1_notmnist.ipynb

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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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     "autoexec": {
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      "wait_interval": 0
     }
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    "colab_type": "code",
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    "deletable": true,
    "editable": true,
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   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified ./notMNIST_large.tar.gz\n",
      "Found and verified ./notMNIST_small.tar.gz\n"
     ]
    }
   ],
   "source": [
    "#url = 'http://commondatastorage.googleapis.com/books1000/'\n",
    "url = 'http://yaroslavvb.com/upload/notMNIST/'\n",
    "last_percent_reported = None\n",
    "data_root = '.' # Change me to store data elsewhere\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "  \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "  slow internet connections. Reports every 5% change in download progress.\n",
    "  \"\"\"\n",
    "  global last_percent_reported\n",
    "  percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "  if last_percent_reported != percent:\n",
    "    if percent % 5 == 0:\n",
    "      sys.stdout.write(\"%s%%\" % percent)\n",
    "      sys.stdout.flush()\n",
    "    else:\n",
    "      sys.stdout.write(\".\")\n",
    "      sys.stdout.flush()\n",
    "      \n",
    "    last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  dest_filename = os.path.join(data_root, filename)\n",
    "  if force or not os.path.exists(dest_filename):\n",
    "    print('Attempting to download:', filename) \n",
    "    filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)\n",
    "    print('\\nDownload Complete!')\n",
    "  statinfo = os.stat(dest_filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified', dest_filename)\n",
    "  else:\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')\n",
    "  return dest_filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labelled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "both",
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      "startup": false,
      "wait_interval": 0
     },
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      {
       "item_id": 1
      }
     ]
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    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./notMNIST_large already present - Skipping extraction of ./notMNIST_large.tar.gz.\n",
      "['./notMNIST_large/A', './notMNIST_large/B', './notMNIST_large/C', './notMNIST_large/D', './notMNIST_large/E', './notMNIST_large/F', './notMNIST_large/G', './notMNIST_large/H', './notMNIST_large/I', './notMNIST_large/J']\n",
      "./notMNIST_small already present - Skipping extraction of ./notMNIST_small.tar.gz.\n",
      "['./notMNIST_small/A', './notMNIST_small/B', './notMNIST_small/C', './notMNIST_small/D', './notMNIST_small/E', './notMNIST_small/F', './notMNIST_small/G', './notMNIST_small/H', './notMNIST_small/I', './notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "  root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "  if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "    print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "  else:\n",
    "    print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "    tar = tarfile.open(filename)\n",
    "    sys.stdout.flush()\n",
    "    tar.extractall(data_root)\n",
    "    tar.close()\n",
    "  data_folders = [\n",
    "    os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "    if os.path.isdir(os.path.join(root, d))]\n",
    "  if len(data_folders) != num_classes:\n",
    "    raise Exception(\n",
    "      'Expected %d folders, one per class. Found %d instead.' % (\n",
    "        num_classes, len(data_folders)))\n",
    "  print(data_folders)\n",
    "  return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAsklEQVR4nO2TsQrCQBBEZ3dPopLC\nQq2Dpb29/ot+YmqtFETBX0gnahEhKCHJrcXpJUXwA8RpBt7s7jVzdJp0mQmAggDvam15p2kgnaAf\nRvPZEE63/SbJHnlhK3itjqqqqoelR8zMIiKG4q0ju5iMiAgzG/sZu6TO06uWn01/o3w/UZUe1WGL\n/uGvhkaci6kRAwSAqvHAkcHoLApAmzdaqulK3QujRaPU6yR75oWt6Nt3eAGEakchfE4CEAAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABCElEQVR4nO3OMS9DYRTG8f9537eJ\noYnhJoRESFc3JokYCRGRThaTkW9g8w18h4bRZLLoYhFNGgNDERED1ZBIiEi5eh9DDY3e8gU84/nl\nnPNAVhzrF8277UzDs6NElWw0RqXXKZetfEKS9EIDs16odq9f8o9/YXBOrYy5AYQ0xdSNAghxfH/8\n3s15SMW5dJhvX+l4xpp0NUbyocbgD8wR1aRdeJKWCJ1VvCcqSw8xlKVV+r43zXlvULyU6tM4lqVq\nhFkIITgwcosHUlIawgGb0tlK1P7qB+a3rqWX0iTmwFw6tzHrm9V6o9U/XCgEno/29m/xqcDMUsYX\nZiZGDN5uaqeVk0fwtAC+AOZMYAYjY5s2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Image\n",
    "#show a random image in each folder\n",
    "for i in range(num_classes):\n",
    "    imagenames = os.listdir(train_folders[i])\n",
    "    sample_idx = np.random.randint(len(imagenames))  # pick a random image index\n",
    "    imagename = train_folders[i] + '/' + imagenames[sample_idx]\n",
