emnist_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EMNIST digit classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test error rates of this model on EMNIST test data:\n",
    "Model was trained from scratch on EMNIST Digits training data using realtime data augmentation.\n",
    "### Dropout 0.2\n",
    "* 0.16% test error rate with ensemble of 10 CNNs at 267 epochs (dropout\\_0.2/weights/267epochs\\_weights\\_model\\_?.pkl)\n",
    "* 0.175% test error rate for best single model at 258 epochs (dropout\\_0.2/weights/258epochs\\_weights\\_model\\_7.pkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras.backend as K\n",
    "K.set_image_data_format(\"channels_first\")\n\n",
    "import keras\n",
    "import numpy as np\n",
    "\n",
    "from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D\n",
    "from keras.layers import Flatten, Lambda, BatchNormalization\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam as Adam\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# used to save and load training histories\n",
    "import pickle\n",
    "from collections import defaultdict\n",
    "\n",
    "import resource, sys\n",
    "\n",
    "# we would reach recursion limit when saving training history otherwise\n",
    "resource.setrlimit(resource.RLIMIT_STACK, (2**29,-1))\n",
    "sys.setrecursionlimit(2**29 - 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load EMNIST Digits dataset\n",
    "Introduced by https://arxiv.org/abs/1702.05373v1 downloaded from https://www.nist.gov/itl/iad/image-group/emnist-dataset (Matlab format dataset). The matlab format dataset can be conveniently imported with scipy.io.loadmat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import io as spio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "emnist = spio.loadmat(\"datasets/matlab/emnist-digits.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load training dataset\n",
    "x_train = emnist[\"dataset\"][0][0][0][0][0][0]\n",
    "x_train = x_train.astype(np.float32)\n",
    "\n",
    "# load training labels\n",
    "y_train = emnist[\"dataset\"][0][0][0][0][0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load test dataset\n",
    "x_test = emnist[\"dataset\"][0][0][1][0][0][0]\n",
    "x_test = x_test.astype(np.float32)\n",
    "\n",
    "# load test labels\n",
    "y_test = emnist[\"dataset\"][0][0][1][0][0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store labels for visualization\n",
    "train_labels = y_train\n",
    "test_labels = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240000, 784)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240000, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape using matlab order\n",
    "x_train = x_train.reshape(x_train.shape[0], 1, 28, 28, order=\"A\")\n",
    "x_test = x_test.reshape(x_test.shape[0], 1, 28, 28, order=\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240000, 1, 28, 28)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240000, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240000, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# labels should be onehot encoded\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verify we have imported the data correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# which sample to look at\n",
    "samplenum = 5437"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f44dba7ba90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dZm7Om1laFX927TbjdSUn+WRDGc9K6pC02d2fbHkT4zCzazS6t5dGr3j8ZZW9mdlWSXdo9KqvAUlrJf1G0q8k/Y2kg5K+4+4t/8NbTm936AJnbm5Sb3kzS7+jCj+7Mme8LqUfzvADYuIMPyAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQf0fPOZqxOiZMBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44dc935240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = x_train[samplenum]\n",
    "\n",
    "# visualize image\n",
    "plt.imshow(img[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show label for sample image\n",
    "train_labels[samplenum][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape test labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels = test_labels.reshape(40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean and standard deviation\n",
    "mean_px = x_train.mean().astype(np.float32)\n",
    "std_px = x_train.std().astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to normalize input data\n",
    "def norm_input(x): return (x-mean_px)/std_px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batchnorm + dropout + data augmentation\n",
    "def create_model():\n",
    "    model = Sequential([\n",
    "        Lambda(norm_input, input_shape=(1,28,28), output_shape=(1,28,28)),\n",
    "        Conv2D(32, (3,3)),\n",
    "        LeakyReLU(),\n",
    "        BatchNormalization(axis=1),\n",
    "        Conv2D(32, (3,3)),\n",
    "        LeakyReLU(),\n",
    "        MaxPooling2D(),\n",
    "        BatchNormalization(axis=1),\n",
    "        Conv2D(64, (3,3)),\n",
    "        LeakyReLU(),\n",
    "        BatchNormalization(axis=1),\n",
    "        Conv2D(64, (3,3)),\n",
    "        LeakyReLU(),\n",
    "        MaxPooling2D(),\n",
    "        Flatten(),\n",
    "        BatchNormalization(),\n",
    "        Dense(512),\n",
    "        LeakyReLU(),\n",
    "        BatchNormalization(),\n",
    "        Dropout(0.2),\n",
    "        Dense(10, activation='softmax')\n",
    "    ])\n",
    "    model.compile(Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data augmentation\n",
    "Use keras data augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = ImageDataGenerator(rotation_range=12, width_shift_range=0.1, shear_range=0.3,\n",
    "                        height_shift_range=0.1, zoom_range=0.1, data_format='channels_first')\n",
    "batches = gen.flow(x_train, y_train, batch_size=batch_size)\n",
    "test_batches = gen.flow(x_test, y_test, batch_size=batch_size)\n",
    "steps_per_epoch = int(np.ceil(batches.n/batch_size))\n",
    "validation_steps = int(np.ceil(test_batches.n/batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f44db9531d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44db98dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load ONE image from training set to display on screen\n",
    "img = x_train[1]\n",
    "\n",
    "# visualize original image\n",
    "plt.imshow(img[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# trick our generator into believing img has enough dimensions\n",
    "# and get some augmented images for our single test image\n",
    "img = np.expand_dims(img, axis=0)\n",
    "aug_iter = gen.flow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug_img = next(aug_iter)[0].astype(np.float32)\n",
    "aug_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44db9535f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# show augmented images\n",
    "f = plt.figure(figsize=(12,6))\n",
    "for i in range(8):\n",
    "    sp = f.add_subplot(2, 26//3, i+1)\n",
    "    sp.axis('Off')\n",
    "    aug_img = next(aug_iter)[0].astype(np.float32)\n",
    "    plt.imshow(aug_img[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensembling\n",
    "Here we create ten models and fit them to our augmented data. We use the mean prediction from all ten classifiers as our final prediction. This should average out errors in our classifiers and enable us to predict with higher accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create ten models from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "weights_epoch = 0\n",
    "\n",
    "for i in range(10):\n",
    "    m = create_model()\n",
    "    models.append(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or Create ten models using saved weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify which weights to load\n",
    "weights_epoch = 267"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "\n",
    "for i in range(10):\n",
    "    m = create_model()\n",
    "    m.load_weights('dropout_0.2/weights/{:03d}epochs_weights_model_{}.pkl'.format(weights_epoch, i))\n",
