simple-image-recognition.ipynb

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      "imported pandas as pd | imported numpy as np | imported matplotlib.pyplot as plt and changed visual style to \"ggplot\" | set matplotlib plots to display inline at retina resolution | imported nitpick.plots as nplt | imported bokeh .charts .plotting and .models as bkc, bkp, bkm | "
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     "text": [
      "\"improved\" the css | hidding the header and toolbar"
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       "<script>\n",
       "// This came from https://gist.github.com/ericdill/227098c569d787abde0c\n",
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   "source": [
    "%run ~/Projects/Jupyter-Mods/ruaridhs-notebook-initialiser.ipy\n",
    "%run ~/Projects/Jupyter-Mods/ruaridhs-notebook-prettifier.ipy"
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# make this compatible with python2 and python3\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "# import matplotlib to be able to plot things\n",
    "import matplotlib.pyplot as plt\n",
    "# make the plots appear in jupyter notebook\n",
    "%matplotlib inline\n",
    "# make the plots high resolution\n",
    "%config InlineBackend.figure_format='retina' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 1) Get The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The tensorflow library has some example datasets included. This one called MNIST is of small hand written digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('.', one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Look at the data to see what's there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    "deletable": true,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55000, 784)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mnist.train.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "image/png": 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yzD8vybVJTmutndF7F3wAWKEVH9H33q/tvf/rwpfne+8/TfLx4ebxWy06LcnTklw5H/lh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\niIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11762b850>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mnist.train.images[90].reshape([28,28]),cmap='gray_r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.train.labels[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 2) Define The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Define The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# add a placeholder to input the images and labels\n",
    "input_images = tf.placeholder(tf.float32, [None, 784])\n",
    "labels = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "# define the parameters of the model\n",
    "W1 = tf.Variable(tf.truncated_normal([784, 64]))\n",
    "b1 = tf.Variable(tf.zeros([64]))\n",
    "\n",
    "W2 = tf.Variable(tf.truncated_normal([64, 10]))\n",
    "b2 = tf.Variable(tf.zeros([10]))\n",
    "\n",
    "# define the computation the model performs\n",
    "logits1 = tf.nn.relu(tf.matmul(input_images, W1) + b1)\n",
    "logits2 = tf.matmul(logits1, W2) + b2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Define The Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Define loss function\n",
    "loss = tf.reduce_mean(\n",
    "  tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits2))\n",
    "\n",
    "# Define the output of the model\n",
    "prediction = tf.nn.softmax(logits2)\n",
    "\n",
    "# Define the trainer\n",
    "train_step = tf.train.GradientDescentOptimizer(0.9).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 3) Train The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Setup The Tensorflow Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "loss_history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132.194\n",
      "1.17699\n",
      "0.798533\n",
      "0.657436\n",
      "0.569862\n",
      "0.497914\n",
      "0.441236\n",
      "0.413106\n",
      "0.39011\n",
      "0.360489\n",
      "0.336264\n",
      "0.362535\n",
      "0.317747\n",
      "0.313818\n",
      "0.307414\n",
      "0.315019\n",
      "0.304089\n",
      "0.301466\n",
      "0.26334\n",
      "0.248717\n"
     ]
    }
   ],
   "source": [
    "for i in range(2000):\n",
    "    \n",
    "    # get some of the images and their labels\n",
    "    batch_images, batch_labels = mnist.train.next_batch(5500)\n",
    "    \n",
    "    # Perform one step of training\n",
    "    _, l = sess.run([train_step, loss], feed_dict={input_images: batch_images, labels: batch_labels})\n",
    "    \n",
    "    # Every 100 iterations print out the value of the loss function\n",
    "    if i%100 == 0:\n",
    "        loss_history.append(l)\n",
    "        print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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C+R0mAwAAAABgNFKwASQpK01Ps9chKS986dDwykvSHntQ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rfDX7Sf93bmVPeTNjbAvl3Efbva52r7Zuz4Tq0mw9QZMGUGTJlOGDGy0zSxuvLa7TYBYiS+8D3i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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e6c150>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 467,
       "width": 876
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_history[1:], 'o-');\n",
    "plt.gcf().set_size_inches([15,8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 4) Check Whether It Works"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Test The Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9453\n"
     ]
    }
   ],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(logits2, 1), tf.argmax(labels, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "print(sess.run(accuracy, feed_dict={input_images: mnist.test.images,\n",
    "                                      labels: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 5) Use The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def prediction_to_number(prediction):\n",
    "    \"\"\" Converts the output of the model to the acual number it represents.\"\"\"\n",
    "    i = prediction.argmax()\n",
    "    return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# pick a random image\n",
    "test_i = 121\n",
    "img = mnist.test.images[test_i:test_i+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rpbwlyfr0Plk7vn8E+Vz2tzl28HWbz7Le36Yl6GfP1T2hlPIDv3MpZff0LiDx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\nTuGH/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a80f810>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the image\n",
    "plt.imshow(img.reshape([28,28]),cmap='gray_r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.66350068e-04   1.02502722e-02   1.97294285e-03   3.17253056e-03\n",
      "    6.97642326e-01   3.93664464e-03   1.43512413e-02   1.85987912e-02\n",
      "    4.28182296e-02   2.06590727e-01]]\n"
     ]
    }
   ],
   "source": [
    "# predict what number this image represents\n",
    "p = sess.run(prediction, feed_dict={input_images: img, labels: mnist.test.labels[0:1]})\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the ouput to the actual number\n",
    "prediction_to_number(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Try real input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    " - read from camera / folder into numpy array\n",
    " - crop to square\n",
    " - resize to 28*28\n",
    " - run prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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