    "    display(Image(filename=imagename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "./notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "./notMNIST_small/J.pickle already present - Skipping pickling.\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "  \"\"\"Load the data for a single letter label.\"\"\"\n",
    "  image_files = os.listdir(folder)\n",
    "  dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "  print(folder)\n",
    "  num_images = 0\n",
    "  for image in image_files:\n",
    "    image_file = os.path.join(folder, image)\n",
    "    try:\n",
    "      image_data = (ndimage.imread(image_file).astype(float) - \n",
    "                    pixel_depth / 2) / pixel_depth\n",
    "      if image_data.shape != (image_size, image_size):\n",
    "        raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "      dataset[num_images, :, :] = image_data\n",
    "      num_images = num_images + 1\n",
    "    except IOError as e:\n",
    "      print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "  dataset = dataset[0:num_images, :, :]\n",
    "  if num_images < min_num_images:\n",
    "    raise Exception('Many fewer images than expected: %d < %d' %\n",
    "                    (num_images, min_num_images))\n",
    "    \n",
    "  print('Full dataset tensor:', dataset.shape)\n",
    "  print('Mean:', np.mean(dataset))\n",
    "  print('Standard deviation:', np.std(dataset))\n",
    "  return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "  dataset_names = []\n",
    "  for folder in data_folders:\n",
    "    set_filename = folder + '.pickle'\n",
    "    dataset_names.append(set_filename)\n",
    "    if os.path.exists(set_filename) and not force:\n",
    "      # You may override by setting force=True.\n",
    "      print('%s already present - Skipping pickling.' % set_filename)\n",
    "    else:\n",
    "      print('Pickling %s.' % set_filename)\n",
    "      dataset = load_letter(folder, min_num_images_per_class)\n",
    "      try:\n",
    "        with open(set_filename, 'wb') as f:\n",
    "          pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "      except Exception as e:\n",
    "        print('Unable to save data to', set_filename, ':', e)\n",
    "  \n",
    "  return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)\n",
    "print (\"OK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6FMRLKAdOsfZQC+b2nMWb2XB8x300S92t7RhxGutdhFZ6FxkEetFYMBQTDzISyAAPxbH6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\nWWYKjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3994dc9190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in range(num_classes):\n",
    "    pickle_file = train_datasets[i]  # index 0 should be all As, 1 = all Bs, etc.\n",
    "    with open(pickle_file, 'rb') as f:\n",
    "        letter_set = pickle.load(f)  # unpickle\n",
    "        sample_idx = np.random.randint(len(letter_set))  # pick a random image index\n",
    "        sample_image = letter_set[sample_idx, :, :]  # extract a 2D slice\n",
    "        plt.subplot(10, 1, i+1)\n",
    "        plt.imshow(sample_image)  # display it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data\n",
      "52909\n",
      "52911\n",
      "52912\n",
      "52911\n",
      "52912\n",
      "52912\n",
      "52912\n",
      "52912\n",
      "52912\n",
      "52911\n",
      "test data\n",
      "1872\n",
      "1873\n",
      "1873\n",
      "1873\n",
      "1873\n",
      "1872\n",
      "1872\n",
      "1872\n",
      "1872\n",
      "1872\n"
     ]
    }
   ],
   "source": [
    "def get_num(data_set):\n",
    "    for pickle_file in data_set:\n",
    "        with open(pickle_file,'rb') as f:\n",
    "            data = pickle.load(f)\n",
    "            print(len(data))\n",
    "\n",
    "print(\"training data\")\n",
    "get_num(train_datasets)\n",
    "print(\"test data\")\n",
    "get_num(test_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "  if nb_rows:\n",
    "    dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "    labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "  else:\n",
    "    dataset, labels = None, None\n",
    "  return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "  num_classes = len(pickle_files)\n",
    "  valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "  train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "  vsize_per_class = valid_size // num_classes\n",
    "  tsize_per_class = train_size // num_classes\n",
    "    \n",
    "  start_v, start_t = 0, 0\n",
    "  end_v, end_t = vsize_per_class, tsize_per_class\n",
    "  end_l = vsize_per_class+tsize_per_class\n",
    "  for label, pickle_file in enumerate(pickle_files):       \n",
    "    try:\n",
    "      with open(pickle_file, 'rb') as f:\n",
    "        letter_set = pickle.load(f)\n",
    "        # let's shuffle the letters to have random validation and training set\n",
    "        np.random.shuffle(letter_set)\n",
    "        if valid_dataset is not None:\n",
    "          valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "          valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "          valid_labels[start_v:end_v] = label\n",
    "          start_v += vsize_per_class\n",
