erogol/adagrad_rmsprop_momentum_compare

## adagrad_rmsprop_momentum_compare
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Experiments are performed by a two layer NN with ReLU activation and Softmax Loss at the end. The skeleton of the model has been provided by Stanford CS231n. I do not give the codes because it is an active assignment for the students and I don't want to violate the honor code."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create some toy data to check your implementations\n",
      "input_size = 4\n",
      "hidden_size = 10\n",
      "num_classes = 3\n",
      "num_inputs = 5\n",
      "\n",
      "def init_toy_model():\n",
      "  model = {}\n",
      "  model['W1'] = np.linspace(-0.2, 0.6, num=input_size*hidden_size).reshape(input_size, hidden_size)\n",
      "  model['b1'] = np.linspace(-0.3, 0.7, num=hidden_size)\n",
      "  model['W2'] = np.linspace(-0.4, 0.1, num=hidden_size*num_classes).reshape(hidden_size, num_classes)\n",
      "  model['b2'] = np.linspace(-0.5, 0.9, num=num_classes)\n",
      "  return model\n",
      "\n",
      "def init_toy_data():\n",
      "  X = np.linspace(-0.2, 0.5, num=num_inputs*input_size).reshape(num_inputs, input_size)\n",
      "  y = np.array([0, 1, 2, 2, 1])\n",
      "  return X, y\n",
      "\n",
      "model = init_toy_model()\n",
      "X, y = init_toy_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Train the network\n",
      "Here we compare naive sgd, momentum, rmsprop and rmsprop+momentum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from cs231n.classifier_trainer import ClassifierTrainer\n",
      "\n",
      "model = init_toy_model()\n",
      "trainer = ClassifierTrainer()\n",
      "# call the trainer to optimize the loss\n",
      "# Notice that we're using sample_batches=False, so we're performing Gradient Descent (no sampled batches of data)\n",
      "best_model, loss_history, _, _ = trainer.train(X, y, X, y,\n",
      "                                             model, two_layer_net,\n",
      "                                             reg=0.001,\n",
      "                                             learning_rate=1e-1, momentum=0.0, learning_rate_decay=1,\n",
      "                                             update='sgd', sample_batches=False,\n",
      "                                             num_epochs=100,\n",
      "                                             verbose=False)\n",
      "print 'Final loss with vanilla SGD: %f' % (loss_history[-1], )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting iteration  0\n",
        "starting iteration  10\n",
        "starting iteration  20\n",
        "starting iteration  30\n",
        "starting iteration  40\n",
        "starting iteration  50\n",
        "starting iteration  60\n",
        "starting iteration  70\n",
        "starting iteration  80\n",
        "starting iteration  90\n",
        "Final loss with vanilla SGD: 0.940686\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>Momentum Update</b>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = init_toy_model()\n",
      "trainer = ClassifierTrainer()\n",
      "# call the trainer to optimize the loss\n",
      "# Notice that we're using sample_batches=False, so we're performing Gradient Descent (no sampled batches of data)\n",
      "best_model, loss_history, _, _ = trainer.train(X, y, X, y,\n",
      "                                             model, two_layer_net,\n",
      "                                             reg=0.001,\n",
      "                                             learning_rate=1e-1, momentum=0.9, learning_rate_decay=1,\n",
      "                                             update='momentum', sample_batches=False,\n",
      "                                             num_epochs=100,\n",
      "                                             verbose=False)\n",
      "correct_loss = 0.494394\n",
      "print 'Final loss with momentum SGD: %f' % (loss_history[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting iteration  0\n",
        "starting iteration  10\n",
        "starting iteration  20\n",
        "starting iteration  30\n",
        "starting iteration  40\n",
        "starting iteration  50\n",
        "starting iteration  60\n",
        "starting iteration  70\n",
        "starting iteration  80\n",
        "starting iteration  90\n",
        "Final loss with momentum SGD: 0.494394\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RMSprop</b>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = init_toy_model()\n",
      "trainer = ClassifierTrainer()\n",
      "# call the trainer to optimize the loss\n",
      "# Notice that we're using sample_batches=False, so we're performing Gradient Descent (no sampled batches of data)\n",
      "best_model, loss_history, _, _ = trainer.train(X, y, X, y,\n",
      "                                             model, two_layer_net,\n",
      "                                             reg=0.001,\n",
      "                                             learning_rate=1e-1, momentum=0.9, learning_rate_decay=1,\n",
      "                                             update='rmsprop', sample_batches=False,\n",
      "                                             num_epochs=100,\n",
      "                                             verbose=False)\n",
      "correct_loss = 0.439368\n",
      "print 'Final loss with RMSProp: %f' % (loss_history[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting iteration  0\n",
        "starting iteration  10\n",
        "starting iteration  20\n",
        "starting iteration  30\n",
        "starting iteration  40\n",
        "starting iteration  50\n",
        "starting iteration  60\n",
        "starting iteration  70\n",
        "starting iteration  80\n",
        "starting iteration  90\n",
        "Final loss with RMSProp: 0.439368\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RMS+Momentum</b> best of the  this naive experiment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = init_toy_model()\n",
      "trainer = ClassifierTrainer()\n",
      "# call the trainer to optimize the loss\n",
      "# Notice that we're using sample_batches=False, so we're performing Gradient Descent (no sampled batches of data)\n",
      "best_model, loss_history, _, _ = trainer.train(X, y, X, y,\n",
      "                                             model, two_layer_net,\n",
      "                                             reg=0.001,\n",
      "                                             learning_rate=1e-1, momentum=0.9, learning_rate_decay=1,\n",
      "                                             update='rmsprop+momentum', sample_batches=False,\n",
      "                                             num_epochs=100,\n",
      "                                             verbose=False)\n",
      "correct_loss = 0.439368\n",
      "print 'Final loss with RMSProp+momentum: %f' % (loss_history[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting iteration  0\n",
        "starting iteration  10\n",
        "starting iteration  20\n",
        "starting iteration  30\n",
        "starting iteration  40\n",
        "starting iteration  50\n",
        "starting iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60\n",
        "starting iteration  70\n",
        "starting iteration  80\n",
        "starting iteration  90\n",
        "Final loss with RMSProp+momentum: 0.435519\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>AdaGrad</b> is better than SGD but not that RMS and Momentum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = init_toy_model()\n",
      "trainer = ClassifierTrainer()\n",
      "# call the trainer to optimize the loss\n",
      "# Notice that we're using sample_batches=False, so we're performing Gradient Descent (no sampled batches of data)\n",
      "best_model, loss_history, _, _ = trainer.train(X, y, X, y,\n",
      "                                             model, two_layer_net,\n",
      "                                             reg=0.001,\n",
      "                                             learning_rate=1e-1, momentum=0.9, learning_rate_decay=1,\n",
      "                                             update='adagrad', sample_batches=False,\n",
      "                                             num_epochs=100,\n",
      "                                             verbose=False)\n",
      "correct_loss = 0.439368\n",
      "print 'Final loss with Adagrad: %f' % (loss_history[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Final loss with Adagrad: 0.643385\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RESULT:</b> Even in a very simple toy dataset results are so demanding for the favor of RMSprop and Momentum against SGD. Best performance is observed by RMSprop+Momentum"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Load the data\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from cs231n.data_utils import load_CIFAR10\n",
      "\n",
      "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
      "    \"\"\"\n",
      "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
      "    it for the two-layer neural net classifier. These are the same steps as\n",
      "    we used for the SVM, but condensed to a single function.  \n",
      "    \"\"\"\n",
      "    # Load the raw CIFAR-10 data\n",
      "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
      "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
      "        \n",
      "    # Subsample the data\n",
      "    mask = range(num_training, num_training + num_validation)\n",
      "    X_val = X_train[mask]\n",
      "    y_val = y_train[mask]\n",
      "    mask = range(num_training)\n",
      "    X_train = X_train[mask]\n",
      "    y_train = y_train[mask]\n",
      "    mask = range(num_test)\n",
      "    X_test = X_test[mask]\n",
      "    y_test = y_test[mask]\n",
      "\n",
      "    # Normalize the data: subtract the mean image\n",
      "    mean_image = np.mean(X_train, axis=0)\n",
      "    X_train -= mean_image\n",
      "    X_val -= mean_image\n",
      "    X_test -= mean_image\n",
      "\n",
      "    # Reshape data to rows\n",
      "    X_train = X_train.reshape(num_training, -1)\n",
