Day 78.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##   Packt Computer Vision with Python3 - Digit Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Liraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import tempfile\n",
    "\n",
    "from sklearn import  svm, metrics\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what kind of data do we already have?\n",
    "from sklearn import datasets\n",
    "digits=datasets.load_digits()\n",
    "\n",
    "\n",
    "example_image=digits.images[0]\n",
    "print(type(example_image))\n",
    "plt.imshow(example_image); plt.show()\n",
    "example_image.reshape((8*8,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-0e8d17c2dda7>:5: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use urllib or similar directly.\n",
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting /tmp/tensorflow/mnist/input_data\\t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "#acquire standard MNIST handwritten digit data\n",
    "#http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "data_dir = '/tmp/tensorflow/mnist/input_data'\n",
    "mnist = input_data.read_data_sets(data_dir, one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zS6vk167VZrwu5SKfbCjja5LaJK11939vehMjMLMPaOhsLw1NYvpgmb2Z2UOSFmjoW197Jd0q6b8lPSLpfZJelbTM3Zv+i7ec3hZo6K3rH2ZuPv4Zu8m9zZf0pKTnJQ1mi2/R0Ofr0l67RF/LVcLrxhV+QFBc4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/B7PeLkBHgOKXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now we load and examine the data\n",
    "train_data=mnist.train.images\n",
    "print(train_data.shape)\n",
    "n_samples = train_data.shape[0]\n",
    "\n",
    "\n",
    "train_labels=np.array(np.where(mnist.train.labels==1))[1]\n",
    "\n",
    "plt.imshow(train_data[1000].reshape((28,28))); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a classifier: a support vector classifier\n",
    "classifier = svm.SVC(gamma=0.001)\n",
    "# Learn about gamma and other SVM parameters here:\n",
    "# http://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html\n",
    "# Exercise:  Experiment with the parameters to see how they affect execution \n",
    "#             time and accuracy\n",
    "\n",
    "# Train the model -- we're only going to use the training data (and not\n",
    "# the test data) to ensure that our model generalizes to unseen cases.\n",
    "# This (training) is typically what takes the most computational time\n",
    "# when doing machine learning.\n",
    "classifier.fit(train_data, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now predict the value of the digit on the test data:\n",
    "test_data=mnist.test.images\n",
    "test_labels=np.array(np.where(mnist.test.labels==1))[1]\n",
    "\n",
    "expected = test_labels\n",
    "predicted = classifier.predict(test_data)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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