    "    models.append(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train models for a few more epochs\n",
    "Train models for some additional epochs and save model weights as well as corresponding training history every num_epochs. Test error rates for ensemble and every single model are written to text files after every iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many iterations with how many epochs we want to train\n",
    "num_iterations = 1\n",
    "num_epochs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 1, cur_epoch 268\n"
     ]
    }
   ],
   "source": [
    "for iteration in range(num_iterations):\n",
    "    cur_epoch = (iteration+1)*num_epochs + weights_epoch\n",
    "    print(\"iteration {}, cur_epoch {}\".format(iteration+1, cur_epoch))\n",
    "    \n",
    "    # train models for specified number of epochs\n",
    "    for i,m in enumerate(models):\n",
    "        m.optimizer.lr = 0.000001\n",
    "        h = m.fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=num_epochs, verbose=0,\n",
    "                            validation_data=test_batches, validation_steps=validation_steps)\n",
    "        \n",
    "        # save model weights\n",
    "        m.save_weights(\"dropout_0.2/weights/{:03d}epochs_weights_model_{}.pkl\".format(cur_epoch, i))\n",
    "        \n",
    "        # save corresponding training history (broken right now)\n",
    "        # TypeError: can't pickle _thread.lock objects\n",
    "        #with open(\"dropout_0.2/history/{:03d}epochs_history_model_{}.pkl\".format(cur_epoch, i),\"wb\") as f:\n",
    "        #    pickle.dump(h, f)\n",
    "        \n",
    "    # evaluate test error rate for ensemble\n",
    "    all_preds = np.stack([m.predict(x_test, batch_size=eval_batch_size) for m in models]) \n",
    "    avg_preds = all_preds.mean(axis=0)\n",
    "    test_error_ensemble = (1 - keras.metrics.categorical_accuracy(y_test, avg_preds).eval().mean()) * 100\n",
    "    \n",
    "    # write test error rate for ensemble and every single model to text file\n",
    "    with open(\"dropout_0.2/history/test_errors_epoch_{:03d}.txt\".format(cur_epoch), \"w\") as text_file:\n",
    "        text_file.write(\"epoch: {} test error on ensemble: {}\\n\".format(cur_epoch, test_error_ensemble))\n",
    "\n",
    "        for m in models:\n",
    "            pred = np.array(m.predict(x_test, batch_size=eval_batch_size))\n",
    "            test_err = (1 - keras.metrics.categorical_accuracy(y_test, pred).eval().mean()) * 100\n",
    "            text_file.write(\"{}\\n\".format(test_err))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Ensemble of 10 CNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_batch_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = np.stack([m.predict(x_test, batch_size=eval_batch_size) for m in models])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_preds = all_preds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16000270843505859"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display error rate in percent\n",
    "(1 - keras.metrics.categorical_accuracy(y_test, avg_preds).eval().mean()) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show test error rate of every model separately\n",
    "All test error rates in percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.204998254776\n",
      "2 0.212502479553\n",
      "3 0.199997425079\n",
      "4 0.197499990463\n",
      "5 0.209999084473\n",
      "6 0.204998254776\n",
      "7 0.217497348785\n",
      "8 0.199997425079\n",
      "9 0.185000896454\n",
      "10 0.207501649857\n"
     ]
    }
   ],
   "source": [
    "for i, m in enumerate(models):\n",
    "    pred = np.array(m.predict(x_test, batch_size=eval_batch_size))\n",
    "    print(i+1, (1 - keras.metrics.categorical_accuracy(y_test, pred).eval().mean()) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Training History"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load and plot saved training histories. Utility functions from https://github.com/fchollet/keras/issues/103\n",
    "Prepending zeros to history files with\n",
    "<pre>\n",
    "for i in `find * | grep \"^..epo\"`; do mv $i 0$i; done\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# which model to plot training history of\n",
    "i = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge training histories\n",
    "def merge_dict(dict_list):\n",
    "    dd = defaultdict(list)    \n",
    "    for d in dict_list:\n",
    "        for key, value in d.items():\n",
    "            if not hasattr(value, '__iter__'):\n",
    "                value = (value,)       \n",
    "            [dd[key].append(v) for v in value]\n",
    "    return dict(dd)\n",
    "\n",
    "# load pickled data\n",
    "def load(name):\n",
    "    filename = open(name,\"rb\")\n",
    "    obj = pickle.load(filename)\n",
    "    filename.close()    \n",
    "    return(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "histories = glob.glob(\"dropout_0.2/history/*model_{}*\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "histories = sorted(histories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dropout_0.2/history/016epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/032epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/048epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/064epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/080epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/096epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/112epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/128epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/144epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/160epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/176epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/192epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/208epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/224epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/240epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/256epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/272epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/288epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/304epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/320epochs_history_model_9.pkl',\n",
       " 'dropout_0.2/history/336epochs_history_model_9.pkl']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "histories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first history would include zero accuracy and mess up our plot\n",
    "h0 = load(histories[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge all saved histories\n",
    "for h in histories[2:]:\n",
    "    h1 = load(h)\n",
    "    h0.history = merge_dict([h0.history,h1.history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = h0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min = np.argmin(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jason Brownlee's script to plot model training history:\n",
