
    "b = [-0.60810912]\n"
    ]
    },
    {
    "data": {
    "image/png": 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+/p+YfP5NdAltz5LD6/j0r+9p7deKt8ZMx27z5fFfX2FbuiyX3TqgFXNvmoG/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+c5VTNWlIbO6fbOrKgjRy9M1whza7hPydGzYiRPL6fM46JvTGvMmoF1joNcFpFAt5hWHK1ysORY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\nRU5ErkJggg==\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x88648f1ac8>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "outputs": [],
    "source": [
    "with tf.Session() as sess:\n",
    " # We stack our two groups of 2-dimensional points and label them 0 and 1 respectively\n",
  3. Søren Fuglede Jørgensen revised this gist Mar 2, 2017. 1 changed file with 17 additions and 26 deletions.
    43 changes: 17 additions & 26 deletions tensorflow101.ipynb
    17 additions, 26 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  4. Søren Fuglede Jørgensen revised this gist Mar 2, 2017. 1 changed file with 11 additions and 2 deletions.
    13 changes: 11 additions & 2 deletions tensorflow101.ipynb
    Original file line number Diff line number Diff line change
    @@ -341,7 +341,7 @@
    },
    {
    "cell_type": "code",
    "execution_count": 11,
    "execution_count": 13,
    "metadata": {
    "collapsed": false
    },
    @@ -359,7 +359,7 @@
    "data": {
    "image/png": 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DHI9nMT6aoW23A68KIX7Fl+LvqUNVmJa9mE9yV1DrOZhrxaYP4+Eel5JqiWVuwVo+zZ5P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\ntGFY9WbW1+5gQ+1O5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    "text/plain": [
    "<matplotlib.figure.Figure at 0xbefec0fe48>"
    "<matplotlib.figure.Figure at 0xbefee5f160>"
    ]
    },
    "metadata": {},
    @@ -398,6 +398,15 @@
    " plt.contour(x, y, predictions.reshape(len(x), len(y)), cmap=cm.BuGn, levels=np.arange(0.0, 1.1, 0.1))\n",
    " plt.colorbar()"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
    "collapsed": true
    },
    "outputs": [],
    "source": []
    }
    ],
    "metadata": {
  5. Søren Fuglede Jørgensen revised this gist Mar 2, 2017. 1 changed file with 23 additions and 46 deletions.
    69 changes: 23 additions & 46 deletions tensorflow101.ipynb
    23 additions, 46 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  6. Søren Fuglede Jørgensen revised this gist Mar 2, 2017. 1 changed file with 33 additions and 25 deletions.
    58 changes: 33 additions & 25 deletions tensorflow101.ipynb
    33 additions, 25 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  7. Søren Fuglede Jørgensen revised this gist Mar 2, 2017. 1 changed file with 57 additions and 22 deletions.
    79 changes: 57 additions & 22 deletions tensorflow101.ipynb
    Original file line number Diff line number Diff line change
    @@ -2,7 +2,7 @@
    "cells": [
    {
    "cell_type": "code",
    "execution_count": 1,
    "execution_count": 2,
    "metadata": {
    "collapsed": true
    },
    @@ -263,7 +263,41 @@
    "source": [
    "The plan will be to find a line separating the two groups, so that if one day, someone comes with a new point in the plane, we will be able to say if that point should be green or blue.\n",
    "\n",
    "We proceed as with linear regression with a few notable differences: Inputs $X$ are now 2-dimensional, which will be reflected in our placeholders and variables, we now have two weights, $w_1$ and $w_2$, and our prediction function will be the *logistic function* $q(X) = 1/(1 + \\exp(w^tX + b))$, taking values between $0$ and $1$. At the end of the day, $q(X)$ represents the probability that the point $X$ should be labelled \"green\"."
    "We proceed as with linear regression with a few notable differences: Inputs $X$ are now 2-dimensional, which will be reflected in our placeholders and variables, we now have two weights, $w_1$ and $w_2$, and our prediction function will be the *logistic function* $p(X) = 1/(1 + \\exp(w^tX + b))$, taking values between $0$ and $1$. At the end of the day, $p(X)$ represents the probability that the point $X$ should be labelled \"green\". Here's what its logarithm looks like:"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "data": {
    "text/plain": [
    "[<matplotlib.lines.Line2D at 0xe95aa8d048>]"
    ]
    },
    "execution_count": 11,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": 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toGNmGlec2z66ZxX0IiKtpL10zyroRURa0Wfdsxnhdc8q6EVEWtnA3rlcNm1k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\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0xe95aa8d7f0>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    "x = np.arange(-10, 11)\n",
    "plt.title('The logarithm of the logistic function')\n",
    "plt.plot(x, np.log(1/(1+np.exp(x))))"
    ]
    },
    {
    @@ -290,7 +324,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Just as we replaced our prediction method, we replace our cost function with $$-\\sum_{i=1}^n p(X_i) \\log(q(X_i)) + (1-p(X_i))\\log(1-q(X_i)),$$ where $p(X_i)$ is the label of $X_i$ (which is $0$ or $1$)."
