pw04/MLP_HOG-p7o4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X_wPegydbafH"
   },
   "source": [
    "# Computing features to train a MLP\n",
    "This notebook will guide you through the use of the `keras` package to train a multilayer perceptron for handwritten digits classification. You are going to computing the histogram of gradients from the images in the `mnist` dataset (LeCun et al. 1998)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "61oGWRyMbafJ"
   },
   "source": [
    "## Loading the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "duI9Ar-8bafS"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:16:52.878943: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-04-30 23:16:53.290719: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.core import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.utils import np_utils, plot_model\n",
    "from matplotlib import pyplot as pl\n",
    "from skimage import exposure\n",
    "from skimage.feature import hog\n",
    "from sklearn import metrics as me\n",
    "\n",
    "from utils import plot_confusion_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpPmUqiRbafz"
   },
   "source": [
    "Load the `mnist` dataset and normalize in the range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "verKKFaFbaf6",
    "outputId": "29e842da-35fa-49b6-a7f7-f35d9e8a71b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "n_train, height, width = X_train.shape\n",
    "n_test, _, _ = X_test.shape\n",
    "\n",
    "X_train = X_train.reshape(n_train, height, width, 1).astype('float32')\n",
    "X_test = X_test.reshape(n_test, height, width, 1).astype('float32')\n",
    "\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "\n",
    "n_classes = 10\n",
    "\n",
    "print(n_train, 'train samples')\n",
    "print(n_test, 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "Y_train = np_utils.to_categorical(y_train, n_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TazeB7LLbagE"
   },
   "source": [
    "### An example of how to compute the histogram of gradients\n",
    "Let's compute the HOG for one image in the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "cL-609oKbagI"
   },
   "outputs": [],
   "source": [
    "n_orientations = 4\n",
    "pix_p_cell = 7\n",
    "hog_size = int(height * width * n_orientations / (pix_p_cell * pix_p_cell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "r2DA2Da_bage"
   },
   "outputs": [],
   "source": [