    "          end_v += vsize_per_class\n",
    "                    \n",
    "        train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "        train_dataset[start_t:end_t, :, :] = train_letter\n",
    "        train_labels[start_t:end_t] = label\n",
    "        start_t += tsize_per_class\n",
    "        end_t += tsize_per_class\n",
    "    except Exception as e:\n",
    "      print('Unable to process data from', pickle_file, ':', e)\n",
    "      raise\n",
    "    \n",
    "  return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK\n"
     ]
    }
   ],
   "source": [
    "def randomize(dataset, labels):\n",
    "  permutation = np.random.permutation(labels.shape[0])\n",
    "  shuffled_dataset = dataset[permutation,:,:]\n",
    "  shuffled_labels = labels[permutation]\n",
    "  return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)\n",
    "print(\"OK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset\n",
      "validation dataset\n",
      "test dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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DcqCFqqUdqTx1Ie6u4mvMSBqATxFf04/AAMTJqk3X70ZCGuqZrk/irUExLL79MfLatsEP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\nb/4v2aW1ghZeUUvUUq1HIm3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LzEaSOGUhy/HWIeoMGmo5KVzwMzISt9AwJ3W9Zsdw4q3T\nEyfu2pSV0HBNMRks/LXXMghIxhYZRblkFuXVft6ZCXhRUtYZtNxvk5E4hLq539TPcU7+bihDx42h\nIuc2ystN2KfANAClfNr/U968/GH+9MAUZGC5BvQliekMjXECDrwEAtX4fRhG5k32j4uN+3IZbnwO\n/2kyxdcFeGP6co6+u5sdf5QgybAWgn4kZZ3BDU6aCvFJZqzJJBQJjkHfdUWTlL0ZeAxJQlxXQVI4\nKR2JTNg0JnBWdzLHjL3piAd9cwKu2ZQVgBLXwEmMVDH45FvOU/fbDL9Vq9iskrKPILFdFyBW/ogr\nfwZ+dHr1NGufhop/lcOj5dhXAOkeF79QSPegVIi9Eo7rKoMJeJUT8RfNC/hr7dPRiIwztmx/n7Gz\nf8Zy8GfYL4FiU/9vHFHPD+Ll1x9nZ3W23rJwO5OU/TOSbXqI+n00Ms5pyvT1uYz6JGWXImFzIM74\nfwN3ORxtCGx0MjsotDM9+wzCz1iPgIsCO7e82OQLb0Z42abYQ1HgVLb7q2ScobFwuw7YS2L6IT2X\nKYiz5HXgVaQxtKt7vfbpFbsAfXhzxGoCN6zkgSHbYPsaoG4cVxhtuu+mb+hKcoZdzt4PilBTPekl\nNNATTsCOFwMwkTUxM/ktTWpGAHDdULh4y6dAANNv/Z5x2Us4uA6SkTiINqGwyzKOg+UrtZPoAR1w\nlJS15WUREiM/H8ekBYpjKk6zi397kgmjf6RobSb+1PYk9cSJ++0n9FYoOYnz4bY4Zlq1DeT60dsJ\nrKmqzR+XkZNLRo42tDSx48BB7QA9wOP2405NScHeAdGUYHJ3JWUnY/UplCLjRieIs9kOhBbhhIV2\npptlKJh+xvljsorc1OGEBYSwpvM8jMxCZ/nwPOQErLwEwb9uZdNjM2uffP7AgfeHwOATQDhzvh3A\nocirGc8MOiNyD+1LYPK0dfxJXzlmnUnK9sUqKXs7YiTrkZS9QX2vUg9vLB5QQ3euGxlFxlq74bZe\n4FFdaXv7KHLmFeN84kb4OZVTxRcLHSY4VfgjGbz/6t7VNw88bj9xbvypGXtjur4Jx7o63G6NdI0v\nRGabuiCdGgsyXHBBvK8CTmcwfUU0mGKof+apCirzGXJHBrO+gFY3XMKpunNgZx+2w6g+iA+lM5IP\nrxVyk0PVfRpQTDTw7x4zaoWtahBWZi5+D5m0KgVC6DLkF8IPGzEerqEYedya5lbWe9azBFtOqpB6\nUYrIhqap22nUk9WlxdQIajCiHIDiTdXq4Y1JxBqBckjSoXakwKP2c316Ip/QCet0njPU4Dga02CE\nmPb2eXXOPrxoU5zD2+JnrkjKjkMa/m7kCbBO/f4pxH3WEusi0kYQhEzshWGVwgzBno8awI93/R8A\nFB7p/I7DWXSA6cgDLRFZW9oP8RB+iPDSEnn8N2Ag/YA2dPxbUq1xDAC6RUDku9vA5tuS2FCqWvjV\n3iwLMkWqM0xHsrdEIElkTyO6PxVYOelLPbMQ9ya+wf2Jr3PNkN8I6e0PdsKx9cEfTC3545degA5V\ntz1sP0NeL1V3dYULZzAxbsxyN49tNnjZptjDBExpA1OiGn41Ba4Mt/OQeKX5iGGIRsZDEeo+9cqE\nOoinRl4qq1RNoEnMGyw1KEeAatslVwfIm/s7+e1h/cf6ErhRkYfw8RWyPPNWpEBhCDcNcALCSyD+\n3cJZUFbJUKQvEAaYrgrAuO8gHOiKJkYfvr+CgJxK9iFTgCZ0KxXqdl1JStiPHxCUuo+uHa5nL0OR\nUmrrk22huZJCaDt0F/vyluOfrx+nmw084mThtUl0CAsiq/gQ0tHq7Gw3J0hBkudYyMkPcffamwse\n2pSGQ4AqDEFEl6yE3G3Yz2weU18a1rl8wWdAUjYOWUwXzmu9EnmqfyIfj0jkka7L+TgukefG76Zb\nv/1Y15CkqMcNou3xx+g3qqkquc0OW07ckpQVwc+rucJUSRyZtY37KJAc2YW8kPbIyCMcDFVUd27L\nH6lSk+KQ8f2fRkc2S+E8gEd1ZdLwYMYA/U9C3xb7uevpNGR5cxraIgTJBWECAsAUw7V3rmRC2xNc\ndTCfnsDI4T2btYBuwCNOEloO4NDK+dxxVVus8dUGHGMgbT8fRxbhjeSRe0/S/2pnontnFR7alEYE\nZRWgpkY9XbX6OoyM8keoL1cfNoJmlpTNQVZhtYaxfrRMnUP+sSzu+7gDby8r4r4PO/Dyux04siMF\n8dO2V49pBwTywoDXqVqd7PzUZw8eS8pCBBgKyOlxksqMckrVbw8DF5Uk0a4qQz11IYaX/s6Yrz8i\nSd1HG6NUJl3l5LxnFR7VFUP4KAqQdWp5G5Pp9+qHnHh8CkOfT4b4x+GBZ2B4J4KT/sYXNTtIvnkK\n5QtWMODHnwlFxvfKHWc9iXVdeMRJ4R9JtBz+JK+/sI6OISakZxSMLDcOxNp3Po70ulsiPcgWzJl2\nmHaTlPgAACAASURBVLzOhXx2f4vmKpu78IKkrCDF2WeD+rLrNTrd02U0q6Rsqw47iYzKAgzEnt5H\nqukCvg+6HkWdrVMwIabfQMjU9oxiHUk7smjdcSn5mZkofMOhYBqPpT2z8FhStlev+RS260TF8q0c\nxLootRw4uAAGPFtExsFwJtx3gv7PGWtv6QkTbDBHknHSwoxs3XngPKor03/2Z8KIjpw67oflRBpK\nNcx7QyGQdbz4UgBGZElBXK9ZdutJ8tuYWGmK5UCEhfUzvBkd5xV4KJ96lJoaE11vG8QFIRt4avsV\n/P5MKN8ejoQj3aD8CGJjgiHwYqInnyByawkXP/c7//ool+TtftjlRtIHPJaUjVffU7COOxuHtjd4\n20jWHVpqcEkSc9KdRt4ZfQgsMHteOTkPjiP/7nzE16YgN1iehtVFNzLv5Yd479kinr5V9KQVPwOv\nL4SdC5pUpjMBj3iZbjxEycZdrMF+1b6C+CZ7/OMbegNXviuRta0fhO0lZlae+gxl7zZSi1YjcyAL\nvV0uT+ARJzCIZRuG0rYLWPz3svf3bjy/9RUKn6hiQKkE07cFbg+FsImw+8HefDhrEPm/D+doaBGn\nk1cizyhdCRB7yIkZGEZFqj+78OOD27rQ6fGLuefA/TwQth+KT4G/hblbS5l2fQ5Lpozjs769iH+j\nE+kHLkEGNSPQWWBUQ5y4JCkbp77H43o0rf3gPB5visr6JDGdwyef6ggfJ47wtR9H+CRl/8fh48UR\nPk4c4ePEOXTJi7sJLrRMwSnYq7296p3LOmcRjTim/0BHam9nGT5OHOFrP86hy7rirqdbsXmPQzSp\nFtnvEuutoYISM7BjfcsN9AbtmuOQ0IYX6u7Q0q+d13hROdY7GuXEq8qAfheeK5xo73E0c/vpENP5\nXOAEzmBdiYkIcpkTd3uSthmlDep2HZnQ40iy4a7IRHA+g6MG8HzUPPIS4KOBZpIN0zi1Iwhxy8Sp\nh8YjDtYTQBUDu6xnpyR/S1R3eJoGV7KcVZxAONViMxx4Kag+CVzP8x2X8GWmlDoWif6TiZwp/Mcw\nGEX5AUPX17BcOITZy+S3WGSlzQEg+9rbKV/+NQgv5zQnUqqRNl8VIEu9FWAF03vdxoCcGSzKt4rj\nmJCo5EUffUDufYuR9B89oHo16L+uNKH9mNWvCpBl7/7AKqZddDs7Og9ix4oVwG9guJRpuzqwdOwm\nMqvb0WNyOsmfbQV6kZX2K+ifE2i4rowH3rGvKynYizjEU79eolndP5CA1smk5R8DFzlx10hqGaW1\ntt0Ta4JME6LdoV7YiNrdSouHkb9jHhlA9fFWnKIVkmfbgHVpogHoBvTFYCgi6sIj7DyWDo7ZnfWI\nBKQWb0AietoiS7DAjpcruObEMTYZ/6AbdVexm1AUI+CHcnQlpp77+CToIgzlwmYvZAb86cgkZsgB\neufFRU7ibA6JR5Z6m4Bgvo/zIyihN0r+QUDiR1qEw9tdJnPqvtPq6SzISG01nBucuNh+4tSv45GB\nnx/mgX4MvGUlc9+xoC3rHb5hBEe2/kaOxY9ut/bDbOlJMvnIY+VX0D8nUH9d0TgZCxxzb37brL5a\nEBhpoVKMpEuceCop2wG5wR8ANyJ3xIlMqDwcSt7uQp4igQkdgnMhrwBpDApSyS0222UoCmwpu9qV\ncpxpNCSJmYvcjUi0BdoCG15qSCWmnmV0GgdVQDX88hP9OlgfrbXRkbleKYc34SEnziBcXFmxlq7V\nx2ofo0bAMKEjhsxM9XTF6HLlduOSsm2QtvQWDbYfDf4QcCljMjfzyvt3QlYRUE27YQqpuWbWTAuk\nOj+VR6dvYPO6VuoxDUkYnDU0ta6c05KyAUgSVQtWZTMb+ccCxNqvAgJ5pfOTsubYAKdvGAa1weRm\nm1ObES5WA/Hkrt2m/XCuSGJ2BF5C+JmDVRnQhpdVLHrxEKewXxdgtjud2tG48AbSU6ysrEd6kq8W\n1a6kOE84ibd5aQkd/IAOBG2Jwe9YBV2Rh0UVsP6PCeRXXoZEUMZjrWuAfupKQ5x0QtrOIsQn2UD7\niUc4CWT24y8z5IUYjqcfABRMIQHcPLKa45OXAQqtr7iF/F82UJo8B1m9Fq/9r144gabXFTtJWXvp\n4RSbU5gb2E5Rj/mF6tJaocZml5T9f/bOOzyKqvvjn91NIY3eS7JAgNBLqEqJIkpRBBFfe6XaEBui\n6AtYeO0NFbCi2BWUjgiEKkivCSGQDZAGaaSQnv39cWayJbvJJpvAkB/f59lnZ3dnZne+e+6Ze889\n93x/QVaRLUZqwd2I3BmsAqJjkK7xdcADtHv2H7KA5mYd2yOfwXL3t78gI5YVmuPUD64ESUxnnIAN\nL8O4Y3YH7kAitiqMNqdrD3Tms1ve5TDiMkMQibmO9y7EtPV+dccawkmY1aO/8p4X6Iwk3+LPxQxp\nKZ5AoQ4GdIiBC42RuF0YFlsDtGMrZUmn+iJiWGOQclhltJ8w4Hp0rTzw3tSQKZ8sUHYz0DowjvxO\ntyI1I7z44uZvWBizGBFO1SQn4JZPAQsnY3BNWdOIxacMp36P8eoHLnHi6nB7N7aqdkFIAdUkpC5D\nMBZlMwcyoXqYOoTVB6XLOXEE8NdWxEn62D0CkBGIn3JsSVl6MyL/2JfLj4pyAk7lU52g03gWPruW\nxDcXoUMGTfUbw5FxC1n8fQJWcf4azMlFdMD0tm9gRqxFqh/p+OrG25GBjMPhpJZsxVpSFiySsieV\n9zoARymz/QB4EBpwhEl/LofDW5C2cpG6If9B9/5fQG3o14bAUwWcWbgRB5XGtMRJFfiUKoFLnLgj\nKfs2EksBUStSA89WMqHhJV9zb7cdav0WtvwyAAJ8EHvxVB568Yv1Aa/DcPEwJCTY/xatS2I64wRs\neFnPL7NPk4ZYRisst4IbfL5meqOvMR2Dk8fkLlYETJoMt7aYyb4P/wKzxKIU1BBOwq3eNiKlwfTQ\nYjzp0+eUKEnmAqMCYc7HgchoyYSTUJVWeHEmKavykoptt8cJJ20oimrNU7PeQYbRUhnphRlzmf3h\nANhTj7vq/MjWp/vB57GIDy6le6MVTirrU4zyMtzqECP2YzDHMKHObmdE2fBSrZKyzmq/WcmEhilf\nEcCTaz5iLXJ/W3fdWSbyLhJnUctg6STYlAE+PqB/uSsfPBaGxLc3w5UhiVlWPTwrXoZxx+xtnJp7\ngkJs63dk58D509JPzEfYywd+/xXubPMx4/VtOPDv7Xzf1x9F1mIINYKTMLuP9ICeGzcc50gH8Spq\ngZeO6wwQcghhzoiDwvxashVnkrLO6o464ERi97p2Q2HJMqQ3cREajiZo2ucciRwExBMQ3ZMloy7w\n2jITsx6dDAknkVV+muOksj5lnbwMq8RXGlFnt2u3zyPzxAbQhqQsgBc0b83Xm+rSHOjaGBp/UMBF\nH0/0ZGBWTN+rIA+fNLNEKhLh9V/HII2gxIVc6ZKYOHkPkGZQBGyvNYnHCu+EviG8NHIuDZYupnhf\nDhdSoVZqFkUcolO/Q3z+xDVM/BioMZKyjuDL2p1jeB0ZaqsR7HpxacD7lApVWaBlW3FdUrYEIt+g\nH+6Fx/JIOBlKkye6sWHYnSTvCqTDpNH4FWfzfP4wCobVYe/AYDifgV1PUsucQAUkZavwO7UiKetF\n15EHabA3ibr7IeOZ9tw68C4kBeqi3b5FSFNQn0GDqR3VKompzy0EImHHXl7f0YLbJj3KvWnvcixG\n1q/pAF8zXPz733LOdMlRxZKy3tCvDxuftjhIA9CjFxQO3Y9l8YSm4Z6krD18kDnxU/DQR//S9J4E\nJgfMpOOZN2lSBOvXdeabnSPZdb42EI8G2w5Uc/upDpTnJFU9Cmv5x52I/OOb2Mo/OsBmwIzn+niy\nc+MoBj6Iegb5AzNxLhxt4jKlRLkCNzkBRzFJC9SGXwB4s/Rzf7w/e4KeUz7mBBZWPCI0l/82mdKS\nsgbgX2T9rSop66RoQbjVthHoRr+H97BlsozBMpDp4F2TR8HkDUioJheN28pkbOVTeyOB+O2IAy3H\nVsKVZx0QJC6kJWD2JMHQkDrD09j+QzTQk7CiYHp2/C+7lvgh1GvGx9ijCtrPNiwxeSNuxiTLRTVL\nyt4AFNC4qDGBSY9R7ANByxLZC5Sd5GpUHnqknW0u7zouJapAUtZ5TNICpTdt1nHqzc6EINN9RuVT\ntQithuCmpGyY1bYHUMC9q/8mGXGFnoDZA3YvvQXJ2lDT641UXiy02lGFksweZPMn59oPAzLw6HuI\nrwb1I/CWe8mLPsHGl0fRe8AnEJWN3Hatb701iRNoOGIkyWvMSEfLVRhxEJN0CdUsKWsAXQHNHz+A\nxwtAO09qdYmEH3rj2gWqQquaQxVJypYF9drNeMdY0u5VaJAVtyRlbWGABnXZcSyITsrJfICAVhCU\nvxUJJdmHajSJKpRP9SDaVJeLocPR6X7grnePMvGRwcSdWc/4707D77DvhwKcy8tqBm5z4kc2yfhX\n+gd4VlDqwNXhdjQyI9UYi/zjS9hO1zvAejDrMSz7m0jAUBzMmqJhyu5lxZNMQCw07kaPPhEc0Jwq\nJpORP3U+krYwB4ukbCaW1A4nDtL5cDvfwwuKDVCsQ+1RFd6QS9HfwsopZT8NKk2rQ0trW3kV6SG4\nYCvhVtsdMbbIoFeXzRSdkDFHHhDtF8yahv0RqlX7MaHh4bab7UfCVQIjBanBxP5yhtAFo0h6bwN5\nvsMoyjXhkVHI/TM+h/AeELcWGWqbquua3IWbnABHlyIh3XwqPtz2gsPnK/SDy0smT0QSXX9HsuB3\nUyH5xyB0QV8yJEru+71iPUhbqWplmewuAKTpn0Y6Hzex8I8s2odfqNAFXSKkALOxSMpGIGvZ/ZFx\nYDmSsrLipg2WFTfFyG00tw/QHKQKjA6dLoRH6n/BWaSrGoZkJj/QuIqvyH24aSthyCqRm4EivDp0\nImBNdMki5iLg9LFizqxoiFCsx6JwEmb1rCm4yUk7LKtLALwg5QJvx/RmxcUJtOoXRYO26Rzc0YAl\njz+C5/QcxIqM1FxOoGHoQ8h1tceBXqKTbSMy0AnDq9W1FfrB1Swpewbz+pbEp8nP7Dw/BbJPIi4h\nDhmZ1lO2PZGmcEq+tukwEq79iuzuZxyf+vLBmpNKSspCIQZOYltYMBaYELOQ0NBQwAQdb6Xo07uJ\n/+UIJ5A/qzYQCUT7TKr6K3MPbtqKH9KZMBMwsZhlSx/gXJoUQAPpUyc3iWPoff8gnXUfbJbzarPn\n5BYnnr1SkHkePZJe6cPCRr9w/cJX+GnNSXZ9dp6U557l8NqDjA7/iYKkWthO2Jiq6bLcgpt2Aju3\nP4XM80TZfWIqZzsXeID/HPikQj+4vOG2Kv94BBlOJWKRf/wU6dwE4ET+sU6IJyvCW7JDeX3O5M3E\ndgehfj57z58mtMcGMMPeA6cJbbwLMnXsjUulfWEmgzo+zbkkHTv8+iNr/zUDlZNDyB/aDhlyf4j8\n0Hzk3zuIrAl9pPQpivh1xV0E6P7E2yx/QiFyiziQCPf92ZtQHwg7FcKcqTKPqwf8vSFnaBcC9mbz\nau4IJLtGM3DLVoaOPYPhj66MfOJr1n36D8uKbLP8CoGihDymbf+ItGmLaLT7BOv3+1GcA5ZJHM3B\nLU5uSt/GyloPQK4HtUK8mHJtEd/XHglp3xLYbTyTJ67Az9SfTz0acPPsjSx/W7UiTcMtTgBuHP0s\ny//px/OJRgYZ9pWsNNiblUCo/z7A8XZKve48YujPVK+BcGCbyz/YFetSpVPXYKtupsKI5H11tXvf\n3MrHm445eeQh8/0PGsC3LuADK7Lhlmay44oEuMUPyIcVF6BNPkQoJdU21+8Fqftc/a2XEpXmBcLA\n0x8f39OMmRvE2y1WoPsG3lwLAwrFatYCjwK920Lho3ruiexB9K67yT67msxUk3L6jaAtXirNybCG\ntTAnS0pYMjIFbqZ06dSbkGlP78A6zKw3nsyDhUgf/JRy+s1QQzjpGNyAgU29yA2CnbTEY+/jRMSm\nQc5K6jS6nYCWPSiuZSDvyIekNBkE0UlIzoMRW8HVGsMJgFlf/0YC2/hT7/hSriuADkqAfkU+3OLl\neLu9ATIawd+Nx2E6ekCR460aTnSIitn7du83Qwb4kchC0v2URjhVJlOgKbU3cI+XmqoMeJWT0rja\nfkqjLE5AeElCUmNnODhec2qJ7khi1mRc5aU0rnJSGlc5KQ13JWWvGGhS+lEDuMpLaVzlpDSucuIY\nmuTlqqRs1UKTkpiXGVc5KY2r7ccxNGkr1SYp26qFT1XFU8we9WuSJGa9KuMFWlwJvFxSTnzrG68U