      "    X_val = X_val.reshape(num_validation, -1)\n",
      "    X_test = X_test.reshape(num_test, -1)\n",
      "\n",
      "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
      "\n",
      "\n",
      "# Invoke the above function to get our data.\n",
      "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
      "print 'Train data shape: ', X_train.shape\n",
      "print 'Train labels shape: ', y_train.shape\n",
      "print 'Validation data shape: ', X_val.shape\n",
      "print 'Validation labels shape: ', y_val.shape\n",
      "print 'Test data shape: ', X_test.shape\n",
      "print 'Test labels shape: ', y_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Train data shape:  (49000, 3072)\n",
        "Train labels shape:  (49000,)\n",
        "Validation data shape:  (1000, 3072)\n",
        "Validation labels shape:  (1000,)\n",
        "Test data shape:  (1000, 3072)\n",
        "Test labels shape:  (1000,)\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Train a network\n",
      "\n",
      "Train this simple model with different update rules."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use SGD\n",
      "from cs231n.classifiers.neural_net import init_two_layer_model\n",
      "\n",
      "model = init_two_layer_model(32*32*3, 50, 10) # input size, hidden size, number of classes\n",
      "trainer = ClassifierTrainer()\n",
      "best_model1, loss_history1, train_acc1, val_acc1 = trainer.train(X_train, y_train, X_val, y_val,\n",
      "                                             model, two_layer_net,\n",
      "                                             num_epochs=20, reg=1.0,\n",
      "                                             update = 'sgd',\n",
      "                                             momentum=0.9, learning_rate_decay = 0.95,\n",
      "                                             learning_rate=1e-5, verbose=True)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finished epoch 0 / 20: cost 2.302593, train: 0.120000, val 0.158000, lr 1.000000e-05\n",
        "Finished epoch 1 / 20: cost 2.302591, train: 0.129000, val 0.130000, lr 9.500000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 2 / 20: cost 2.302589, train: 0.139000, val 0.174000, lr 9.025000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 3 / 20: cost 2.302574, train: 0.166000, val 0.177000, lr 8.573750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 4 / 20: cost 2.302539, train: 0.170000, val 0.193000, lr 8.145063e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 5 / 20: cost 2.302430, train: 0.188000, val 0.199000, lr 7.737809e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 6 / 20: cost 2.302224, train: 0.195000, val 0.193000, lr 7.350919e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 7 / 20: cost 2.301709, train: 0.177000, val 0.181000, lr 6.983373e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 8 / 20: cost 2.300819, train: 0.165000, val 0.179000, lr 6.634204e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 9 / 20: cost 2.296373, train: 0.151000, val 0.181000, lr 6.302494e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 10 / 20: cost 2.290793, train: 0.169000, val 0.197000, lr 5.987369e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 11 / 20: cost 2.284688, train: 0.184000, val 0.187000, lr 5.688001e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 12 / 20: cost 2.244895, train: 0.184000, val 0.193000, lr 5.403601e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 13 / 20: cost 2.196050, train: 0.156000, val 0.180000, lr 5.133421e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 14 / 20: cost 2.254494, train: 0.135000, val 0.183000, lr 4.876750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 15 / 20: cost 2.185508, train: 0.196000, val 0.185000, lr 4.632912e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 16 / 20: cost 2.192414, train: 0.163000, val 0.186000, lr 4.401267e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 17 / 20: cost 2.227328, train: 0.179000, val 0.188000, lr 4.181203e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 18 / 20: cost 2.073986, train: 0.181000, val 0.188000, lr 3.972143e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 19 / 20: cost 2.080279, train: 0.186000, val 0.190000, lr 3.773536e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 20 / 20: cost 2.142814, train: 0.198000, val 0.199000, lr 3.584859e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "finished optimization. best validation accuracy: 0.199000\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use Momentum\n",
      "from cs231n.classifiers.neural_net import init_two_layer_model\n",
      "\n",
      "model = init_two_layer_model(32*32*3, 50, 10) # input size, hidden size, number of classes\n",
      "trainer = ClassifierTrainer()\n",
      "best_model2, loss_history2, train_acc2, val_acc2 = trainer.train(X_train, y_train, X_val, y_val,\n",
      "                                             model, two_layer_net,\n",
      "                                             num_epochs=20, reg=1.0,\n",
      "                                             update = 'momentum',\n",
      "                                             momentum=0.9, learning_rate_decay = 0.95,\n",
      "                                             learning_rate=1e-5, verbose=True)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finished epoch 0 / 20: cost 2.302593, train: 0.110000, val 0.109000, lr 1.000000e-05\n",
        "Finished epoch 1 / 20: cost 2.273101, train: 0.133000, val 0.154000, lr 9.500000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 2 / 20: cost 2.057101, train: 0.201000, val 0.241000, lr 9.025000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 3 / 20: cost 1.888748, train: 0.311000, val 0.288000, lr 8.573750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 4 / 20: cost 1.839703, train: 0.344000, val 0.339000, lr 8.145063e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 5 / 20: cost 1.942659, train: 0.333000, val 0.366000, lr 7.737809e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 6 / 20: cost 1.847249, train: 0.361000, val 0.389000, lr 7.350919e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 7 / 20: cost 1.742119, train: 0.407000, val 0.391000, lr 6.983373e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 8 / 20: cost 1.523142, train: 0.392000, val 0.397000, lr 6.634204e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 9 / 20: cost 1.710355, train: 0.411000, val 0.411000, lr 6.302494e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 10 / 20: cost 1.675986, train: 0.426000, val 0.417000, lr 5.987369e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 11 / 20: cost 1.759964, train: 0.449000, val 0.433000, lr 5.688001e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 12 / 20: cost 1.400295, train: 0.445000, val 0.430000, lr 5.403601e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 13 / 20: cost 1.473600, train: 0.441000, val 0.430000, lr 5.133421e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 14 / 20: cost 1.665528, train: 0.437000, val 0.447000, lr 4.876750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 15 / 20: cost 1.608525, train: 0.471000, val 0.445000, lr 4.632912e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 16 / 20: cost 1.420895, train: 0.454000, val 0.450000, lr 4.401267e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 17 / 20: cost 1.560292, train: 0.472000, val 0.444000, lr 4.181203e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 18 / 20: cost 1.585418, train: 0.450000, val 0.449000, lr 3.972143e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 19 / 20: cost 1.623252, train: 0.503000, val 0.445000, lr 3.773536e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 20 / 20: cost 1.554016, train: 0.476000, val 0.454000, lr 3.584859e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "finished optimization. best validation accuracy: 0.454000\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use RMSprop\n",
      "from cs231n.classifiers.neural_net import init_two_layer_model\n",
      "\n",
      "model = init_two_layer_model(32*32*3, 50, 10) # input size, hidden size, number of classes\n",
      "trainer = ClassifierTrainer()\n",
      "best_model3, loss_history3, train_acc3, val_acc3 = trainer.train(X_train, y_train, X_val, y_val,\n",
      "                                             model, two_layer_net,\n",
      "                                             num_epochs=20, reg=1.0,\n",
      "                                             update = 'rmsprop',\n",
      "                                             momentum=0.9, learning_rate_decay = 0.95,\n",
      "                                             learning_rate=1e-5, verbose=True)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finished epoch 0 / 20: cost 2.302593, train: 0.102000, val 0.098000, lr 1.000000e-05\n",
        "Finished epoch 1 / 20: cost 1.948840, train: 0.356000, val 0.329000, lr 9.500000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 2 / 20: cost 1.933427, train: 0.383000, val 0.359000, lr 9.025000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 3 / 20: cost 1.866497, train: 0.390000, val 0.395000, lr 8.573750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 4 / 20: cost 1.748220, train: 0.420000, val 0.414000, lr 8.145063e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 5 / 20: cost 1.647285, train: 0.417000, val 0.427000, lr 7.737809e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 6 / 20: cost 1.636752, train: 0.406000, val 0.438000, lr 7.350919e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 7 / 20: cost 1.683645, train: 0.433000, val 0.439000, lr 6.983373e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 8 / 20: cost 1.727223, train: 0.455000, val 0.444000, lr 6.634204e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 9 / 20: cost 1.771403, train: 0.446000, val 0.454000, lr 6.302494e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 10 / 20: cost 1.662157, train: 0.484000, val 0.450000, lr 5.987369e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 11 / 20: cost 1.783750, train: 0.451000, val 0.451000, lr 5.688001e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 12 / 20: cost 1.572829, train: 0.455000, val 0.459000, lr 5.403601e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 13 / 20: cost 1.539926, train: 0.457000, val 0.459000, lr 5.133421e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 14 / 20: cost 1.699544, train: 0.439000, val 0.458000, lr 4.876750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 15 / 20: cost 1.600255, train: 0.443000, val 0.463000, lr 4.632912e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 16 / 20: cost 1.619370, train: 0.468000, val 0.464000, lr 4.401267e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 17 / 20: cost 1.571197, train: 0.476000, val 0.464000, lr 4.181203e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 18 / 20: cost 1.608766, train: 0.468000, val 0.469000, lr 3.972143e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 19 / 20: cost 1.630492, train: 0.484000, val 0.477000, lr 3.773536e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 20 / 20: cost 1.481858, train: 0.489000, val 0.470000, lr 3.584859e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "finished optimization. best validation accuracy: 0.477000\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# use RMSprop+Momentum\n",