    "Just as we replaced our prediction method, we replace our cost function with $$-\\sum_{i=1}^n l(X_i) \\log(p(X_i)) + (1-l(X_i))\\log(1-p(X_i)),$$ where $l(X_i)$ is the label of $X_i$ (which is $0$ or $1$)."
    ]
    },
    {
    @@ -316,34 +350,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "We are now in a position to run our optimization and plot the resulting values of $q$."
    "We are now in a position to run our optimization and plot the resulting values of $p$."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 10,
    "execution_count": 1,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "W = [[ 0.48370111]\n",
    " [ 0.3921538 ]]\n",
    "b = [ 0.35555899]\n"
    "ename": "NameError",
    "evalue": "name 'tf' is not defined",
    "output_type": "error",
    "traceback": [
    "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
    "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
    "\u001b[1;32m<ipython-input-1-f3b6409e0b53>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mtrain_X\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroup1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mtrain_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m40\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m40\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
    "\u001b[1;31mNameError\u001b[0m: name 'tf' is not defined"
    ]
    },
    {
    "data": {
    "image/png": 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JpaeRMm8DroCXTGN8uCDb0ylsqWZ3QwU+1U+ONbmt3Ko3MSEhh4Jj+yio2YdF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ASr1WrE3eYJFzfVSSCJEBickA2DELJJ0IXXIYc8OAQD+j5CLjA6cQU8nPa30xzopNB4TshSt5BP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\nuOk4O+oPU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05ayDajhRsmL2LVkfWsOrwOvaTjqnEzkjpXk/Ewosl46NBiH3qSEXJ/sfcM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\nZKyhoaExAvj/GIr+tGRs8xsAAAAASUVORK5CYII=\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x63e33b9048>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    @@ -358,7 +384,7 @@
    " # We stack our two groups of 2-dimensional points and label them 0 and 1 respectively\n",
    " sess.run(optimizer, feed_dict={X: train_X, labels: train_labels})\n",
    " \n",
    " # Plot the predictions: the values of q\n",
    " # Plot the predictions: the values of p\n",
    " Xmin = np.min(train_X)\n",
    " Xmax = np.max(train_X)\n",
    " x = np.arange(Xmin, Xmax, 0.1)\n",
    @@ -378,6 +404,15 @@
    " plt.contour(x, y, predictions.reshape(len(x), len(y)), cmap=cm.BuGn, levels=np.arange(0.0, 1.1, 0.1))\n",
    " plt.colorbar()"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
    "collapsed": true
    },
    "outputs": [],
    "source": []
    }
    ],
    "metadata": {
  8. Søren Fuglede Jørgensen revised this gist Feb 28, 2017. 1 changed file with 28 additions and 29 deletions.
    57 changes: 28 additions & 29 deletions tensorflow101.ipynb
    28 additions, 29 deletions not shown because the diff is too large. Please use a local Git client to view these changes.