\nTtTnMBy0nyZVF48016lX50rgBC6hrTSx/AflotokZc/E5fDfUJi6VypsrqW0Hl6Y3bZe2b4eyQ6b\ncFiH76ynyf3zXbjyJTEBhkvRE/W/D6ek5ua8Gxkysx9DkVuo+glWew5F0u3zroG5/7wJ5hlQIyRl\nrTkB2IpH6Fim9NzJ4S++ZzSSQbkRGIUsvMsHdgBtenel/xdd+LLHj0x7RMdtX5pA+7ZSbvtRhVMH\nFc/kNb0XzuVS5fWs4msw6OfZfLINGAk8k3YBtM8JuCQp66j94OD1LrZPXstfCx3vpaxmrzJJ2a8Q\n2zyHZUr+JKIV64kUrfTAgSTmsb1ye8xz4UvAUhKrEMXNX4BatXLVWkFaksR0xEl94DVk2BSLLLuq\nj0P5VHsU8nTqe0rhD+cwIynX37cYC+aSAiFa4aWKOKkFgfUZP/wArV7/nkjEJtoEwn4/aNnXgMf2\nIo5ES2pwyz2HyexxGB0QbhqHFLy+IjgZjOV+2BaHkrLuoRAZyyvQCidQcVuxk5StMlSZpOzXlFY2\new74EbmYfMQjl5LEDPaVyjWV6a6aAVLAx1+TC/YdcfICMit3D9LZMeKSfKoBDI3QfyUslVW4Qg/0\n6QbHlsnKFI2hCjjxBv+2DM+KoPPrX6IDuvrB33O/5I2L/2VLci9ePzSJV9MeJXHMTHyUOjEZCBv+\n12gukbosTm4FspHiZy5KytYYVNRWLqukrKvyDUa799TyV0lIUYNwxGn2RRznGYD4erDyoiSD1bI7\ngbGMbROyoDP+CzM6T5P60UGkesgzuFS0tVpRHicbseVkK6XkU0EuI57mXQvx63qU1t9ZXJ/9yY2I\nlays3wUKzyBLXQFZ4lUDOJFiDqHNv8UvPhKDjy85rw/k/advhlfikVtEKAnn2wK57PmjJbP6DyBq\n5z+cBvJ84I1PSmR2tWIr5XHSBeFkFhJRsGk/6cDm2VuRjpR1C7I/pdFmy6Q8bER2tcMJVNxWSjgR\nhCvPqqSseirrUwaXbBmxLYNidfEucVLZiZsmyALaAOSimuBA/vGbR+FtP+kv97c7gbGMbSOSODVn\nENy45A71Iy1JYjpCWZyATdcvTHncBgTSPC2Y0E0HCLE6mdHu5G2AHrWg+ckx2EmF1hBObkTn/w7T\ng4oZmAW/PvgxsxfchtwMVDNtrpyuCEjgfyE/0A6xlXu2TYLUburJtGwrTRAufJGeZBOcSMqOAYbM\nHoStzC6U5ySNOBDZ1TYn4KJPEYRRvqRsO5t3jXZHKahWSVl1JX0S8kfXxoH8Y/4gPb7NK7f2Rx1u\nB/tEWb+lZUnMsjgBh7KY3kAxxt8SOXC27DLEPkBGl8bED1dr4ZXYTQ3hpIgFFz7kxPoo9u6ey74F\nCZij0pGIdj62gQiR+ChM8iYfsfQnpizAqpailmzFXlIWpIKUyktdXJKUdRta4qTSPqWK4RInlZWU\nzQIWIHUUmyH14EpJyj6/1I+6+Zn8o+xkdPGXm5CyICl/wdFaK60/0rIkZlmcgBOpXWpdy4erZvEd\nzsul6hC3kJhQl11RschKtitCUtZlTmoHHmKt4Veyghpw6ObjYO5G2YV1i2DNdubcsBBO7IXd19nv\noBVe7CVlk5Ce49vIcHIT2AwiSjgJB4pLhttGXG1BJpwG77TCSWV9ilFehlsdZsQVXkxUnhNXepJb\nsZkkA6SsUR9l+wGk5JEKVf6Rke/0ZPYgGQ4ZXfgiFUYkqjvbCDOfL6nifhCJWUyvwKmqCxXlBKx4\nsXT8R2CY/wDLy0kh9kYCVt0+6gAZvZEkqTD14xrByayGhXTTwbBRXUhK6UT5FbaVyv9/X4DYYGzr\nLmrKVuwlZZcjJfV0lMNJGNbDbaPLX2jEwkaY5W0tcVJZn2IlKas+jC59oZHKc1JZtcQ9SN3EYmQs\n1MfuuDUA23XXcGOrLZWa3S4GSIZG51LUt7QiiVlZTsBeapcivqvzMCdzy/4jdEB9X5gauwBYopy+\nBFqRlHWklrgNmIZFAvMfnMinRvfMp9t+HY/r3oXCjdhdowN4IBwWONpXK7biTC1xL5LOAlL06TNk\nckJFGTK7lYZWOHHHp1wWSdnyepJqqfVgZDH+OWQk7IEkYBoQ5bcljg4+mNALgio3O1QMkAYNEpMr\ncXS1wi1OLDBAcCeSns3Ck7JzSQ3A6JeBF7ajwdQfFZMRh30EqaTcGyl39TGS3vEmElp1uBZ3xP7V\nPBwGfJNO+Qo+nuDXEm4fCiHXYPFBmsNkoB8y7xaBRS1xFcKDQXl/+eX6gZcBVdR+Lh0qq5Z4A9JN\nNSN/9lZHB2+fuYHZyC2iIRWLScYBWaeA/5XXo7jkcIsTQTgAbWv/SkRWBME4d32eSLBm6rCPYOZa\npMOhOTlZcKyW6IMltcMHyQV0qJb4wT4z+0cD2W8jdDqL09cGXSajbshnyO99yPusNS9PaY2GOam0\nrYRT5TFJLaDK2o/AiJsxyXJRkZGwJxKA3oXkRiYp7w/ESWfxQvuXmTnxC5Z8V2RTaL88GJFI9sQC\naNBBIrkaRYU5EVwHmOmeaibkzN4ydSM9gO6tYOWY+sjNNwhLT2uzWz++GmFEeDFhSXmZjqUsVinc\nAIw4pmNuh1fh+BZkglO9Th1CtQ5aNeJuzwDGrZ9E4zG+DHr3C2SBojrrr1lOKmwrYUDR7EFsnetV\noS8yomGRXVtUsv2EVfiLjFSek4pIynogljseEbzKxyL/6HC0WHAmiYzGtfAguwI/SVAMJBVAg6MV\nPvRSQI01SWBRZuV8kD9ZlZTV41RSVo9OV0SfyVu4MLPsL8oD9lzXg/iDUXA2ANcXeV5yWMffTiE9\ngVeAOoitqJKyDm3OAKyINjMz7FvmHVfXaamicQVAMU07BjOx37c0/mYNScDnkV/BiS1UPt232uFW\n+6mhuASceACNnCpZVHSOxBVJ2VHA60hd+EeQ4HsaspbSerq+NPYs4K3Z+ZxAovVGF3+UCdFMjAIC\nXZeiuJR4DJmY+AXJs9qLRAh+R5JT1dQOJ4vmN2L28SPq9fCS6gaOoKb+ZK3qSsKFxsrXnHKy92XH\nZGSJnbWtTEF6juXayjrEUdY/OJ/QQS9SfCKNwmbgmVdEbmIP+nx6nmcm3cXab8QlRj8+lK0bTEj9\ngxOOTqkFuNV+wqmRw233fApQ9nBbJ69H3AkJd5e8a8LCib+OCoX2y3OSSUgy7DHl+RokflBeaoeg\nWSjPz/6R1XMLSl2xCculOdpuiwwuC8oai14e6IA3kCGCqs8Rgdz5uiPxFuecAHAj09fryb12FU2x\nDCpN2PLQFijSQf4DxfDebuXTQKs9NTWQcstWrkd48EiDelvf4JZekBPqxcnofFqeh/g7pQW1B/qO\n0PHTzuvIjohCwg8tqImcGIGg2deydW4DJM/aT/nkJGIdlLzWK8KcJixuQ93WFCPu+hRAZIf1yEJd\ndflhEbIAOggCWzJt5FhyX5dPTAgPrZGh38X2sOq46z+4vHGKqmx2HbIqYCTSLd6GVO7KQ/KXPnN4\ndIKlro3J7iNTOdtFSMr0H+VNdF56WHOyH+FlAPAsYrn/Q0TWW+BUUrYd785/gePYrkYy2W3Ln6Pj\n4/SnkL61oz01A7dsxUd5LkRGEJH7IPHzfLZsgvjzcjc/BYTdBX3XbGbrHrPyjvUtRnNwi5MEIBcf\nZEQaqTzbb8vrYsVaTFbHW29rCO75FEDmBTMQP5uhPLKBM+hqjeK9f3/n4LQ/2ZIoe59B1jp6ILfU\nk4ODHZ3UKcrrSW5DUTNF+rh3IB5+BzJdD/AqEnsqLZ0av5e3ZudxEktinB5xDNbOQYcMKNTtWKQe\nkg5N/tEqJ2DhZTLyXwxAfDsILw4lZTv2/YTrfpTrbIXzQZQ6ItBnFCquwGT1CK/8FVQP3LKV9XUa\nYL6QQjFiK9lIOnkBEplsPkbP+Z09uSPufsxKMQx5mKipnMQ2MDCg7WzuvdmTxGR4/uG96DGzeHk+\nD4zeU7Lf4uX5jBmwmRXKaxMaZsRdnwJcM/4PPHKyiT2eQtjoi7TvEEADUvj3u8NM2tObrU0tJUGK\nAX895PWGFdldKWrmxZ7PK5Yx40oM0xOJtS3B0gU+R0kRTLwpXeRHUCeUwbPrM2Pp37xwFFqZZVaj\nBRJFCkQagbqSvWkdSC+GqUVw6qJc6A/IQk8NotK8zI4+ykkkDmcs4wvMAGYzL6T/yBt4IR1VI5Yy\nohobSLnByRHDEp7dvYjbZyzj2Y1iE4ObQHpfWNflaxL/OMHB5EOQmIOl8ihYBpfh1DROUnWhrIr7\nhEYv7aPdb08SMCmL3ch49eDKgpL9krAshocrgBHnnIDwchcS345C8mttsLjIROLKC7wPDHh3N0WI\nNVwEPHRwd3tIaAiD/hfIKz6vcHD0DuLTa3OguB2FG7Kp6lx9HY7lH1sgAQEjUmYojdKqZuFUXQn6\n8Cq8pqqAO7z8f5NPvcrJ1fZjDWecNEP6RdHAXGTy05Fa4iWXlC2vQM9AYAsizKOO/l5Elg8NQoJC\nMcgqiyz+/wgZXeWlNK5yUhpXOSkNZ5zchUziNEbqSU4GHlI+v6y8uBqTtIcfkvIyUXl9L7L86v8L\nrvJSGlc5KY2rnJSGM07WALcjSxZVXs6iAV4qKwSm3gFM2EpiPlEFv+lKhhm4E/ljNSOJeZlxfDi0\nxQAAIABJREFUlZPSuNp+HEOTtlLZpQpqEUwzEhteiOh0qBjeMKhJVcVTzNDEjKTb7Ke0NoaWEIfE\nVcKQ5VZfUEoZsColZUt4uaI5qVd1sTdzp0CuBFspt/0o/22VPAJatbgSOIGybWU4EFl17ae1y5xU\npmg4SA/0OJKb2QdZWXIXklRtUD5rC9eBbihQAOYtSGpULSTBYysSltEBm0A3DB3t6TzhW/ZuWcW8\np6cxe3IrpDjxJnd+66WEB5K50gdJ4voXh7x8CR88RNfU0SycfYb+8w9x8DV46pyZMOT2+Rey2j8P\nuZNtB4YhM3gZwLtBuyG2D2ifF5c4+a/VAeE4Fk+13nb02XBKckiuBE5caD9DsIgtb0T31Ab00wop\neu8NdM+8BBEGzkZ7cO8Tkl7drjNMDplIwnsfocv3wvzBHJj4EvTwBu1zAs5tJQrhRFFLVK0lHIsF\n+AJ/47FlOa8N9mENpe3GH3hn/kGSHn8aWezjGieuyjckYVu9tzZwAZmmjwcaIQFZsFF78wRzMdfc\n05La3RsCegaFFPGwV1PqdddzfXBtRr5vxtgggLCgJpib53Hk80zufGMJzQ6c58JvL9PKx4AG4YiT\n+khcRYesH8xAAvMRyuclvMx7aAVvPKWnydyVhOsP8saTZlaeM+OJ/NW5SFK1BxK8qg3Uawu7Rz6A\n75BhLLz5DojVXGKHW5w4gh7xGF7Kw3rb/rUBS66thlAWJ97ILHcsUiTLQftRIelO5g/mUdT6Pfh4\nG2bjB5jv/Z24TUJuDrC8xS0kbzRB0ELM7d6ET7ZBD01K2lTUVlxQS1RSw8orBlUI5Zfis0VlJWVf\nAH5ClJlmIqXYH0O6hlYKeP7o2j7MI1uncfv5dXS49xoeHzSPQUVT6ZS0m2Hxz9Jn+hzapazkJtNE\nZnaJhI6hzAufSrNN2xj0v8X0Dz6kfudBRBnQySqWSwpnnKxHhlEvIkOoNggnYMVL7soN6JCgi5rj\npT4bgNvGQLfRMGBnC07sfoEP79zJ5rRr+TW6H89tHknWynSw1A6qEZw4ghkLN0V22/avi7EE+tCO\nrZTFSUtkeev7yNDSQfuxh3rFZkBH/YXt2LRUGnEB0C61gII0M+Iw1P1KqjxohROouK2UaSduwCVO\nqkJSdjG28o+mkr3qZTNtYEu+XyyD7LuWXM8J5M7fN6uIa73bUuxTRFp2CoW5Gfise50HgLciwEQG\nN9w/nlPflZytB2WsYrnEqCgnW7Fqw5vPZ2JGVpZEI2tKDUD3FpDW9zk6HgyCUwd5f3mgcsQ6oCOk\nHkGWpKVilSKsqr1d0ZyEWx1UC1lG1m5hI2Z/9xnhowZBfhHE7CC89TWWHWN2yHGmHZB2hvD6rWDT\ne6AdWymLE5Dl6OGIg1iGffuxEYVV148UI8swilkTcStrUVYv63RsvLs/7ElAbMSkHP+3ejKtcALu\n+BTAuaRsm5I9rE9uVPaKB7LWfIakpQIuclLZ2W1n8o9zkG5yK4A37wvk4kcSiVR7SV7ARR10mNyX\nwe8+ALoCXpi+i8L3PsdXuUwdYgZe38o4REnJNyOB3BVoE2VxAlaKb9djW8XJgCxBaLDrOWa1bK8c\nri7HV+uWByLMtFSOMqMYS43gJMzuQE/gH66BbYdhWywWSRRHmk0BQCfrN7RsK2VJytq0H+fKHJ2g\nVxs2fVofPWdoAQz3hzmLeiA2k4eD6ola5gRc9CmCMCencO4kQUTOD42YSva6lkhOumucuOIkvwJu\nUX68ClX+UVWD98Yi/+iBovbWceXnnMBi3sXIXwhA/QtAOpjz+Lj9HTzL50rVQAvyKLVeSytqbxXl\nBKxU8DwpXequGHjW7x1m8R6SDVIENuV4Q6H9LTT+5QxvdJvBj4tuZcMUoIZwYo9CoBHnkIjsg5St\nnKhCj1WJZq3wYi0p21V5bzaSLF0H+bOPIsNLm/bjHB4M7/M3uj2nCEgQTxuSpAPfw5QTb9MKJ5X1\nKcZq+C3lclIdkrKqstnqH9Is4qdG5ZELGMyQdcxI6LZ+/NDuDoqn+fATFsdhUh4egL9FNuogsjph\nsgu/ubpRUU7AipfNCA9mLCWcdMCGekC9QdDYxNhO68g62YFWj0cy5eQXGL6DRVEQ1UMWvfoG/qae\ndwg1gJNwqzeNyqPW5H9YxD8U84zTLz2OTH2C8Kkk1WnJVioqKVvCieO6iR5ABr7rG+EZn0kuoqgV\n8EkhEuIEJxUltcRJZX2KopYYbnWYEVflGyzD7ZI6aS5x4mpMcqDde+XVflsDcHd9OJZmKw5ahNw+\ndsX4kjB3F6NS3uG50R8Dx6mF9BeMWIbdLQfcyp9r/gTtqL1B5TgBhZfJgyF6i1gFWMLr+3Rmdr9y\nH8vme9Ho+HEKI3LRTxKTKELq2wci/BWfLlDHCFpRS3SLkzAHH+RjqefvDPWB/sq2FyVOUku24khS\ntgdl1x1VKjCEOfjIA3SFtHzwJPmvKPIe44AXPlc+L8aJWIGWOKmsT1HUEsMq/IVGrIfbK1DSCl3i\npLokZctEPnBLsx955cVl/HNfbZrGNCUe6V2pKAZ67enDEyOGIMoAmoHbnGxoOZKOrCYby8xFMVBs\nhrXTI/BDWlW+8vBEBpK1AQ8D0N2H1Lh2kHTIwdkvGyorKVsmKpLWo8EUoMpKyjqB5ETovJpyx645\nbEdCUl90mwi/xyMj1oIyz6ABVItPqU64It9wE2LUq5T9dwKhSOrP28j02RIceOUfUksPt0H+2JO/\nF3Ly9yz0ZJFwLB4DpYfb//beTbdGMTblZjUAtzgBiNwVw2HlwJbIcNsTsQwD4jjzEWupBXT3hMZd\nYPmAJvzyR3vQXSCSDkiNAM1gMtALKW01AHEEnyL+PRnx8TchM7ml1BLDrbaNSOmXJvf6MX/zu9IF\ncFICMDPtOJlpMuDWAcSscv9Kqg6TkQGAaivWkrKnsdjKchzaSrjVthF1YsLjk3vZP2EqZsDXH1IX\nN1D2UZMETWiyEqvA7fZThcNtl1CtkrKOhtsgkepO98Hb89cTHdCaWbzGOf03JZ8bsVx27fPJrHT9\nei4F3JbEHPV8bdIni9dQJ7UKEUd5WwPoMA8WTLif/66fx/n/NIP0k7B/FRxIw2wG4usj/e5fq+cK\nKwe3JGXD7F77AMcH3cDeJYlwJgCcakrWwbbToSkn6aathDl4z0ze8NrMQwKb2SH1Se2ZC6caYbEm\nIxrWS6wCSdkboWS8ZUDMrewsciPKcDtsKtnrllOmhI4dXBluf4tcRJDyfJ/ynXuRjmIjKjjS8QEW\nHZ3M1pBTkHCSx+hP/Dur+OrZ8yXjMhVZTs5xmdEKCbq3BzogqwabAlOBCco+XjhRS8ygdsm2B9C4\nCezt+xKL4v/DrL2nYNJRmOSNpIuponHeYFZzXg1oUGBP5aQdcqtuhEhaBCG2ApL24unwaAcwUIi0\nmbpoWBGxLFRx+/GEzl3Z3c5MMbLkzT+hJZuz+1I9udbVAvc5aWik29gThBDJqYI27FnfBuLKi14r\nbrQSmlmuDLdzkbkCT6Tz86RynF557MGSHOsSikAi7uljkUmuDP58egQez35bKqKiPYkbAD5Ccng9\nkczupxEHMAW5vN0IJw7VEgvxLGnyRcCF/I4sO9IGYrYgzk8Ny5iBETChFf3u38rzeW8S+L9UzLGe\n9B2wAL5zdPbLho+Q0ul6JIKQjSTpeiEj4d3I8DPC2QmsUQCEXdjO658uwq97OgX55TtJg6GYnMGV\n+/HVhCpuPx6MmriL1U/JibKBPv+7APfFosGbpjO4zcncLvMo3p9KPZLpVlyPaV3qcV/cKmCJU9eq\nvm3YV/Ef7MpwezSSr6SuGd+FBFsXIfleTuUfncUkc4BH/lnIW81Wce77Njz90+3c+u23fIW4BxMa\njqgI7sDCyQfITFwv4GeEsx8ooz//+TLorGx38YY/P36V8/eehIA6BAbG4RMdRp8d//L6qtkcfeUl\n9n0BxV/IF55BHOuwnOdZX40XWAmMRYze2lbmI4VUP8diK+ccHRxutW1UHtufT+dVWpT5pTHKA6Qh\n/F3GvpcBbrUfqU2rIhj07RiyeA/ZWLJEBl/YinTgVTdQjCx5NlXldVQl3OQENocfKbnaYJIwkET0\nm90JnjELXVPHw+4zQLofFG6cjdWKG5dQnpPUIWsbjyGOYDoSZD2OeP+xSJfHoWhEVx8wKDFJk9X7\nXsDqrkE8mfoErf7Kol2Hf4lbK39vDLYO1YTGIiq2nHyA/NSeSPpid0Ro/VnEGTicyU2Mm8t2v9XM\nzYapAdBx+W8UtTzPz7k+jOp2ggZn5rErVBbHq/3Ks4hj7YisQzw/qAP8VInbYvXBLVsJs9o2WZ0w\nFss6ilNYr6mQ18HK4xSSbKgxJ+kWJ9IxV684Dv/Wek40NFCExHmGT4Tpz+1EcrCNyn7WLciEBkVl\n3eREVvAVIFfXGGU0ttEAJNGwaTxgu1jxNEp2fnAfNiSFQmIsFVG0cFVSdqhyUUVIftPDwD4kvNgA\nWT1YCoetZmxMdl+6s8CXcwEGGj/eFr93ltB7lwwuY2xP4UpexKWGtSTmQYSXb5A1oN7IH3wYWSng\nuATL4XW0HiN/3s/JkPjrT5w7uwFd8kq8fjxOSoaYvR4JT9dD+MtExim1O0GXcU2r7worB7dsxRom\n5bkYEYzLUx7W2/avT6DJ5Bf3OJnSFCJegBMvwFN1CXltCLecXcVZZHRWZyTw5zh4sgmceAF9zDPQ\nrwXih/RotDfptp14ImXPYpH+cxugd4c4YB9rvUaUjEZBetunlZO+8uh8UhO9sSw/cA2u1FOzHkJ9\n4OBzI7L2savd++bOtaBprvyh6dhmPW8DXvaBbTlS+kMVpw9XThiLdHNNPhCd4/JvvZSoNC9wHTe/\nksLhuYe4HukBFSO9oOtQakXp4cHOYG4LiY0a8/HWxoSm1WFds3okHjiMmIbm6mxWmhPrAJS1rYRT\ndj1JI5bwTDBKlcAawklrpC9ZjAwQb0duBJsQHgxIG/kbme/1Ad667j/EbfJBWtAprHqSNYETAHPH\nLr3oem4fe87BSG+oO+5lfvbzR799JU8d20oKwtFNQMgAeP18CBG1J5CxbxWWgF4suMhJRYbb1hfT\nDBlafoB4/bOlD4WuRugaKV2rcLvPioAdOZaJGasyV7QDpvhB4HwdnReNhX+WojG4xQuE8ffqh7gv\nMJCep6GVHnp0h/MdIPrJV9jw5UjyvprP6XO1obCAzLN+nEg+zpHkYZBkRkL2YVQkjeESwC1Owqy2\nwyvwpUYs8qk3UOIktQK3OGmNZfZiI5biZyqsC6d5A4GhULQ3HnGZrZU9wtDwcNuekwQkI2Qx4vNn\n4EBSNuL8w3j734pX3x28PGod2xd9QqEhgCOxsQTr4NYRENW8Dl/V/x2/n/5kb/oJiC7GkmEUTlWO\nUQciTB/AUup8BDKvmqdc6J