    "fd, hog_image = hog(X_test[0,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=True)\n",
    "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s2Ow4uJtbago"
   },
   "source": [
    "Show the image and the corresponding gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "83RZeljKbagu",
    "outputId": "e1507d91-5cd9-484c-e0bc-0673b046a274"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True)\n",
    "ax1.axis('off')\n",
    "ax1.imshow(X_test[0,:,:,0], cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax1.set_title('Input image')\n",
    "ax1.set_adjustable('box')\n",
    "\n",
    "ax2.axis('off')\n",
    "ax2.imshow(hog_image_rescaled, cmap=pl.get_cmap('Greys'), interpolation='nearest')\n",
    "ax2.set_title('Histogram of Oriented Gradients')\n",
    "ax1.set_adjustable('box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Yv-ep9ybag4"
   },
   "source": [
    "Compute the HOGs for the train and test datasets\n",
    "\n",
    "> Indented block\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "y8Q3cdrebag6",
    "outputId": "4c21e6ce-56d2-4287-eab6-634cda1ad94b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train done\n",
      "X_test done\n"
     ]
    }
   ],
   "source": [
    "X_train_hog = np.zeros((X_train.shape[0], hog_size))\n",
    "X_test_hog = np.zeros((X_test.shape[0], hog_size))\n",
    "\n",
    "for i in np.arange(X_train_hog.shape[0]):\n",
    "    X_train_hog[i,:] = hog(X_train[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_train done')\n",
    "\n",
    "for i in np.arange(X_test_hog.shape[0]):\n",
    "    X_test_hog[i,:] = hog(X_test[i,:,:,0], orientations=n_orientations, pixels_per_cell=(pix_p_cell, pix_p_cell), cells_per_block=(1, 1), visualize=False)\n",
    "print('X_test done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kos0aTYmbahU"
   },
   "source": [
    "Create the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "id": "NnawtqztbahV",
    "outputId": "c99078e7-4754-41c7-dbe9-e953931c5f6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               16640     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19,210\n",