  9. Søren Fuglede Jørgensen revised this gist Feb 28, 2017. 2 changed files with 406 additions and 2 deletions.
    2 changes: 0 additions & 2 deletions README.md
    Original file line number Diff line number Diff line change
    @@ -1,2 +0,0 @@
    TensorFlow 101
    ==============
    406 changes: 406 additions & 0 deletions tensorflow101.ipynb
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,406 @@
    {
    "cells": [
    {
    "cell_type": "code",
    "execution_count": 123,
    "metadata": {
    "collapsed": true
    },
    "outputs": [],
    "source": [
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "%matplotlib inline"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "TensorFlow 101\n",
    "==============\n",
    "\n",
    "Some of the main objects in TensorFlow are \"placeholders\", which are things that you can feed things into, \"Variables\", which contain intermediate results of calculations, and \"sessions\" in which one carries out predefined calculations."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
    "collapsed": false
    },
    "outputs": [],
    "source": [
    "a = tf.placeholder('int64') # a will be an integer\n",
    "b = tf.placeholder('int64') # b will also be an integer\n",
    "c = tf.add(a, b) # c = a + b\n",
    "\n",
    "with tf.Session() as sess: # Start a TensorFlow \"session\"\n",
    " # Feed in the values of a and b and evaluate c\n",
    " result = sess.run(c, feed_dict={a: 2, b: 2})\n",
    " print(result)"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Linear regression in TensorFlow\n",
    "===============================\n",
    "\n",
    "Let's do a slightly more interesting example: OLS linear regression. Let's first set up some data outside of TensorFlow"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "Number of samples: 15\n"
    ]
    },
    {
    "data": {
    "text/plain": [
    "[<matplotlib.lines.Line2D at 0x5d4b9edb38>]"
    ]
    },
    "execution_count": 3,
    "metadata": {},
    "output_type": "execute_result"
    },
    {
    "data": {
    "image/png": 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5HRFDwH3ATXVnAsjMoxFxN/A3wJdrjkNEXAU8l5nfAAZqjnO8I8AtmXkJ8KfAl7vgukPD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\nkgpiqUtSQSx1SSrI/wNo3zTRfcwsbgAAAABJRU5ErkJggg==\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x5d4b60ecc0>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    "train_X = np.array([7.33892413, 6.09065249, 3.6782235 , 1.5484478 , 5.2024495 ,\n",
    " 1.55973193, 1.90577005, 3.52333413, 1.89705273, 7.16303144,\n",
    " 6.69186969, 6.35116542, 8.40910037, 6.81104552, 9.0276311])\n",
    "train_Y = np.array([6.01218497, 7.15272823, 4.39711002, 0.78859037, 4.66163788,\n",
    " 2.1366047 , 0.75108498, 2.4262601 , 2.01357347, 5.1216737 ,\n",
    " 8.01817765, 7.07644285, 9.11215858, 8.64146407, 8.13432732])\n",
    "\n",
    "n_samples = train_X.shape[0]\n",
    "\n",
    "print(\"Number of samples: \", n_samples)\n",
    "plt.plot(train_X, train_Y, 'o')"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "At the end of the day, our model will be $y = Wx + b$, where we feed in the training values for $x$ and $y$, and the model will have to determine $W$ and $b$."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
    "collapsed": true
    },
    "outputs": [],
    "source": [
    "X = tf.placeholder(\"float\")\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "# Set model weights; initialize all weights as random numbers\n",
    "W = tf.Variable(np.random.randn(), name=\"weight\")\n",
    "b = tf.Variable(np.random.randn(), name=\"bias\")\n",
    "\n",
    "# Construct a linear model\n",
    "pred = tf.add(tf.mul(X, W), b)"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Recall that OLS works by minimizing the mean squared error, $\\frac{1}{2n} \\sum_{i=1}^n (W x_i + b - y_i)^2$; in TensorFlow, we explicitly specify this \"cost\" and how to go about minimizing it."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
    "collapsed": false
    },
    "outputs": [],
    "source": [
    "# Mean squared error\n",
    "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
    "\n",
    "# Gradient descent\n",
    "learning_rate = 0.001\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "With the variables in place, we can run the TensorFlow session and examine what it learns."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "W = 0.88705\n",
    "b = 0.708046\n"
    ]
    },
    {
    "data": {
    "image/png": 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uj/awv/EgX/Z9RZQjis15r/F20VYSYxJn/N66SEdE5lrElvq4Z4Jjbac41X4G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\n",
    "text/plain": [
    "<matplotlib.figure.Figure at 0x5d4cfa2240>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    "with tf.Session() as sess:\n",
    " sess.run(init)\n",
    "\n",
    " # Run the optimization algorithm 1000 times\n",
    " for i in range(1000):\n",
    " sess.run(optimizer, feed_dict={X: train_X, Y: train_Y})\n",
    " \n",
    " # Visualize the results\n",
    " print('W = ', sess.run(W))\n",
    " print('b = ', sess.run(b))\n",
    " plt.plot(train_X, train_Y, 'o')\n",
    "\n",
    " # Make predictions for new values of x\n",
    " x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    " predictions = sess.run(pred, feed_dict={X: x})\n",
    " plt.plot(x, predictions)\n",
    " plt.show()"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Logistic regression in TensorFlow\n",
    "================================="