      "from cs231n.classifiers.neural_net import init_two_layer_model\n",
      "\n",
      "model = init_two_layer_model(32*32*3, 50, 10) # input size, hidden size, number of classes\n",
      "trainer = ClassifierTrainer()\n",
      "best_model4, loss_history4, train_acc4, val_acc4 = trainer.train(X_train, y_train, X_val, y_val,\n",
      "                                             model, two_layer_net,\n",
      "                                             num_epochs=20, reg=1.0,\n",
      "                                             update = 'rmsprop+momentum',\n",
      "                                             momentum=0.9, learning_rate_decay = 0.95,\n",
      "                                             learning_rate=1e-5, verbose=True)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finished epoch 0 / 20: cost 2.302593, train: 0.166000, val 0.165000, lr 1.000000e-05\n",
        "Finished epoch 1 / 20: cost 1.800040, train: 0.373000, val 0.390000, lr 9.500000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 2 / 20: cost 1.636812, train: 0.459000, val 0.437000, lr 9.025000e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 3 / 20: cost 1.609279, train: 0.472000, val 0.450000, lr 8.573750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 4 / 20: cost 1.540035, train: 0.467000, val 0.451000, lr 8.145063e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 5 / 20: cost 1.507733, train: 0.487000, val 0.460000, lr 7.737809e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 6 / 20: cost 1.642292, train: 0.518000, val 0.473000, lr 7.350919e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 7 / 20: cost 1.497452, train: 0.504000, val 0.468000, lr 6.983373e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 8 / 20: cost 1.533577, train: 0.502000, val 0.473000, lr 6.634204e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 9 / 20: cost 1.442068, train: 0.482000, val 0.464000, lr 6.302494e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 10 / 20: cost 1.549564, train: 0.486000, val 0.473000, lr 5.987369e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 11 / 20: cost 1.494527, train: 0.502000, val 0.471000, lr 5.688001e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 12 / 20: cost 1.462458, train: 0.495000, val 0.483000, lr 5.403601e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 13 / 20: cost 1.515679, train: 0.543000, val 0.480000, lr 5.133421e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 14 / 20: cost 1.510962, train: 0.525000, val 0.485000, lr 4.876750e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 15 / 20: cost 1.541044, train: 0.508000, val 0.492000, lr 4.632912e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 16 / 20: cost 1.577317, train: 0.536000, val 0.493000, lr 4.401267e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 17 / 20: cost 1.525123, train: 0.538000, val 0.491000, lr 4.181203e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 18 / 20: cost 1.351778, train: 0.552000, val 0.503000, lr 3.972143e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 19 / 20: cost 1.590443, train: 0.536000, val 0.499000, lr 3.773536e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 20 / 20: cost 1.448961, train: 0.530000, val 0.507000, lr 3.584859e-06"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "finished optimization. best validation accuracy: 0.507000\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# AdaGrad\n",
      "# use RMSprop+Momentum\n",
      "from cs231n.classifiers.neural_net import init_two_layer_model\n",
      "\n",
      "model = init_two_layer_model(32*32*3, 50, 10) # input size, hidden size, number of classes\n",
      "trainer = ClassifierTrainer()\n",
      "best_model5, loss_history5, train_acc5, val_acc5 = trainer.train(X_train, y_train, X_val, y_val,\n",
      "                                             model, two_layer_net,\n",
      "                                             num_epochs=20, reg=1.0,\n",
      "                                             update = 'adagrad',\n",
      "                                             momentum=0.9, learning_rate_decay = 0.95,\n",
      "                                             learning_rate=0.01, verbose=True)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Finished epoch 0 / 20: cost 2.302593, train: 0.117000, val 0.108000, lr 1.000000e-02\n",
        "Finished epoch 1 / 20: cost 1.976317, train: 0.375000, val 0.393000, lr 9.500000e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 2 / 20: cost 1.819465, train: 0.396000, val 0.360000, lr 9.025000e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 3 / 20: cost 1.946780, train: 0.393000, val 0.396000, lr 8.573750e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 4 / 20: cost 1.948320, train: 0.429000, val 0.388000, lr 8.145062e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 5 / 20: cost 1.817160, train: 0.445000, val 0.463000, lr 7.737809e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 6 / 20: cost 1.781161, train: 0.433000, val 0.433000, lr 7.350919e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 7 / 20: cost 1.726162, train: 0.437000, val 0.431000, lr 6.983373e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 8 / 20: cost 1.945575, train: 0.467000, val 0.438000, lr 6.634204e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 9 / 20: cost 1.717542, train: 0.463000, val 0.468000, lr 6.302494e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 10 / 20: cost 1.653255, train: 0.472000, val 0.443000, lr 5.987369e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 11 / 20: cost 1.757400, train: 0.487000, val 0.448000, lr 5.688001e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 12 / 20: cost 1.612132, train: 0.487000, val 0.470000, lr 5.403601e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 13 / 20: cost 1.575829, train: 0.462000, val 0.453000, lr 5.133421e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 14 / 20: cost 1.739385, train: 0.465000, val 0.471000, lr 4.876750e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 15 / 20: cost 1.649168, train: 0.497000, val 0.461000, lr 4.632912e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 16 / 20: cost 1.844438, train: 0.482000, val 0.483000, lr 4.401267e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 17 / 20: cost 1.755178, train: 0.502000, val 0.466000, lr 4.181203e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 18 / 20: cost 1.623927, train: 0.494000, val 0.476000, lr 3.972143e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 19 / 20: cost 1.760897, train: 0.502000, val 0.474000, lr 3.773536e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Finished epoch 20 / 20: cost 1.795621, train: 0.501000, val 0.469000, lr 3.584859e-03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "finished optimization. best validation accuracy: 0.483000\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RESULT:</b> In a real dataset perfomance differences are more significant. SGD gets 0.18 and we can scale ti to up to 0.46 by RMSprop+Momentum with the smae number of iterations. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Look at the results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>SGD</b> results ---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plots show that SGD takes so much time to reduce the loss and it only achives 0.19 accuracy at validation set. In addition, train and validation accuracies are very dispersed at many segments of the learning. I think SGD is not very reliable by these observations."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the loss function and train / validation accuracies\n",
      "plt.subplot(2, 1, 1)\n",
      "plt.plot(loss_history1)\n",
      "plt.title('Loss history')\n",
      "plt.xlabel('Iteration')\n",
      "plt.ylabel('Loss')\n",
      "\n",
      "plt.subplot(2, 1, 2)\n",
      "plt.plot(train_acc1)\n",
      "plt.plot(val_acc1)\n",
      "plt.legend(['Training accuracy', 'Validation accuracy'], loc='lower right')\n",
      "plt.xlabel('Epoch')\n",
      "plt.ylabel('Clasification accuracy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.text.Text at 0x7fbb51db12d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E3X139PfINDJ11KjM11RVpT8+YUL1yZozNf2uXFkaq1comBMRaeSaNcu+bFN8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\nW5sMd+DCBT3L9PvvoUgRq9XYlxdfhKFDdePjcuWsVmMNjq5kTUjCqlZ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tKudlxGWWFagN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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb5179a710>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>Momentum</b> Results ---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Better than SGD. However, still it needs a set of epochs to find a way to the minima. It is observed through the stationary Loss at the begining. Validation and Train accurcies much more correlated as well.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the loss function and train / validation accuracies\n",