+I2tv/F0lMcI+XmiqfepWT0rjafkrDGSffIqsj8pBQVQs0IilbWQzA\nNr3F4SqK/4e4yktpXOWkNK5y4hia5KWy9STVSsEmbNXe/j/oBpeFFkhg+hAaUnu7zLjKSWlcbT+O\noUlbqewyBrPVcxgwCyloYIWgqhoqmJVzXQlQf3MYkhb039K7VJ0Knpeu85XAyyXlJLB98yuFE/U5\nDAftp3WvRlXGScce/lcCJ3AJbcW3UVeXOalsT7KkojQSiG1FKenUWGTVkRHxxakEtf+CZ6L6YEYC\nMYORGbpNwE1FjzPD0AyZ6w7CMolvQgmy7lfemImTlSwaQBzCqTpr5oCXJNRa7Q2J4z9EEwT8fM9C\n9n7/AzAG7riDR35pQUvgv1t1GB7ZgznqeWjUjT5emzkXt48QYJ35KAgvNYCToUAhzQL9aXp6Fbcr\nB7zY87+w3wvpWGzAdopnO/r2nejgt4mC/YcJAjZExYP2baXc9hOz7zytkdJ44HzG3/51OA5m/A9k\ngfY5gbJtZTjwgdjKEMSnxLG8aRSHEy2Tw2HKzvbbRoQPAxDvBZHnD4OLnFTWSaoVpQ2I5bbHUiDT\ngKy0UH5aGDLj/zfzXpnGmXulZqQ6K2cA/No0ZoZhJJImZcY2nTwcxUlaFy7VKvYgF7sFSWlshGQi\ngA0vg4G61O2xhCBdAH77M7l78GL2fu9JSGAzXlp7DQt9anFu1hT0gxpTkld7viV+H4/H+MQ+hlJS\ngVTrvLjIyTDQ5dDufn88X1uFDhjSCdh/CCm/aH/j1wHF9Mj35uVmh1kE3BjSmA2R5+DK4KTc9tMG\nS9HFzVjW1OiwXX2ns/usrfLYiMxzKzP+WucEnNuKyokiKRsG+OLZOYpDR6MwI7GKIKOBr1/8HgM6\n0pb/zOnRtwOQtvy3ku1CPMj8+WfY8Du4yIm7krJNkT/4E6TCyyCcyoS2x3f6DpuKV15A3UZwRtcE\nWZlkL2qgWZQliZmMePgGyE1ITYq146WAU2ltiEltQz5wTe4uAN7vNoPYzFiSb+7H6g+CUGsISrZp\nMf8a+lbfVbkHNznRowNGtlyBAUlpiZzUGVka701pJ+kBFJPSZySxW6VF6cMC0RjKk5RtiLSl9yij\n/aiNtBHSqpsitw310dTJdnMg9LZWaBAVtRUHkrJ6Qjv+W1IHqBZwtH1HTk+KJGZSCukrs4iZlEDM\npASb7TOTzpC+IYOKwFX5ho+RKXoVqqTsW0guUz0sam+xlJQkUdXePPAa1YeDqyz9aCOSB3CyQ28O\nxI5CKfyE7WBBPQcgq1v2AM9QgaKt1QRnnKwHhmHhJAeLMqCVLGY44EFxnJkz5BAEZKf7MPjLazn0\n2GYCf+xP5J1qjQh1HUkb0J3imh8/5jw2y+++pEZwsgazWcfJBVtKli0t3PcksAOLgzTafWV7Huo0\nmbW/iiv9OrakHIpWbMVVTmYhf7RN+0lDepCeeug6oA5TmxxTBA/+4SesVCPZYfV6Bz8pz3CGH5Zm\nq6fTCidQcVux8imgrmk3rI/jFNJLuwCYj/ZHbCUFSbtUnWEzZC7IpLyumE9xpSe5ldIrwkcjgeYA\nJPFzLNKzP4zNLX8M0jUexvQ56eixeOVgJAHqUOOpcMYTcQhgGWqHKY+SdTo9sMinXm5UlBOw4SUM\nGAbe02mhCyAHOPt2FjOmvUFm8CCefvIh5K8vRO6TPkB7guoPZEyCH48ANw0qWbVVQzi5Dp3nZ9x4\nXLpTfv7QZv0hZOCojj+MVqfWwW2T6PReFEOAL/roOHDucfVDrdiKM04WI1WjfkMMXFVLtGk/44FR\nwJP9YcfpR2Hpt8gK2EikM6Y+Iu22I5GOWXckqwbQDifglk8BGA5N76BDyxvogHS8avnCoBZpSMe8\nFlLpwE95dFYeI5D5gIr5FFeH27uxVXALQkrxJ2GpM6Aqm1kHpRXkM37dnyVTSyDO8ofd77B+aRwu\nrg4yI/KPWhhvVpQTcMRLMJjry+a5LPg3GxIeb0fyuVwkGKFOWORD2/vZPvBe8qIjSNFBxj0lxl9D\nODFgXt6OyBwxSu8gqH1tLBZ9zVpIgd36SEPwZf2LdxKVKbeR0OFm2Bunfq+WbMVaLRGkMvlUZHn+\nYaQe6VGctB89yCX7pCB51oVIp9PVR0lVHC1x4rZPadn0NO06/4sP4kuy/PwJv6YXliK82Q4eOdj5\nW5c4qaxaIkiZ9feU7VQsgWc7mVBvoA5p8/0wI03eGzD0CmHv6GzEDJzU5i8NrUhiVpQTcMBLq9AY\nau9Pxz9JYmr9Hg1jzpThMETPRP1iuADmiC6Y70xj4MbW/L7dTG7nQfw4bQJRk86iyAXXGE5+6TqW\naOSWeSE/AKNvIRMbH4DAbGgOaU0COEAPohM6E5x6gcTh+SWjky8aTrT/PVrhxV4tEaRxqrykYttF\ndiqzWwXQCieV9SlGeZlHvZi6BJ03EYPcBrxyLzKwQwSBiwIpdODWDHiQVdyUrXNzKYq3KeVdJZKy\njpTNsqCkvLZ9jUA7SUwvqNOARRl6uipfmK+D1HHtiZ3jj+Pa4yY0LolZUU7AhpfNgI76WxIouBiD\nF9DLCLfvmgVE813GGnLSV6DLg+KCFaR8Db1awohab5IWs56cSd9h1UMYQo3gZDtHB69mC9KFaHMq\nkwbJa2mQjwyIsiA33ptgtpOS2YgGOSmYLp4hHsjpD988n4B0zgBt2Yq9WmISUsQmi3I4+RvpF+05\nDpk58chUjCswUcPaj52k7EZMQe346pAEtY2AIbOYdlOX0N7Bl+mRAMQe/04U5Qdh1SmrMklZRyiv\n9puVJGYdAm5tRJMDh/E9JOk/N/TswMCXnkDKyBVjKUhczBUiiekIrtTDU94bBuQTmF+flvHTKNTB\nkvueIO3VreDTibUx3vg17EXDXPBqDLUT9rHyLPz48mLGvf0iOZzGqmyIViRlHcFlTnT9Z8PO1VwP\n1AuEU4m9ONc0k4Y+yegwo8NARlZj6owL4X+mBUzr/SqNX/qU9piJvPtB2BmEhPk2grZtRZWULavu\n6BqQfBcvoH0HiIpy1UFCDW0/VpKyQ5n+WQz+11pmMtTAlKMMcQ9k1v/RHv7s2naNstcGqGZJ2elI\n8mVHxMufdHYCKGDk+TW0uyhZDV17wpjez8C+ncrnvkjsRHWURWg8FagKONGh08HoO78k5y3ID/Ln\n/NY6gB/kRPJ97XHQ3heiDFCvkBl1f6bese85+vYxJk34nTfmd8MSq9MMHEnKPoxUm+6jvN8CEZ8v\nNZN44wOrYadYQ8R9L/LJh9fQpEsiIfWPYKCIAjw5dL47F17PBX7nfK1ztO1goHB/MXuWDkC08DRn\nN84kZVcgGT0ggcaPEZ2X/w+ogvbTmKlvv8zXygn0QHi3j/E8FIWZWqX2NgCJ6DF7q5pJFbOTykrK\nPoSUMHoLWW/6sOPDtwGFbDnkj19cHkbgrboLSV4Uh9w8c4ACdG+PJOqZUbR7aSXMW4MMFcrg6PLC\nTU4A1mNuOohf3zpEM6BlXAgrz/VFisIUQ9JJWO2L3FDrcniSL2E+ARzbm0neD8uQUYnmyhE7kpQ1\nAP8i629/QQLzDosWeH60kHBkeubgvEAoPkDSr3VIorXVXqmIzeQR8EMgXoUFHAcuhv+J9Ck0Jxbm\nTFJ2O+JAy7SVKh5uawXut5+Wh7nzD4ldtwOmdIdZBz2R+44zB2iCDaZK/eDyrCoRKU0EtvKPY5Fp\nepARtLfjw4eg043j3Xsj6QLUa92UQk4DDagT4sOdLVN5dE4Ui+f2YrU+gW+3hHLXk9FILbyhWNKA\nNAU3OQF4kD1zf+J6JP7m+0tHuHgYi/huHnKDzQZSWb1pNCdj2tMY6JgKXPsNwo+m4EhStiuWlJex\niJN0GB4IjA8jDAmwevTrjRh7OuIY1Uc6oAMdmGekkVkgU6LNgm+lJK1KW3DLVm5ArujuJhCgD1F2\n80A6pmU92ilHDlPOoim43X7uXFCXociVtQd2T+mhnNYLua04erTF4k/CKvSDXR1u11e+qRMyJd8a\niyZVHk41yrwxk8Kx1h3w4CRH5z7B7kXXE77zP2y8pRZN802c/28+ZxG2crdD19Sl/Hj/Vvh2LXKv\n0KQKnPUwqgvS5QtEMlbrIX+yH04kZbu8WMCuSVEYkK7XqBcnU2Z5u7NAgXB0EWRMqr1ekzUnBcgf\ndxGx4zPK9hlwrOyVPBLa/qjcJpyKzuqAAnTAQ0e+YTNiwPdPfZ+Nz/wHbRXfBtxuP0La8X/hved+\nYsg/u5SQa0XgVCrmcsFtTu77ail7sLSH31ZMQBbqVE+orjyraqo8opEZqcaIA98HvITtdH19u2PN\nEAbeAdwfsIIWyfDU6Tqc/a0hXw0ZyyeHn6ahMZHFSePYMykT/YVky1puY1NmJvYmx1sHek9IW+rK\nb72UaIr8qfORtIU5ynvvIVI0amqHPScA5hsataXw/Em8gN4fduGNaeMRp6dDBpw5WCax6tLgP3W5\n98hb5B+NIL+5ji/jH0CGU+GgHV7cspXbQ3RcjDTjE+zD7pyRnI7rSWnVGh2gQ0dntte9nfB0+bIM\n4I+mz1CYuJeaxImU/JCLaY0k87XC8eSENQ4rDx3g1dGbbyLyoGZwAmDuWSuYOrnRGICOXpDbcApf\nxDdTPnYmQm3CNgThuqSFK8Pto4gS4GIknaEF5ad2KGjL0GVTuS4T/ulxD1Mfu43QF5/jk9Cm8OB/\nSQ7bzKjJ04hq2ZpCpH9tAooyEpk/IxdW/AkdNDl5m4JIX36HLM+MQNGKR4gvgxMY1teLW4GenrDs\njxlWnxTQaUFd9IGtEPkifyCfvuYT5J6JoAMQ0P8hpJncWPVX5R7cspWbjpvpA/T3HsLpuCZYjN1k\ntVeM8uxLTpa0MiOShDhr+k40GoKoNCdtoCQEoUPkmfchyYX7rR5L7bYLkdkPb6BjhOZGYm76FBg+\n0JNRSKiqSbMAjg/xRValnbLay2S3bcQy1DZW6AeX5ySt9Sj+QOzRWv7xIJLS4KRPH8P6b0aR3/xh\nwg+ks3RFN8g9j3S2ooAsSL/AhSRZKeEF1L