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Next up, let us take a look at a classification problem instead."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 149,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "data": {
    "image/png": 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    "text/plain": [
    "<matplotlib.figure.Figure at 0x5d505bb8d0>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    "group1 = np.random.multivariate_normal([-4, -4], 20*np.identity(2), size=40)\n",
    "group2 = np.random.multivariate_normal([4, 4], 20*np.identity(2), size=40)\n",
    "plt.plot(*group1.T, 'o')\n",
    "plt.plot(*group2.T, 'o')\n",
    "plt.show()"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "The plan will be to find a line separating the two groups, so that if one day, someone comes with a new point in the plane, we will be able to say if that point should be green or blue.\n",
    "\n",
    "We proceed as with linear regression with a few notable differences: Inputs $X$ are now 2-dimensional, which will be reflected in our placeholders and variables, we now have two weights, $w_1$ and $w_2$, and our prediction function will be the *logistic function* $q(X) = 1/(1 + \\exp(w^tX + b))$, taking values between $0$ and $1$. At the end of the day, $q(X)$ represents the probability that the point $X$ should be labelled \"green\"."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 150,
    "metadata": {
    "collapsed": false
    },
    "outputs": [],
    "source": [
    "# Inputs are now two-dimensional and come with labels \"blue\" or \"green\" (represented by 0 or 1)\n",
    "X = tf.placeholder(\"float\", shape=[None, 2])\n",
    "labels = tf.placeholder(\"float\", shape=[None])\n",
    "\n",
    "# Set model weights and bias as before\n",
    "W = tf.Variable(tf.zeros([2, 1], \"float\"), name=\"weight\")\n",
    "b = tf.Variable(tf.zeros([1], \"float\"), name=\"bias\")\n",
    "\n",
    "# Predictor is now the logistic function\n",
    "pred = tf.sigmoid(tf.to_double(tf.reduce_sum(tf.matmul(X, W), axis=[1]) + b))"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "Just as we replaced our prediction method, we replace our cost function with $$-\\sum_{i=1}^n p(X_i) \\log(q(X_i)) + (1-p(X_i))\\log(1-q(X_i)),$$ where $p(X_i)$ is the label of $X_i$ (which is $0$ or $1$)."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 151,
    "metadata": {
    "collapsed": false
    },
    "outputs": [],
    "source": [
    "# Similarly, the OLS cost function from before is now replaced by cross-entropy\n",
    "cost = -tf.reduce_sum(tf.to_double(labels) * tf.log(pred) + (1-tf.to_double(labels)) * tf.log(1-pred))\n",
    "\n",
    "# Gradient descent\n",
    "learning_rate = 0.001\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.global_variables_initializer()"
    ]
    },
    {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
    "We are now in a position to run our optimization and plot the resulting values of $q$."
    ]
    },
    {
    "cell_type": "code",
    "execution_count": 152,
    "metadata": {
    "collapsed": false
    },
    "outputs": [
    {
    "name": "stdout",
    "output_type": "stream",
    "text": [
    "W = [[ 0.32362193]\n",
    " [ 0.22263546]]\n",
    "b = [ 0.1163578]\n"
    ]
    },
    {
    "data": {
    "image/png": 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/8eLc+OyYIz4EjHvr4rDLrrj5k2BvO4DWj59uTdNkIUTLSGYtsBSo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\nPFn9pmU8i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    "text/plain": [
    "<matplotlib.figure.Figure at 0x5d505a5d68>"
    ]
    },
    "metadata": {},
    "output_type": "display_data"
    }
    ],
    "source": [
    "with tf.Session() as sess:\n",
    " train_X = np.vstack((group1, group2))\n",
    " train_labels = np.array([0.0] * 40 + [1.0] * 40)\n",
    "\n",
    " sess.run(init)\n",
    "\n",
    " # Run the optimization algorithm 1000 times\n",
    " for i in range(1000):\n",
    " # We stack our two groups of 2-dimensional points and label them 0 and 1 respectively\n",
    " sess.run(optimizer, feed_dict={X: train_X, labels: train_labels})\n",
    " \n",
    " # Plot the predictions: the values of q\n",
    " Xmin = np.min(train_X)\n",
    " Xmax = np.max(train_X)\n",
    " x = np.arange(Xmin, Xmax, 0.1)\n",
    " y = np.arange(Xmin, Xmax, 0.1)\n",
    " \n",
    " plt.plot(*group1.T, 'o')\n",
    " plt.plot(*group2.T, 'o')\n",
    " plt.xlim(Xmin, Xmax)\n",
    " plt.ylim(Xmin, Xmax)\n",
    " print('W = ', sess.run(W))\n",
    " print('b = ', sess.run(b))\n",
    " \n",
    " xx, yy = np.meshgrid(x, y)\n",
    " predictions = sess.run(pred, feed_dict={X: np.array((xx.ravel(), yy.ravel())).T})\n",
    " \n",
    " plt.title('Probability that model will label a given point \"green\"')\n",
    " plt.contour(x, y, predictions.reshape(len(x), len(y)), cmap=cm.BuGn, levels=np.arange(0.0, 1.1, 0.1))\n",
    " plt.colorbar()"
    ]
    },
    {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
    "collapsed": true
    },
    "outputs": [],
    "source": []
    }
    ],
    "metadata": {
    "anaconda-cloud": {},
    "kernelspec": {
    "display_name": "Python [Root]",
    "language": "python",
    "name": "Python [Root]"
    },
    "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"
    }
    },
    "nbformat": 4,
    "nbformat_minor": 0
    }
  10. @fuglede fuglede created this gist Feb 28, 2017.
    2 changes: 2 additions & 0 deletions README.md
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    TensorFlow 101
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