      "Trainable params: 19,210\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:17:08.854528: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:08.864317: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:08.864367: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:08.866123: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:08.866164: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:08.866197: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:09.358699: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:09.358760: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:09.358768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1722] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-04-30 23:17:09.358803: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-04-30 23:17:09.358824: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20894 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:0c:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    Dense(256, input_shape=(hog_size,), activation='relu'),\n",
    "    Dropout(0.4),\n",
    "    Dense(n_classes, activation='softmax'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(\n",
    "    model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    "    show_layer_activations=True,\n",
    "    show_trainable=True,\n",
    "    to_file='mlpf-model.png'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EGeviXzJbahY"
   },
   "source": [
    "Define some constants and train the MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "laKFY8vcbahZ",
    "outputId": "5849045b-3563-4afb-edfa-5658aac89ec8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " 31/422 [=>............................] - ETA: 2s - loss: 1.8591 - accuracy: 0.4960"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-30 23:17:11.875140: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:637] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422/422 [==============================] - 3s 6ms/step - loss: 0.6776 - accuracy: 0.8097 - val_loss: 0.2892 - val_accuracy: 0.9152\n",
      "Epoch 2/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.3421 - accuracy: 0.8895 - val_loss: 0.2451 - val_accuracy: 0.9212\n",
      "Epoch 3/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.3005 - accuracy: 0.9018 - val_loss: 0.2227 - val_accuracy: 0.9287\n",
      "Epoch 4/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2794 - accuracy: 0.9095 - val_loss: 0.2089 - val_accuracy: 0.9332\n",
      "Epoch 5/10\n",
      "422/422 [==============================] - 3s 8ms/step - loss: 0.2631 - accuracy: 0.9146 - val_loss: 0.1986 - val_accuracy: 0.9355\n",
      "Epoch 6/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2508 - accuracy: 0.9183 - val_loss: 0.1892 - val_accuracy: 0.9372\n",