      "plt.subplot(2, 1, 1)\n",
      "plt.plot(loss_history2)\n",
      "plt.title('Loss history')\n",
      "plt.xlabel('Iteration')\n",
      "plt.ylabel('Loss')\n",
      "\n",
      "plt.subplot(2, 1, 2)\n",
      "plt.plot(train_acc2)\n",
      "plt.plot(val_acc2)\n",
      "plt.legend(['Training accuracy', 'Validation accuracy'], loc='lower right')\n",
      "plt.xlabel('Epoch')\n",
      "plt.ylabel('Clasification accuracy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "<matplotlib.text.Text at 0x7fbb51f9d890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QpXZt3Uc0qKVL9TXo29d+2rpiUKjP2GIKVK08fbyJSGcA3QEYvwXZF66Aeve2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\nA1YXGZKQwsULAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb5209b1d0>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RMSprop</b> results"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Main difference here is the convergende of the algorithm compared to others. You can see a very declined slope at he beginning of the Loss value. It starts to optimize very quickly. Also, accuracy is peaked from the begining of the training."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the loss function and train / validation accuracies\n",
      "plt.subplot(2, 1, 1)\n",
      "plt.plot(loss_history3)\n",
      "plt.title('Loss history')\n",
      "plt.xlabel('Iteration')\n",
      "plt.ylabel('Loss')\n",
      "\n",
      "plt.subplot(2, 1, 2)\n",
      "plt.plot(train_acc3)\n",
      "plt.plot(val_acc3)\n",
      "plt.legend(['Training accuracy', 'Validation accuracy'], loc='lower right')\n",
      "plt.xlabel('Epoch')\n",
      "plt.ylabel('Clasification accuracy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.text.Text at 0x7fbb51d82a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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1BmuNh3VKs8ce082NuXLddfoL+ZprUgNBu0Ev5vv/+leyhiSTRNtGuhPzMV94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TgfaEHQf+XXwHTgr8BJwBpgpLvvilWMUj2U8rvyIJBC0MTqwGpgXFh/KDmBmdm5QAawkMNNqfcB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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb52030b50>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>RMS+Momentum</b> Observations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Much better convergence and best performance. It peaks the validation accuracy almost at the 5th epoch and we see only a little improvement there after."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the loss function and train / validation accuracies\n",
      "plt.subplot(2, 1, 1)\n",
      "plt.plot(loss_history4)\n",
      "plt.title('Loss history')\n",
      "plt.xlabel('Iteration')\n",
      "plt.ylabel('Loss')\n",
      "\n",
      "plt.subplot(2, 1, 2)\n",
      "plt.plot(train_acc4)\n",
      "plt.plot(val_acc4)\n",
      "plt.legend(['Training accuracy', 'Validation accuracy'], loc='lower right')\n",
      "plt.xlabel('Epoch')\n",
      "plt.ylabel('Clasification accuracy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<matplotlib.text.Text at 0x7fbb515bbb90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PqZqp1OqySl5Qt9gCuOmmyh2f0sVgrbrVx/OTuaBtu+3M/3//uzLHf+EF0wHyl1/y1ztr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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb5167c350>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>AdaGrad</b> Observations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "AdaGrad is known as a optimization method that is robust to learning rate choice andeven you give a initial learning rate value, it finds a way to tune it to a optimal value in the couse of training, at least theoretically. \n",
      "\n",
      "I used much greater learning rate for AdaGrad traning since it cannot improve the accuracy or the loss value otherwise. \n",
      "\n",
      "One improtant behaviour of AdaGrad is , it needs a initial period to tune the learning to a good value. Therfore, we observe a non-decreasing even wildly increasing Loss value initially but it functions well at the end. It takes the second best accuracy as well after RMSprop+Momentum"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the loss function and train / validation accuracies\n",
      "plt.subplot(2, 1, 1)\n",
      "plt.plot(loss_history5[100:])\n",
      "plt.title('Loss history -- after 100th iteration')\n",
      "plt.xlabel('Iteration')\n",
      "plt.ylabel('Loss')\n",
      "\n",
      "plt.subplot(2, 1, 2)\n",
      "plt.plot(train_acc5)\n",
      "plt.plot(val_acc5)\n",
      "plt.legend(['Training accuracy', 'Validation accuracy'], loc='lower right')\n",
      "plt.xlabel('Epoch')\n",
      "plt.ylabel('Clasification accuracy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "<matplotlib.text.Text at 0x7fbb51116290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3iSRli3POMWOAl15yXp95pkk9M326E8hNngzcdZd5fsQR/ulhChHVHO0WtyZv\n8GDgllui9/vzn4F7A8adjx8PnHKKeT55MvDBB/HLWYyw67V4cXH7F2PjjYHjjkvn2HUJk19TpYoT\ntG2uqteq6nRV/UZVqwFsHrWTqo5Q1QaqupOqdrF+3lDV+1T1Ptd256pqJ1XdUVW/LOK9kMX9B9+e\nWQHIv+m5J20PC1K87D5ofuc7/vjcdTNnAj/+aJ57R2F691+92r9ZcuZMYNo089xbY+T+7zxJs17Y\nTdFed9JJ5kaf1LHHBjflus/7/fdAdTXw3HPRx7T3O+qo4AD+7383gZjNHUDb+//nP8AFF5jnL71k\nRv56FTJdlN/nmda0U488YvrF2d55J/gzrMSpr1q2jK7RvvnmdM69ZElh3+lyUC1P8LR0aWEzwxDV\nhjhB2+8i0sN+ISJ7AViaXpGoWO5pqd59N94+J50U//hhQZubiOkzZvPmO/Pur+p/rJGuMcctWjj5\n6AATqBTCfTNwB0HuG4U7ZUoSzz1nAgv3MW12vjhb//65AzU23tgEckHlfeGF9GduKFUtT6mDA7tc\nNTUm4PVbVymiyrOUf0EBAB06mNpyv+DshBOA7bdP79zDh/vnsow7nzRROcQJ2s4EMFBEZonILAB3\nW8soA55+2nleqonKvX9IP/44eFs7yW0Y+wZ39dXx0iR07Wr+0C9enB9Aes2d69807L5JuGs1ghIS\nX3UVcO65ueW1RdXmuM+1zTb+29jH/OEH0xctaL19vkJqthYvNgmaAeCOO/KPW4xS12h9911wPzy/\nc4X94xBnu0KMHJmsWbgQf/wB7L9/uucoteuuA9ZbL962s2aZEa9+NewjRiRrAUjqT38CLrwwf3ml\n/QNQn4kUN6inLoozenSMqu4AYAcAO6jqTgD2Sb1kVLG8gU2S/nZ+7NkU7r3Xf3opP+efb5qZ3Nyz\nRUTxGxAR5oYbzKwUhYgzsnLLLcPXe28k3tx9q1b550VzGzHCeX7RRcHbeQOdhQuD+4x5y6caL1C3\nLVniXwPbtm1wfkK/oC2or1pazaOrV5sRm+5aUre4/Tij/PST6cYQ9c9JKcWpIf311+Cmy08+MTN2\nJFFbgVJtjHKm0nK3HFG8mjYAgKouVFU75r0kpfJQBpWqBg8ARo2Kt93XX+cvK0X/l6RTZwWZMMEJ\nZuPckKZNc2ZtCKpJsmfUEMmvkfTmavNT6Nyqzz8ff9Tl8OFAp07h27jf37RpwX0dw/arTX7/DJx3\nXvg+pQrkwfrVAAAgAElEQVTa7PccNCtKkgA5jgULzCjbKOuu69TWloL32j76qKklL6V3342Xx441\nbVTJYgdtREGCmhQLUe4JyOPcsNx/1H/8MXjKrgMPTHZTPeig3OOfc46zbr/9zGjQIEly2CXRvXuy\nmSIuizMrsUuSG2S5bqatWuX3RRwzpnbObQcz7lkk9t/fqYXs1Km0fQd33jn+tsXWsIf5299Kf0y/\nKfn8pPE9GzUK2HTT0h+3tlx1VbLvRilV4mCicspM0MZM3+RWCb/IIqaJzK/WDzD9cTp1Sn4TsN/b\nPfc4y9zJggt57/36xUslcuSRwMUXO68//hh4663o47/0kqkJHOmdqA4mj1zQZxDnRnr55cAmrsnt\nkrz/Un1PvAMHChld6O6fVUxgMGyYGehif++S5l0LO7c3OC3kOJXwu+kn7md+wAGlP/dHH8UPGivR\nG2+YVEjlUKnfp3IJDNpEZImILPb7Qf7E76lLs0MqZU85R3i5//hPn55s+zgKzT0XZujQeOV48cV4\n78mPX02gqpmxwa+JccqU3BQlQUaMiDftlUh+nsBCBiJMnmwCVXe/R+9xooI273l++MHMmmF7+21n\nEMNHHwVf06DlH34IdO6cv/zHH4H27c3zlSvTrQ0rpUpqkixXcGL75ZfytzhQ5QoM2lS1uaq2CPip\n9Sw2m0dmhqP6xG+EZX0xe7aZLaC2ffEF8O9/J9snbJRroWkv7P39+sJNnZq8PAsWAGPHmlrGoUNN\nE/mBB+YOMth999zAMWqOXu95vIH42WebwTQzZuQ2b3q3SxqgT5vmBGp33OEEcEmPafcP/eYbM7+t\nn3LUgKxe7T+/cV3Svz/Qs2e5S5ErzWuddFCY1yOPZCf3YClkpnmUqFI88USyTtilTgtx2WWlO2bY\nHKtvv537epddwqcT8xO3BuXRR03+NT/elDL2DeTEE4H33w8/bpybzfnnAzvtZGoZn3zSf5tFi3L7\nsdnHjTtwJkjHjrnNZqVMJmtPDxfH/PkmYLXZc5/26wfcdJP/PsU2jxYy88cpp8T7B37Fivj5JCvJ\nhAmVWc40yjR3rvn92Wqr/HX9+kX/U/fxxya/6Mknm0C3vmDQRpTQ/feHp8zwipvGJG1+f3hfeCG3\nv5yb3benmL449owVIs7IXL8mu7/9Lfm8oA0aAPvum7ssaQ49IH5Te6lrG+JMMVfMOcOmhfO69VYz\nUb1t6lTzM2RI4edPIm5Q8Omn8f5h2X574JBDspfEePvt8/9ZivLLL+n0wyvE1KnxP/NDDgF2281/\n3c03m9pvIPh34PHHnQTolRjopoVBG1E9t8ceySa9T8KduHjddZ3nZ52V/4c2bi2TvV2pgqik6TeK\nOabfoIGgbT/+ODeQqk2qwaOibXfemTt7x6+/mvx6tj/+CA9I3e/bvqZJUqV8801w/88pU0zw065d\n+PEmTkw2+CLIL7/kzvvrleS7k7S/7osvJg/0kopb/i23DG5O94o7gKaQ+aPrskwFbe7cVGuvXb5y\nEGVR2B+5tdc2c3imxfuH93//y7/h2oMW9tgj/Fju+XSDzmNnUk8a2Lm39wYc7nWFBozffRd/22ef\nNXnv3EoxAEckujbEG7R9GTArtLtZas6c3PfXrRtQVeW/X6dOwF13+Z83yOzZuX0WO3UyWQW6dw/e\nxy8xqzuFzfjxwD4RqeIXLYr+3G+/PXxQTSkDjL//PTd5caFT+aUlrX8AychU0Nahg/OcQRtRMu5U\nHn7Gj493nBUr4uefs0dz7r579LaqJnAMaq713viiBgMsXOgM2HD/vVi8OHc7dwDmDmYmTwaaN/ff\nLu5N+PffTS3SM8+Y1+6bf9ykwoDTWduvD6JdlpUr/Wu2ZszIPe/y5cBaa8U/N+AEWEuX5o6CdXMH\nzID57D//PL/p7tVXzffHL5WMan6gagvKcxY2jZ732IDJOeYWVNN2/PGmeb9NG+Coo+KdoxSi/iF4\n8MHi+1KWUqF9B6O2C/sc6vPUVpkK2gDz3+c55+Re0KR9YYjqo6hAK+4f27PPNn3hkpg2LXqbQvPZ\nhR3vuOPMc/u//7lzzfRn9o16xgzgt9+cfYYNyz2Ge50I0KVL8Htx18jY72XcONMH6x//SP4+3AMJ\nwm5SXbo4z/v2zV/fsSNw7bXO6zhTYgU1Xf/4Y3D6Jb+mylWrcpvu/vxn/ynH3Of705+iy1eMuLWk\nTz8NPPyw+Q4UMiq5kHPGVSm5y2bNAho3Lmxfv8/siivipfdZZ538f77qi8wFbcccA9x9d+6Xlol3\niYoXd9qgoGTCSXkHOKiGzzPovVElmTjeZgdhH39sOjp37GgSh4Yd07ZypRlBesYZudt9/bU579pr\nA9ttl1sObw3jnnvmH9d9LPf0WIUMYAkKLtxBX5zg2JsnzD23rNdDD5nH666LPu477/in64nbp82W\nJGgJGvnr5ff9j3OeOXNyZ4UZPNjUMAKmdej115117nIMG2aaZr3/TMU55wEHmFHsQUaOzK9RTIPf\n76t9DWfONImxk7jpJjNtnlvQ5+H+J6E+9W/LXNBmK0X/EiJy2Gkeonz4YXjNUVzeydaTpruICtrC\n/pAPGGDSfCRxyCHm0RvQdO7s3KSjajOj5rYNyz9onz8u9/sPSqdi89aSeRMsh12bU081j2FB/8qV\nTn41+zFJyo9ipju7/PLc2sgg7dol63Noa98eeOAB5/Vppzmd8WfNyq1ZdNfcPvmkuS7euXrj3s/C\n+qDedRdwww3xjlOMsLI+8wxwyy3B64sN0EsVqP30U7Zmq0g1aBORB0Vknoj49pYRkQ1E5E0RGSMi\nE0Tk5DTLQ0SVK+kk6/Pn529z9NFOs0lYB+04/70nYdc2ASYoSXJDGTgw3nbuGpukpkwBvv8+f/lP\nP5k8dXfeGb7/yJHxcxNOmuQ8tz+Hxo3NIBPAPzluWE0eADRtGn7OK680jyL5iVa9TdZ+fSHtJlzv\niEa73920acBtt4WXAYjuhL/XXslrFdPy1lvpVHh4r+WSJYXVisfp/hD3WGEOOMD0l4zTLFsJ0q5p\newjAgSHrzwUwWlV3AlAF4D8i0ijOgZPWtBXa7k5EtSOqj4rdmT+K3Wk+bCqgoNGQhbr3Xud5166m\n360tKlt7GqPtvv46/7h206wdNKxeDbRqFZ3aAzA1iFG5Ce2/w+5aOvfN1J6XNixoGzDA/9hRwfSN\nNzrPo9Jf+N0v4uQ5u/vu6G38Bsi99FJun8LddjMBwiOPRB8PMAmk0wjwvFO+hSkmuHOPdA2yZEny\nOXTjzIns5dd9wO476p7fuJKlGrSp6nAAYZfsewAtrectAfysqjG6yQJrruk8Z/MoUfYlyeAfJu5o\nwmKENU3ZSUFtBx8c75jXX194ebw6dzZpVdy8TUDevkNxhAUPSQML9/Z2bWMx/bDskb9xEiy7037E\nVWjgNGVKbgD9+efhSYu95d133+JzyX33XXTTfPPmwLvvFncewPmc7OA9TvPmJpuY/upJuP/JixMD\nzJ5t8siFlSMLyt2n7X4A24rIdwDGArgg7o5vvWVGMd12W27nXSKq34ppRiynoFQXpeLtl5b0Jlkq\nfjfYUswnaqcxiboJr1jhPyAECM9f5z7u1KnFDciJ03/TPRBl9WrzuYV9R9x947yfcdu2QK9eucu8\ngeNvv5U2lcj994evd3+eCxcGj0q+9trouZbjBF52Td4NN2R7/tpYTZEpuhLAGFWtEpHNAQwTkR1V\nNa+hpNqVeruqqgpVVVVo29ZM7gyYIcD2fxJHHGGyRAe58MJkc0cSEaUtzo0n6dyvhZ6nEFHNW/ZA\nhdGj0y1HELsJO6zZ3B79G2W77cyNP076FD/u5lwvezBLnz5mfl3AmRPX3fQ8cWL+fiLmc/ULjH/8\nMfe1nQ/Rb/tZs4D11gNatAh/H0E1XJde6jyPmpUi6tiDB5s+jXZtbLHfm6uuMoNFWrf2P96LL5rP\nNunvWk1NDWqiRvyUQLmDtj0B3AAAqvqNiMwA0BnA594N3UFbIdwXxm+CWiKiSufuO1eoYm56YfvG\nTc5c2+ybvx0sFmrWLNP0veOOThLjoKAlLHUNkB9ARbnmmvzzffaZ//mnTnVqkp56yhkA8vXXJr/i\na6+Z1Bq2Pn1Mvzn38Tt0MPs9/XRwmWbPdgLHZcucLks//RRvzto4NaJ+24aNOp40yanIics7F/KR\nR5rHpEGbXZlk65/SLPblbh6dDGA/ABCR1jAB2/TQPVLQr58ZiRY1nQkRUVqGDQtuIiolv1GkxfL2\n44sjbhNV0iDT25evlOL2Lfvyy9wRxVFGjPBfXkgOty23NMEaAJxwgpNyZPVqM7PD4MGmn5ztiSec\nVCfuz/qZZ5y5Ze3zduvm9AfcdFPgr381z921rG++GW+6taj0Ln5J8ydODO77Om8esM02wedzj2q2\n3493BpG0uyiUQtopP54C8DGAziIyR0ROFZEzROQMa5N/A9hFRMYCeAfAZaoa8T9KKcqVv6xVK2CN\nNaL37dmzfH1BiIgqUdKcd0B4M6XbzTcnO663r1kpB6olCSDdiXSj9Ojhv/zNN+MfI8iFF+YvC6oV\ntVOn2Lx5Az/7LH/WEKCw2ttZs3Jfe4N4v39gttsuOJVPVFO1e+CN3TTqnRs37dk4SiHV5lFVPT5i\n/U8ADkuzDH6CMqvH+eW2R41xxCoRUfoKqcVLS5LgpFEJ7q6l6PcX1Ynfy52PbsYMc6/bdVdn2e+/\n55crbjk/+cQMAhk8OH+dXYtp1365g7BvvnFq79wJiov1xRelO1ZtKXfzaKrcQ5wPPRQ4MCBjnD0B\nMgMxIiIKsnp1/JlDKiVoS+qSS5zn3pkxANMfzi9BcZTvvnNG7YaNvPWbbeXtt52mWj9Bgy8AU9Fy\n1FH+eemymL+13AMRUuW+iPYE1+5lTZuatni7XT5J0Bb2JSEiqouOO672z5k06apXKf9Oz5kDXHxx\nvG2TBjZ+yaPd/bCA2r/nxA0a42zXtq3zvJCgz66ESRrIPvOMc//3yuI9vM7VtLnbxYMuiL388MP9\nl6fhvvsK37dHj+COqkREteWzz2r/nMVmUUiS+T9KkoAh6f3Eb65Q72jhpKNOS2XatPD1U6YkO14x\nibTtka5e55/vvzzsOmQtsS5QB4O2JBF8UN+2IO7/FOKyh0Svs07yfQEz9PrDD4GGDeNt7+1YWQpx\npnghIkpDnGmQwtj5yGpb0qAtTsqUuLV8pRZ1DYKSFQcZNCh5GaICrKFDnecXXZQ/MtTrgw9K2z+u\nttS5oA0wNVOtWkXXtDVpkrs8KuCzk0Mm0bFj7jmB3Pnpoqa4sRMTdusW73zuL7Z35E+h+vYtzXGI\niLIsaioot6jaKUomKved2x13OPnXgoK9NCo4akOdDNouvtjkXbMDpZ9+8t+ucePcC2oHbU88YR7j\nTOmx6abh6+1jumvK7IEPQO5UJX7s9xC3BtH9ftzzsxYji+3+RESldt118bd99tn0ylEfhSXsdd/3\njjrKPE6eDOy9d927DnUyaLPZwUbUVBy2e+4xfcdOOMF8CexhzueeG30OP5MnOyOI3LV67n2iAiL3\nend26pde8t/e/eWNk3cuDgZtRERUqdw52OxBB8cdZ7oWLVpUnjKlpV4EbXGXt24NdO+eu2z58tzm\nTLeHHvLPvn3hhWZEaufOTg1ZocO///Y357m73L165Vf79umTu2zjjUvTZs+gjYiIsiSLgwziqBdB\nW1DQ0apV9DEaNw7e/+STczvpr7WWebz99txmz+XLnXVnnx0+1Ybt1FPN4ymnRG8LmI6iDzzgfFGX\nLzeDH5o1i7d/GBHg+NA0yURERJS2ehG0+S3/9tv4/RPOOw947LHo7bp29V/u7js3cKATwAHBfdXC\npoVxN7Vut515XGcds3z1auecpSKSP2gDADbZxH/7gQNLd+40dOhQ7hIQEVGasjgyNI46E7T17Zvb\nlAiE17S1aeMfiPjZcEPgxBOD19t9za65BrjxxnjHtAWl8mjfPr8vnf0+fvjBWTZgQO42dtDm589/\nDl535JHB60T8P8OgoM1vot9CvfVW6Y5lK0XtIxER1U0bb1zuEgSrM0HbLbcADz+cu6y2+mLZaT16\n9gT69Uu2r7uv27hxwH//a543beo891p33eDjhQVtG21kHv2yQ4floGvQwP+zLLbPwMsvB6+76irz\nuP/+yY8bN6cdERGRlz0CtRLVmaDNT1BNWyV1rLcDjCOOALbf3gxeAAoPiML2s9/3EUfkLv/ss/yB\nEvbkvPZ+7s/s6quLK6OtTRvn+Zln5q7zqxW0R8M2bRp+3KAawNq0227lLgERERWikmIErzodtNWW\nQoOXUaOCR5X27Jm/zO+LtP76ua/bt8/fxq/2z+4LBwC77AJce23u+qZNczNMu8/durV5/Pe/zaCL\nRx/1nzXh8stzX9v7xeH3Xj/5BDjjjOicdd7zep1/fn6QCAB/+Uv88kVxB6RR7EEnREREYep00BY1\nerRU4gRtftvYeeDc6+30In797fyOscsuuVm6H3882bxudvDiN82WnTjY/fmtWOEMkujZ06Q36dMH\nWG+9/P29eeK8qVO6dIlfTsCc93//i76eZ50Vvv6MM/Ln9Cu1JN+5sO/PU0+F7+utNSUiorqrXgRt\nbo0bh4/MLMSGG0Zvs8suwBVXRG/XrRvw/ffJzu8OhtZcMz+ACgsK/IKXZ54xj+6g97rrTELfRo2A\nVavy9/E7h7fDv3fiZ3eNmbcfWrFNrxtsEL3NkiXm8YsvzGMpg/ugYyWd/Drqu7rHHv7LFy9Odh4i\nIjLqbfOoiDwoIvNEJHAqXBGpEpHRIjJBRGpKe/7cR8DkLyt10LbZZtFBxlprmebEIO797QEDXoV+\nkexj77EH0Lx59PbHHmse7WBUxDT39eplXnfuDGy+efgxxowBLrgAePVVZxRs2Iic66+PLpctzucw\nb170Nu7UK97jvvde/vZxgnNb0ICQvfeOfwwA2Gqr8Amvg5qK41xnIiLKlrRr2h4CcGDQShFZB8BA\nAIep6nYAji7lySs5Wq5Ndv+1M89MVgNjd+j3fo6tWwdPhmwHiDvuaPrFHXJIeDoRm908u/fewMKF\n4dvGua52MNO7d/S2Xn37Avvs47yeOTP+vlttZR579AC23NJMoxKlmFrFuHPSlut4RERZU8mxQ6p/\nolV1OIAFIZucAGCoqs61tg+Y2r2YMlT2BbClOeWGd3qrMO7BB7Y4n1+pPuMmTYCWLeONgo2jkBon\n9+CQG25w+vbFYQe6F10ETJligjcAOP10YPRo/32CpkmLo1Tzy9qy8LtCRFRflfv/6i0ArCci74vI\n5yLSp8zlKZs4QVUpb6hJkgfGOW/coFAVqK52Xm+xhfP8L38BTjrJPA8LRoLKYwdIbltvHa9cbknn\niY0zGKBLF6dZ/rbbnOX33hvebB4laXNrFAZtRESVq9xBW2MAOwM4GMABAK4WkS3Cd6mb4qSISJJG\nIsrQof4DHvyCr3btoo93wgnxmkGB3Nqviy92ng8Z4sw80a0bMHGi//5+gUWDBk6OOy/vTBlB7Dlh\n/ZoIhw0DLrnEfz+7f1/PnsHBq3v5RRc5z888M3iGhjhJgksRZLmTLYddQ790MkREdU0l//OasE6h\n5OYA+ElVfwfwu4h8CGBHAFO9G1a7qmeqqqpQVVVVS0VM37x5pkkwyh57AH/8UZpztmhhftxOOSW/\ntipuDdphh5kfP4X8Aog4QVSUHXc0Ax+8U4g9+qjpU/f558Ajj4Sfa9kyM7K4f//cYMl+//vtB3zz\nTf6+O+zgPH/nHbNdIYYNy00ofMEF8QZn2DWmLVoUPmLUnQi4Rw/guef8tzv9dGemirgaNvQfbUxE\nVKkKuWfV1NSgJml6gAKUO2h7CcDdItIQQFMAuwG4zW9Dd9CWdfvtB7zxhvM6yajEuPOlFuLBB9M5\nblhTbCF9+dZbzwxW2HJLoKrKmaD+yCOByZOd7fpYje1hU2EdeaRpog2aZcEd2Pr9InfunLs8yftx\n1zh6g70mTeL1x7OnNDv6aDOd2tVXmwDQLkvUH5+BA831Ofts4J574pc9rjFjzEwfgHlPy5fnru/d\n20kxE6Z163gjgomIysFbmdS/f/9UzpN2yo+nAHwMoLOIzBGRU0XkDBE5AwBUdTKANwGMAzASwP2q\n+lWaZaoEhxySG1zUdVttlRvMJP0vxjtX6uDBwGOPmY7+993n9EHbaqvwGjU/zz8fHBxNnWqCGVtQ\nuffZB+jQIdl5AVMD6GUHOIX8p2c363prUOMYOBD49ttk+7RqFb5+9ercmTe8s3cAZtRwnIEYd9wR\nvU2pU/lUiscfL3cJChOUQ5Co0lVy82jao0ePV9U2qtpEVdup6oOqep+q3ufaZoCqbquq26vqXWmW\nh7LpsMOAkSOd1/vs4/R9S4MdYHbqlDsowe8XeeutgYMPBmbMSH6eNdfMX3bggcHn+vLL4GO5a9V2\n3NFZfvLJ5p+EOJL2mezTJ7w5OM4fvvPOi571ATB9HKN07Bi9TVKVEHjsu2/wukKb42tDJd/4iLKq\n3AMRqB7q3Tt/rtMwDRvGu2lHCUpaHJf3JvTuu/nv48YbTe1fHH5NqX7TidniTvvlHnn70EPxaqmi\nLFqUv+yii5ymWC+/PpqdOuUv23bbeAMu4uSPSyNtTm11nW3UKLfLhJtfjab9+VZyYFTJZSPKKgZt\nVOvats1N+1EbVE2NTtzBDX68N6E11sgPJrp1Mx32o/Tokd+XUTV4BGwx/IKlpPyaXO3PY86ceMcI\nqvHr2TN63zgBQNeu8cqRRNL0L4Vq2tQMhPET9t63qOCx9gzaKKsq+bvLoI3qjaqq4DQicdi/yGPH\n5r4uxIcf+jeP+qXciFMTVaqbd1gTY9AI5002AUaNCj/uE08Ef14NGwZP+2VzB40vvwycdlr+Nv/8\nZ34Tb9++wceM812Ie43/8x//5XGbnEWAXXdNXobbb6/9f4DiquQbH1FWMWgjism+CbnTfMRlT28V\nxa8ZsGtX4P33g/dZuhTo1y/eTdI7kte7j7c2zD1I4KyzgK+/9t93113DA8c11ggvn0hwkmJVM2K4\nXz/zunlz4Jhj/LetqQFefNF5PWhQcA1cnD5wcQOPc87xXx73ugP+QfHtt4eXoUkTZwRxpSl30HbU\nUeU9P2VXub+7YRi0UVlttlm5SxCfPbLTFvcXWzV3LtOkRML7Vq25Zrw+X61amfxvcTvsq+b2E2vU\nKDwwi5p6zP15+SUqvuWW8PLESa2yxRbAzjvnLosa5Rr3nGGCakNLUQtW6hvIWmuV9nhB3OXu3r12\nzukWlLQ6jDft0eDBpSkLUakwaKOyOvBAYMWKcpciV1BA0LVrcalLkkjj2BtvDFx+uX+S4CBhgViS\n1CIiZvaH116Lv08Sp56ae664ZSrFNkBw0Najh+nDafvzn+Ml0v7ii3jnBZwynnBCvO39RsSuWlX6\nGju7XLfdBvzrX8n3HzSoNOdPIs05oMMcdFB5zptlftMWlgpr2ohC1FZn71Irxy/2okXBiYCj+I1M\n3XJL/23vvNM8braZf03VrFnJg7bmzU16FPu1V9QN072PN/A47zznuTfvWzE3YnfAFUbEjCb2c+21\nTqLn004zwWsQ+33YtYXu9xxUFrs/oLeGMchZZwEffZS7rEED4Jpr8redMMH0FfSz557+yy+4wDxu\nsAGwZIkZZVxI7sD27Yubl7eQ38+ovpVpqeQgoVJ9+GF6x3bn56w0DNqICvD00/FvkqVUyM0PAGbO\nzE9SfMUVwSM37US4w4ebJMZefjeZTTcNvvmUoknOHXy1bOk/GAEwn5EdHHrNmmUCFPex/Gqe7AEE\n7hq8IHaeOW8+tauvNo99+5op1Wy9ejnPDz00d5+//MV53rSp0x/v/vvNKN1ly/Lz9f32m3ncYIPc\nsgc56ij/gMuv/51I8HRqft+dpUvNoIzZs01To33dowJnv8TIIrndCjbdNPwYYeLU9IWl6rGvZRz2\nDC1JhAVtcfrmsaautOzfpUrEoI2oAL17Z6uGcNNNTWf+OLbayhnJ2LKlf7OZ303mxReBH3/MXz51\nau4N/p13wmvaBgww018F5dWzyxbWVP3f/+bvt8supvbGngvV3kfE+a/d7ntlJ60tpgbkoovyl4mY\ncxx7rP8+7ve0bBmw117m+WmnmX2bNs3P17dkiXns0wf44Ydkgx8Ap1/pgQfmB/Z+7CDiuuvy1625\npmkqbtfO/x8Md42o2+rVuUmhbVGff5wm4YYNnc/11VeDt2vRIji49NbABSWEfvfdwnL7hb3PONPL\nvf568nPWdc8/X/i+lVzzyaCNyKNc/VrcokZaJt0nyfaTJgU3m4Zp3tzU0G2zjQmQbJ065Z4rKi/b\nJZeYG/illzpNbYBzXfymHfPOyduxo9NX0t7v00/9z7f++k7/mDSvfevWTmBSynl+7VrRBg3MOTbZ\nJP6+vXvnTqlnX/fDDw/e5447ghMBB7E/17vuAkaM8F8/ZkzusqDvpzuAeuKJ4HPag3OWLjVJnIHc\nvod27ZQ7UXHU9R8xwnQzOO44c0xvDWHY7BVhgt7rs8+aWp+99vKvjayujpcSqDa1bu08v/ji8pWj\nmM+FQRsRldy4ceU7d9gftdGjgTffTL6/t+bykkvCZ3M47zygf38TdGy9dfTxgv6IP/ZYfrmSBG9+\n+fb8/PCDExR5m4sLOa/twguBX35xXgc183kDW8B8Ru7ldvLp3XbL3W70aOd5u3bOdGtxud+X30hS\nv75k3u+I/TlH3VB3283UPtrbNWliagdVc9/rWWeZZe4+kEGfv91c1r07sGCBeT5hgvlc3DOQxDVl\nSu730S7rySfnbnf44Wa74cPz0/UAph/i8uXh5wprEYgz00zSfzDcU6uVM/jx+323uxJEYdBGRImE\n9QE75hjzx9ybgqRPH+Dmm+Ofo5BJ7uNo0iQ4u7/Nr6l2883DR00ecwxw/PHO6x13NDetYmeRiDOa\n87Balo8AACAASURBVL33cmuk3A47rLDzdu5sRpO6bb998lFxDRvmNmEHBRHFTOPmV8sD5A5u8Zvq\nzBYVjPo1Z7v3e/550/Todw2eeSb3ddu28fpQ2seOEyivvXZpa2G33DL3d8QOErxNoX6BNmBq9K6/\n3uxn1ygG1TraNc7u/IVJnHJKcB9RP0lG2BczQ00hmjXLbQUIwqCNiGIbP94/j5nt0Uf9/wC3aQNc\ndlm8c/z2W3BH/jiK/aN28cVmcIRX2OCOnXcGnnyyuPO6ufu0Ba0DzMwJVVXBwWGcHHl+Jk8Gzj8/\nd9kGG8QbFbfhhslrePxyIhYbiLgHKYQNkgk6j11j55fH0H0NjjzSfL/ta+Cu/XH3D9x88+h8f2G8\n5bQDq7DPafjw3Boyv++TX62PuwuCfS3DRoa7y3DkkfmjeqP694VNRed2ww3O8/bt88/tdvvt+cvs\nfphBx3cL6tuZRDF5GIMwaCOi2LbbrrAmlySaNSvvH6bGjYsbDZgWv0EX22yT+1n9+99OsPXHH8HH\nKiS5a1wffwxMnx6+zemnA3ffbZ4XmuDZ/b4ffzz5/ja/pj3A5HDzG7xinzvpd/SII0zgZu8fxg5E\nHnnEWeatAT7uuNxt/eyyi/8IZDe/wH7ECDODBwDcdJN5DCtzsTki7X2GDw/fzt1Ubf8dCqq59Ptc\n3ANh7FQ3fnbdNb+muRBBf0fifkZ+NW+V0K85CIM2Ikqskv8T9UryB/iSS0wwVFUVnDqjZUsnAPJr\nvurY0Zyz0Hx6cbRqFRwI2bbYwqkpAUzNzE8/xTt+ixamJtZdO+c+VlIdOvhfh4YNc9MrFDtzgvt7\nGfUdtWu63IMHjjkG+PZb53VUM78tKqDye+8tWgB/+pN5bverKzYx9J//bD7DAQPM65deMo+HHOIM\n4ImqCbNHV7sVmt7I24UDcH6vRo3KTT1T6MABvz57e+8d/HvvXd6pU/42lfz3LUNJC4hqRyX/l1Up\nKvmPWjHatDGBymabmb48ftZZx/Rj8xsFed55/qkrymHDDc2PrVEjZ6Spza82cOpUE2R5b4Z+vxfF\n/q54v0cffujcvBs2LK7GOSwx8n33+Q9eEYnOc1eIoKS9SQagxNnmrbfM4yuvmEc7J+Crr+YOJrH5\njTT2C9pqw5gx/kGeV8+ezkCZa64x/0x4R4b37es832GH8EFb++5r8m66pd3SUQwGbUREMB22o3Lv\nzZplbnQNGvjXCt11VzplS2r+fFODJeLkcfP66iv/m7ZfzUOUQoK3//43/1zuZsSGDU0AHDQAxI+7\nX5c79YRXVC2lV9T769q1uObwODMxxGkeTZoqaM89gSFDTE2anbT5kENM8/u77+b3+1SNNwdwkOOO\nM10Q7r3XWfbuuyYQi3usd95xnvfvb8oa5vXXw9Pg9O1ruhK0b2+SQle6VJtHReRBEZknIuMjtttV\nRFaKSIzcz0Tpqqu1SKXklyutUsW9GcRJlty+feEDD2pTq1bO9zioP9LWWxc+w4at0Jo2VeDcc/1/\n16691jzaNW5+A0AOPRS48krn9ahRwNy5uYmHi31vSeyyS3g6iVLU3peqT5vfMnfN0m675QZGQPQo\nT3sU8f3356876STn+VNP5R/Lm9uud+/cWtK33sofsOPmTU/j1bat6SccpVJqyKOk/efnIQChGX1E\npCGAmwG8CYC3Syo7No+GU81W0JYFWf1HoZg0IkGqq82ju2nXq1Wr3FGOu+6a3xx6wgkmUXQlsBP5\nBl3nJk3ypyfzShK0+f0NC9tnyJDw4/XqFd48Hnbshx82A3ai/q66j+U+3hprOHMht2uXv1/Dhs4s\nKTZveeL0x8zK72CqzaOqOlxEOkRsdh6AIQB2jdiOqFaUYp5MorhOPDG/r1lWHH10bmLfUonTVB2l\nYcP8Kb3swGCLLYo7dtR53QYONBOQhwUFa6yRPz2ZV7E1bUE1xD//7Iya9Q44KGSWFW+tlkhwvrmg\nY7i5A644ORX9PPss8Pvv+cvdtYBZUdaKfhFpC+BwAHYLN+s4qKwmTkw2OTRVvkqvOX3ssfCZHypF\nUO2NX5qUYqU9r6/f/KzuYMGbMDnJd6hTp9wZQc4+23m+7rr5zYPemRoA//5xe+5Z3ETm7vd31lnm\nsV274DmJmzRx0qe4eWdtsI/dvbtJDN2qlZPKJKocftwzVCxf7iQB79vXNKkXcsy11nI+O/e1dKd7\nyYpyD0S4A0A/VVUREYQ0j1bbdeYAqqqqUFXIrLxEEWo7QzeVT6NG/jcg8rf77vEmL88qdyDXpYsz\nAhNIFrSJAPvv77+uRQvzj2GUDz7InW0CAP71L/NTSP46u1y2e+4Bbr01fPDE/Pn+NWQPPWSaPIHc\nz8VvNHVUOdxUgWnTTJP7q6+aZe6UK4MGxTt+FHdQaLv9dhN0vvxy4cetqalBTVi0WiLlDtq6Anja\nxGvYAMBBIrJCVfM+OnfQRkQUV9ANV8TcgCiepk2dGpq6ptS1sSKmH5g7CJg0Kf48tW3bho9wLcVA\nBG83kJNPzk0S7Bfc2H7/Pfy9FDJLwYYbJh/Va0uy33PPAW+8kZv498ILCzuvm7cyqX///sUf1EdZ\ngzZV7Wg/F5GHALziF7ARERFlyamn5gY+fk2yhVhzzcKS3UYFekn+gXGPNh00KD/A22YbYNmy+Mfz\nBs077JAsX9zjj5v5Ue3ZHsLe6/rr+0/plhWpBm0i8hSAvQFsICJzAFwLoDEAqOp9aZ6biAgwI/fi\nzgRAdVv37sGT04cppCbu8MPNTyHCgo6lSws7Ztho3EKp5iaydQuaESSoD53bCy8kC9qaNzcDIOyg\nbeONw1OwbLGF/0jULEh79OjxCbYNyD9ORFS4Sy81P0RrrBHcmd3tmGOAOXPSL0+QNNJPrLde6ZuB\nCzle797R05UlGW1qGzAA6NfPDDho1gx47bXgbTfcMBuJdP1kIE0kERFR7dluO+DBB3Nf15ZjjolX\nG5VVIunUcjVrZtKDFDMzRRbSPZV7IAIREVHFqu2UMc8+W/wxdt4Z2Gmn4o+TZWkkfq4ErGkjIiKq\nQ9q1858gvtQqOQdi165mJoYkKvn92Bi0ERERUZ1TSN+4SsegjYiIiBKpqgpOIpxVac/EUQqiGagP\nFBHNQjmJiIgom775xsx7W4qceiICVS35OGAGbUREREQllFbQxuZRIiIiogxg0EZERESUAQzaiIiI\niDKAQRsRERFRBjBoIyIiIsoABm1EREREGcCgjYiIiCgDGLQRERERZQCDNiIiIqIMSDVoE5EHRWSe\niIwPWP9XERkrIuNE5CMR2SHN8lB51NTUlLsIVCBeu2zj9csuXjvyk3ZN20MADgxZPx3An1R1BwD/\nAjAo5fJQGfCPT3bx2mUbr1928dqRn1SDNlUdDmBByPpPVHWh9XIkgE3SLA8RERFRVlVSn7a/A3i9\n3IUgIiIiqkSiqumeQKQDgFdUdfuQbfYBMBBAd1XNq5kTkXQLSURERFRCqiqlPmajUh8wKWvwwf0A\nDvQL2IB03jgRERFRlpS1eVRE2gN4HsCJqjqtnGUhIiIiqmSpNo+KyFMA9gawAYB5AK4F0BgAVPU+\nEXkAwJEAZlu7rFDVbqkViIiIiCijUu/TRkRERETFq6TRo3lE5EARmSwiU0Xk8nKXhwwRaSci74vI\nRBGZICLnW8vXE5FhIvK1iLwtIuu49rnCuo6TRWR/1/KuIjLeWndnOd5PfSQiDUVktIi8Yr3mtcsI\nEVlHRIaIyCQR+UpEduP1ywbrWky0PvcnRaQpr13l8psgoJTXy7r+z1jLPxWRTSMLpaoV+QOgIYBp\nADrANKmOAbB1ucvFHwWAjQDsZD1vDmAKgK0B3ALgMmv55QBusp5vY12/xtb1nAanlncUgG7W89dh\nBqSU/T3W9R8AFwN4AsDL1mteu4z8AHgEwKnW80YA1ub1q/wf6/OfDqCp9foZAH/jtavcHwA9AHQB\nMN61rGTXC8DZAO6xnvcG8HRUmSq5pq0bgGmqOlNVVwB4GsDhZS4TAVDVH1R1jPV8CYBJANoC6AVz\nQ4H1eIT1/HAAT6nqClWdCfNl3k1ENgbQQlVHWds96tqHUiIimwA4GMADAOyR2bx2GSAiawPooaoP\nAoCqrlSToJzXr/ItArACQDMRaQSgGYDvwGtXsdR/goBSXi/3sYYC6BlVpkoO2toCmON6PddaRhXE\nysPXBWZGi9aqOs9aNQ9Aa+t5G5jrZ7OvpXf5t+A1rg23A/gHgNWuZbx22bAZgB9F5CER+VJE7heR\ntcDrV/FU9RcA/4EZePcdgF9VdRh47bKmlNfr/+McVV0JYKGIrBd28koO2jhCosKJSHOY/w4uUNXF\n7nVq6nt5DSuMiBwKYL6qjoZTy5aD166iNQKwM0yTys4AfgPQz70Br19lEpHNAVwI03TWBkBzETnR\nvQ2vXbaU43pVctD2LYB2rtftkButUhmJSGOYgO0xVX3RWjxPRDay1m8MYL613HstN4G5lt8id77Z\nTaxllJ49AfQSkRkAngKwr4g8Bl67rJgLYK6qfma9HgITxP3A61fxdgHwsar+bNWqPA9gD/DaZU0p\n/lbOde3T3jpWIwBrWzWygSo5aPscwBYi0kFEmsB00nu5zGUiACIiAAYD+EpV73CtehmmYy2sxxdd\ny48TkSYishmALQCMUtUfACyyRr8JgD6ufSgFqnqlqrZT1c0AHAfgPVXtA167TLA+9zkisqW1aD8A\nEwG8Al6/SjcZwO4isqb1me8H4Cvw2mVNKf5WvuRzrKMBvBt59nKPzogYuXEQzMjEaQCuKHd5+PP/\n12UvmP5QYwCMtn4OBLAegHcAfA3gbQDruPa50rqOkwEc4FreFcB4a91d5X5v9ekHJvG1PXqU1y4j\nPwB2BPAZgLEwtTVr8/pl4wfAZTBB9niYDuiNee0q9wemNeI7AMth+p6dUsrrBaApgGcBTAXwKYAO\nUWVicl0iIiKiDKjk5lEiIiIisjBoIyIiIsoABm1EREREGcCgjYiIiCgDGLQRERERZQCDNiIiIqIM\nYNBGRJkhIkusx01F5PgSH/tKz+uPSnl8IqJiMWgjoiyxE0tuBuCEJDta08SEuSLnRKrdkxyfiCht\nDNqIKItuAtBDREaLyAUi0kBEbhWRUSIyVkROBwARqRKR4SLyEoAJ1rIXReRzEZkgIn2tZTcBWNM6\n3mPWMrtWT6xjjxeRcSJyrOvYNSLynIhMEpHHy/A5EFE9EvWfJxFRJbocwKWqehgAWEHar6raTUSa\nAhghIm9b23YBsK2qzrJen6KqC0RkTQCjRGSIqvYTkXNUtYvrHHat3lEwU0ftAKAVgM9E5ENr3U4A\ntgHwPYCPRKS7qrJZlYhSwZo2Isoi8bzeH8BJIjIaZg6/9QB0staNcgVsAHCBiIwB8AmAdjATO4fZ\nC8CTaswH8AGAXWGCulGq+p2a+QDHAOhQxHsiIgrFmjYiqivOVdVh7gUiUgXgN8/rngB2V9VlIvI+\ngDUijqvIDxLtWrg/XMtWgX9TiShFrGkjoixaDKCF6/VbAM62BxuIyJYi0sxnv5YAFlgB21YAdnet\nWxEwWGE4gN5Wv7lWAP4EYBTyAzkiolTxv0IiyhK7hmssgFVWM+dDAO6CaZr8UkQEwHwAR1rbq2v/\nNwGcKSJfAZgC00RqGwRgnIh8oap97P1U9QUR2cM6pwL4h6rOF5GtPceGz2siopIR0xWDiIiIiCoZ\nm0eJiIiIMoBBGxEREVEGMGgjIiIiygAGbUREREQZwKCNiIiIKAMYtBERERFlAIM2IiIiogxg0EZE\nRESUAQzaiIiIiDKAQRsRERFRBjBoIyIiIsoABm1EREREGdCo3AWIQ0Q4qz0RERFlhqpKqY+ZiaAN\nAFQZt1G06upqVFdXl7sYlBH8vlBc/K5QEiIlj9cAsHmUiIiIKBMYtBERERFlAIM2qlOqqqrKXQTK\nEH5fKC5+V6gSSBb6iomIZqGcRERERCKSykAE1rQRERERZQCDNiIiIqIMYNBGRERElAEM2oiIiIgy\ngEEbERERUQYwaCMiIiLKAAZtRERERBnAoI2IiIgoAxi0EREREWUAgzYiIiKiDGDQRkRERJQBDNqI\niIiIMiDVoE1EDhSRySIyVUQu91lfJSILRWS09XNVmuUhIiIiyqpGaR1YRBoCuBvAfgC+BfCZiLys\nqpM8m36gqr3SKgcRERFRXZBmTVs3ANNUdaaqrgDwNIDDfbaTFMtAREREVCekVtMGoC2AOa7XcwHs\n5tlGAewpImNhauMuVdWvUiwTERERAZgzB3j+eWDoUGDiRKB9e2CzzfJ/OnQAmjUrd2nDrVxp3s+M\nGf4/Cxfi/9i77/ioqvz/46+ThJBASKMGCIReBKQIhB6KFINiWYqCgPIVWDuIiixq/IlrWXFXV1dQ\nERSiWFBBQUHQUCQFkCY1hBJSgIRASK/n98dNhgSSMCQzmRn4PB+PeczMnTv3fgbC5M0595yDiwvU\nqGHcX3krb3tlX7MWa4Y2bcY+fwL+WutMpdQo4AegbVk7hoSEmB4HBQURFBRkgRKFEEKIm8exY0ZI\nW7UKjh+Hu+6C556Dnj0hLs4IOMePGyHup5+M56dOgbc3tGxZdqjz97duUAHQGs6evVzflaEsIQEa\nNixd14gRlx/7+BjBrrxbXt71bb/ytUOHwjhwIIzCQigosN6fg9LanGxViQMrFQiEaK1HFj1/ASjU\nWr9ZwXtOAD201ilXbNfWqlMIIYQoj9awdi188QV06QIDB8Jtt4Grq60rM4/WRgBbtcpoVTt7Fu65\nB+67DwYNMlqKrqWwEBITrw5KxeHp7Flo3Lh0YCoZ8Bo2BGXGhVAXL5bfUnbyJNSuXf45mjWzr78T\npRRaa4tf/mXN0OYCHAGGAglAFHB/yYEISqmGwDmttVZK9QK+1loHlHEsCW1CCCGq1aZNMH8+pKXB\n3/8O0dGwZYtx36uXEeAGDoTeve2r+1Br+PPPyy1q2dlw771GUOvTB5ydLXu+3FyIjS07bB0/DhkZ\nRhdrycDl7n71vrm55bfmBQRAnTqWrduaHC60ARR1ef4HcAaWaK1fV0rNANBaL1ZKPQb8HcgHMoHZ\nWuuIMo4joU0IIUS12L7dCGunT8Mrr8D48aWDzsWLxj5bthi3ffvg1lsvh7i+fcHLq3prLiyE8PDL\nLWqurkZIu+8+6NHDvJYua0lPvzqgZWVdHczq1bNtnZbkkKHNUiS0CSGEsLY//zTC2oED8NJLMHmy\ned2HGRkQGXk5xEVFQbt2l0PcgAFGILG0/HzYvNkIaj/8YJyjOKjdcsuNE4AckYQ2B6hTCCHKorXR\nupCScu19LcXJCTp1sq/rfOxVcUgLD4d58+CRR6BmzcofLycHdu0yAtWWLUarXNOml0PcwIHQpEnl\nj71xo9GatmaN0UJ1331G92ebNpWvWViWhDYHqFMIIcDoqjp06HLLy5YtxnY/v+qrIScHkpLgoYeM\nENKyZfWd21EcOwYhIbBhAzz7LDz2mHWuTcvPh717L/8sbN1qjMYsGeJatCi/ZSwzE375xWhRW7cO\nOnc2Qtq99xoX4Av7I6HNAeoUQtycqvpL2VqOHIGPPoLPP4fu3WHmTBg92rwuvxtZbCy8+ip8/z08\n9ZRx8/SsvvNfGeo3bzZaRot/Vpp3P8ZZ9ScZh/rz2+rGbNxoDHy47z64+25o1Kj6ahWVI6HNAeoU\nQtwccnNh587S3V/+/qWvYaps95c1ZGfDt9/C4sXGaL5p0+D//u/ma6VJTIR//hNCQ40AO2cO+Pra\nuiqj+3zHwXO8u+krNpwJ5ULhCXT8bTgHRNDAvTF3dxrBXbcMZ0CzAbjXcLd1ucIMEtocoE4hxI0p\nMxMiIi63jOzYAW3bWv9Cc2s4cMAIb6GhxijHGTNg1CjLTwNhT86fhzffhE8+gSlTYO5cY+4wW0vP\nTeeHwz8Quj+U8NPhjG47momdJzKs5TCUroFyKmBnwk42xGxgw/EN7Dmzh77+fRnecjjDWw2nU4NO\nKBltYJcktDlAnUKIG0NqKvzxh31N6WBpmZnw1VdGgEtIMK57mzbNmCT1RpGaCu+8A++/D2PHGiND\nmza1bU15BXlsiNlA6P5Q1kWvo1+zfkzsPJEx7cZQ27V2he9NzU7l95O/syFmA+tj1pOVl8XwVkaA\nu73l7dSvXb+aPkXl5Rfmc/zCcZIyknBzcSt1c6/hbnrspKy5NLr1SWhzgDqFEI4pKcm4Dq04pNn7\n5KmWtmePEd5WroTBg43Wt9tvN66zckQZGfDf/8LChXDHHfDyy7YdiKG1JjwunNB9oXxz8Bta+7Zm\nYueJjLtlXJWCVkxKDOtj1rMhZgNhJ8No5duK4S2HM6L1CPr698XV2XZDh7Pzszl6/iiHkg5xKPkQ\nB5MOcij5EMdSjtG4TmMaeTQiOz/bdMvKyyr13MXJpVSIc3dxLzfgXfVaGfvWca1D/dr1qV+rPvVq\n1cPbzduqrZQS2hygTiGEfStevzA62rhFRRkhLSEB+vW7HNJ69Lg5p8pIS4Mvv4RFi4xWqkceMUaf\n2kNXojmys43w+cYbxt9jSAh06GC7eg4lHSJ0fyhf7P+Cmi41mdh5Ig90foCWPpZPkHkFeUTERZi6\nUg8nH2Zg84GmrtS2ddtaJaSk5aRxOPmwKZQV38ddiqOFdws61u9Ih3od6FC/Ax3rd6Rt3bbUqlHx\n/4C01uQW5F4OdPlZ5Ya78l4ruT07P5vUnFSSMpJIzkwmKTOJrLws6taqS71a9ahfqz71a9ennns9\nU7CrX7t+qdfqutelhrP5I3gktDlAnUII+3D+vBHKjh69HNCKbzVrGvNZtWkDXbsa6y/eeuuNdU1X\nTEoM3x/+niZ1mjCs5bDrbs3R2hhosWiRMR/Y8OFG69vgwbadsPXEhROsjV7Luuh1/HH6DxrXaUyH\neh1o59uRswc6sHZZB3o0b88/X6lF1662qTEhLYEv939J6P5QzqSf4f5O9zOxy0S6NepWrdefnc88\nz6YTm0xdqc7K2dSVOrTFUHzcfa7reMmZyVe1mh1MOkhKVgrt6rYzQlm9jnSo34EO9TrQ2rf1dYWc\n6paTn8P5rPMkZSSRlJlUKtAlZSSRnJVsei05M5mUrBQ8XD1MLXUlW+3KCnktfFpIaBNCVJ/sbGNC\nWC8v8PEBNzfr/cLOzMtkyZ9LSMtNY2rXqTSuc+0Lq1JTrw5kxSGtsNAIZW3bXg5oxTef6/td5TCS\nMpL46sBXhO4PJSYlhnva38OZjDOEnQyjjW8b0y/s6+02u3gRVqwwWrByc2H6dJg6FerWtd5nKZZb\nkMu22G2si17H2ui1pGSlcEebO7ij9R0MChhE4qVzfPT9Qb789RCuTQ7i0eIQ8dnRNPJodLmFp57R\nwtOhfge83bytUmdqdiqrDq0idH8ouxN3c3f7u5nYeSJBAUE4O9n+fwNaaw4nHzYFuG2x27ilwS2m\nrtReTXrh4uSC1pqEtISrWs0OJR0ityD3qlazDvU60Ny7ucNff2aOQl3IhawLpYNdOSEvKSOJ07NP\nS2gTQlhferrxC3rhQqhd2+gyu3DBeM3Hx7h5e1d8f+U2L6+yr4+6lHOJD3d8yL8j/k1f/77Ur1Wf\nbw5+Q1BAEDN6zKBvo9s5HuNUZqtZRsbVgaw4pN1IaxhWJCM3wzT6cPvp7QS3DWZi54nc3vJ2UytH\nbkGuqdtsfcx6jp4/auo2G9F6BG1825jVAqS1sWLAokXGTPyjRxutb/37W/bPOjEtkZ+P/cza6LVs\nOr6JdnXbMSzgDgY2CqaFW3cyM5xISzOmLnnjDSM8LlgAQUHG+/ML8zlx4cRVoeNQ8iE8XD3KDHMN\naze87lawnPwc1kWvI3R/KL8e/5UhLYYwsfNEgtsE2/20HDn5Ofxx+g/WH1vPhuMbOHnxJK18WhGd\nEo27i/tVrWYd63ekkUcjGal6HaR71AHqFLZVUFjA+pj1DG0xlJouVViD5iaVmmqMsnv3XeMX4Lx5\nlOpiysoyWl0uXLj6vqxtJe/T0qBOncshzqP+BZJbvcfJ+u8TUHg7w2rOo71vJzIz4cCxNMLTvuB4\n3UUUOF+i/ulH6O70EF1aNiwV0Pz8bo5gdqW8gjx+Pf4roftDWXt0LX39+xqjD9uPwcPV45rvT85M\nZtPxTaYL2F2cXBjeajgjWo1gSIshZnWbnT9vTNi7eLHRrTxjhrFOp7e30RqXnm78nZe8L2tbWhpc\nSi8gNj+Kk67rOFNnLVk1T+CeOBznmGDyD48kK7kBNWuCh4dxq1PHuK9bFx59FEaMMO/nQGtN3KW4\ny2Eu6RAHk437Ql1YKqAUtyY182pWqhWpUBey5dQWQveF8t3h7+jcoDMTO0/kbx3/dt3djfbkbPpZ\njl84Trt67fB1t4OJ624AEtocoE5hW8//+jyf7f0MFycXnu37LI/0eOSaF7wK4xfwu+/C//5njLR7\n4QXLX7xdUACXLkF0wjk+3PNvVp38iNs87mKI6wu4ZbQ1hbuS15u1aaNJdNrBx38uZtWhVYxoPYKZ\nPWYSFBB00/2PX2tNRFwEoftD+frA17TybWUafdigdoMqHfdQ8iFTi8u22G10btDZ1JVa3G1W/vuN\nCYYXL4Yff4S8PKNrujhYlQxZJbe51EnhjMd6Tris5WjherxdGtHbJ5j+De8gsElfvD1dTO+pXRtc\nyi/BIpIyksrsEryYfZF29drRsX5HvGt6s/rIanzcfZjYeSL3d7offy9/6xYmHJaENgeoU9jO8r3L\neTnsZaIeiSI2NZbXtr7GH7F/MCtwFo/2fJQ6NevYukS7c+aMMYfVkiXG8jjPPw+tWlnnXAlpCfzr\nj3/x2d7PGH/LeJ7v/zwB3gFmv/9i9kVW7FvB4l2LyS3IZXr36UztOpW6tarhwiobOpx8mNB9oXzx\n1xfUcKphGn3Yytc6f1HZ+dn8EfuHqRXuVOophrQYYupKrejvLCvL6AJ3db265Utrzb6z+0yDrA+Y\noQAAIABJREFUCPad3UdQQJBxfVqbO2jmZZ9LM6Rmp3I4+TCHkg9xNv0swW2D6dSgk63LEg5AQpsD\n1ClsIzIuktFfjub3Kb+X+kL969xfvL7tdTbEbODxno/zZO8nHboLw1JOn4Z//cu4uHzSJGOhbH8r\nNRicvHiSt/54i5V/rWTKrVOY03cOTTwrv76T1prtp7ezeNdi1hxZw+i2o5nRYwb9m/W/YVrfEtIS\nWPnXSkL3h5KYlsiEThOY2Hki3f26V/tnPJN+hl9jfmXD8Q1siNmAV00vU1dqUEBQhf8ZSs9NZ+Px\njayLXse66HXUdKlJcJtggtsEMyhgEG4ubtX4SYSoXhLaHKBOUf3iLsXR+5PefBj8IXe1u6vMfY6e\nP8ob295g9ZHVzOgxg1mBsxxi5nBLK75we9UqY+b72bOtt/B09PloXt/2OquPrGZ69+nM6jOrSt14\nZTmfeZ7P937Ool2LcHFyYUaPGUy+dbLVRgha06WcS3x36DtC94eyM2GnafTh4IDBdjH6EIzrufaf\n3W9qhYuMj6S7X3dGtBrB8FbD6e7XnWMpx0wjPSPiIujdpDfBbYK5o80dVpsnTAh7JKHNAeoU1Ssz\nL5OBSwfyt45/Y27/udfcv2Srz9SuU5nTd45ZU0s4usOHjUWy160zLtx+6inrTddw4NwB/rntn6w/\ntp7Hexmtm9a+sFlrzeZTm1m8azE/R//MPR3uYWaPmfRq0suuQ0JuQS4/R/9M6P5Q1sesZ3DAYCZ2\nnsjotqPtfvQhGP/+Np/cbJrM9eTFk3i7eXNHa6PLc1jLYXJZgrhpSWhzgDpF9dFac/+q+3FxcmH5\nPcuv65dzQloCb29/m2V7ljGh0wSe7/c8zb2bW7Fa29i3D157DX7/3Qhqjz1mjO6zhj8T/zRdR/h0\n4NM82vNRPGt6WudkFTiXcY5le5axeNdi6rjWYUaPGUzsMtEmtZSlUBeyLXYboftCWXVoFbc0uMU0\n+tDRR+2lZKXg4+Zj10FZiOoioc0B6hTVZ8GWBaw5sobNUzdXulXiXMY5/hPxHxbvWsyYdmN4of8L\ntKnbxsKVVr+oKCOs7dgBzzxjTMfgce2ZICol/HQ4C7YuYM+ZPTzb91mm95huFyN2C3Uhm45vYtGu\nRfx24jfGdhzLzNtm0t2vu1XPm5OfU/bEm5nJnM04y/qY9XjV9DJGH3a+324vwBdCVI2ENgeoU1SP\n7w99zxM/P0HUI1GlujcvXYKDB40JVn2vo9HiQtYF3ot8j/d3vM/wVsOZ138etzS4xQqVW9fWrcYk\no4cOGSNBH34Y3K3Qy6a1JuxkGAu2LiAmJYa5/ecytetUu72wPDEtkU93f8pHf35Eg9oNmNljJhM6\nTaC2a+0K36e15lLOpXJDWFLm5SVuimdCz8nPqXCJm/7N+tOlYZdq+uRCCFuR0OYAdQrr23d2H0M/\nH8q6B9bRybcnf/wBv/1m3A4cMOb3OnbMCG3du0O3bsZ99+7GZKwVKTk7f79m/fjHgH9YvWWmqrSG\nTZvg1VchPt6YY+3BB62z2LnWmvUx61mwZQHnMs4xb8A8JnaeaNfrC5ZUPPnyop2L2Ba7jfs73c8t\nDW4pN4QlZyZT06WmKXRVtM5g8WPPmp7SPSiEsF1oU0rV1Vqft/SJr4eENgEQf/Ecty3uRf+c10n+\n/X527DAW+h4yxLj16WOsj1lYCDEx8Oefxm33buPexeVygCsOcwEBV88plZmXyce7PuZf2//FrY1u\nZf6A+fTx72OTz1werWHtWqNlLTUV/vEPmDDBOpOQFupC1hxZw4ItC8jOz2b+wPmM7TjWbkY1Vsbp\n1NN8uvtTEtMTy14AuiiI2WvroRDCvtkytEUDe4ClwM+2SE8S2m5OBQWwd6/Rirbx91w2Nh5KvcyB\nPNj4NYYMMdY8rGPm4DStjfnJigNccZjLyCjdGtetm9G96uxsXJ+0bM8y3vjjDVr6tGT+gPk2n42/\nsBC++84IawDz58O995a9rmdVZeRm8OPRH3lt62u4Orsyf8B8xrQfc1MsDi2EEFVhy9DmBAwDHgZ6\nAl8DS7XWRy1dTAU1SGi7CWhtTE/x229Gl9/mzdCwIQweojnS9hFcvZP56cHvLBoazp27OsidOQNd\nulwOcp1uzWOfDuWt8H/SoHYD5g+cz4hWIywa3rKzzVvHMzzcCKovvgjBwZVfezMnP4e4S3GcvnSa\n06mnS98XPc7Kz6Jn45680P8FRrYeKd1+QghhJru4pk0pNQRYAdTGaH17QWu93dJFlXFeCW03qJMn\njYBWfF1azZowdKjR3Tl4MDRuDO9GvMsnuz9h+8Pbq2Xep9RU2LOndJA7fhzadyjAq+83HG34Gp61\naxIyZD5ju9yFk3KisNAYCFHRoukVBbKCAmMx9eIF1Uvel3zcti0MGFBxWMsryCMhLaFUELsyoF3I\nukDjOo3x9/LH37Po5lX6vl6tehLUhBCiEmzZ0lYPmAhMBs4CnwA/ArcC32qtAyxdVBk1SGi7QSQm\nGvOGFYe0zMzL16QNHQotWpTef0PMBiZ/P5nwaeG08GlR9kGrQWamMe/Z7t2w689Cfk9Ywwn/BTh5\nnkFn1qUw1w1n7UYN3HF1cqOmixtuLm7UquFOLVc3PGq64eHuhqe7O5613fCu7YZ3HTfqerpT19MN\nz1puuNdww72GO25F73VzccPd5fJzZydnCgoLOJtxttzWsdOXTpOUkUSD2g3KDGRNPZvi7+VPw9oN\nHfqaNCGEsGe2DG1HMVrXPtVax13x2lyt9RuWLqqMGiS0OagLFyAs7HJIS0yEoKDLQa1Dh/JbjY6e\nP8qApQP4Zuw3DGw+sDrLNktOjmb7kWM418zEyS2LfJ1Ndn42WXlZZOdnm25Z+Zefl3qt4Dr2zc/G\n2ckZhcLH3ad0y9gVrWR+Hn4OM6JTCCFuRLYMbTZPTHZQgrhOaWnGBK+LF0Ng4OWQ1rWrcZH/tVzI\nukDgkkDm9JnDIz0esX7Bdk5rTW5BLkopXJ2tMJ+HEEIIi7FWaDNngoANSqmxWuuLRYX4Al9qrUdY\nuhjh+AoLYcUKY76wYcOMudMaX+fynvmF+UxYNYERrUZIYCuilKKmS01blyGEEMKGzAlt9YsDG4DW\nOkUp1dCKNQkHFRUFTz5pBLdVq4wWtsp4dsOzFOpC3hnxjmULFEIIIRyYOXMnFCilTKtpK6UCgEJr\nFSQcz5kz8NBDcPfdMHMmRERUPrB9uvtTfor+ia/+9hUuTlaYKVYIIYRwUOb8VvwHsFUptaXo+UBg\nuvVKEo4iNxfefRfefNNY5/LwYfD0rPzxtsVuY+7GuWx5aAu+7texeKgQQghxE7hmaNNa/6KU6gEE\nAhp4WmudbPXKhF1buxZmzTLmDdu+3bivilMXTzH2m7F8dvdntK/X3jJFCiGEEDcQc/uf8oFzgBvQ\nsWhUxJZrvEfcgI4cMcJaTIzRyjZqVNWPmZ6bzpiVY3i277OMamOBAwohhBA3oGte06aUegTYAvwC\nhADri+7FTSQ1FZ55Bvr1MybB3b/fMoGtUBcy+fvJdPPrxqzAWVU/oBBCCHGDMmcgwlNAL+CU1now\n0A1ItWpVwm4UFsKSJdC+vbHc0oEDRnhztdBUYSFhIZzNOMui4EWyZJIQQghRAXNCW7bWOgtAKeWm\ntT4MtDPn4EqpkUqpw0qpaKXU8xXs11Mpla+Uute8skV12L4devUyQtuPPxr3DS042cvXB77ms72f\n8d2472QOMiGEEOIazLmmLU4p5QP8APyqlLoAnLzWm5RSzsD7wDAgHtihlFqjtT5Uxn5vYnS/SlOL\nHYiPh+efN5afeuMNmDix4gXKK+PPxD95bN1jbJi0gYYeMu2fEEIIcS3mjB69u+hhiFIqDPDECFjX\n0gs4prU+CaCUWgmMAQ5dsd8TwLdAT/NKFtaSnQ3vvAMLF8KMGcYUHh4elj9PYloid6+8m0XBi+jm\n183yJxBCCCFuQBWGNqWUC/CX1ro9gNY67DqO3QQ4XeJ5HND7iuM3wQhyQzBCmywwagNaw+rVxrVq\nnTsbKxu0amWdc2XnZ3PPV/cwrds07ut4n3VOIoQQQtyAKgxtWut8pdQRpVRzrfWp6zy2OQHsP8Bc\nrbVWxlXo0j1azQ4ehKeeMrpEFy2C22+33rm01kz/cTr+Xv68OOhF651ICCGEuAGZc02bL3BAKRUF\nZBRt01rru67xvnjAv8Rzf4zWtpJ6ACuLRg3WA0YppfK01muuPFhISIjpcVBQEEFBQWaUbv8SEyEr\ny+iG9PAAd3fLXz9W6nxpibjXcEdneRMSAl98AfPnw6OPQo0a1jsvwNvb32b/uf1se2gbTsqcMTBC\nCCGE/QsLCyMsLMzq51FaV9wgppQKKmv7tbpKi7pWjwBDgQQgCrj/yoEIJfZfCvyotf6ujNf0tep0\nJHFx8M038NVXEB0NXl6Qlgbp6cbSUMUBrk6d0veV3VajBiRnJjP/t/l8deArsnMKyMlypq5zC3q3\na0H7hi1o4d2CFj7GfYB3AO413C36mdceXcsjPz5CxP9F0MyrmUWPLYQQQtiTokUILN4EY85AhLDK\nHLioa/VxjMl4nYElWutDSqkZRa8vrsxxre31ra9zKecSz/V7Dh93H4sd98wZ+PZbI6gdPAhjxsAr\nr8CQIaVbuPLyICPDCHDFQa74/sptZ88aKxNcuV/J/S9l5KF6fkhh/wV4nLwfjx3H6dTUm1ffTsGj\n6QmOXzjOiQsn2H9uP2uOruHEhRPEpsbi4+5DC+8WtPRpWSrQtfBpQVPPpte1mPvBpIM8tPohfpjw\ngwQ2IYQQopLMaWlL5/L1aa5ADSBda12FpcGvT3W2tLX5bxs6NejEtthtzOkzhyd6P0GtGrUqdayk\nJFi1Cr7+GnbvhtGjYfx4GD7ccpPTVmTj8Y089ctTNKrVmJA+/6Gp6y3k5EC7dhV3wRbqQhLSEjhx\n4QQnLp7gxIUTHL943PT8XMY5mtRpcjnIlQh1LX1a0qB2A9NEueczz9P7k968OPBFpnSdYv0PLYQQ\nQtiYtVrarhnarijCCbgLCNRaz7V0MRWct1pCW3JmMq3ea0XKcylEp0Qz/7f5RMRF8NKgl3i428Nm\ntS6lpMD33xstalFRxlJP48fDyJHg5mb1jwDA8QvHeWbDM+w9s5d3RrzDmHZjLLraQE5+DrGpsaZA\nd+KicStutcvKzyLAO4AW3i2IT4tnaIuhvD38bYudXwghhLBndhHaShSzR2vd1dLFVHC+aglt66LX\n8U74O2ycvNG0LSo+irkb5xKfFs9rQ17jvg73XRWAUlONKTO++gq2bTNGYI4fD8HBUKtyjXSVkp6b\nzutbX2fxrsXM7jOb2X1m4+ZSTUmxhLScNFOgu5RziQc6P4Czk3O11yGEEELYgs1Cm1Kq5GRaThgj\nPgdprftYupgKaqiW0PbS7y9RqAtZMGRBqe1aa349/itzN87F2cmZN4a+Qa/6Q/nxRyOohYVBUJAR\n1O6807j4vzpprfli/xc8v/F5ggKCeHPYmzTxbFK9RQghhBACsOFABOBOLl/Tlo+xhNUYSxdiDyLj\nI3m85+NXbVdKMbzVcPo2GsaLX37DmE9mkn0mgMC0N5h+Xw8+/9wYAWoLuxJ28eQvT5KTn8PXY7+m\nr39f2xQihBBCCKuqVPdodauOlrZCXUjdt+py5PEjNKjdwLQ9Oxt+/tkYTPDzz8YC6veNyyO9zRIW\n7vx/9G/WnwVDFtC2blur1nelcxnnmLdpHmuj17Jg8AIe6vaQzH0mhBBC2AFrtbRd87e8UuozpZR3\niec+SqlPLV2IrR09fxQfNx8a1G5Abi789BM8+CD4+cF//2t0f0ZHw4YNMOP/avDMoJlEPxFNt0bd\n6LukLzN+nEFCWoLV68wtyOWd8He45X+34FXTi8OPHWZa92kS2IQQQogbnDnXtF016OBGHIiwbM8y\nNsRsoP/ZL3jxRejY0bhG7W9/g0aNKn5vSlYKb2x7gyW7l/BI90d4vt/zFp3jrdjP0T8za/0sWvi0\n4N8j/k37eu0tfg4hhBBCVI0tByLsBQZrrVOKnvsCm7XWnS1dTAU1WD20zfxpJh3qdeCDyU+xbBn0\nrcSlYXGX4ngl7BV+OPJDled4Kyn6fDSz1s/iyPkj/HvEvwluE2zRKTyEEEIIYTk26x4FFgLhSqlX\nlVILgHDgX5YuxNYi4yNp4x5IUhIEBlbuGE09m/LxXR+z9aGt7EjYQdv/tuWjXR+RX5hfqeNdyrnE\nc78+R58lfRjYfCB//f0vRrcdLYFNCCGEuAldM7RprT8H7gXOAWeAe4q23TAycjM4ev4oFw93pV8/\ncKri5WHt67Xn23Hf8t3471j510pu+d8tfHvwW8xtLSzUhSzbs4z277cnKTOJvx79i+f6PUdNl5pV\nK0wIIYQQDsuc7tFA4KDW+lLRc0+gg9Y6shrqK67Bqt2jW05t4blfn6PXvgiaNoXnnrPcscua421o\ny6Hl7h8ZF8mTvzyJQvHeqPfo1aSX5YoRQgghhNXZsnt0EZBW4nlG0bYbRkRcBIFNA9m6FQYMsOyx\ni+d42zl9J3P6zGHm2pkMXz6cXQm7Su2XmJbIlB+mcO/X9/JYz8fYPm27BDYhhBBCmJjVEViymUtr\nXQDcUGsSRcRF0MmnN8eOQY8e1jmHk3JifKfxHHz0IPd1uI+7Vt7F+G/H89e5v3hz25t0/rAzfh5+\nHH7sMJNvnSxTeAghhBCiFHOSwQml1JNKqRpKKVel1FPAcWsXVl201kTEReCUEEjPnuDqat3z1XCu\nwYzbZpjmeOv/aX/+OP0HEf8XwRvD3qBOzWpeA0sIIYQQDsGca9oaAu8Bg4s2bQKe0lqfs3JtJWuw\n2jVtp1NP0+OjHjx0/ixuNRWvvGKV05RLay2jQYUQQogbiM3WHtVanwXGW/rE9iIyPpLApoFs+0Hx\n//5f9Z9fApsQQgghzHHN0KaUcgemAR0Bt+LtWuuHrVhXtYmIi6BHw0De2lP5+dmEEEIIIazNnGva\nlgMNgZHAZsAfSLdmUdUpMj4Sj9TedO4MtWvbuhohhBBCiLKZE9paa61fBNK11p8BdwC9rVtW9cgr\nyGN34m5S9ve0+FQfQgghhBCWZE5oyy26T1VKdQa8gfrWK6n67Du7jwDvAKK2ekpoE0IIIYRdMye0\nfVy0SPx8YA1wEHjLqlVVk8j4SHo1DiQyEvr3t3U1QgghhBDlM2f06MdFDzcDLaxbTvWKiIvAXw+k\neXPw9bV1NUIIIYQQ5bupp92PjI8k51hvBg60dSVCCCGEEBW7aUPb+czzJKYlEr29o1zPJoQQQgi7\nd9OGtqj4KG5rfBvbtjpLaBNCCCGE3bvmNW0ASql+QECJ/bXW+nNrFVUdIuMjaVUzkFPe0KSJrasR\nQgghhKiYOSsirABaAnuAghIvOXRoi4iLoEXy36WVTQghhBAOwZyWth5AR6ut2G4DhbqQqPgo3P9a\nxp2DbV2NEEIIIcS1mXNN21+An7ULqU7R56PxcvNix++NpKVNCCGEEA7BnJa2+sBBpVQUkFO0TWut\n77JeWdYVGR9JJ+/e7MiH1q1tXY0QQgghxLWZE9pCiu6Lu0dViccOKSIuAs9LgQwcCErZuhohhBBC\niGu7Zveo1joMOAx4AnWAg1rrzVauy6oi4iJIO9xbukaFEEII4TCuGdqUUuOASGAsMA6IUkqNtXZh\n1pKZl8mR80c49Fs3WQlBCCGEEA7DnO7R+UBPrfU5AKVUfWAT8I01C7OWXQm7aOfTiRNn3OjUydbV\nCCGEEEKYx5zQpoCkEs/PF21zSJHxkTTK741fX3B2tnU1QgghhBDmMSe0/QKsV0p9gRHWxgM/W7Uq\nK4qIi6Aw9l4GS9eoEEIIIRyIOfO0PQcsBm4FOgOLtdbPmXNwpdRIpdRhpVS0Uur5Ml4fo5Taq5Ta\nrZTapZQacl3VV0JkfCSnw2UQghBCCCEci7LWQgdKKWfgCDAMiAd2APdrrQ+V2Ke21jqj6HFn4Hut\n9VUzpymlLLIgQ9ylOLou6kbW/zvHhRSFq2uVDymEEEIIUYpSCq21xS8lK7d7VCn1h9a6n1Iqnavn\nZdNaa89rHLsXcExrfbLoeCuBMYAptBUHtiIeQPJ11H7dIuMiaenam9o9JbAJIYQQwrGUG9q01v2K\n7j0qeewmwOkSz+OA3lfupJS6G3gdY6ms4ZU8l1ki4yNxSw6UrlEhhBBCOBxz5mlbbs62MpjVn6m1\n/kFr3QG4EzDnuJUWERdByr5AmZ9NCCGEEA7HnNGjpWYzU0q5AD3MeF884F/iuT9Ga1uZtNZblVIu\nSqm6WuvzV74eEhJiehwUFERQUJAZJVyWX5jPn4l/Uri1J4FfXNdbhRBCCCHKFRYWRlhYmNXPU+5A\nBKXUPOAFwB3IKvFSHvCR1npuhQc2wt0RYCiQAERx9UCEVsBxrbVWSnUHvtFatyrjWFUeiLA7cTf3\nrphE/W8OEBVVpUMJIYQQQpSr2gciaK3/CfxTKfXGtQJaOe/PV0o9DqwHnIElWutDSqkZRa8vBu4D\nJiul8oB0YEJlPoQ5IuIi8M3qLV2jQgghhHBIZk35oZTyAdoAbsXbtNZbrFjXleevckvb1B+msuO7\nvvzzvumMGWOhwoQQQgghrmCtljZzBiI8AmwBNgCvYLSchVi6EGuLiIvg5LZA+ve3dSVCCCGEENfP\nnBURnsKYc+2k1now0A1ItWpVFnYh6wJxqQkE1LqFunVtXY0QQgghxPUzJ7Rla62zAJRSblrrw0A7\n65ZlWVHxUTTSPRg4QFaIF0IIIYRjMmfKj9NF17T9APyqlLoAnLRqVRYWGR+JU4IMQhBCCCGE47pm\naNNa31P0MEQpFQZ4Ar9YsyhLi4iL4MzO6QyYZ+tKhBBCCCEqx5yBCIFKKU8ArXUYEIZxXZtD0Fqz\nPTYS74zeNG1q62qEEEIIISrHnGvaFmHMoVYso2ibQziWcgznfA8G3+Zn61KEEEIIISrNnNCG1rqw\nxOMCjMlyHUJEXAQeqbJIvBBCCCEcmzmh7YRS6kmlVA2llKtS6inguLULs5TI+EguHZRBCEIIIYRw\nbOaEtplAP4wF4OOAQGC6NYuypC3HI1DxgbRpY+tKhBBCCCEqz5zRo2eB8dVQi8Vl5WVxJOUgwe26\noSy+mIQQQgghRPUpN7QppZ7XWr+plPpvGS9rrfWTVqzLIv5M/BPPnFsI6u9u61KEEEIIIaqkopa2\ng0X3u4CSq7WrK57brYi4CPJPBjLgb7auRAghhBCiaioKbeOAHwFvrfV/qqkei9pyPJKcmLvo0sXW\nlQghhBBCVE1FAxF6KKUaAw8rpXyvvFVXgVWxPTaCnn6BODvMBCVCCCGEEGWrqKVtEbAJaInRRVqS\nLtputxLSEkjPyWRkr1a2LkUIIYQQosrKbWnTWr+nte4ALNVat7jiZteBDSAyLhLXpN4MHCjDRoUQ\nQgjh+CoaPeqptb4E/KOs7lCtdYpVK6uizccjyIruzW232boSIYQQQoiqq6h79EsgmKtHjxZrYZWK\nLOS3I5G095hHzZq2rkQIIYQQourKDW1a6+Ci+4Bqq8ZC8gvzOXJpF0926mXrUoQQQgghLOKay1gp\npfoppTyKHj+olHpHKdXc+qVV3oFzB3DObMrwgd62LkUIIYQQwiLMWXt0EZCplLoVmI2xWPznVq2q\niraejCDveCB9+ti6EiGEEEIIyzAntOVrrQuBu4EPtNbvA3WsW1bVrN0bQWPdGw8PW1cihBBCCGEZ\n5oS2NKXUPGAS8JNSyhmoYd2yqmbXmUgGtgy0dRlCCCGEEBZjTmgbD+QAD2utzwBNgLetWlUVXMy+\nSEpBLHf37WTrUoQQQgghLKaiKT8A0FonAgtLPI8FPrNmUVUREbsDEnsw6MlrfjQhhBBCCIdhzujR\nPkqpHUqpdKVUnlKqUCl1qTqKq4zVuyLwTg+kXj1bVyKEEEIIYTnmdI++DzwARANuwDTgf9Ysqiq2\nHI+ke8Peti5DCCGEEMKizAltaK2jAWetdYHWeikw0rplVY7WmmPZEYzpLoMQhBBCCHFjMefCrwyl\nVE1gr1LqLeAMYJersB9LiSE/y527Bje2dSlCCCGEEBZlTkvb5KL9HgcygabAfdYsqrK+3xGJ2/lA\n/P1tXYkQQgghhGWZM3r0ZNHDLCDEmsVU1c/7I+jgIV2jQgghhLjxlBvalFL7K3if1lp3sUI9VbI/\nJZKH2423dRlCCCGEEBZXUUvbndVWhQVk5WWR4nyABwZ3t3UpQgghhBAWV1FoqwE01FpvK7lRKdUf\nSLRqVZXw857dOF9oz60da9m6FCGEEEIIi6toIMJ/gLIm0b1U9JpdWRUZSTPnQJRdjmsVQgghhKia\nikJbQ631vis3Fm1rYb2SKiciLoJAf5lUVwghhBA3popCm3cFr7mZewKl1Eil1GGlVLRS6vkyXp+o\nlNqrlNqnlPpDKVWpAQ6nCyP5W28ZOSqEEEKIG1NF17TtVEpN11p/VHKjUuoRYJc5B1dKOWMsgzUM\niAd2KKXWaK0PldjtODBQa52qlBoJfARcV/r669QZ8p3SuLNvm+t5mxBCCAem5HoYYQe01tV2ropC\n29PA90qpiVwOaT2AmsA9Zh6/F3CseK43pdRKYAxgCm1a6/AS+0diTN57XUJ/j6RuTi9cXOQfsBBC\n3Eyq8xemEFeq7v84lBvatNZnlFJ9gcFAJ0ADP2mtf7uO4zcBTpd4HgdUdOHZNGDddRwfgI1HIri1\nrnSNCiGEEOLGVeGKCNr4L8xvRbfKMPu/QEqpwcDDQL+yXg8JCTE9DgoKIigoyPT8cHoEIYFXXS4n\nhBBCCGF1YWFhhIWFWf08yppNy0qpQCBEaz2y6PkLQKHW+s0r9usCfAeM1FofK+M4urw6L6YW4POm\nDwnPncTP29fin0EIIYR9UkpJ96iwqfJ+Bou2W7zv1JwF46tiJ9BGKRWglHIFxgNrSu7nZbhcAAAW\nVElEQVSglGqGEdgmlRXYruXLTQdwz28sgU0IIYQQNzSrhjatdT7wOLAeOAh8pbU+pJSaoZSaUbTb\nS4AP8KFSardSKup6zvHT7khau8v8bEIIIW5Md9xxB8uXL7f4vsLxWLV71FIq6h5tNGMaY/vexn+n\n/L2aqxJCCGFL9tw96uHhYRpZmJGRgZubG87OzgB89NFH3H///bYsT1hIdXePVjgQwd7l5MA51wjG\n9XvM1qUIIYQQJunp6abHLVq0YMmSJQwZMuSq/fLz83FxcehfxdVC/pwM1r6mzap+D09FeZ8isEVn\nW5cihBBCXFNYWBhNmzblrbfews/Pj2nTpnHx4kVGjx5NgwYN8PX15c477yQ+Pt70nqCgIJYsWQLA\nsmXL6N+/P88++yy+vr60bNmSX375pVL7njhxgoEDB+Lp6cntt9/OY489xoMPPlhm3deqMSUlhYce\neogmTZrg6+vLPfdcns519erVdO3aFS8vL1q3bs2GDRsACAgIYNOmTab9QkJCTOc/efIkTk5OfPrp\npzRv3pxhw4YBMHbsWPz8/PD29mbQoEEcPHjQ9P6srCyeeeYZAgIC8Pb2ZuDAgWRnZxMcHMz7779f\n6vN06dKF1atXm/NXZlccOrR9vW0HjehGDecati5FCCGEMMvZs2e5cOECsbGxLF68mMLCQqZNm0Zs\nbCyxsbG4u7vz+OOPm/ZXSpWaxDUqKor27dtz/vx5nnvuOaZNm1apfR944AECAwNJSUkhJCSEFStW\nlDtZ7LVqfPDBB8nOzubgwYOcO3eO2bNnm84/ZcoUFi5cSGpqKlu2bKF58+Zl1lrWubds2cLhw4dZ\nv349AMHBwRw7doykpCS6d+/OxIkTTfvOmTOH3bt3Ex4eTkpKCm+99RZOTk5MnTqVFStWmPbbu3cv\nCQkJBAcHl/dXZL+01nZ/M8q8WpuHF+gx788p8zUhhBA3tvJ+N5Tep+q3qgoICNCbNm3SWmv9+++/\na1dXV52Tk1Pu/rt379Y+Pj6m50FBQXrJkiVaa62XLl2qW7dubXotIyNDK6X02bNnr2vfU6dOaRcX\nF52VlWV6fdKkSXrSpElmfaaSNSYkJGgnJyd98eLFq/abPn26nj17dpnHKPnnorXWL7/8sun8J06