0OPlx7hAlzJ8CQN2DnVxW6oEsAe40OI7aSsk8gtpyH\nzE6VgmFVBHrgQNc65GYcRnqPem5/5QfGT53Imd4PQac6QBa6sOeYu2YeZzLgXO9WrGsQjCQOas74\n3bKVDLOcIKD5eSSrTR02maz2Urd3UTtMXnkg0YdNM7ZD8PAqu5gqglucWDf5k1gGk6estu1f229r\nUEHSTZ8CneIiMCsHnM9uzNYYtXSayWovZ9uOXpeNapWUDRxShy9ME5gccz3SqNOxSoIGPKBhU/71\n6kuL8aG8ee+fvL1pOKf7rsRsjgKz07DE5YTbkrJmZCyRkdWMmEg1RNeC4L9j8DOb+XqpmdGx77F3\n1hyWhOvZmWUmOcSI6abrifi8EA06SHDTVvY0nMjeIRPZ5mWfY2wPb8w6WNZ5rJSICYDMO7oT0Lwd\nRO8v59hLDrc4qYrxseYi11UgKZsUYWkBDwZehJ1xKNH6akF5EzfbkIa/Ellvaa1upk5DJj/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pRxMiWbXCqZ20bBE8iNqG2lD7KkWYFUWFbbikdJ2S8XF1AOzC+QrYdfhEKnC3bmHHqG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\n4v8Bv+PKHIjjJGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80f63751d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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60AnFeMSLeR7CQUvEhSMbkZXGiHOwV1mZj8yL56PVJ9kVghhmvEEF8ojpVEKa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+sUkEbocCSncWOExovlLEcB9UmKepmhzeswznYhtzjxVFI2BGnupSt3URub/GO83bFWYD8Yyr4M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\nBoPBYDAYDAaDwWAwGAwGgyGc/B+eerT1i3C9WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80f64354d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s3fZFHNCbkCDydHV/78quk4AlBMg/pKvTX2QdApRQgxu8DKgxJwAxXRMYgDwq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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80fdffde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show 50 images\n",
    "def showImage(data_set):\n",
    "    plt.figure()\n",
    "    Images = data_set[0:50,:,:]\n",
    "    for index,Image in enumerate(Images):\n",
    "        plt.subplot(10, 5, index+1)\n",
    "        plt.imshow(Image)\n",
    "        \n",
    "print(\"training dataset\")\n",
    "showImage(train_dataset)\n",
    "print(\"validation dataset\")\n",
    "showImage(valid_dataset)\n",
    "print(\"test dataset\")\n",
    "showImage(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "id": "QiR_rETzem6C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK\n"
     ]
    }
   ],
   "source": [
    "pickle_file = os.path.join(data_root, 'notMNIST.pickle')\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise\n",
    "print(\"OK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800441\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "deletable": true,
    "editable": true,
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8466\n",
      "0.8541\n"
     ]
    }
   ],
   "source": [
    "#using tensorflow\n",
    "import tensorflow as tf\n",
    "\n",
    "def next_batch(batch_size, index, data_x, data_y):\n",
    "  data_length = len(data_x)\n",
    "  start = index\n",
    "  index = index + batch_size\n",
    "  if index > data_length:\n",
    "    perm = np.arange(data_length) # arange() create Arithmetic sequence\n",
    "    np.random.shuffle(perm)\n",
    "    start = 0\n",
    "    index = batch_size\n",
    "    images = data_x[perm]\n",
    "    labels = data_y[perm]\n",
    "  return images[start: index], labels[start: index]\n",
    "\n",
    "\n",
    "sample_size = 5000\n",
    "x_train = train_dataset[:sample_size].reshape(sample_size, image_size * image_size)\n",
    "y_train = np.zeros((sample_size, num_classes))\n",
    "#y_train = np.ndarray(shape=(sample_size, num_classes), dtype=np.float32)\n",
    "\n",
    "x_test = test_dataset.reshape(len(test_dataset), image_size * image_size)\n",
    "y_test = np.zeros((len(test_labels), num_classes))\n",
    "#y_test = np.ndarray(shape=(len(test_labels), num_classes), dtype=np.float32)\n",
    "\n",
    "for i in range(sample_size):\n",
    "    y_train[i][train_labels[i]] = 1\n",
    "\n",
    "\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], image_size * image_size)\n",
    "for i in range(len(test_labels)):\n",
    "    y_test[i][test_labels[i]] = 1\n",
    "\n",
    "\n",
    "W = tf.Variable(tf.zeros([image_size * image_size, num_classes]))\n",
    "b = tf.Variable(tf.zeros([num_classes]))\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, image_size * image_size])\n",
    "y = tf.placeholder(tf.float32, [None, num_classes])\n",
    "\n",
    "y_ = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "\n",
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_), reduction_indices=[1]))\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "tf.global_variables_initializer().run()\n",
    "\n",
    "index_in_epoch = len(x_train)\n",
    "batch_size = 50\n",
    "for step in range(sample_size/10):\n",
    "        batch_data = next_batch(batch_size, index_in_epoch, x_train, y_train)\n",
    "        batch_x = batch_data[0]\n",
    "        batch_y = batch_data[1]\n",
    "        index_in_epoch = index_in_epoch + batch_size\n",
    "        sess.run(train_step, feed_dict={x: batch_x, y: batch_y})\n",
    "\n",
    "#evaluate\n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(sess.run(accuracy, feed_dict={x: x_train, y: y_train}))\n",
    "print(sess.run(accuracy, feed_dict={x: x_test, y: y_test}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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