    "https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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hMoEuPSZzLfCWpmkd+r58TdMahBCjkERi9g2dA5RpmlY1hOO3YMHCx4gouexp5FtnSsMd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\np+AiIiJ1p+AiIiJ1p+AiIiJ19/8ButhL6yICGCoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92539b5c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WAXNGuN6FwB1KqYRSaiuwybShKFTFSmmjlgY6A/EA4vHkMEflIW2FZ/Kz2jwej+dgUEzh\nmQPscF63MFg8RtxHRBYAJwPPOpv/wYTmbhaR+lFcDxG5QkSWi8jykRIIhsM6ntrMARgIRKMvkRjm\nqDzYUFvGh9o8Hs/hwSGdTi0i1cDdwGeVUl1m8/XAkcBiYDfwndGcUyn1E6XUEqXUkubm5jG3rdKM\n8ZRn+nMmf3rH4/F4PMNTTOHZCcxzXs812wraR0TK0KJzq1LqHruDUmqvUmpAKZUBbiQIpxVyvQmj\nMhqhjWn6Rfeu7PZ4coyOZ8ALj8fjOTwopvA8DxwjIgtFJIoe+L8vtM99wGUmu+10oFMptVtEBLgJ\nWKeU+q57gIjMcl6+G3jJOdfFIhITkYXohIXnJv5jaZqqYyRjZpXPzkDfRi883vF4PJ7Di6IJj1Iq\nDXwKeBCdHHCnUmqNiFwpIlea3ZYBW9CJADcCnzTbzwA+BLwlT9r0t0TkRRFZDbwZ+EdzvTXAncBa\n4AHgqmJltAFURCN8/2Nv1S+6AseTSIw11ObHeDwez+FBUSeQKqWWocXF3XaD81wBV+U57kkI1d4M\n3vvQMNf7BvCNsbZ31FRN149dgeNZv+sAv/r1X/jWe19LSUnej5BLNrnAOx6Px3N4cEgnFxzyVJnk\nBEd4Vm3fz10rWmjtKTDkZh2PH+PxeDyHCV54xkNpFGK1OfN4SpQWkJ0d/YWdwzsej8dzmOGFZ7xU\n1OW8LEUPK+0qWHj8GI/H4zm88MIzXirqc15G0CuJFiw8aV8yx+PxHF544RkvQzqeeGHH+yKhHo/n\nMMMLz3ixjicS0w9GeAoe4/GVCzwez2GGF57xUm4cT1kFAKVkmN9QOYoxHl+rzePxHF544Rkv1vEY\n4YkwwNKFDWNILnDGeDY+Ak/9cAIb6fF4PIcOXnjGixUeIxxza8tY2FTFgb4UfckCwmfZeTyO41n9\nK3jmRxPcUI/H4zk08MIzXmxyQaofpIQPLJnD3HrtfloOFOB68s3jGUj4ZAOPx/OqxQvPeLGOJ9kD\nJaWQSXNcYynnlKxg3a6OkY/PV6stnfBjPh6P51WLF57xYpMLUFnhObrtj/xv9DvMeubfRz4+63ic\nMZ50wpfQ8Xg8r1q88IwXdwJpSSlkBig1ZXOW7r0DWl8e/vh86dTphE+v9ng8r1q88IyXHOGJaMFI\nBZNHVev64Y8fyJNckI77UJvH43nV4oVnvLiVC0yojXQgPJ0dB8hk1NDH5wu1DST1edQwx3k8Hs8U\nxQvPeCmrDJ7nEZ5tu/Zw1rce5Zant+U/Pm9ygTneh9s8Hs+rEC8840Wcxd7MGA+pII36z2u2srOj\nn5XbD+Q/Pl86tS+j4/F4XsV44ZkoZp4UjPGkExCtJiVRIqluALbt78t/XL4JpNbxFDKXRylf2drj\n8Uwpiio8InKeiGwQkU0i8sU874uIXGveXy0ip5jt80TkURFZKyJrROQzzjHfFpH1Zv/fiEid2b5A\nRPpFZJX5uSF8vaLxpT3w8UedUFs/lJZDrIYa+ikReKV9COHJVzInPYrF4Tb9Eb55BMQ7x/cZPB6P\nZ5IomvCISAS4DjgfWARcIiKLQrudDxxjfq4Arjfb08DnlVKLgNOBq5xjHwZOVEq9FngZuMY532al\n1GLzc2UxPldeyiogUmaEJ6WFo7Sc0spa/uqIGJ9401G09ybpjoccjFL5i4SOZqmE9s2Q7Ibeton5\nLB6Px1Nkiul4lgKblFJblFJJ4A7gwtA+FwK3KM0zQJ2IzFJK7VZKrQRQSnUD64A55vVDSilrBZ4B\n5hbxM4wOd4ynrByJ1TCvcoDXzqkFYHs43OYKi3U3So0uuSBlzpnsHWfjPR6PZ3IopvDMAXY4r1vM\ntlHtIyILgJOBZ/Nc4yPA/c7rhSbM9icROStfo0TkChFZLiLLW1tbC/kchZMd44lDaQXEpkGim/mN\nOvPtlfY+UgOZoHiodTZllUH6dI4YFeB4bCJDaohQnsfj8RxiHNLJBSJSDdwNfFYp1RV670vokNyt\nZtNuYL5SajHwOeA2EZkWPqdS6idKqSVKqSXNzc0T22A3nbo0ZoSniyMaqwDYtr+Xbz2wnvf86Cm9\nv00siOr3yQzkpGIXVDbHCo4XHo/HM0UoLeK5dwLznNdzzbaC9hGRMrTo3KqUusc9SEQuB94BnK2U\nnmWplEoACfN8hYhsBo4Flk/Q5xmZrPAk9bhPrAYSXVTHSmmsirK9rY8Nu9rYva+dgYwiYhML7Fwg\nOz5kGY3jSXrh8Xg8U4NiOp7ngWNEZKGIRIGLgftC+9wHXGay204HOpVSu0VEgJuAdUqp77oHiMh5\nwD8D71JK9Tnbm01CAyJyJDphYUuxPlxe7BhP1vHUQEKnUy+aPY3nt7dzfuvPuKP0q7T1JIJQW9bx\npINt9vVI+FCbx+OZYhRNeEwCwKeAB9HJAXcqpdaIyJUiYjPOlqHFYRNwI/BJs/0M4EPAW5z06AvM\nez8EaoCHQ2nTfwWsFpFVwF3AlUqp9mJ9vrzkhNp0OjXxLvjVpbyvdh1bWnuZQStzpI09nfEg1GYd\nz0DI8RSS1eZDbR6PZ4pRzFAbSqllaHFxt93gPFfAVXmOexKQ8Hbz3tFDbL8bHZo7eJSUagFI9etQ\nW/k0UAOw7ne8k9/zaW6lnBRVxNnT2c/rysKOZyAUahuF4/GhNo/HM0U4pJMLphxZx5MIQm2WabOJ\nlZZQLkkiomg90OEkF1Trx0wqlFwwGsfj06k9Hs/UwAvPRJJTucCkUxtk2mzOOLqJ6eW64vSBjgNB\n1YKoTS5Ij93xpApYZtvj8XgOAbzwTCQlETOBND7Y8VTU84NLTua4xggAnR0dufN4QDucAZ/V5vF4\nXt0UdYznsKOk1CQIxIN0akuyj6pYaVZYeroPQNp8/UON8YxqHo8PtXk8nqmBdzwTSUmpcSDKZLU5\n81eTPfrROJTe7q786dSjnscTzzmvx+PxHOp44ZlISkoDgbHp1Ba73SQPJHo7SSWNWJhQWyKZIOMs\nmz26Wm0+1ObxeKYGXngmkpJIIDBl5VA7F45/F8w5NSjiaZxJJN3HDf+3Xm8zWW3v+9ET/OrpjcH5\nCgq12eQCH2rzeDxTAy88E0mkLHAppWaphPf/AuadBoncUNsHT25kZ5tZQ8dktZUywJodTuHSkUJt\nmYEgXOdDbR6PZ4rghWciqZ4RPC+NBc+jVdoJOULx2ukRohhhKQuEZ2Gdk+8x0jweV2x8qM3j8UwR\nvPBMJNNmB8/LKoLn0WpAQX9HdlONJKg32pSK6H1LZZSVC1zh8aE2j8czRfDCM5G4whN2PAC9QRhN\nkj3MrdFf/7ZuXR2oJgpqNMkFNrGgpNQ7Ho/HM2XwwjORTHMWQy11HI/NbnOEh2Qvs2v0ZNI1rTqk\ntnhWNWo0RUKt46ls9GM8Ho9nyuCFZyLJCbWVB8/zOB6SvcysEhKqlPvXtgEwvy5KSSaJKjHjPCMl\nF1jHU9nkQ20ej2fK4IVnIil3JoyWusJjioD27Q+2JbuZWxMhLaVsadc126qjihhJVLZo6BChtr52\n/ZN1PA3BAnQej8dziOOFp1jkE56Q44lJmpKycgbMr6GqDKKk6UyX6X2Gmsdz/RnwrYWB46lq0o9+\nTR6PxzMF8MJTLNystpgVHh1SQyJ6Xk86QXmsgncsng9AZakiJik6U6WkVQlqqDGe7l36cdMf9WOl\nFx6PxzN18MJTLErzjfHs049VTbqSwUAKKY3yubct0ptLIUaKuColTYS+eJy8VE3Xj89erx8rG/Wj\nz2zzeDxTgKIKj4icJyIbRGSTiHwxz/siItea91eLyClm+zwReVRE1orIGhH5jHNMg4g8LCIbzWO9\n89415lwbRORtxfxsQ9J8vH7MG2ozjqeySU8oHTALxkV0aK0ykiFKigRlpChle2snF/34aXoToZBb\neOzHh9o8Hs8UomjCIyIR4DrgfGARcImILArtdj5wjPm5AjC38KSBzyulFgGnA1c5x34R+KNS6hjg\nj+Y15v2LgROA84AfmTZMLh++Dy66JVjcDQYLT5URnnQSIlE9DweoKFXESJFEO54Nuw7w3NZ21u/p\nyr1GohsWnBW8to7HC4/H45kCFNPxLAU2KaW2KKWSwB3AhaF9LgRuUZpngDoRmaWU2q2UWgmglOoG\n1gFznGN+bp7/HPgbZ/sdSqmEUmorsMm0YXKpng6LQh+zNKoFxiYXZENtiVzhiShqpI9uVUmaEvr6\ndahtS6uTKp1O6DTrI98UbMuG2nxKtcfjOfQppvDMAXY4r1sIxKPgfURkAXAy8KzZNEMptds83wPY\nAmmFXO/gEa2GuCmZU9Ws3YldqdQIT0QNUCd9dFJFmlJKGQBg235HUBLd+rG8Dma9Tj+vqNOPfhKp\nx+OZAhzSK5CKSDVwN/BZpVRX+H2llBIRNcpzXoEO6zF//vwJaWdBRKuhv10/t1lo/Qe0+zFjPGRS\n1EkvnaoKSkopNWM5W9tc4ekKzvd3D0DXTkCX3PGhNo/HMxUopuPZCcxzXs812wraR0TK0KJzq1Lq\nHmefvSIyy+wzC9g3iuuhlPqJUmqJUmpJc3PzqD/UmHGrGlQ26Mf+9pxQGwMpquklHZ2GRMooJcNr\nyvbw4a3/DD0mTGeXV4jV6HGkpmOC1G0favN4PFOAYgrP88AxIrJQRKLogf/7QvvcB1xmsttOBzqV\nUrtFRICbgHVKqe/mOebD5vmHgXud7ReLSExEFqITFp6b+I81RpqP1Y+lFUHttr52E2qLgJRAbxsl\nKMqqGigpLaOcJA9EPsdp6eVkWlboY2yozV3d1CYyDBVqU6MyhR6Px1NUiiY8Sqk08CngQXRywJ1K\nqTUicqWIXGl2WwZsQScC3Ah80mw/A/gQ8BYRWWV+LjDvfRM4V0Q2AueY1yil1gB3AmuBB4CrlFID\nxfp8o6bpOP2Y7g+y3GxyAegEgf2bAHjX6YuoLC/nbZHl2cPXbt/JFbcsZ/UWM4xlJ6UClJl5Qvnq\ntT1/E3ytDu69auSiox6PxzMJFHWMRym1DC0u7rYbnOcKuCrPcU+SHbgY9N5+4Owh3vsG8I1xNLl4\nNL8meG4nlEKwfEL1DGh7GYD6xulQUQ7B8j3c9vgaHhqYyfTt63gtQMypCxcp09UQ8k0gbTXLa7/w\nSzjqLXDieyfk43g8Hs9Y8ZULJgsbaoNct2IdT/V0kygAVNQHCQeGi06s4cbLlpCJm1Bb1DmHiBaz\nfKG2ZC/UzIb6hfDsjyfgg3g8Hs/4KEh4ROQzIjLNjMXcJCIrReStxW7cqwp3rZ5oPuFxls0urwsS\nDszzxc0lnLtoBq+xdRrcMR7Qy2fnC7UlunXV7KUfhx3PQtvGcX8Uj8fjGQ+FOp6PmHTmtwL16PGX\nbxatVa9GSpyv2hWebKhterCtwhGeijoor4V4JwBH1GTIKOHZljjfffhlVmw3KdplFflDbcle7YZm\nnKhfd+8prL2bH81ZqntI4p1+7Mjj8YyKQoXHjrdcAPzCDOTnHYPxDMOHfgMfvCt3jGcox2NDbeV1\nejzHCM/cijQ9lHPlrSu59o8bueTGZ8lk1DChth4tdGUm8y09ROFRl/4O+MW7YdVtI+97w1nw5++N\nvJ/H4/EYCk0uWCEiDwELgWtEpAbIFK9Zr1KOeot+dNfZcZMLQAtRWQWUGOGxVQnMxNHmWIoeKjjQ\nl2JeQwU72vvpiqeoGyrUluzVE1btXJ9CJpn2tgEqqLQwFJkB6NgOB7aNfE6Px+MxFOp4Poouxvl6\npVQfUAb8XdFa9WonUhpUr3aTC0A7HJHcMZ7ywPHU0E8flURKhA+/YQEA7b1J0pFytuxqza1yAHqM\nJ1btCE8BZXVshYWRJqTaOUWmbR6Px1MIhQrPG4ANSqkOEbkU+DLge5vxYMNt4VCbdTgRO8ZTb8Z4\ntOORZDeRihrOP3Emx8zQCQYH+pJs64J4Xw93PPdK7nXsGM+ohOdAcOxwWMHxwuPxeEZBocJzPdAn\nIq8DPg9sBm4pWqsOB2yCQTi5oMKkrbmhNie5gEQPC2fP5AeXnExDpRat1u4kW7sUFcRJZ0JVCrJj\nPKMQnj7jeEYKy9m6cQdDeFb+An732cm/rsfjGTeFCk/aTPa8EPihUuo6oGaEYzzDYYXHOp7yOv28\n3DiegUQcnQulAAAgAElEQVSwPeYKTzfEahAR6qu0OD28di/7ExEqJUHLAUcsBtI6mcBNLihkjKfQ\nUNtoHE+yVy/pMFG8/ACs/pUvB+TxTEEKFZ5uEbkGnUb9BxEpQY/zeMaKDbVZxyMCdUdAzUz92nb6\n1vGkerWQGOEBaKjSovXCKwfoJ0aVpDh5953QvsWcwxYUrTYCJ4VltfXlEZ5kH3Ttzt0vPgrH88u/\nhQf/ZfD2ZB/8YAlsfWLkc7j07NUiOlIChMfjOeQoVHjeDyTQ83n2oCs/f7torTociIUcD8AHfw1n\n/5t+bjt9m1wAOrSVDISnoixCrLSELW299BOjml6u7Psx/OWO3HNEq7SwlVWSihdQwTqf43niv+F/\nQ5WK3FDbSM7jwDbobBm8vXcf7N8Iu1aO3C6Xnr36sXNQAXKPx3OIU5DwGLG5FagVkXcAcaWUH+MZ\nD+HkAoCGhVAVWk3UOh7Qd/eO4xGRrOuJxIK5Qcnu/eaJcTwmrDdQWs6vn36ZP29qG75t+cZ4WjdA\n9+5cgbFOR2WCaw1Fsid/6C5lHJhNaCgEpYJlIrq88Hg8U41CS+ZchF5i4H3ARcCzIvK3xWzYq56o\nGSKzobYw7kqjtiBo9x7dyTuVD+pNgkGsIhhy6+8ywhISnpTEiJJkdcsIobGs43HEpHOHvrYrRnFn\nbb7hwm1K6XPlS2xIm22jEZ5Ed3CcFx6PZ8pRaKjtS+g5PB9WSl0GLAX+tXjNOgzI53hc8jkeG1Zy\n6rRZx1NeHWxL9RjHk3DGeIBkSYxyEhyx4aZgHCgffXnSqW2YLOGIkTu+MpzwpPqMaOURnrE4nt5W\np11eeDyeqUahwlOilNrnvN4/imM9+YiF0qnDZIWnPhjj6TKdvyM89UZ4qquCbZm+A7y07Mfs/dP/\n6g1G5OLEmCEHuGD3dfDiXUO3Let4jLtJ9UNfKHwHwRgPDC88VqzyZdRZ52LDe4Vgx3cAunYVdkxn\niy7v07135H09Hk9RKVQ8HhCRB0XkchG5HPgDoXV2PKNkJMez9ONmvxrH8QwWnoZKnVxYM602u036\nD1Dx/A+ZsV0v+Kqi1fQm0vRlypiJcRY2lJeP7BhPL2Qyua7CFR5XbIYTnuRwwmNSrAspSGqxwhOt\nCcR4JPauhT2roW1D4dfxePLR1w7fmA3bnjzYLZmyFJpccDXwE+C15ucnSqkvFLNhr3rC83jCnPt1\n+MoBXdW6okFv6zBVCRzhqTNjPHW1gfCUpTppyAQOYtmGbpb8+yN0pEqZLlp4VGKIZIBUv3YhVuzS\n/Xp8x5ITauvSdeBgBMfTHZw73/VgdKE2m1gwe3HhoTY7L2ogWfh1PJ589O3XN2XtWw92S6YsBYfL\nlFJ3K6U+Z35+U8xGHRaEKxeEEQmWUojV6NptVnic5ILGai08jfV12W3TVC/1EgjEr188QH9qgH3x\nEmKiC5TGezvh0f+Ap38EgFKK3kQ6cDu18/Vjsjc3DfrANvj95/TYTKIL6ubp7f0dcGC7Xk4BtJN5\n+N+0OA3reMYwxtOzF6QEZp6kkwsKmURqnVV6EoWndYOf4PpqxC4DkvHLgYyVYYVHRLpFpCvPT7eI\ndA13rDn+PBHZICKbROSLed4XEbnWvL9aRE5x3rtZRPaJyEuhY34lIqvMzzYRWWW2LxCRfue9G8LX\nO6Q49jw465+g4ciR9xXRYz15HM8FJ83iy28/njnNOg07FaunRHI7u8e36w4/TuCu4r0dsPZe2KAj\npvf9ZRdLv/EIPR0mjGUFJdmbmzm2/vew/CbY/RftcmrNfvFOeOoHcNdH9OuW5/VyCVsfD1xSJj14\n7R7reFKjqGzQuw+qmqF2rhau8CTSVbfDK8/kbrPnHpjA6gnDsX8zXLcUtv5pcq7nmTwyprq8X4dq\nzAy7LIJSasxlcUQkAlwHnAu0AM+LyH1KqbXObucDx5if09A14U4z7/0M+CGhmnBKqfc71/gOucVK\nNyulFo+1zZNKdTOcPYrEwIqGIJvLplcDTdUxPnbWkWaQXSiZfzpsvD/n0IwqoaEqSn8icFfpvi5I\ndmbX/XnhlQ56kwO07m6hGvRS2WAczw6QCKiBQPz62kyorUE7sHin3mbdjV1wLtkLJZGgMak+iARh\nwZxKCv0dUOOsSzQUPfuganpQ5aF7T1DjDuC3V+rHrzp/GvY6k+V4rHPsaR1+P8/UwwvPuClmZtpS\nYJNSaotSKgncga715nIhcIvSPAPUicgsAKXU48CQqU4iIug5RbcXpfWHGpUNwfNY9eD3p82Gq54j\ncuplg976xF8dyXknzqTfcTzpeDfJngN0HGiD1g3M3nYPAF1tJqzWYIQn1adDbdaZHdiuH3vbtNiU\n1wZFTOOdegxlIK3FAbSTcRMZwqukuuM+hYbb4p06zbzaCs/u/Pu5YS47tjNZjsdeL11AUdYJuV4K\n7v4YtL48Odc7nMkM6Ec/Xjhmiik8cwBnVJoWs220+wzFWcBepdRGZ9tCE2b7k4icle8gEblCRJaL\nyPLW1il0N2oTDEqctXzCNB+bK1CGay44nsXz6nJCbdJ/gKiKI4lu1B0f5Ir2/6aZA/S3m04863h6\ntPA0H2deGxHp2qU78azwdASZaen+IPMs2ZubCRce58lxPAUKT6pfV9vOOh4nRdoVG3dsKh1KLvjT\nt+Hlhwq73liw10kVUBtvIujaBS/+2of2JgM7tuMdz5iZynNxLiHX7ewG5ptQ2+eA20RkWvggpdRP\nlFJLlFJLmpubJ6mpE0ClCSXFavSYz1C4yyp89kW48s+AHgtavHBWdreGAV3doFr1Ejc3cGdHXmCg\ney+UVelQIJhQ206oXwClFcF1Wteb6zVAZaMOLdmxllRIeNxMuHBm21gcTzquxbfGcTzxTvivhbDx\n4WC/3aucY0LJBc9cBy/dXdj1xsKkOx5zvZEqinvGjw21+eSCMVNM4dkJzHNezzXbRrvPIESkFHgP\n8Cu7TSmVUErtN89XoNcMOnZMLT8UsY4nOsKwmxWemllQNx9mnghAdayU04+bm90tKlptIqLYl9Dj\nPOeUrEB6W+mNNpAoMSLTsUN3nrVzc0N8tlOvnaddVt/+wPEke4dxPKGO2HU8O5frEJ5Sw2eDWccT\nrdJLRnTv0aG9/nZ9DsuuF4LnA05ygVI6/DdSfbnxMNmOxwvP5JEd4/GhtrFSTOF5HjhGRBaKSBS4\nGLgvtM99wGUmu+10oFMpNUTAPodzgPVKqWwsRUSaTUIDInIkOmFhmLowUwwbQouNIDx2PR/rBlzK\nKgZvA6r69Nd4VmQNsZ4drOuu4LEtugPbsdEKzNycNG4ObAu2VzaZZAMzmJ/qD8Z4kqExnlSoY0zF\nAzF94jtw71U6M+4/Zg/9Ga3jsZ+ze3fgaNzxnt2rnWMcx5OO685jPMKzc2UQ68/bRns973hG5MC2\nqRW2yo7xTKE2H2IUTXiUUmngU8CDwDrgTqXUGhG5UkRM2hHL0OKwCbgR+KQ9XkRuB54GjhORFhH5\nqHP6ixmcVPBXwGqTXn0XcKVSahR1WA5xrOPJl1jgUhrVApEvO2wI4WmSLrpVBTGSvE4206pq2dyp\nHcfezX/RO02bk//atXNMqG2/znoDLTxuVlvY8exbD4/+p3Ye6ThUNQXvv/I0rLln+AXrUvHgs9TM\n1NfKCs+eYD+3pI87xmOLmw41iXYk1v0ebnwzrLp16H1spzRZjseGEIvp4opBsheuO00v6jdVGPBj\nPONl2HTq8aKUWkaotI5S6gbnuQKuGuLYS4Y57+V5tt0NFDFof5CpcMZ4RuKYt8KCMwdvt6uQ5qFv\n7lnU7HyIUsnQquqywrPQRD5TNXMoi4aEp7xOt8cVDoBEZ1DbLdWrO8VITIe5Un2w+g548n/g9R/T\nwlNWCVc8BqvvhGeuD86jVP7xrHR/ruN55ekglGYdTySaG9ZzQ23WgY3VHaw26x0NV3Zo4DByPHd9\nRP99vv07oz820a3/Bnr2jbzvoYIPtY2bqZxccHhRaKgN4H0/hdd/dPB26xJitYPemnHsUtIxLW7x\nWAPb2xMkiNIo3SRUGRu6YkGoTcyfjZ08WtmYe7KOHYAZo7GOp9o4sFR/MBeoa6cZrymH2SfDEWcE\nx9l9w2QG9D982PHYfa3jqWzKnZDqhtqsEwq7g84WWHWbrk/n0rNPV2To2afFcIvJHFNmv999VleB\ncJlsx3Mwhadlua6FNxayv5dJSnOfCLLJBemD244pjBeeqUI2uWCEUNtwWJdQO3fwe5X1lM7Rc28b\nZ8xj9c5OtmWmA9BFJS/u6gpCbXVH5J4nLDwHnBpWdozHZsml+o0woYUnHQ+y5WackHueZI8+9r5P\nBxMybTJC1vHM0p2uTWawk2wrG3PdRjqP43Edy86V8D8nwG//HnauyG3H7z4Dv/gbuOFMnVRhhcse\n/8ozsOO53GMO2hhPEUJtvfuDm4UwSmlBHut1J7uixETg5/GMGy88U4Ws4xmUIV44NtSWT3gqGmDW\n6wAor59NMp3hX1LaNcUkzZMb2+gTIxBNx+aeZ5DwbNOPVdP1hNFkj34OWoiyjmdX4HhAC5qbtZfo\n1qV3Vv4cNj2it1kHYR2PFeSe0HIHVY25d9HZ9OZk/lDbjmed6zoVDzIDQRXinr26FI7FjhXZEKKL\nm9X2f/8euKRiUUzH8/BX4LaL879nF+Ubs/DYihJTSXjsGI8XnrHihWeqUNGg5+bkmSBaMLaztoLh\nhtwqG2HuEgCqZujJoy9HT2Dgfbfwg+n/jz+8uJvfrDEdctMxuecJj/HYiZv1RxjX0hPs038Aekw4\nLOt4jPCUmMKflmRP0LnvXaMfrYOwx1jRClfHrmzMDXPZDm7AEZ5MKujwXOFwkw72vKgdzmxTRtBd\n/8c6n/QwwpPuhz9/X49fFZNsaG+YpIzRsOuF4Hvqahl6+Qk3e3EsZJ1hkUOSyT64/wu5q+YWwp+/\nD8/+OHdbdozHh9rGiheeqUJpFC7/Q/6xm0KZNlsP8s85Vb+uagpcUGUDHPd2+MiD1C/UnezJR9QT\nOeFCLrv4Ei49fT5tKVP5oPk1+nEox9O1O7ieDbWV12qx2L/J2c84HrcSw4U/hPO/rZ8negJB2WfG\nEMKOxx4bXs+nsikUanNK5uSU8DEdpjue5Hai2/UEXI47P2gz6Cy/rPDEB5cCssJjywh1F7hg3Vix\nHfhEOJ6BFNz0Nlj+U/26b7/5HHk6Wus0x5ohODBJYzw7V8CzNwwuHjsSD38F7v/n3G0+uWDceOGZ\nSsw/LbcY5mipng7XtMCxb9OvbbkbMI6qBOafzoLGKkpLhNMWanc1r6GSi5bMo1fpTv6SR6u5oeYq\nPrtqDptbe/TSDm6IzDqamtm600r3Q/V0kiUV9LSYYuMlZboTT8cD1wLQeBTMMe4i6QrPOv0Ydjyl\n+RyPqeY9kAwSBdwioW6adTbs5ghHjvA8pcsH2ZJB9s6/ZlZw95zP8diO1BYJLXSl1LEykWM8yV4t\nCLaShF0KPVwFHALhsYsGjpbs72UUjmfNb2HlL0Z3HXtjMZoxt6EmMQ944RkvXngON+w8H9CFNq3w\nOCG82soyfnvVGXz0zIXZbcfOqOFBdTrfHXg/y9ujPD7tXdy7tp17V+0Kjo/VakHJpCFWS7qsKpjb\nUzOLroFSqnu26dezT9YhuVR/bikeCNqX6A4EpXOHfh52PPbR7RSjVYGYZUNseZILIHetIBt6TDrv\nH9gG048Psgk7d2qRragbwfGY0FevCUV1FTIvehzY643F8SS69dwqS3jcxS6Fnm95cjcNOjw5GLRA\nDPfZx5LVtuJn8NS1Whjt2N9I2BuDfJmSQ9E7RC1Hn9U2brzwHI6UxrRAlNfqZIXSikGTS0+cU0t5\nWbCcQXlZhFjzQq5NXcgbjmrmto+fzrz6Sra0mo67qgkqaoPQXUUte+PONLHqGfQrsyyDRPR4Uji5\nwGKz51zHA9r1FOJ4olWBmIWXQxhIDR1qq6jVRVjdzjvRrUXHJnV07QxeJ7p18kEmPfQYj+2cEp1B\nOKp9i67SMJGLxGWrbydHv/TDsz+GG98SOBbXHaTiwWfrzyc8TlJHONzW3wH3fQpeGMadjCW5IB3X\nhWGfvwl++beFhfnsdUYzBmYrsYc5mKG2zp26rNQUxwvP4YiIdiiVjVp8CkxYOGG2dgRvXaTn5BzV\nXMXmVtNJ1x0B0+YGAlZex+4+58+rZhZxdE04ZizSdeQGEvpnKMeT7NWuwi4P3rphGMcTFh4jcuFQ\nTjrkeLLLcvfq4qjRqlzhSfbo9ljH07PPCE+NDrXZDjOTyp3Jnq9TspNbf/tJ+OPXoW2j/jzXnzH+\nrDc3HTmf87Bs+/NgYerZp4/Juj8rPIlcsRnJ8YTdVjY5YZgwo23LaEJt6bgW8raNgMoNnQ7FWBxP\nx0jCcxAqF/z6cp0kMcXxwnO48v5b4czPwdHnwPHvLOiQU+bXEY2UcK4RniObq9na1kMmo/Ss9Yt+\n7qQ517Gj1xWemRyT0fN7Uqd8JJgLBIMdTzbUZhxPw1H6dfeePI7HCExYeGw7bEczEEouKDFuzO1s\no5X62u4ddKJHO7DsxF0F5dP0T6Irt8N0O958wmM7YHvttpf1eNjel/SKruPB7QSHCrd1vAI/uwDW\n/jZ3u/0O7Hdov+NUf67YjOR43BCl246h1kuCsTkee/Nhi8AWkqmWXel2FI7HCk+44sfBFJ7efUH4\ndgrjhedwZd7rdZ2106+E8/+roEMuWTqfR6/+a2ZM053+Uc3VxFMZdnX2a9dUPZ2uAe1qBmK1bDP9\nQR/lEKsho3T5m/aj3g2zXhucOOx4IqV6W9KM8VQ16eSHnj15strMo5vVFq12HE9oDMHO47GLyLmh\ntjIjPLYjTie1UEVrcitGxGr0eFA6HiqAmmfCqoubEQdacLKFVceZBu0KnSs8Pa3BOIgdawmPXdjP\nsHM5fHN+MN6TjhfgePYGHXNY8LKr0RYyxjNKxwNauN32D0eqP/dxOPZv1mFHG2orCVUWK0ao7bdX\nwYt3jbxfsm/weOIUxAuPp2BKIyXMqQtE4qjmKgCuuedFPn37C9zx3CvsMH3ArngsKDSq6uhPZXhH\n8hu8J/FVOlKlQecLgx0PaJdhHU95rSmNs3foeTzu2ig5YzxO2AiM4+mCaWZtIutukr3BUguJLrjt\n/bBhWdCWsirA1I2LTQuEyI23u+KR727YplTbtrrCM940aFfo3HMtvwluvUhnYvWZtsa7tHvMFks1\nv7TtT+n2ZOdMxYd2PErBb/5e72sdaXisJRtqm2DHkxUpM0ZWUKitQOHp3gM/fL0uVGsdT7ht4fV4\n1t4L65cxIk/9QFfhyMdLd8PWx0c+R6pvalYgD+GFxzNmjmzWIbEnNrbx501tfPGeF+lM67vDF1oV\ne/p1csJeVc+mfT2sVQtYqY6loy+px5nsctphxwOB84h3aeGpnjG848k51s1qC5VksckFNUZ4bHgo\n63iqdCbbyw8EwhOt1qnmVmxiNTrUBrnuISfUFuqsyioDx+NOis3neNb/Ae7/4uDPNRxDhdps1fBU\nX9DWRBfcciE88lWzv0162Jr7mVLxoNirlOSKUKIL/nIbHPVmOOffcs8Tbkdvqy7+mu+Ofiwlc8Lu\nKOx47vmETt5wKTTU1tmiv6+9awLHY9dwsoRDbU9+D56+buR2b34Utjw2ePtAyiRyjCCKSpnah154\nPIcxTdVRZkyL8foF9Tz3pXP413csoqlBrwe0viOSXUxur6pn3Z7grrSj3/zDNpm5MZLnz7BQxxMp\nHRwKiVYH76f6g8wzCJILqpp0dl041BarCUr62AoMWcGZFjzG8ghPTjVsRwhKyvSYlr3zt2LTvjUQ\nIzd8cscH4Nnr83cw6WT++TJDhdrcNZJc4enYEawi62bbQeCM3FBb3RHB83hncI3j3wnTF5nrhoXH\nvlbw0JcHVwCA4SeQ7v4LfPuYwZWrw4VXw45n0yODHUi6QMdjHWzby9rx2L+tnPJLoVBbsqew+UH9\n7WYtqIFQBYzu3DYORaofUFNv6Ys8eOHxjBkR4bF/ejN3fuINREqEj565kGPn6JpstfXN/L/3LQW0\n8KzfHdyVvvBKB9c9ugllO6xwnTXQ4hHv1I4k63j2Bh2HW+3Auh47JylaFbyfTgyu2ZboNuJRrTvj\n/gP6TtiG2qxI2WKmNr3bClC5G2oLzWPJZEz9MueadukI6x4SXeiwnQoKkrqZaGLS2O2kWUuiB65/\nI9x/9eDvK0d4nI7JdVS2U+1r19ezAmv3t697HeHpO6DDjDWz9POdK+G/FgTJENFq/Z3Z9rm47cik\nc4vHWoYb49m7Vn+/bn08u4aTi+t4MgP6e257OdelFOp4rOhu+ZOuPj7TjEW6jizseBI9gwWt/wD8\n+E265FL23Pu1aL50D1x7cjBB1705GA7b9vE6nudvGns18QnCC49nXFREI4i7Zo4ZaP7E205h8dHz\nASM8juO5+cmtfPvBDRw49VNw6uVwyocGnzhaHYyJWMeTSTHQuUuX/Slx/nRtIkGVqYCdIzz9QacR\nienOcCChxSBaDStvge+/Tv9TRyuDThT0nB0IqjLkDbU5YzzJPh2r/+6i3Cy78mlmsTxnjMUWWm3b\nGBxraTATd5+9Af77uKDo6sNfgf0bYfP/Df6+BsyaR5DbMdmkC1d4bAipa6cJPZrfjR2zyK6l1K/v\n0isb9E9/u05pV5mghFG0Kjf9HfQ5V/96cAist3XwtuHGeOy+toO2nxOVf7/svibF2k1qKDSd2rpC\nG4K1STDpYYQn2TtY0FpW6ErmLc8H2/rMDU63qdhhHbX9/kdqW/b7TYw9o04pWHY1LL95bMdPEF54\nPBOLk05N9XR47008EjuH9XuCziE5oENFe/pL+UXTPxIvzVNxO1atJ8sBxKaRqdIp3Lu2rR+cjJC9\nZr1e02fOqcE+qXjQaZRPC9bQqWwMhCXeaRxPZe6yE7bqQtjxuKG2nJn7fdC6Luj0rHPJ53gaj9bP\nrfCk+vTA85+/H1TcfvHXelzr6R/pDmPlLdp9tG8JROSVZ/QKnn3twXyskUJtdtBcZfR3EHYqWceT\n0OetqNc/fe2Bw7PrHkWrcrMQQYe67vlY/sFyK6IWd55VeEKt7ZBd4Ql3ziWluenUbuizdYNznJ1A\nWmCoDQCBGSea9uUTnqQZd+kefN5W41azy3kk9H4ZZwJzt61z54wzDocrbmN1Pal+/Xc9XKbhJOCF\nxzOxZCeQmppyJ/0t0+qbae8dnHq67MXd/Ou9a7jvL7v42Z+38tM/b6Urbu7kotXBHXh5Lb1RXYg0\n1v3K4IQCt4rB3y3T4w5Zx+MIj5sSnW/SbFlF/vWOomHhcSoZ9IaExx1HKXfGhCqbdAeaTmjX1WSE\nx3bWyV7tZLY9mdupSARe+KUOg2VSejAfgjvp3/69HqvZ86JeEdaey5Iv1OaGwNo2BgJrcVdP7T8Q\nTDbubw+E1nZc1iFGqwIBs/vs36zdYiSqJxdDkMBgsRNIVQY2PqQXlbPkczz2d1lSpsWwqlkL6S/f\nq0VtSOHpCx4f/gq84iyD4WJvDkBPcrbh23zCk0mZjjwzWDT2hYTHTcywz21Nw3ihjickPC0r9ITS\nzMCQhww+h/nbKHbtwBEoqvCIyHkiskFENonIoDQd0Vxr3l8tIqc4790sIvtE5KXQMV8VkZ0issr8\nXOC8d4051wYReVsxP5tnCLIlc+qym9wU7OaaWPb5qh06DHTn8zv46u/W8rXfreVf7jExcUckEqU1\ntJdooZg+sBc1yPGEJpNCAcLTCFf8CU75sHOeqtxQmyU2ilBbj9Px2U4rVhNU8LZhrppZJkXbkOrT\nImHHtSxv+mc9HmMz7I56s77LtwvP2YSAVJ9ue6w29242n+Nx2fvS4G2WtEk9j03T6e8DySDEZu/W\ns2HI6qBTs513Z4v+7Gd/Bd7+3+bzh4XHGa/5/efg8W8Hr/MKj+mc3/QF+MCd+vybH9Uua/nNuZ+x\nzRUec1x/h3aV63+ni42+dHdue3pbg7/h5uMGV8CA3BptQ83DssJjEzJy5kOZ78f+nrI1/0YSHueG\nIdUHW/8Ea34zuDL7sOewbutV6nhEJAJcB5wPLAIuEZFFod3OB44xP1cA1zvv/Qw4b4jT/49SarH5\nWWautwi4GDjBHPcj0wbPZOKUzLHMqQ+EZ67zfHWL/odZvl13LGce3cTjL7cykFE5zuP5PQO0qqAq\nd0ocgYHAAUXyC086af6h3UX0Khpg9mI48k25bY8N53jM8eW1ukOKxEJZbb25r13hqTLCY4UiNk2H\nIi2Jbt0BxU1Nt1mL4eRL4bXv1+/byZLT5ujwz84Vobk7pkp483HBnf5AKkhaSPbk3s1bhhtkTvXr\nu/HYtGAJDFstYJDjcSbe2uuoAf3+G/9BLytRUZ/H8TifoWdP/nJG+RxPw0KYt1S3zX7GF+8O3FbD\nUdD6snOcEQ57p5/o1ineT/0gtz29bbqALWKEx/wd5UsucNuWSQfjLplMkDGYdTzOd58Vnr1BMgqM\nMtTW4yRmhI4brkq4vTno2XtQ1xMqpuNZCmxSSm1RSiWBO4ALQ/tcCNyiNM8AdSIyC0Ap9TiQZ6r0\nkFwI3KGUSiiltgKbTBs8k8miC+HNX84JZVnHUxWN0FgVzW7vigd/+K+bW8v7lsylK57mpZ2d9B/5\nNtarI9ilGnhgZ5S2ZIQ9Rnx6M2W517R3pTmOJwYIpOI8uUGP5cQjTukT60DctYTyhdoiUV3RG5zQ\nmeN83A4l7Crc9OvKkPCUh4THdpjxLt2pHPkmuPA6nc0HwTpGFfVaBPr2w65VobaWwfTXBOML7thH\n1y4tBJXOon3R6sDB4CSIZFH6OuWO8NiO1h3jsefKCo/zb+sKef3CwPFkBvSclrCTGEl4bOdsb3Bc\nF9vVAut+p9Pzpx8/OAwKgYAkzLLq4XI7fft1iO0Dv4I3/ENQJzBfqA1ynYy9RucrwXP795EzEdd8\nntJyyRgAACAASURBVJ3L4T9m6xCj+9ninflvCHJqCPY6KeLOd/jbT+pzPvilYNu+9XDj2cFNDejw\n4EEsvVNM4ZkD7HBet5hto90nH/9gQnM3i4i9FS7oXCJyhYgsF5Hlra1DlD33jJ3Go+BNV+sJogbr\ncmrKy6itiFIWEWaasjtN1TFKBN75utm88SjdKT65qY3rN07jvMR/8qFpP+X+jf20did4IaPHRbrS\nISObnUzqCI+IvltNx9nbrjuXXpyxIbuuUY7wOMkF1j25QhQOubnJCqBDbUM5Htvhu47HZuFBMM7S\n16ZDWjaEFa3Ujq7NER5boHSnMx4CupNsPl53dj2tuUtF2FRpO2kXgRknBO7IClwYmwEYXi7djr9Z\n4bHzriBXjN3vr35BkFyw/g96Eqt1UBY3nGSFp7cV7vqILufz/I16m3Ui9mageoYOQW7/s/6uq5qG\nT0pIdAcu06KUvlZVk16zqmZGcJ32rbDu9+Y7yeN43GtsM4sH1i90lpRwHY/ZtusFLR5bn8g9/gen\nwvVvYBDh5AIrOFaAkn2w6lb92r0p2blc/+zflPv9FnupjmGYiskF1wNHAouB3cB3ht89F6XUT5RS\nS5RSS5qbm0c+wDNu5mSFp5T3v34eXzz/eGbV6X/oU4+oY9lnzuLyNy6guSbGa2bW8N2HX+baP27k\n7SfN4tNnH8P+3iSPbWhlZcYsuR3OwnIKhv5+9S52d/ZnX9PZQtNe3RF0K+N4YtMCF5PjAJx0apt1\n5t6xn/AeeOs3gqoHbuiuvFZ3Wm7nkBNqM9exd/zl0/J39nY+jnvdqqYgtbyi3izJ0DV4YmUkqh0P\naNcznPCU10LjMYGA1A5zvxer0dctC41/RWLaZdl97HjHUMJTN087LzcUFZ78meN4zHu7XtBjMfFO\nXdYHgt+5vQloOhZmvU4/r2rWodT+A0GmXFh4kj0mtOlcP9mrHZj7N2H/Tu79JPzqg1rQ84XaIPjd\nL79Jt+foc/I7nnDI0wpHJqXnjmVTukPzdpLhUFvI8bgZgz179ZjX7Zc4zrEjV3iKvSruMBRTeHYC\n85zXc8220e6Tg1Jqr1JqQCmVAW4kCKeN+lyeyWFune7wa8pLWbqwgY+euZDmau0oZtVW8JqZ0yiN\n6D/Fb7z7JD525kK+9q4T+MElJ3PyPO1Mnt3azoYy3anOS2nXsLWtl1uf3c5ARHdCCcr41G0v8JPH\n9fsJicGaezi79RYAOjOms6qoZ9mLu1m7qys3u811PM1mno27suq0WfDGTwVuzg3zVNQPLqNfPV3P\n6p+9OEiRzobaaoNQm3sNi9thu66svE5fN9GthcUuXgeB4wEdXnHnEtnU8XpTFbyiTrtTi3U07vks\nsRr9mcOuxxXHaXNMOE/ldqzuPrXztLD2tuYuge5Wnkj0BGWNrPC4naUVW5tQYsW/dh7MNy6hqkn/\nXjPpoefIJLr0NQYSOiX94a8EHb7rREtDiSzbnxxGePq1SO5cAa//mP692SXD3ZDccOWBnr3BOXe7\nzgz85hE6NT0VCrWFHY+9qZm7VH9Pmx/VmZJWeNxQG7xqHc/zwDEislBEouiB//tC+9wHXGay204H\nOpVSw34bdgzI8G7ApuXcB1wsIjERWYhOWHhuIj6IZ3xMqyilOlZKTXkwNmOz22bX5f5jn3pEPddc\ncDwffuMCSkqEOfUVREtL6Emk2VOpS+yUoFBK8a0H1vOl37zE5gM6TLWxTf9Dv7RTd7idqdw/7/YB\nfS1V2cg/37Wam57cmrtsd1mFEQPRg/uQP9nAYh1NxKzqGl44LFoFn3xa3/mWRnWnng211QYd3LRZ\nDCKWR3ii1eY8NWYuxh6obtapxbYdNTN1u/atDQmP+beyy1FU1OcKj013rnPv3WxbzPcTFh43A7B2\nru4A+/bn3t27AmqP72zJFZ5yR+zS/Tqz7YazBk82bTgqECGbUJIVnrkw/3T93Doe0G3JDOjO3r1O\nT2sQ3lzxc53pZjvuKtfxhIRn2wjCY9O0F/1NcFMT78if2JGPbU8Gz/v2a+HIpGDjw4PTqcOOxyZu\nzD9Nr1fUvkU7OJt5Ge/IHSd6NToepVQa+BTwILAOuFMptUZErhSRK81uy4At6ESAG4FP2uNF5Hbg\naeA4EWkRkY+at74lIi+KyGrgzcA/muutAe4E1gIPAFcpFZ6g4DkYiAiL59Vx9PSgE7LCM6s2T5FP\nh0iJsKBRO6bq6hpenH8Zn05+iq1tvfxxnb77fWmf/sdr6dFhlTW7uhjIKPpVNOdc+5P6dTpWR08i\nTWe/CWvZjLOySt3xXvUcLP6A3hbL40YsttMrLdeiFR6sjYSy71x35YbaavIIj+uCbEdox6XsuEZn\ni1lF1nyvpVHtTGacmFv5GoKqCXXzg3PZcCLAzBN1e5uOyfM5TYdthSM7/hRyM6A7v0Qn2WSFsDiB\nHnwfSnhAj08c2JrbqVc2Bm4NgvE8+/upmwfzrPA0Bd+1rY9mz2FxyzTZm4E95h7WdTyR3L8htj4x\nfKitvx0Q0wZzvV9fDqt/lXse6/LCDrN7dyB2ffvhlaf18x3PatFwq1NYwelvh99cqcOQ5bXQbMKt\nNqXbnSht06mrph9Ux1M68i5jx6Q6Lwttu8F5roCrhjj2kiG256mvkn3vG8A3xtRYT1G55SNL3XyD\nIR1PPo5squblvT00VMXYeeqXuO/lFVT8aUu2AsK+/hIohR2d+j6jLznA1rYe0gO591W7E/pavRH9\nz95pi5VWNur4eHb+xrFBamy+CaUW2+mVxnIXC7MZXuFOywpGaYUeG7HjKvVHQLiMWY7jscJjUtSt\n4HXsgLmn6uv1HwiuN+t1sPyngfMor9WdjpTAtNlmW52TaIBO2z76HHj6h+Yz1ASdVNbxGHFpPFLX\nassnKrtXBZ/pwLZc4bbH73ohVFIo1Pna8SiVCWr0NR6du184q612rnZ/539LV6/Izik6EITZKhsD\nkXHL7thttv6ceyMQdjxtG3ITSsKOp2+//j2VRIIbhW0meWDOkiAh5Ohz9fdX2QjPOcVTe/bp8Oy+\nNdC7PxjT2vWCTlaoqNfXsONRoMXwL7fr57MWBzc09r670+Rc2fk+kSic8Zn8TnuSmIrJBZ4pSEmJ\n5NR0O/PoJs5dNIPjZ+UplxPiSLPuT1N1NJshd88LLRw9vZoT50zLLqnd2h90Jiu3d1CtdMf59dSH\n+LvU1axL6rvgDnRnFQiP7tivfaKFtBEzImVmATgtAM9tbec7DzkTEsERkvLcTtHWWisNCY+9Ez3p\nvfpx1mL44N1w/LsGf+icMR5z927DR7azTZjK3dlMPEd40v16rEEiwR18eW0wv6qiXnfetfPMsg8R\nHWa057Kdpnu9o94CC84KysjkJA4YJ2U7b1t53N2nvFZ/p+GlAWKhvwErPO55G47KmRuWFYSmY7WQ\n27Gt0z6h3ZvreOygf4XjOF2sU939Fy3OOWM80aH3h8GOp689f6r+Rx6C9/wkeF2/AP72Jr0MPDjf\nt9LCDrBrpXZAR75Zj41tfypIgEn2BoLqjoE1LMxN04eg9JStKh6t0mOVJ743//cxCXjh8RwUjmis\n4sbLllAZHdl0H2XW/WmoimbnBKUGFG95zXQWz6sjbkJqCaJURiOUl5Xwx/V7mYHuFNarebzScCZ7\nzU1wW0afL8fxANc+sYvntrXzo8c26RI/b/kyLL4UgF8v38EPH90UCBMEHWYkqmfSl1boMRTrUMKO\n5x3/A/+0Sc/PAR0WO+ac/K4qnNUGQefkugg31GavZysqb3tCv2/dWHmdEUsJOsXGo3Kvb12MU3ki\ne725p8Llvw/a4zqeinp9Het4sskZzj42QcGKk3VA5SHhcQfRrfA0HpUr7lZ4jngDXNMy+O7dHeNJ\n5Qm15WP/Jh2Cijh/k67jsVl9iZ5gjKkvn+Mx13ZDq/NPyxUE69js2Jqb4Vi/EBA9rgN6Ai7oEGWZ\nKcrqOh63ekb9wsHZktnitJ1apPIls0wyRQ21eTwTgXU8DVVR6irLqIxG6EsO8NfHNtOdSPP8Ch22\nS1LKzNpy6irKePzlNsoiOtSwLTOT0+fWsXoVEIM9Kd0RZ4WnZgZJiZGmlK//bi3r93STTGf47DlX\nZtvwSnsfSsGBvlRQ9icbaivXd9nXtOjO4Ld/r7eHhSc6REmeqE3zrjXjI+TPassKTyiNO+x4mo7V\nbYp3wrHnBSGWinrt5N7/Cx32AXjdB5xJpDjCk8fxhF+7bRTRQmKXAbDuLnys7Yynn6DFpHPH4FCb\nix2Haj4uKKgKuYIQydONVdQBkut48tXny0Hp5AwX93dYM0OH5dSAFg5byy4S1Y7EjvHY1XXtDcjs\nk02bnfFMezNgw63VM4IU88pG/f3v36g/wxFv1ALV1aL/VgaSWkCs47HZeNUztCutbCK77IZLvMOU\nVRomfDxJeMfjOeQ5ftY03nRsM284qhERYU5dBVXRCEsW/P/2zjs8rurM/5931OuoS5Zk2Wpucrex\njW26AZtAbMD0hBoIBLJhSVjCDwgJ2bBhaSlLD2yAZSEGAhhCaKYYew3YBrk3uUouarZk9Xp+f5x7\nZ67GM5JcJFny+TzPPJq599w758zV3O+873nP+yZwzqhU7jxfR6A1qRDSYsMZk+GmocUbV7KXBMZm\nutmjkqhLHs+KNv1rvLGlnabWNph2Kw8lPwjgyaL9j9UdJ16L9+ubV2WdIxTW42qzhCgoWH+pnZZQ\nd7B/Sbsz8U7K+5vj8WPx2HWFnO8XFKytnoh4bWU5M4aDTqJqWwjjLoOzf+M9X2euNk8bW3h8RNQZ\n9ZZ7lrYY88/u2MaeVP/eo14LwOlC82XIdLjqDRh+XscoQlcXty5XkG7f4JzjsYQnKiXwcb7CI+Kd\n0HdaErZwNBzwnq+lQVtAnijESLj2fbjmXavfwd4IRPvHhnuwPpcz0CMiznuOuMH6+qVbUZb2jxen\nxWNbNDcvhexT9Ps4I/Ns7HBqfz9+ehlj8RiOe8JDgnjxem/2o/PHptPc1kZosL75hIXrL3EzWnhG\nZ+gb1DOt3+NS9wbGJMVzUnYCjRLO2N136VxwFltKawkPieCz+jxAu3jSYsPZUlbL5tIahqXG0NTa\nxt6D+kteWevIsm1HJPlOQNs36u4Kj30TsrMStNR3zMLQlavNFgvnfMS8p3T0VWx6R1dbl33xsXjs\n+R8nYQGExxa4iddoYTvVT8G68x/XEXdDToZtn3nHAB0tPs97xULGxI7t/JU790dkgna1tTqCC0AH\nPgRKF+MrPKCvb1uTf1dZS52OBDxY4nC1OUR76IyO5wqJgKYWR3BENNy2Qq8rWvG8NU5LeCq36EW+\noOcDN76nQ8N953jsIBLn9YhO1YtjnWuGGqr0HFZnATO9hLF4DP2On83K585zR3g3WDf+ZoJJiQ1n\nbKa+wT4uPyTuF9+y8LaZFKS7+ewXpzMxq+PN9843VvODv3xNSVUDp+QnkRQdxh8uH0+QS3h9hY4G\n2n2gwbMAvqLWp7IoHDoBHWh7IDzC4PYKiTME0J2pXWODLfHt4GqLO9TVBrrsgp3FwNfi6U5f/Imc\njW/qIJsZt8Mpv4DvPRb4/Im5Op8feC0551ocG0+4sTOxq9X/YJ8w9UBExFuuNh/hiXOEZXuyMVif\nt7/Qdvs6RjtEyZkhPdytLRlb5Dpz6dnXwhkF6c44dDGyZw7OsoRsi6dyqyU8tY48d0r3P8QhyO5M\nna/OWVbeGVzQxxjhMfR/rC9cE6GkxYaRmxxFeIiLQe6IDpF0QxKjeOKqiQxLjebKqXrSeuO+g+w7\n2EhzazvnFKSx4t5ZTMtJZM7oNF79ppiDjS3s3O9duLe1vI4H3l1PQ3Nbx6g2J+GH6WqzbwThbst1\n5nNDD4mAGxd5F0gGBXcUK4+rzSd5quf8h2Px+LjaOhMe3xvY4JPgrPv8z7n4w75B2/13Co8dpdbB\nurP671sWIxARlsVjz3ElDYcz7oEJV+nXweHeG7wdWh7I4gE9x2PjGz4fEqmtHug8iME+l/N439cR\ncd7Pxl5XZacDOljiDY93ZmPw/bFy3sNw8fNeK9EVrOd4mmo7X5vWSxjhMfR/rCii08bmcU5BGsFB\nLiZmxZOTdOgvu5SYcD7619O4dLKOqHIWvXSWbPjxqbnUNrWyYHmxZ34H4LVvdvHC0u0sKapwLCDV\nv8CXbKngoQ82Olxt3l/mOyrqKD3oyCLsJChEt3VaPF3hSVTqDC4IYAn4qZEUEN+otsMRnsPFvkEH\nh+uHMwtDwVxt5XVYdHsErraafbDu79qSScjR9Y08efhivT8S7LDmaH/C08kcD2jhDInwlrIOFLYN\n/i0e53bQAmu7V+2+RqfA4Kkw90lvXkBn8IBvwEBclrZ67f/R2Azteq0rMxaPwXBMyJwM177P9Zdd\nQroVbv3kVRN57NLxAQ9xRxxqHQyO994MxmS6yUmO4pvt+9lVWU9YsIvkmDDKarSrbU1JVceoNuD5\nJdt46vOt1It1HocFcv1fl3PPW50UXZv3pM7vlTXNa9l0hrM2UFdzSs7S4F2RmAuTrtXRUUGh/oXH\nTisUKKN1d0nI0euM3Jl6bVDaOO/7jZ6vrTynFecRnm662vLP0TV+ij6B8Vd5LTFnRVn7nCkF+q8/\ni8cTXOB0tTmEIjbDEh5rvUy3XG0BquiCFn07YCFpmHf7DR9pay087tDCc4HExB6fHZbe2nhczPGY\n4AJD/0fkkEncuMjO3VxO4YkJD6amsbWDxQOQlxzNtoo62toVQxIjCXa5KLeEZ/XuagjNB4S1pY18\n9eU2VuzQazo+rh9GbORs3vykgXvnNeIS2FZRR2NLJxmcxszXf2fdH7BJdUMLkaFBhAS5Alg8AVxt\nhxNcEBQCF/xRPw+OOHRxJ+ib2M1L9Ar7oyF+CNxZpG/UNy7S25b9Wc/L+Lt52zdR35t2IEZfDGte\nh80f6qJ6Nr4lzCUICubp7ADOG72Nx+JxLCx19mHIDP0edq63Tl1t1nGhPhaPiN7X1qz7NP4K/Tn7\nyxruL/y8K+Gx10zBcRFObYTHcEISG+7917/v/FEcbGghPKRj9FZuSjSfbiyjuqGFmXlJHQIL1pRU\no0SQqGS+K2vnwfc3YAfLPfBZOZV1V8P+KmaMLPOI3J7qRqobWvxaW06e+nwrH6zbxzu3esVUKcWs\nx77guhlD+cnpeT7CY910AlkCh+NqcxIWHdhKSht9eOcKhK/AhMbo+Qh/ghccpm/O3bV4ROCi5/Q6\nJTubBGhxDQ73lnuIStIT8Vct8H8e+/0i4r1rdpzCkzW14+sjcbXZ+0IidL8j4mHk+f7P4e86BrJi\nPMKT2XXbXsQIj+GEJDjIRUxYMDVNrcwenUZs+KFikJscTWu7orymiYL0WNbt0Sn2UyyX257qRqLm\nL+CxZzZi5zOICQumsq6ZyUPiWbfnIFtKa1E4w7drmDy0443pYGML0aHBuFx6cnjlzgOsKq6isaXN\nI4aVdc2U1zSxqtiaKA+P1TdoZ7nurlxt3bF4nFz8vDe3W29hi51zotxJuLv7czygPyd/rkvb2jnl\nDhh7aefn8CQkjdWfZVuzzxxPjI/wdOLSDORqs7d1R1SPxOJJn6A/t4RsvcaqjzFzPIYTltiIEOIi\nQ/yKDkBusvfLPDrD7SnbfdFE/evxpWU72NCexQFiiQ4LJjspimm52s0ya1QquSlRFJXXsnLnAYZY\nGbY3lXZM9X+grplpDy7i3dXeFPW7q3S00i5HUMMea9u2ciudTLjbe4NOtCaR44f6H2jycP0r3F/J\ng84YcnLHjNC9QVhM5xaDO6NjLrUjJSFX50RLyofcMzpv6yw6F+xjsdh56+wolfSJnYfRe6La/AhF\nSET3fhw429hh510JT+oouGevLtOR7Med2MsYi8dwwuKOCCEhKvBNIifZ65IYlR7Ld7u0tXHRxAyq\n6pt55ottrLTmdV69cRqxEcG8u2oPH68vZdbIFDbuPcgXm8upbmjhtjPyeGHpDjbt6yg8hcVV1De3\nsaXUm+jRFpkdFXUMS43puK2yjta2doJn3A6jLtQHxA+Fu4sJyNCZcJdv+uvjlJNv65i52pfL/7f7\nrrbOuObdjmtcOsOut+QKcpRZD4cbPvamBqrYrP/Oeajzc3Vm8YRGd51PDjpaPBHxOsKtK1dbWGxg\nK7IPMMJjOGG5YWa2J/uBP9wRISTHhBEVGkRseAjnFqRSVtNIbnI0v7twDN/tqmLFzgPEhgczOiMW\nEeGHJw9l5KBY8lJiyE+N4e1CbcmcOTKVJUUVbNzbUXhWlWgxK6vRoda6TpDOIee0eHZX6f0tbYqS\nAw0MTcr3Xzunv9OV9eEv6uxI6O7iXtDWjWcNkR2xGOxd0Atw8XM6l5xzm99zOYTLlzn/2b3ACecc\nT1fCk3uWzi93uG7WHsYIj+GE5eJJmV22+f64dGKsQISc5Gjuv6DAs+/KqVncv3Adw1JjPAtV3REh\nnDVShxnbWbWTY8IYm+Fm3OA4Xv1mF02tbYQF67mb1SX6131ZTRNvfVfSISXPzspDXW0AX2+vJMxa\nIHu4NLe2e8T2+SXbCRK4dkZ2F0ed4Mz4F2/UoS0MLp9bZ87p+tEVsRn64S/XXNbU7vXH1+KBwK62\nwSfpx3GGmeMxGDrhvvNHcfss/z7xeRMyiAwNYlS6/5pC+alaeM4cnoLLJUzLSaSxpZ1VxVpslFKe\nYIGyg038x/sbefB9XTUy2CXsqPSWB9hT1UBStHYx3fXmGi59Ztlhj2V/XTPjH/iILzaXo5Tiqc+3\n8srXu7o+8EQntcCb7DSQ8HSXaT+BW5YeXX9CY/Ck+OlKeI5TelR4RGS2iGwSkSIR+aWf/SIif7L2\nrxaRiY59L4hImYis9TnmYRHZaLV/S0TirO1DRaRBRAqtx9O+72cwHEvcESG8c+sM7jjbvzBlJ0Zx\n7fShXD9TWxRTsxMQga+2VQI6iKCyrpnQYBe79tdTVtPkCckePzjukOACp8AV73ekS+km2ytqqW9u\nY8Peg+ysrKeitomd++tpb1eHtK1tavW7/YTnaIUnOLR7C3k7w+XyWj3OZK79iB4THhEJAp4A5gCj\ngCtExHfF2Rwg33rcBDzl2PdXYLafU38MjFZKjQU2A3c79m1VSo23Hjf7OdZgOKbkp8YEXKzqcgm/\n/n4Bw9N0gEBcZCgj02L5cN0+CourWLlTByackpdEbVOr57hglzB5aAIlBxqortfzPburGsmIC+eh\ni8dwco6egK6qb6YzHv1oE/Of+j/P633Veh1S2cEmlu/QGY2bW9s9mbdtmlrbmPnQp7z81c5ufw4n\nDEcrPMcKX+E5DhaFHg49afFMAYqUUtuUUs3Aa8BcnzZzgZeU5isgTkQGASilFgP7fU+qlPpIKWV/\nS78CunbUGwzHCWeNTGHdnoNc9ORS3incQ1RoEKcN94YHuwTS3OFcME5nSf7Ne+tobGmjoraJdHcE\nl52U5bGgtlfUsbe6gQff36CTlgKfbSzz5IT7dGMZK3cd8GRM2FutraSymkZPlgWAhYV7ePzjzZ7X\n2yvqqKpvYfHm8h78JPopdnBBXwuPHWBgXG2HkAE4YzxLrG2H26Yzrgf+6XidbbnZvhCRU/wdICI3\nicgKEVlRXm6+WIbe5Y6zh/HWT6bTrrQwTBwS3yFI4ObTcrlgXDoF6W5uPT2Xv3+7m58v0KWix2Tq\nX7lDrTVBOyvreerzrTy7eBt/+nQLVfXNXP/icu59ey2NLW1s2leDUt61P7YgldU0sXLXAUYO0q67\nRz/axB8XbaG+Wf+es0O7vyuuQinjbuvA8WrxGOHpHUTkHqAVeMXatBfIUkqNB+4A/ldEDpn1VUo9\nq5SarJSanJx8DBaiGQyHgYgwISuecZaITBmaQIpVSjs5Jox/mz2Cu2brtSG3nZnPiLQY/rFmLycN\njee0Yfr/dXBCJCKwYe9B3vp2N6HBLp5bvI3XlhejFHy8vpSFq/bQas3RbC3XQrK3WgvPnqoGtlfU\nccbwZEKDXZ52+6z9W8p0+/11zR3mmaobWnh31Z6jFqOK2ibeWFlyVOfoM0IOTQDbJ4TH6XVI/sqQ\n9wN6Unh2A86l0pnWtsNtcwgici1wPnCVsr4FSqkmpVSl9XwlsBXo+yW6BoMfLhinU9GclJ1ASqwW\nnuzEjr9aQ4NdPHbpeMZkuPnV+QWekO3wkCDS3RE8s3gbNU2t/OnyCYjAYx9vJizYRXiIi9++u95z\nni1lteyqrPdYPCUHGmhrV+SlRDMkwZv6pfSgngPaWlbrCbn+dpfXJfeXL7fx01e/Y7nDTefkr0u3\nc9ajn3c59jdWlvCL11dRFqhMxPGMvf7meLB4giO8WRyORTaHXqQnhWc5kC8i2SISClwOLPRpsxC4\n2opumwZUK6X2+p7IiYjMBv4N+L5Sqt6xPdkKaEBEctABC9uO3XAMhmPHD6YN4Q+XjWdqdgJJ0WE6\nwXbSoYkjR6XH8u5PZ3rcbDYtbTo73My8JM4tSGXWyFSaW9uZNCSeO88dQU1TK8kxYQxJjOS5xds4\n/ZHPPLnmbLKTohjqqFlkC9OWshpm5iURFRrkydYA8OG6fQD8bbn/LAkrdh5ga3kdu6sauPftNdQ0\ntvhtZ1tWu6sOPzKvz/G42oI6b9fTjLzAW77ilmW6nEU/oseExwoAuA34ENgALFBKrRORm0XEjjh7\nHy0ORcBzwE/s40XkVWAZMFxESkTkBmvXfwExwMc+YdOnAqtFpBB4A7hZKXVIcILBcDwQHhLEvAkZ\niAghQS5+cc5wLjspq9vHXzhBT4U+dtk4RMRT2G5qdiLXzxjK9TOyuXJKFnnJ0TS0tNGuoL65jeQY\nb7qZnKRo/uXMfB65RFe33HewkZa2drZX1DE8LYaCDDdrd+s1R9sr6thcWos7IoT31+yl0lkC3KL4\ngBaSt74t4X++2sX7a/z/hrSzNNiuv36FJ7igj11tw86F2Q/qNDipR1meog/oUXtRKfU+WlyctFr4\nMAAAFSNJREFU2552PFfArQGOvSLA9rwA298E3jzizhoMfcitZ/j9tw7IXbNHcPusYUSE6l/epw5L\n5t7vjWTueC1mv7pA34z+458bWLSxzJNRe2yGm0Uby0iMCsUdGcKYSDdjMt38euE6Sg82sr2ijpY2\nRX5KNE0t7fzvNztpa1f8danO9fbIJeO49ZVvueSZZbx24zRSYsN58vMiEqNCPZVa1+7WltWH60r9\niqnt0tvTry0ek/TlaOi3wQUGw4mMyyUe0QEIcgk/OiWng0UDcMOMbJ67ejLXTB8KeCPjsn3KgqfE\nhlF6sJFCy7U2bnAcozNiaWxp5643V/Pisp1cOTWLs0el8vINU9hVWc+Tn29laVEF//nBJv74yRb2\n1+l1RWssK2nJlgpm/P5T7nt7rSfcG7wuvUAWT2NLm8cqOu44XsKp+zlGeAyGAUxKbDhnj0pl3oQM\npmYnMHu0TrKZk9xReNJiw9lX3ch3xQdwR4SQnRhFQboWqTdWlnDG8GT+fa4uATA1J5Hvj0tnwYpi\n7lhQCOgidza7qxoIDXLRbM1DvfzVTh76YCOg0wSVWRbPvgDC84dPtnDu44tpaG6jeH89Zz7yOdvK\na/227XVC7OCCPp7j6ecY4TEYTgAy4iL4249PZnhqDJOHxHPasJQO+9Niwyk92MR3u6oYPzgOl0vI\nTY4izIpuu+3MPE+hOoAbTsmm3rJi7jlv5CHvN2lIPG/fOoNFPz+NcwtS+XDdPqobWigqq/UI0h5r\nQWubT2qezaU1HKhv4f01e3n6i61sq6jjH6u980Ufry/tu3Ds4yWcup9j7EWD4QRCRHjjlumHbE+J\nDfdEmdlWUXCQi4lZ8bS0tTMxq2N+sYJ0N6/8aCr5qdGEBQfxOyu5aViwi6bWdlJjwxg/WK+uP2tk\nKh+uK+W8P37pKfkQHRbM3qpGGprbmP77RfzqglFcOEEnIbHnil5bvssjbs7yFQ99sJEdFXWMHxxH\nXsrhrV9paWsn2CWe0PTDJm0sDD3FW4fHcEQYi8dgMDDI7a0P4xSZZ66exF+vn+L3Rj0jL4mUmHDc\nESHkJEd5qrACpDrOd8ZwbV3trmrw5KQbm+mmrEYHMxyob+G9VdqiUUrXG3JHhLB8xwFPCHiFFUVX\nVtNIUVktre2K3/3Du1bJSXu78pvgdH9dM+N/8xFz/vgl631Cy7tNTCpc+x5EJR3Z8QbACI/BYADO\nHzuIf501jD9cNp4Zed6bamx4CNFhXTtGzi1IY3puoie4IS3WKzzJMWHMyEtk0hCvoI0fHEe78hbC\nW7atkubWdirrmmloaeOnZ+Yxf1ImQZZ7r7xGC8+yrZXW+6Xy2aZyiqwsCy1t7dz5+iqe/mIrpz78\nGfe90yGpPQArdx6grrmNjftqeGFpP6nIOkAxwmMwGEiMDuNns/KZNyHDc7M/HO6aPYJnr57sV3gA\nXr5+Kgt+fDIjrEzdEyyr6pvteqldfXMbK3bu97jZhiZG8fD8sSy7+0wmZsVRUdvMJ+tL+dvyYmLC\ng/nt3NGEBAn/vXQ7RWW1bC2v5fWVJfz+nxspOdDA6ytLqK5voaishj9+soXWtnYKiw8Q5BKm5yby\n3S7/2RcMvYOZ4zEYDMeMZKtYndPVBngCEy4/aTDvrd7rEaClRRW4RIeD3/5aIQVWzaHMhAhEhJSY\ncJKiwygqq+WWV1bS0qaYMzqNlNhwzilI45Wvd/HK17v45Rw953L7rHwK0t3c+NIKFq7ew86KOv6y\nZDt1za2s21PNiLQYTs5J5NGPN1Pd0II7wgQJ9AXG4jEYDMeMVMvSSQ9QlvvaGdm8cct0MuIiCAt2\nUVbTREpMOM9Z1tJnm3TG+MHx3vRByTFhbLMWtt557nAemj8WgDvPGc5VU/UCVTtLwnXTs5k1MoUR\naTEsLNxNkRWG/ezibXy9bT/jB8cxPksHPay23HzV9S2e0O66plZufGmFJ2ODjZ0YtXh/Pec+vviw\n0v18sr6UTftqut3+RMAIj8FgOGZcPCmTJ6+aSJqPxeOLyyXkJOuItPS4cE4fnsLD88d59kc55pXs\nkt8Apw9PJjZcWylDk6L41QWjCHIJq0uqcUeE4I4MQUSYkZfE6pJqNu2rYc7oNE4aGk9ru2Lc4DjG\nZmrh+f0/NzLrsS8Y98BHnPXo59Q2tfLhun18vL60Q32ihz/cyNmPL6a8pokVO/ezqbTGM9cUiP11\nzZz28Gd8t+sAP3ppBef+YTE7KnQeu083llK8v54rnv3Ks+j2cKhtavUUCOyvGOExGAzHDHdECOeN\nGdSttvYi1kFx2joalR7LxKw4pmQndGhnzxu5BHKTO4ZPhwUHeeoT2X9BR801tbazt7qRkYNiefKq\nSVx98hDOGZWKOyKEKdkJ7K5qICshkiunZlHX3MbSogoWrtoDwKKNZZ7AhUUb9PMfvbicnZV6Dmpz\naUcL5tVvdnG/I6BhVXEVOyvrOyRU/c276/ivT7dw00srWbShlGXbKiks7jjXVF3fQunBRtrbFQcC\niNLPFxRyzX9/E/BztftXWFzVaZu+xMzxGAyGPsEWkXSHdfT6zdPxjW2wLZ6shEjCQw7NGDAsNYat\n5XVkOcpKjMnwZvPOS4kmOSaMB6zMCwB/u2kaoNc1tbS1s7BwD2+uLGHJlgoumZTJu6v38G9vrOKZ\nH05mU2kNyTFhrCqpJtaaE1q7u5oL/ryElrZ27po9gqe/2MrOynpmjx7EybmJbLKE6QMro3duchTf\n7qoiMz6C1nbFJxvKAF2iwqa1rZ2rnv+KuqY2rpsxlH9/bwN//8l0RjvG0tTaxheby2lubae+uZXI\nUP+38Cuf+5qK2iaW3X1mh0KDxwvG4jEYDH1CrmXxpMd5b4xBfhZ32hZPfmqM3/MMs7Y7LZ6hiVHE\nhOubcr6fRaYi3vcJCXIxIy+Rj9aX4hLhx6fl8IfLxlNYXMUP/vI1SsH8SXpxq+1i+7+tlazZXc2e\nqgZu/1uhxxJ64L31rN1dzWZrTqfKcoldNDGT6oYWz7qkZdv0eYodhfb+56udrN19kO0Vdfz50yKa\n29q5Y0EhTa3ePHcrdx6gsaWdduVNxgo6nPynr37HmpJqz2uAB971v9aprzHCYzAY+oSCdDcidJl9\nwI6U8ycg4BWeLEdRO5dLGJPhJsglDEnsuiy07R58+JKx5KXEMHv0IK6bke2xXC6zyk60OhamRoUG\n8dt5oz3ZGH51/ih2VtYx74mlfFlU4WmXEBXqWURrY6cJ2l5Rz62vfMv976zlwfc3ctLQeIJcQnlN\nEzPzkthcWtvBXffllgpPuPsqhytt7e5q3l21h3dXa1ehvfbqg3X7PItvjyeM8BgMhj4hLyWaxXee\nwcy8zrMApMeFM39Spqdqqy/TchI4OSeR6T7nuXxKFj+cNqRDup1AzB2fwcp7ZzF3fIZn2y2n5xIR\nEkROsi6Yl2pVih1npQI6fUQK3xsziIy4CMZkuLl+Zjaf/vx0QC94tV2IecnRDEuNJjxE9yPKkVV8\nSVE5/1izlxeX7SQrMZLnrp7MVGuO63cXjuakofE89flW6pt1xofPN5UzKSuejLgICku8wmMX7LMz\nMuyva+bknESUgs82lnnafbG5nPrmVj7bVMauSq+11dsY4TEYDH3G4ITILvOmBQe5eOSScYwcFOt3\nf2J0GK/eNI2MuI5zGd8fl86vv1/Q7b4kRncsKZEUHcZjl47j7jk6CerwNP3+548ZRExYMPMnZRIc\n5OLlG6bwxJUTAUhz62zgAN8bq62ovNRogoNcFKS7iQ0P5uRcLZDxkSE0tmiX2PPXTGbBj08mLjKU\n22cN446zhzEkMYqfnTWMvdWNTH1wEX/5chsb9h5k9ug0xg+O4/+KKvho3T72VTd6AgnW7ammsaWN\nhpY2ZuYnkRYbziJrPmlreS3XvPANv31vAz9fsIrfvLuu25/NscYEFxgMBkMA5jgi9EakxbB4czkT\nsuJY/etzPIKZ4xNpd92MbD7dWMYlkwezfMcBj5vttjPz2FfdSPH+ej7ZUMqpw5J5p3APgxMiOGtk\nquf4KdkJnsi+mflJvHbTNO59ey3//o8NBLmEC8alM3FIPIXFVdz08kqCXEJ4sIsgl3CgvoX1e7XV\nkxAVypkjU/j7tyW8vGyH5/yvfrML0BZdX2EsHoPBYOgGM/KSiIsMIT8lplMrbUp2AusfmM2w1Bje\nvnWGxwI6Y3gKV0zJYs7oQZw9KtUjSCcNSQh4LoBpOYn8bt5oTx+SY3Tm70U/P01nCE+Jpq65jbNG\n6PMt3aLnl+IjQ7jltFzGZLi57511/OnTImKsuZ8p2QlMHtr5+/YkPSo8IjJbRDaJSJGI/NLPfhGR\nP1n7V4vIRMe+F0SkTETW+hyTICIfi8gW62+8Y9/d1rk2ici5PTk2g8FwYnHasGQKf3UO7siu0+x0\nlu9uTKab566e7Amq6I4ATM1J5JFLxvH/zvOWYwgPCWJGXhJPXDWRSUPi+emZ+QAsKbKFJ5TBCZG8\neuM0hiRGUl7TxDkFaTxyyTgevHBMl+/Zk/SY8IhIEPAEMAcYBVwhIqN8ms0B8q3HTcBTjn1/BWb7\nOfUvgUVKqXxgkfUa69yXAwXWcU9afTAYDIbjjoL0WP58xQQumpjRdWN0SPeItEPnuXKTo3nzlumM\nyXQzOCGC76z5noSoUEDPkf34VO1Wm56byPxJmYddx+hY05MWzxSgSCm1TSnVDLwGzPVpMxd4SWm+\nAuJEZBCAUmoxsN/PeecCL1rPXwTmOba/ppRqUkptB4qsPhgMBsNxh4ier/G3KPZIyU2OprlVByzE\nW8IDcOnkTB67dFzAyMDepieFJwModrwusbYdbhtfUpVSdh3cfYA9K9etc4nITSKyQkRWlJeXd/FW\nBoPB0H/IcwQ6xDkybwcHubhoYma3Qst7g+OjF0eI0iljDy012PkxzyqlJiulJicnJ/dQzwwGg6H3\nsV1oseHBBAcdv7f3nuzZbmCw43Wmte1w2/hSarvjrL/26qgjOZfBYDAMGGzhSXC42Y5HelJ4lgP5\nIpItIqHoif+FPm0WAldb0W3TgGqHGy0QC4FrrOfXAO84tl8uImEiko0OWOg8havBYDAMIGzhiYs8\nvoWnxxaQKqVaReQ24EMgCHhBKbVORG629j8NvA+chw4EqAeus48XkVeB04EkESkB7ldKPQ/8Hlgg\nIjcAO4FLrfOtE5EFwHqgFbhVKeXNrmcwGAwDnLjIUJKiQ4nvRsh3XyJ2Zb0TkcmTJ6sVK1b0dTcM\nBoPhmPHmyhJSYsM4Jb/n5rBFZKVSavKRHm9S5hgMBsMA4mKrhMPxzPEb9mAwGAyGAYkRHoPBYDD0\nKkZ4DAaDwdCrGOExGAwGQ69ihMdgMBgMvYoRHoPBYDD0KkZ4DAaDwdCrGOExGAwGQ69yQmcuEJFy\ndNqdgUYSUNHXnehBzPj6N2Z8/Z/hSqmYIz34hM5coJQakHURRGTF0aSzON4x4+vfmPH1f0TkqHKN\nGVebwWAwGHoVIzwGg8Fg6FWM8AxMnu3rDvQwZnz9GzO+/s9RjfGEDi4wGAwGQ+9jLB6DwWAw9CpG\neAwGg8HQqxjh6eeIyA4RWSMihXaIo4gkiMjHIrLF+hvf1/3sLiLygoiUichax7aA4xGRu0WkSEQ2\nici5fdPrwyPAGH8tIrut61goIuc59vWbMYrIYBH5TETWi8g6EfmZtX3AXMNOxjhQrmG4iHwjIqus\n8f3G2n7srqFSyjz68QPYAST5bPtP4JfW818CD/V1Pw9jPKcCE4G1XY0HGAWsAsKAbGArENTXYzjC\nMf4a+IWftv1qjMAgYKL1PAbYbI1hwFzDTsY4UK6hANHW8xDga2DasbyGxuIZmMwFXrSevwjM68O+\nHBZKqcXAfp/NgcYzF3hNKdWklNoOFAFTeqWjR0GAMQaiX41RKbVXKfWt9bwG2ABkMICuYSdjDES/\nGqPS1FovQ6yH4hheQyM8/R8FfCIiK0XkJmtbqlJqr/V8H5DaN107ZgQaTwZQ7GhXQuc3gOOdn4rI\nassVZ7sx+u0YRWQoMAH9i3lAXkOfMcIAuYYiEiQihUAZ8LFS6pheQyM8/Z+ZSqn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f925b79d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "axes = plt.gca()\n",
    "\n",
    "# set x label range, starting with 16\n",
    "r = np.arange(16,288)\n",
    "\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(r, history.history['acc'])\n",
    "plt.plot(r, history.history['val_acc'])\n",
    "plt.title(\"model {} accuracy\".format(i))\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "#plt.xlim(32,224)\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(r, history.history['loss'])\n",
    "plt.plot(r, history.history['val_loss'])\n",
    "plt.title(\"model {} loss\".format(i))\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "#plt.xlim(32,224)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try evaluation only with best performing models\n",
    "Most of the time, this does not improve prediction accuracy.\n",
    "<pre>\n",
    "267 epochs, dropout 0.2\n",
    "0 0.204998254776\n",
    "1 0.212502479553\n",
    "2 0.199997425079\n",
    "3 0.197499990463\n",
    "4 0.209999084473\n",
    "5 0.204998254776\n",
    "6 0.217497348785\n",
    "7 0.199997425079\n",
    "8 0.185000896454\n",
    "9 0.207501649857\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_numbers = [2,3,7,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models = []\n",
    "\n",
    "for i in best_model_numbers:\n",
    "    best_models.append(models[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = np.stack([m.predict(x_test, batch_size=eval_batch_size) for m in best_models])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_preds = all_preds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17250180244445801"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display error rate in percent\n",
    "(1 - keras.metrics.categorical_accuracy(y_test, avg_preds).eval().mean()) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train single model some more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select a single model, train for a few epochs, and check accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# which model to train\n",
    "i = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reload weights in case of overfitting\n",
    "# models[i].load_weights(\"weights/weights_emnist_model_0.2475_\"+str(i)+'.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models[i].optimizer.lr = 0.0001\n",
    "history = models[i].fit_generator(batches, steps_per_epoch=steps_per_epoch, epochs=4,\n",
    "                   validation_data=test_batches, validation_steps=validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# calculate accuracy of single model\n",
    "pred = np.array(models[i].predict(x_test, batch_size=eval_batch_size))\n",
    "print(i, (1 - keras.metrics.categorical_accuracy(y_test, pred).eval().mean()) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# models[i].save_weights(\"weights/weights_emnist_model_0.2475_\"+str(i)+'.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_1 (Lambda)            (None, 1, 28, 28)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 26, 26)        320       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 32, 26, 26)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 32, 26, 26)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 24, 24)        9248      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 32, 24, 24)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 12, 12)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 32, 12, 12)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 64, 10, 10)        18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 64, 10, 10)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 64, 10, 10)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 64, 8, 8)          36928     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 64, 8, 8)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 64, 4, 4)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 601,578\n",
      "Trainable params: 598,250\n",
      "Non-trainable params: 3,328\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "models[0].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix for python3\n",
    "import pydotplus as pydot\n",
    "\n",
    "from keras.utils import plot_model\n",
    "plot_model(models[0], to_file='model.png')\n",
    "\n",
    "# visualize our model\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "SVG(model_to_dot(models[0]).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Predictions\n",
    "Here we compute the predictions from a single model or ensemble to visualize them later. As well as looking at the overall metrics, it's also a good idea to look at examples of:\n",
    "1. A few correct labels at random\n",
    "2. A few incorrect labels at random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute predictions for best performing single model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load saved weights\n",
    "m.load_weights(\"dropout_0.2/weights/258epochs_weights_model_7.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37888/40000 [===========================>..] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "# predict classes\n",
    "preds = m.predict_classes(x_test, batch_size=eval_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or compute predictions for ensemble\n",