c\n0EopfeLEiXJruHDhglZK6UuXLumCggLt7u6u9+3bd9V+WVlZ2sfHRx87dkxrrfUzzzyjH3vsMbM+\n57WU9zNYtN3iechhW9oKCuBEXgR39ZCVEIQQQpTNErHN0urXr4+rq6vpeWZmJjNmzCAgIAAvLy8G\nDRpEampquYMsGjVqZHpcq1YtoPQ1dObsm5CQgK+vL25ubqbX/f39y625ohpPnz6Nr68vXl5eV70v\nLi6OVq1alXvcaylZU2FhIXPnzqV169Z4eXnRokULAJKTk0lOTiY7O7vMc7m5uTFu3DiWL1+O1pqV\nK1eW2w1s7xw2tO3Zo9FNIhneUab7EEII4Tiu7AZcuHAhR48eJSoqitTUVDZv3lyyp8kq/Pz8SElJ\nISsry7QtNja23P0rqtHf35+UlBRSU1Ovep+/vz/HjpU9BWvt2rXJyMgwPT9z5sxV+5T8swoNDWXN\nmjVs2rSJ1NRUTpw4ARg9hvXq1cPNza3cc02ZMoXQ0FA2btxIrVq16N3bMbODw4a2HzafwK2GK009\nr3t9eSGEEMJupKen4+7ujpeXFykpKbzyyitWP2fz5s257bbbCAkJIS8vj/DwcH766adyr2mrqEY/\nPz9GjRrFo48+ysWLF8nLy2PLli0ATJs2jaVLl/Lbb79RWFhIfHw8R44cAaBr166sXLmS/Px8du7c\nyapVqypcgD09PZ2aNWvi6+tLRkYG8+bNM73m5OTEww8/zOzZs0lMTKSgoIDw8HByc3MB6NOnD0op\n5syZw+TJk6v852crDhva1h+IoKOnYyZlIYQQN68rg8nTTz9NVlYW9erVo2/fvowaNarc8HLlxftl\nHc/cfUNDQwkPD6du3bq8+OKLjB8/vlS37fXUuHz5cmrUqEH79u1p2LAh7733HgA9e/Zk6dKlzJo1\nC29vb4KCgkwteq+++ioxMTH4+PgQEhJSalBBWZ9r8uTJNG/enCZNmtCpUydTECv29ttv07lzZ3r2\n7EndunV54YUXKCwsLPX+/fv3M2nSpDI/oyNwyMl1tYba9z3F09Oa8s/gZ21YmRBCCFux58l1HdH4\n8ePp2LEjL7/8sq1LsYrly5fz8ccfm1oBLeFGW3vUKo4cgYLGEYzqJIMQhBBCiMrYuXMnMTExFBYW\n8vPPP7NmzRruvvtuW5dlFZmZmXzwwQdMnz7d1qVUiUOGtt+25FBQ9y96NO5h61KEEEIIh3TmzBkG\nDx5MnTp1mDVrFosWLeLWW2+1dVkWt379eho0aICfnx8PPPCArcupEofsHh31SAQHAh4l9h9/2rAq\nIYQQtiTdo8LWpHvUDJHxEfRtLoMQhBBCCHHzcLjQdvo0ZPpEMlKuZxNCCCHETcThQtvWreDcPILA\nptLSJoQQQoibh8OFtl+2nUXXvEjbum1tXYoQQgghRLVxuNAWFh1J1/q9cVIOV7oQQgghRKU5VPJJ\nToazNSIZ2k66RoUQQty4nJycOH78OAB///vfWbBggVn7Xq/Q0FBGjBhRqfeK6udQoW3bNvBoH0Hf\nZjIIQQghhP0aOXJkmSsLrF69Gj8/v1LLK13Lhx9+yPz586tc08mTJ3Fycip17okTJ7J+/foqH1tU\nD4cKbZu3FpDhtYNeTXrZuhQhhBCiXFOnTmXFihVXbV++fDmTJk3Cycl2v35vhrnt8vPzbV2CVThU\naPt19yHquzeibq26ti5FCCGEKNeYMWM4f/48W7duNW27cOECa9euZfLkyURFRdGnTx98fHxo3Lgx\nTzzxBHl5eWUea+rUqbz44oum5//6179o3LgxTZs25dNPPy2179q1a+nWrRteXl40a9aMV155xfTa\nwIEDAfD29sbT05OIiAiWLVvGgAEDTPts376dnj174u3tTa9evQgPDze9FhQUxEsvvUT//v3x9PRk\nxIgRnD9/vsyaL168yOjRo2nQoAG+vr7ceeedxMfHm15PSUnhoYceokmTJvj6+nLPPfeYXlu9ejVd\nu3bFy8uL1q1bs2HDBgACAgLYtGmTab+QkBAefPBB4HIr4qeffkrz5s0ZNmwYAGPHjsXPzw9vb28G\nDRrEwYMHTe/PysrimWeeISAgAG9vbwYOHEh2djbBwcG8//77pT5Ply5dWL16dZmftTo5TGhLS4Po\nrAgGtJTr2YQQQtg3d3d3xo0bx+eff27a9vXXX9OhQwc6d+6Mi4sL7777LufPnyc8PJxNmzbxv//9\nr8xjKaVQyphc/5dffmHhwoVs3LiRo0ePsnHjxlL7enh4sGLFClJTU1m7di0ffvihKWwUB8jU1FQu\nXbpEYGDpS41SUlIIDg7m6aefJiUlhdmzZxMcHMyFCxdM+3z55ZcsW7aMc+fOkZuby9tvv11mzYWF\nhUybNo3Y2FhiY2Nxd3fn8ccfN73+4IMPkp2dzcGDBzl37hyzZ88GICoqiilTprBw4UJSU1PZsmUL\nzZs3v+rPofj5lbZs2cLhw4dNXb7BwcEcO3aMpKQkunfvzsSJE037zpkzh927dxMeHk5KSgpvvfUW\nTk5OV7WS7t27l4SEBIKDg8v8rNXJxdYFmCs8HHw7R9JPrmcTQghhJvVK1VcS0i9XrjtxypQpjB49\nmg8++ABXV1c+//xzpkyZAkD37t1N+zVv3pzp06ezefNmnnrqqQqP+fXXX/Pwww/TsWNHAF555RVW\nrlxpen3QoEGmx507d2bChAls3ryZMWPGXLNbdO3atbRr184UbCZMmMB7773HmjVrmDJlCkopHnro\nIVq3bg3AuHHjWLNmTZnHurL1bN68eQwZMgSAxMREfvnlF1JSUvDy8gIwtfYtWbKEadOmMXToUAAa\nN25cbr1lfZ6QkBDc3d1Nz6dOnWp6/PLLL/Puu++SlpZG7dq1Wbp0KZGRkfj5+QGYQuydd97JjBkz\niImJoVWrVixfvpwJEybg4mL7yGT7Csy0dSsU+EUQ2HSmrUsRQgjhICobuCyhX79+1KtXj++//57b\nbruNHTt28MMPPwBw9OhRZs+eza5du8jMzCQ/P5/bbrvtmsdMTEykZ8+epufNmjUr9XpkZCRz587l\nwIED5ObmkpOTw7hx48yqNyEh4arjNW/enISEBNPzRo0amR67u7uTnp5e5rEyMzOZNWsW69evN7XU\npaeno7Xm9OnT+Pr6mgJbSXFxcVVq0fL39zc9LiwsZN68eXz77bckJSWZriNMTk4mKyuL7OxsWrVq\nddUx3NzcGDduHMuXL+fll19m5cqVrFq1qtI1WZLDdI/+vv0Sl1yO06VhF1uXIoQQQphl8uTJfP75\n56xYsYKRI0dSv359wJjGo2PHjhw7dozU1FRee+01s0aU+vn5ERsba3pe8jHAAw88wN13301cXBwX\nL15k5syZpuOW1Z1YUpMmTTh16lSpbadOnaJJkyZmfdaSFi5cyNGjR4mKiiI1NZXNmzejtUZrjb+/\nPykpKaSmpl71Pn9/f44dO1bmMWvXrk1GRobp+ZkzZ67ap+RnDA0NZc2aNWzatInU1FROnDgBGC10\n9erVw83NrdxzTZkyhdDQUDZu3EitWrXo3ds+Ls1ymNC2K3En3Rp1o4ZzDVuXIoQQQphl8uTJ/Prr\nr3zyySemrlEwWp3q1KlDrVq1OHz4MB9++GG5xygOO2B0SS5btoxDhw6RmZlZaqBB8XF9fHxwdXUl\nKiqKL774whRk6tevj5OTEzExMWWeZ9SoURw9epQvv/yS/Px8vvrqKw4fPszo0aNL1WKO9PR03N3d\n8fLyIiUlpVSdfn5+jBo1ikcffZSLFy+Sl5fHli1bAJg2bRpLly7lt99+o7CwkPj4eI4cOQJA165d\nWblyJfn5+ezcuZNVq1ZVGETT09OpWbMmvr6+ZGRkMG/ePNNrTk5OPPzww8yePZvExEQKCgoIDw8n\nNzcXgD59+qCUYs6cOUyePNmsz1wdHCa0+XaJoG8z+0i6QgghhDmaN29Ov379yMzM5K677jJtf/vt\nt/niiy/w9PRk+vTpTJgwodyL7EtegD9y5EiefvpphgwZQtu2bRk6dGipff/3v//x0ksv4enpyauv\nvsr48eNNr9WqVYt//OMf9OvXD19fXyIjI0sdu27duvz0008sXLiQevXq8fbbb/PTTz/h6+t7zbqu\n9PTTT5OVlUW9evXo27cvo0aNKrXv8uXLqVGjBu3bt6dhw4a89957APTs2ZOlS5cya9YsvL29CQoK\nMrUmvvrqq8TExODj40NISEipQQVX1gZGYG7evDlNmjShU6dOpiBW8u+gc+fO9OzZk7p16/LCCy+U\nau2cPHky+/fvZ9KkSWV+RltQjjBfi1JKt/zHXbxx/yTG3jLW1uUIIYSwA0qpm2LOMWEby5cv5+OP\nPza1ApalvJ/Bou1VHwVzBYdpaTvvHkFgUxk5KoQQQgjryszM5IMPPmD69Om2LqUUhwltbq7ONPVs\nausyhBBCCHEDW79+PQ0aNMDPz48HHnjA1uWU4jBTfvRtFnjNkS9CCCGEEFUxYsSIcqcysTWHaWnr\n3UQGIQghhBDi5uUwoU2uZxNCCCHEzcxhRo+m5aTh4eph61KEEELYCRk9KmytukePOkxoc4Q6hRBC\nVB+5zlnYg+oMbQ4zEEEIIYQoSf4zL242Vr2mTSk1Uil1WCkVrZR6vozX2yulwpVS2UqpZ6xZi7g5\nhIWF2boE4UDk50WYS35WhD2wWmhTSjkD7wMjgY7A/UqpDlfsdh54AnjbWnWIm4t8sYrrIT8vwlzy\nsyLsgTVb2noBx7TWJ7XWecBKYEzJHbTWSVrrnUCeFesQQgghhHB41gxtTYDTJZ7HFW0TQgghhBDX\nyWqjR5VS9wEjtdaPFD2fBPTWWj9Rxr4vA+la64XlHEuuNhVCCCGEw3C00aPxgH+J5/4YrW3XzRof\nXAghhBDCkVize3Qn0EYpFaCUcgXGA2vK2VdCmRBCCCFEBaw6ua5SahTwH8AZWKK1fl0pNQNAa71Y\nKdUI2AF4AoVAGtBRa22fK7UKIYQQQtiIQ6yIIIQQQghxs7PrBeOvNTmvECUppU4qpfYppXYrpaJs\nXY+wH0qpT5VSZ5VS+0ts81VK/aqUOqqU2qCU8rZljcJ+lPPzEqKUiiv6ftmtlBppyxqFfVBK+Sul\nfldKHVBK/aWUerJou1W+X+w2tJk5Oa8QJWkgSGvdTWvdy9bFCLuyFOO7pKS5wK9a67bApqLnQkDZ\nPy8aeKfo+6Wb1voXG9Ql7E8eMEtrfQsQCDxWlFWs8v1it6ENMybnFaIMMqhFXEVrvRW4cMXmu4DP\nih5/BtxdrUUJu1XOzwvI94u4gtb6jNZ6T9HjdOAQxpy0Vvl+sefQJpPziuulgY1KqZ1KqUdsXYyw\new211meLHp8FGtqyGOEQnlBK7VVKLZHudHElpVQA0A2IxErfL/Yc2mSEhLhe/bTW3YBRGE3UA2xd\nkHAM2hiRJd85oiIfAi2ArkAiUOZk8OLmpJTyAFYBT2mt00q+ZsnvF3sObRabnFfcHLTWiUX3ScD3\nGF3sQpTnbNG0Qyil/IBzNq5H2DGt9TldBPgE+X4RRZRSNTAC23Kt9Q9Fm63y/WLPoe16JucVNzml\nVC2lVJ2ix7WB4cD+it8lbnJrgClFj6cAP1Swr7jJFf3iLXYP8v0iAKWUApYAB7XW/ynxklW+X+x6\nnrayJue1cUnCTimlWmC0roGxPFuo/LyIYkqpL4FBQD2M60teAlYDXwPNgJPAOK31RVvVKOxHGT8v\nLwNBGF2jGjgBzChxzZK4SSml+gNbgH1c7gJ9AYjCCt8vdh3ahBBCCCGEwZ67R4UQQgghRBEJbUII\nIYQQDkBCmxBCCCGEA5DQJoQQQgjhACS0CSGEEEI4AAltQgghhBAOQEKbEMLhKaUKlFK7S9yes+Cx\nA5RSMpGqEMLmXGxdgBBCWEBm0bqzQghxw5KWNiHEDUspdVIp9aZSap9SKlIp1apoe4BS6jel1F6l\n1EallH/R9oZKqe+VUnuKboFFh3JWSn2klPpLKbVeKeVmsw8lxP9v745ZuorCOI5/H8LBoUAoEFxa\nagqNfAW9hgiLpnCpIZp0cO8NmJOLU68hEEJwcI2W1pqUDEJwSeTPz8GDXOMfDaFyr9/Pcp/zcLmc\nsz3nOQeuri2LNklDMPnH8ejTlg9wkGQWWOP0t3gA74GNJHPAB2C15VeBrSQPgUfA15a/B6wleQAc\nAE8ufkmSdJ6/sZLUe1V1mOTmmPw34HGS71U1AewluV1VP4HpJKOW301yp6r2gZkkx51v3AU2k9xv\n42VgIsm7S1iaJJ2x0ybpOunuUusv74zLH3XiEd4HlnQFLNokDd1C57nT4h3gWYtfANst/gS8Bqiq\nG1V167ImKUn/4m5R0hBMVtXnzvhjkpUWT1XVF+A38Lzl3gAbVbUE7AMvW/4tsF5Vi5x21F4BPzjf\noWPMWJIunHfaJA1Wu9M2n+TXVc9Fkv6Xx6OShsxdqaTBsNMmSZLUA3baJEmSesCiTZIkqQcs2iRJ\nknrAok2SJKkHLNokSZJ64ASmns6EnhfxigAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb50b95e90>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>AdaGrad</b> updates takes some epochs to tune learning rate. I printed loss values for first 50 epochs to see this effect."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loss_history5[0:50]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "[2.3025928278904635,\n",
        " 75.934736079622795,\n",
        " 92.182474572629786,\n",
        " 100.5019278755542,\n",
        " 84.844181635490045,\n",
        " 49.409391098629875,\n",
        " 43.328290141355289,\n",
        " 35.325688200166425,\n",
        " 25.519854900507241,\n",
        " 21.356402138599851,\n",
        " 26.218600967478551,\n",
        " 31.59622812204384,\n",
        " 26.22040589643569,\n",
        " 23.227517734307192,\n",
        " 17.767912373878527,\n",
        " 16.157541492684935,\n",
        " 12.608468084163864,\n",
        " 12.321226053503693,\n",
        " 13.24122535188368,\n",
        " 12.928515216653592,\n",
        " 10.796928215446016,\n",
        " 9.1984105449830302,\n",
        " 11.298781826011448,\n",
        " 10.804694477862794,\n",
        " 12.568968109093376,\n",
        " 10.56363563705977,\n",
        " 7.9977924016860049,\n",
        " 7.34338664646741,\n",
        " 6.6929796435282283,\n",
        " 7.1677274578861159,\n",
        " 7.2066434970512532,\n",
        " 6.7933009055358866,\n",
        " 6.7913307562240677,\n",
        " 6.6829018897838459,\n",
        " 7.0043970733245429,\n",
        " 7.3813129930707051,\n",
        " 6.4100443076342426,\n",
        " 5.7477095369608451,\n",
        " 7.3967044022923663,\n",
        " 5.8068102116770044,\n",
        " 6.1110745504385751,\n",
        " 5.4339376326257485,\n",
        " 5.3695441754045357,\n",
        " 4.6965492787415659,\n",
        " 5.7784881299439856,\n",
        " 5.4864936711432595,\n",
        " 4.4452802054476042,\n",
        " 4.7502021504025134,\n",
        " 5.2173199263390639,\n",
        " 3.9212560040704405]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Conclusion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Despite the simplicity of the both Momentum and RMSprop tricks, performance improvement is very high compared to naive SGD. Also from the above figures, you can see the problems that I've been told in my blog post http://bit.ly/1FGKb4K. If we look at the loss changes of SGD it is very unstable and after we apply Momentum it is stabilized relatively. Then implication of RMSprop makes things better and modest. \n",
      "\n",
      "Another fact is the convergence rates of the methods. As you can see RMSprop+Momentum reaches the very solid values after only the first epoch.\n",
      "\n",
      "I add AdaGrad to experiments lately. As I stated above, if you like to see good values in a short time AdaGrad is not the choice but in some way or another it concludes a good model, if you are patient enough.\n",
      "\n",
      "The accuracy values of train and validation are also more correlated as we improve the SGD with the additional tricks. Of course, I didn't suppose that this model is the best and you can see that we can increase the number of learning epochs or make the model larger since there is no gap between train and validation accuracies yet after 5th epoch.\n",
      "\n",
      "As a side note, this notebook and the blog post is inspired as I was working on Stanford CS231n assignment2. Therefore I did not provide any replicable code for the sake of honor code. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}