      "Epoch 7/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2388 - accuracy: 0.9218 - val_loss: 0.1809 - val_accuracy: 0.9435\n",
      "Epoch 8/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2286 - accuracy: 0.9252 - val_loss: 0.1784 - val_accuracy: 0.9443\n",
      "Epoch 9/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2208 - accuracy: 0.9286 - val_loss: 0.1711 - val_accuracy: 0.9462\n",
      "Epoch 10/10\n",
      "422/422 [==============================] - 3s 7ms/step - loss: 0.2150 - accuracy: 0.9297 - val_loss: 0.1670 - val_accuracy: 0.9470\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "n_epoch = 10\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train_hog, Y_train,\n",
    "                    batch_size=batch_size, epochs=n_epoch,\n",
    "                    verbose=1, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aezTZuCMbahf"
   },
   "source": [
    "Show the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "u6E66I52bahg",
    "outputId": "52139b93-823a-4fca-de59-a64f77f3b0ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.18654173612594604\n",
      "Test accuracy: 0.9386000037193298\n"
     ]
    },
    {
     "data": {
      "image/png": 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79u1YsGCBz/hahbkF1zy7E4IAHHkqF1G6no/TUd901iYkD7ZHcAnH9mhpacGFCxeQmZkp2y/KzrhcLpjNZkRFRXmHWEJVY2Mjhg0bhnXr1mHRokV+ndtVG/X093do//T6SUKUDqkxeogicLioTu5yiIiIAurQoUPYunUrzp07h4MHD+Kee+4BANx6662y1MMw0omcdGnBM05iJSKiwejFF1/E5MmTMXfuXDQ2NmLv3r29moMSCJzA2oms9CH4+6EShhEiIhp0pk6dioKCArnL8GLPSCey06RJrIeKauF0Bf20GiIiopDFMNKJMUlGRGiUaLQ5carM0v0JRETUb0LgXouwFYi2YRjphFIhYKq7d6SgiEM1RERy8Nw11NTUJHMl1BlP2/TlDi/OGelCVvoQfHy2CgcLa7Hw2nS5yyEiCjtKpRImk8n7XBWDwRA0zwxzuVyw2WxoaWkJ+Vt7e0MURTQ1NaGiogImkwlKpbLX12IY6YJn8TNOYiUiko/nCbWeQBIsRFFEc3Mz9Hp90AQkOZhMpm6fItwdhpEuTEk1QRCAopomVFhakGAMrgV3iIjCgSAIGDp0KBISEmC32+Uux8tut2PPnj2YPXt22CxCdyW1Wt2nHhEPhpEuROvVGJ1gxKlyCw4W1uHrE/uW/IiIqPeUSmVAfvEFilKphMPhgE6nC9swEijhN8jlp+wM93NqOImViIioXzCMdMOz3gjnjRAREfUPhpFueCaxHrtUjxa7U+ZqiIiIBh+GkW6kxxoQG6GBzenC8cv1cpdDREQ06DCMdEMQBGTxFl8iIqJ+wzDSA1xvhIiIqP8wjPRAaxip4/MRiIiIAoxhpAcmDYuGWimgqsGK4ppmucshIiIaVHoVRjZu3IjMzEzodDpkZ2dj7969XR5vtVqxcuVKpKenQ6vVYsSIEXjllVd6VbAcdGolJiRHAwAKimpkroaIiGhw8TuMbNu2DcuWLcPKlStx6NAhzJo1CzfddBOKioo6PeeOO+7Azp078fLLL+PUqVPYunUrxo4d26fCBxrnjRAREfUPv5eDX79+PRYtWoSHHnoIALBhwwZ88MEH2LRpE9auXdvu+Pfffx+7d+/G+fPnERMTAwDIyMjoW9UyyEkfgpc/voCCwjq5SyEiIhpU/AojNpsNBQUFWL58uc/+3Nxc7Nu3r8Nz/vWvfyEnJwfPP/88Xn/9dUREROCb3/wmfvnLX0Kv13d4jtVqhdVq9b42m80ApIcSBfIhSZ5r9eSak5IjAQCnysyosTTDqONjffqDP21C/Y/tEVzYHsGF7dG9nv5s/PqNWlVVBafTicTERJ/9iYmJKCsr6/Cc8+fP4+OPP4ZOp8Pf//53VFVVYcmSJaipqel03sjatWuxevXqdvt37NgBg8HgT8k9kp+f36PjYrRK1FgF/PGdfIw18a6a/tTTNqGBwfYILmyP4ML26FxTU1OPjuvVf+8FQfB5LYpiu30eLpcLgiDgz3/+M6KjpUmg69evx3e/+1387ne/67B3ZMWKFcjLy/O+NpvNSE1NRW5uLqKionpTcofsdjvy8/Mxb968Hj1x8cPGo3j3aBnUSaOx4GsjAlYHtfK3Tah/sT2CC9sjuLA9uucZ2eiOX2EkLi4OSqWyXS9IRUVFu94Sj6FDh2LYsGHeIAIA48aNgyiKuHTpEkaNGtXuHK1WC61W226/Wq3ulwbv6XWvzozFu0fLcOhSPf/g9bP+amvqHbZHcGF7BBe2R+d6+nPx624ajUaD7Ozsdl1S+fn5mDFjRofnzJw5E5cvX0ZDQ4N33+nTp6FQKJCSkuLP28suy/0E38NFdXC6OExDREQUCH7f2puXl4c//elPeOWVV3Dy5En89Kc/RVFRERYvXgxAGmK57777vMd/73vfQ2xsLL7//e/jxIkT2LNnDx577DE8+OCDnU5gDVZjk4wwaJSwWB04U2GRuxwiIqJBwe85I3feeSeqq6uxZs0alJaWYuLEidi+fTvS09MBAKWlpT5rjkRGRiI/Px9Lly5FTk4OYmNjcccdd+Dpp58O3HcxQFRKBaakmrDvXDUKCmsxNilw81eIiIjCVa8msC5ZsgRLlizp8Gtbtmxpt2/s2LGDZrZxdvoQbxi5Z1q63OUQERGFPD6bxk9Z7pVYD3IlViIiooBgGPGTZxLrxeomVDVYuzmaiIiIusMw4qdovRqjE6XVWNk7QkRE1HcMI73Ah+YREREFDsNIL3iGahhGiIiI+o5hpBc8PSNHS+phdThlroaIiCi0MYz0QmZcBIYY1LA5XDh+uWfr7hMREVHHGEZ6QRAEb+8IJ7ESERH1DcNIL2VxEisREVFAMIz0UrZ7EuuBwlqIIh+aR0RE1FsMI710VYoJKoWASosVl2qb5S6HiIgoZDGM9JJeo8SEYdEAgINFHKohIiLqLYaRPsjmeiNERER9xjDSB547ag5cZBghIiLqLYaRPshKNwEAviozo8HqkLcYIiKiEMUw0gdDo/UYZtLDJQJHiuvkLoeIiCgkMYz0EdcbISIi6huGkT7KTjMBYBghIiLqLYaRPspOjwEg3d7rcnHxMyIiIn8xjPTR2KFG6NVKWFocOFvZIHc5REREIYdhpI/USgUmp0qLn3GohoiIyH8MIwGQ4x6qYRghIiLyH8NIAHgWPzvIMEJEROQ3hpEAmOq+o+Z8VSNqGm3yFkNERBRiGEYCwGTQYGRCJAAO1RAREfmLYSRA+NA8IiKi3mEYCRDOGyEiIuodhpEA8SwLf+RSHWwOl8zVEBERhQ6GkQAZHhcBk0ENq8OFE6VmucshIiIKGQwjAaJQCMjivBEiIiK/MYwEEOeNEBER+Y9hJIA8YeRAYQ1EkQ/NIyIi6gmGkQCanGKCUiGg3GzF5foWucshIiIKCQwjAaTXKDEhOQoA540QERH1FMNIgHknsV6skbkSIiKi0MAwEmCeeSMFRewZISIi6gmGkQDzhJGTpRY0Wh0yV0NERBT8GEYCLNmkx9BoHZwuEUcu1cldDhERUdBjGOkHWVxvhIiIqMcYRvoBn+BLRETUcwwj/SAnw90zUlQHl4uLnxEREXWFYaQfjBsaBZ1agfpmO85XNchdDhERUVBjGOkHaqUCk1NMADhUQ0RE1B2GkX7iXW+EYYSIiKhLDCP9pPWheQwjREREXWEY6SdT3XfUnK9sRE2jTeZqiIiIghfDSD+JidBgeHwEAOAQl4YnIiLqFMNIP+J6I0RERN1jGOlHnMRKRETUPYaRfuQJI0cu1cHudMlcDRERUXBiGOlHI+IjEa1Xo8XuwslSs9zlEBERBSWGkX6kUAjISjMB4FANERFRZxhG+hnnjRAREXWNYaSfZbnDyEGGESIiog4xjPSzySkmKBUCLte34HJds9zlEBERBZ1ehZGNGzciMzMTOp0O2dnZ2Lt3b6fH7tq1C4IgtNu++uqrXhcdSiK0KowbagTAoRoiIqKO+B1Gtm3bhmXLlmHlypU4dOgQZs2ahZtuuglFRUVdnnfq1CmUlpZ6t1GjRvW66FDDxc+IiIg6p/L3hPXr12PRokV46KGHAAAbNmzABx98gE2bNmHt2rWdnpeQkACTydSj97BarbBard7XZrN0W6zdbofdbve35E55rhXIa3ZkckoUAKCgsKbf3yvUDVSbUM+wPYIL2yO