    "Load model weights and compute predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify which weights to load\n",
    "weights_epoch = 267\n",
    "dropout = \"0.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "\n",
    "for i in range(10):\n",
    "    m = create_model()\n",
    "    m.load_weights('dropout_{}/weights/{:3d}epochs_weights_model_{}.pkl'.format(dropout, weights_epoch, i))\n",
    "    models.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = np.stack([m.predict(x_test, batch_size=eval_batch_size) for m in models])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_preds = all_preds.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# decode a one hot encoded string\n",
    "def one_hot_decode(encoded_seq):\n",
    "    return [np.argmax(vector) for vector in encoded_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = np.array(one_hot_decode(avg_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16000270843505859"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display error rate in percent to check we have loaded the weights we wanted\n",
    "(1 - keras.metrics.categorical_accuracy(y_test, avg_preds).eval().mean()) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize computed predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000,)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = np.where(preds==test_labels)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "incorrect = np.where(preds!=test_labels)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39936,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incorrect.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of images to view\n",
    "n_view = incorrect.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import permutation\n",
    "\n",
    "# get a random selection of correctly predicted images\n",
    "idx = permutation(correct)[:n_view]\n",
    "idx_incorrect = incorrect[:n_view]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_correct = preds[idx]\n",
    "preds_incorrect = preds[idx_incorrect]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = x_test[idx]\n",
    "imgs_incorrect = x_test[idx_incorrect]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 1, 28, 28)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few correct labels at random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4489b20358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,4))\n",
    "\n",
    "for i in range(30):\n",
    "    sp = f.add_subplot(3, 10, i+1)\n",
    "    sp.axis('Off')\n",
    "    sp.set_title(preds_correct[i])\n",
    "    img = imgs[i].astype('float32')\n",
    "    plt.imshow(img[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 8, 5, 3, 8, 4, 1, 0, 7, 7, 2, 3, 6, 8, 3, 8, 6, 6, 5, 3, 2,\n",
       "       1, 8, 9, 2, 3, 5, 9, 9, 4, 9, 0, 4, 3, 2, 7, 9, 1, 2, 0, 7, 0, 3, 8,\n",
       "       0, 8, 7, 3, 3, 6, 5, 7, 8, 9, 3, 5, 9, 9, 0, 0, 7, 2], dtype=uint8)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ground truth labels\n",
    "test_labels[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All incorrect labels\n",
    "Display all predictions not matching ground truth. There are clearly some labeling errors in EMNIST Digits, limiting our overall prediction accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4470deee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,10))\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=1.15,\n",
    "                wspace=None, hspace=None)\n",
    "\n",
    "for i in range(n_view): \n",
    "    sp = f.add_subplot(7, 10, i+1)\n",
    "    sp.axis('Off')\n",
    "    sp.text(0.8, 1.2, \"Pred.: {}\\nLabel: {}\"\n",
    "                 .format(preds_incorrect[i],test_labels[idx_incorrect][i]),\n",
    "            horizontalalignment='right', verticalalignment='center',\n",
    "            transform=sp.transAxes)\n",
    "    plt.text(0.5, -0.12, \"#{}\".format(idx_incorrect[i]), \n",
    "             horizontalalignment='center', verticalalignment='center',\n",
    "             transform=sp.transAxes)\n",
    "    img = imgs_incorrect[i].astype('float32')\n",
    "    plt.imshow(img[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 8, 3, 2, 4, 4, 1, 9, 3, 8, 9, 0, 1, 7, 4, 4, 9, 8, 4, 2, 4, 1,\n",
       "       3, 8, 9, 7, 5, 5, 4, 1, 4, 5, 4, 8, 2, 5, 1, 0, 4, 3, 3, 4, 8, 7, 1,\n",
       "       3, 7, 4, 0, 9, 5, 8, 6, 5, 9, 9, 7, 6, 6, 4, 3, 3, 9], dtype=uint8)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ground truth labels\n",
    "test_labels[idx_incorrect]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  505,   582,  1656,  3291,  3928,  4043,  5421,  6538,  7055,\n",
       "        7499,  8127,  8132,  8308,  9272,  9699, 10017, 11258, 11882,\n",
       "       13365, 13710, 13909, 14174, 14696, 15095, 15169, 15407, 16688,\n",
       "       17747, 18158, 19413, 19506, 19845, 21788, 21860, 23309, 23325,\n",
       "       24013, 25056, 25171, 25758, 26046, 26191, 26544, 26969, 27337,\n",
       "       27957, 27969, 28864, 31031, 31121, 31239, 31917, 32081, 33381,\n",
       "       33694, 35166, 35329, 36008, 36597, 37283, 38090, 38166, 38539, 39677])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_incorrect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot unnormalized and normalized confusion matrices. Code adapted from \n",
    "http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# calculate confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cnf_matrix = confusion_matrix(test_labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[3997    0    0    0    0    1    1    0    0    1]\n",
      " [   0 3994    1    0    1    0    0    3    1    0]\n",
      " [   2    1 3996    0    0    0    0    1    0    0]\n",
      " [   0    0    3 3992    0    3    0    0    2    0]\n",
      " [   1    0    0    0 3986    0    1    2    1    9]\n",
      " [   0    1    0    3    0 3994    1    0    1    0]\n",
      " [   2    0    0    0    1    0 3997    0    0    0]\n",
      " [   0    1    2    0    1    0    0 3995    0    1]\n",
      " [   0    0    1    2    1    0    2    0 3993    1]\n",
      " [   0    0    0    0    4    0    0    2    2 3992]]\n",
      "Normalized confusion matrix\n",
      "[[  9.99e-01   0.00e+00   0.00e+00   0.00e+00   0.00e+00   2.50e-04\n",
      "    2.50e-04   0.00e+00   0.00e+00   2.50e-04]\n",
      " [  0.00e+00   9.99e-01   2.50e-04   0.00e+00   2.50e-04   0.00e+00\n",
      "    0.00e+00   7.50e-04   2.50e-04   0.00e+00]\n",
      " [  5.00e-04   2.50e-04   9.99e-01   0.00e+00   0.00e+00   0.00e+00\n",
      "    0.00e+00   2.50e-04   0.00e+00   0.00e+00]\n",
      " [  0.00e+00   0.00e+00   7.50e-04   9.98e-01   0.00e+00   7.50e-04\n",
      "    0.00e+00   0.00e+00   5.00e-04   0.00e+00]\n",
      " [  2.50e-04   0.00e+00   0.00e+00   0.00e+00   9.97e-01   0.00e+00\n",
      "    2.50e-04   5.00e-04   2.50e-04   2.25e-03]\n",
      " [  0.00e+00   2.50e-04   0.00e+00   7.50e-04   0.00e+00   9.99e-01\n",
      "    2.50e-04   0.00e+00   2.50e-04   0.00e+00]\n",
      " [  5.00e-04   0.00e+00   0.00e+00   0.00e+00   2.50e-04   0.00e+00\n",
      "    9.99e-01   0.00e+00   0.00e+00   0.00e+00]\n",
      " [  0.00e+00   2.50e-04   5.00e-04   0.00e+00   2.50e-04   0.00e+00\n",
      "    0.00e+00   9.99e-01   0.00e+00   2.50e-04]\n",
      " [  0.00e+00   0.00e+00   2.50e-04   5.00e-04   2.50e-04   0.00e+00\n",
      "    5.00e-04   0.00e+00   9.98e-01   2.50e-04]\n",
      " [  0.00e+00   0.00e+00   0.00e+00   0.00e+00   1.00e-03   0.00e+00\n",
      "    0.00e+00   5.00e-04   5.00e-04   9.98e-01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc789c7ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc789c7e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "class_names = [i for i in range(10)]\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure(figsize=(20,5))\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure(figsize=(20,5))\n",
    "plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}