4sD2619OfjV9hxGazoaCgAMuXL/fZn5ubi3379nV57tSpU9HS0oLx48fjiSeewA033NDpsWvXrsXq1avb7d+xYwcMBoM/JfdIfn5+wK/ZVr0VAFQ4XlKPv7+7HVplv77doNDfbUL+YXsEF7ZHcGF7dK6pqalHx/kVRqqqquB0OpGYmOizPzExEWVlZR2eM3ToUGzevBnZ2dmwWq14/fXXceONN2LXrl2YPXt2h+esWLECeXl53tdmsxmpqanIzc1FVFSUPyV3yW63Iz8/H/PmzYNarQ7Yda8kiiJ+f2YPyi1WJE+8FtMyY/rtvULdQLUJ9QzbI7iwPYIL26N7npGN7vg9TAMAgiD4vBZFsd0+jzFjxmDMmDHe19OnT0dxcTFefPHFTsOIVquFVqttt1+tVvdLg/fXddvKyYjBf46V4kiJBdeNTuz+hDA3EG1CPcf2CC5sj+DC9uhcT38ufk1gjYuLg1KpbNcLUlFR0a63pCvXXnstzpw5489bhzyuN0JERNQxv8KIRqNBdnZ2u/Gx/Px8zJgxo8fXOXToEIYOHerPW4c870qsRbUQRVHmaoiIiIKH38M0eXl5WLhwIXJycjB9+nRs3rwZRUVFWLx4MQBpvkdJSQlee+01ANLdNhkZGZgwYQJsNhveeOMNvP3223j77bcD+50EufFDo6BVKVDXZMf5qkaMiI+UuyQiIqKg4HcYufPOO1FdXY01a9agtLQUEydOxPbt25Geng4AKC0t9VlzxGaz4dFHH0VJSQn0ej0mTJiA//znP1iwYEHgvosQoFEpMDnFhP0Xa1BwsZZhhIiIyK1XE1iXLFmCJUuWdPi1LVu2+Lz+2c9+hp/97Ge9eZtBJyt9iBRGCmtxx9WpcpdDREQUFPhsmgHUdt4IERERSRhGBlBWmgkAcLaiAXVNNnmLISIiChIMIwMoNlKLzLgIAMChojp5iyEiIgoSDCMDLIsPzSMiIvLBMDLAcjIYRoiIiNpiGBlgnkmsh4vr4HC6ZK6GiIhIfgwjA2xkfCSMOhWa7U58VWaRuxwiIiLZMYwMMIVC4LwRIiKiNhhGZOAZqjnAMEJERMQwIodsPsGXiIjIi2FEBpNTTVAIQEldM0rrm+Uuh4iISFYMIzKI1KowNikKAHCwsE7eYoiIiGTGMCIT73NqOFRDRERhjmFEJnxoHhERkYRhRCaeMHK8pB4tdqfM1RAREcmHYUQmKUP0SDBq4XCJOHqpXu5yiIiIZMMwIhNBEDhvhIiICAwjsmIYISIiYhiRVZZn8bOiWoiiKHM1RERE8mAYkdGE5ChoVArUNNpwoapR7nKIiIhkwTAiI61KiauGRQPgUA0REYUvhhGZZbcZqiEiIgpHDCMyy+IkViIiCnMMIzLz9IycLm9AfbNd5mqIiIgGHsOIzOIitciINQAADnGohoiIwhDDSBDw3uLLoRoiIgpDDCNBgA/NIyKicMYwEgQ8YeRwUR0cTpfM1RAREQ0shpEgMCrBCKNWhUabE1+VWeQuh4iIaEAxjAQBpULAlDQTAK43QkRE4YdhJEjwoXlERBSuGEaCBMMIERGFK4aRIDEl1QSFAFyqbUa5uUXucoiIiAYMw0iQMOrUGJMUBYDrjRARUXhhGAki2ekmAByqISKi8MIwEkS4+BkREYUjhpEgkp0WAwD4sqQeLXanzNUQERENDIaRIJIao0dcpBZ2p4gvS+rlLoeIiGhAMIwEEUEQvPNGDnDeCBERhQmGkSDD9UaIiCjcMIwEGU8YOVhYC1EUZa6GiIio/zGMBJkJydHQKBWobrShsLpJ7nKIiIj6HcNIkNGplZg4TFr8jEM1REQUDhhGglBOhnSLL9cbISKicMAwEoSy0lrnjRAREQ12DCNBKMt9e++pcgvMLXZ5iyEiIupnDCNBKMGoQ1qMAaIIHC6qk7scIiKifsUwEqS43ggREYULhpEglcUwQkREYYJhJEhluyexHiqqhdPFxc+IiGjwYhgJUmOSjIjQKNFoc+JUmUXucoiIiPoNw0iQUioETHX3jnC9ESIiGswYRoJYVjrXGyEiosGPYSSI5XASKxERhYFehZGNGzciMzMTOp0O2dnZ2Lt3b4/O++STT6BSqTBlypTevG3YmZJmgiAARTVNqLC0yF0OERFRv/A7jGzbtg3Lli3DypUrcejQIcyaNQs33XQTioqKujyvvr4e9913H2688cZeFxtuonRqjEk0AgAOFtbJWwwREVE/8TuMrF+/HosWLcJDDz2EcePGYcOGDUhNTcWmTZu6PO9HP/oRvve972H69Om9LjYceeeNcBIrERENUip/DrbZbCgoKMDy5ct99ufm5mLfvn2dnvfqq6/i3LlzeOONN/D00093+z5WqxVWq9X72mw2AwDsdjvs9sA9q8VzrUBeM9CmDIvCmwAOXKwJ6joDJRTaJJywPYIL2yO4sD2619OfjV9hpKqqCk6nE4mJiT77ExMTUVZW1uE5Z86cwfLly7F3716oVD17u7Vr12L16tXt9u/YsQMGg8GfknskPz8/4NcMFHMzAKhwpLgW//z3dqjDZMpxMLdJOGJ7BBe2R3Bhe3SuqampR8f5FUY8BEHweS2KYrt9AOB0OvG9730Pq1evxujRo3t8/RUrViAvL8/72mw2IzU1Fbm5uYiKiupNyR2y2+3Iz8/HvHnzoFarA3bdQBJFERvP7EJNox2pV81AVppJ7pL6VSi0SThhewQXtkdwYXt0zzOy0R2/wkhcXByUSmW7XpCKiop2vSUAYLFYcODAARw6dAiPPPIIAMDlckEURahUKuzYsQNf+9rX2p2n1Wqh1Wrb7Ver1f3S4P113UDJTo9B/olyHCkxY9qIeLnLGRDB3ibhhu0RXNgewYXt0bme/lz86vTXaDTIzs5u1yWVn5+PGTNmtDs+KioKx44dw+HDh73b4sWLMWbMGBw+fBjTpk3z5+3DFp/gS0REg5nfwzR5eXlYuHAhcnJyMH36dGzevBlFRUVYvHgxAGmIpaSkBK+99hoUCgUmTpzoc35CQgJ0Ol27/dS51jBS1+mQGBERUajyO4zceeedqK6uxpo1a1BaWoqJEydi+/btSE9PBwCUlpZ2u+YI+WfSsGiolQKqGqwormlGWmzgJ/ESERHJpVf3ZixZsgQXL16E1WpFQUEBZs+e7f3ali1bsGvXrk7PXbVqFQ4fPtybtw1bOrUSE4dFAwAKimpkroaIiCiwwuRG0dCXncZ5I0RENDgxjISItvNGiIiIBhOGkRDhWRb+VJkZlhau9kdERIMHw0iISIzSIWWIHi4ROFxcJ3c5REREAcMwEkK43ggREQ1GDCMhhGGEiIgGI4aREJLlvqPmcFEdnC5R5mqIiIgCg2EkhIxNMiJCo4TF6sCZCovc5RAREQUEw0gIUSkVmOJ+ai+HaoiIaLBgGAkxXPyMiIgGG4aREONZb+QgwwgREQ0SDCMhZqq7Z+RidROqGqwyV0NERNR3DCMhJlqvxujESADsHSEiosGBYSQEcb0RIiIaTBhGQlAWJ7ESEdEgwjASgjw9I0dL6mF1OGWuhoiIqG8YRkJQZlwEYiI0sDlcOH7ZLHc5REREfcIwEoIEQfAO1XASKxERhTqGkRDFSaxERDRYMIyEKE8YOVBYC1HkQ/OIiCh0MYyEqKtSoqFSCKi0WHGptlnucoiIiHqNYSRE6dRKTBgWDQA4WMShGiIiCl0MIyGMD80jIqLBgGEkhHnnjVxkGCEiotDFMBLCstJNAICvysxosDrkLYaIiKiXGEZC2NBoPYaZ9HCJwJHiOrnLISIi6hWGkRCXxfVGiIgoxDGMhLgchhEiIgpxDCMhzjOJ9WBRLVwuLn5GREShh2EkxI1NMkKvVsLS4sDZyga5yyEiIvIbw0iIUykVmJJqAgD89Yti2J0ueQsiIiLyE8PIIDB3fCIA4E8fX8D8l/bg/S/L+LwaIiIKGQwjg8ADMzKw5tYJiI3Q4HxVIxa/UYDv/v5TFBTWyF0aERFRtxhGBgGlQsB90zOw67E5WPq1kdCpFSgorMV3Nn2Kxa8X4DznkhARURBjGBlEjDo1/l/uGOx+7AbcdXUqFALw/vEyzHtpD37xjy9RabHKXSIREVE7DCODUGKUDs995yq8v2w2bhybAKdLxOufFWLOC//Fr3eeQZONS8cTEVHwYBgZxEYnGvHyA1fjzR9Mw1Up0Wi0ObE+/zTmvLALW/cXwcE7b4iIKAgwjISBGSPi8I8lM/Hru6ciNUaPCosVK945hq//ai8+PFHOO2+IiEhWDCNhQqEQ8M3Jyfgw73r84hvjYTKocbaiAQ+9dgB3bf4Mh/mgPSIikgnDSJjRqpRYdF0mdj92AxZfPwIalQKfX6jBbb/7BI+8eRCF1Y1yl0hERGGGYSRMRevVWH7TWPz30Tn4TlYKBAH499FSzF2/G6vfPY6aRpvcJRIRUZhgGAlzw0x6rLtjMv6zdBZmj46H3Sni1U8u4vrn/4uNu86ixe6Uu0QiIhrkGEYIADA+OQqvPXgNXl90DcYPjYLF6sDz75/CDS/uwlsHiuHkE4GJiKifMIyQj1mj4vHvpdfhpTsnY5hJj9L6Fjz2t6O4+dd7setUBe+8ISKigGMYoXYUCgHfmpqCnf/vevx8wVhE6VT4qsyCB179Ave+/Dm+LKmXu0QiIhpEGEaoUzq1Ej+cPQJ7fnYDfjArExqlAp+crcY3fvMxlv3lEC7VNsldIhERDQIMI9Qtk0GDlTePx87/dz1um5IMAPjH4cv42ou78ez2k6hvsstcIRERhTKGEeqx1BgDNtw1Fe8+ch1mjIiFzenC5j3nMfuF/+KPe87zzhsiIuoVhhHy26SUaPz5oWl49ftXY0yiEfXNdjyz/SRuXLcb/zhUAhfvvCEiIj8wjFCvCIKAG8YkYPtPZuH5716FpCgdSuqasWzbYdzy24/xydkquUskIqIQwTBCfaJUCLgjJxX/fXQOHps/BpFaFY5fNuOeP32O+1/Zj5OlZrlLJCKiIMcwQgGh1yjx8A0jsfuxOXhgRgZUCgG7T1diwa/34tG3juByXbPcJRIRUZBiGKGAio3UYtU3J+DDvOtx81VDIYrA3wou4YYXd+F/3/8K5hbeeUNERL4YRqhfZMRF4Hffy8Lfl8zANRkxsDpc2LTrHK5//r945eMLsDlccpdIRERBgmGE+tXUtCHY9qNr8af7cjAyIRK1TXas+fcJzF2/G+8euczl5YmIqHdhZOPGjcjMzIROp0N2djb27t3b6bEff/wxZs6cidjYWOj1eowdOxYvvfRSrwum0CMIAuaOT8T7P5mFtd+ehHijFkU1TVi69RBu+90n+Ox8tdwlEhGRjPwOI9u2bcOyZcuwcuVKHDp0CLNmzcJNN92EoqKiDo+PiIjAI488gj179uDkyZN44okn8MQTT2Dz5s19Lp5Ci0qpwN3XpGH3Y3OQN280IjRKHLlUj7s2f4ZFW77A6XKL3CUSEZEM/A4j69evx6JFi/DQQw9h3Lhx2LBhA1JTU7Fp06YOj586dSruvvtuTJgwARkZGbj33nsxf/78LntTaHAzaFT48Y2jsOuxG7Dw2nQoFQJ2flWBr2/Yg5X/OI7iBnDhNCKiMKLy52CbzYaCggIsX77cZ39ubi727dvXo2scOnQI+/btw9NPP93pMVarFVar1fvabJbWqrDb7bDbA3c3hudagbwm9ZxJp8CTN4/BvdekYN2HZ7DjRAX+WlACQIVXzu3CzJGxmDUyDteNjEW8USt3uWGJf0eCC9sjuLA9utfTn41fYaSqqgpOpxOJiYk++xMTE1FWVtbluSkpKaisrITD4cCqVavw0EMPdXrs2rVrsXr16nb7d+zYAYPB4E/JPZKfnx/wa5J/bo4Gxk0APrqswOl6ATVNdrx7tAzvHpX+XA0ziBhrkrbhRhEqTr0eUPw7ElzYHsGF7dG5pqaePd3drzDiIQiCz2tRFNvtu9LevXvR0NCAzz77DMuXL8fIkSNx9913d3jsihUrkJeX531tNpuRmpqK3NxcREVF9abkDtntduTn52PevHlQq9UBuy713o/sdrz3QT7ixuTg04v12Hu2CscvW1DSJKCkScDOy4BercC0zBjMGhWHWSNjkRFr6PbPH/UO/44EF7ZHcGF7dM8zstEdv8JIXFwclEplu16QioqKdr0lV8rMzAQATJo0CeXl5Vi1alWnYUSr1UKrbd8tr1ar+6XB++u61DsqBTBjVAKuHz8MywFUNVjx8Zkq7DlTiT2nq1DVYMWu01XYdVp6/k3KED1mjYrH9aPjMGNkHKJ0bMtA49+R4ML2CC5sj8719OfiVxjRaDTIzs5Gfn4+vvWtb3n35+fn49Zbb+3xdURR9JkTQtSVuEgtbps6DLdNHQZRFHGy1OIOJpU4cLEWl2qbsXV/EbbuL4JSIWBqqgmzR8dj9uh4TBoWDaWCvSZERMHM72GavLw8LFy4EDk5OZg+fTo2b96MoqIiLF68GIA0xFJSUoLXXnsNAPC73/0OaWlpGDt2LABp3ZEXX3wRS5cuDeC3QeFCEASMT47C+OQoLL5+BJpsDnx2vhp7Tldhz+lKnK9qxIHCWhworMX6/NMwGdS4bmScFE5GxSMpWif3t0BERFfwO4zceeedqK6uxpo1a1BaWoqJEydi+/btSE9PBwCUlpb6rDnicrmwYsUKXLhwASqVCiNGjMBzzz2HH/3oR4H7LnrL0QKdrUbuKqgPDBoVvjY2EV8bKw0TFtc0Yc+ZSuw9XYVPzlahrsmOfx8txb+PlgIAxiQaMXu0FE6uzoiBTq2Us3wiIkIvJ7AuWbIES5Ys6fBrW7Zs8Xm9dOnSoO0FUXy+CTeefAFCXDlw3U8AtV7ukqiPUmMMuGdaOu6Zlg6H04XDxXXYc7oSu89U4eilOpwqt+BUuQV/3HsBWpUC04bHYvaoOFw/Oh4jEyI5EZaISAa9CiODgihCKPoUSpcN2L0WOPJnIPdpYNw3Af5CGhRUSgVyMmKQkxGDvNwxqG204eOz0nDOnjOVKDdbpc9PV+Lp/5xEcrQOs0ZJc02uGxmHaAMnpBERDYTwDSOCAOdd23Dwz08iu+afEOqKgL/eB2TMAr7+HJA0Ue4KKcCGRGhwy+Rk3DI5GaIo4nR5gzeYfH6hBpfrW7DtQDG2HSiGQgAmp5ow2x1OJqdEQ6Xk4iZERP0hfMMIAAgCSmKmY/Idy6HevxH45FfAxb3AH2YB2Q8ANzwBRMTKXSX1A0EQMCbJiDFJRvxg9nA025zYf7HG21NypqIBh4rqcKioDr/aeQZROhVmeibCjo7HMBOH9IiIAiW8w4iHJgK44efA1HuB/CeB438HDrwCfPk2MGcFcPVDgJJd9oOZXqPE9aPjcf3oeADA5bpm7HWva/Lx2SrUN9vx3pdleO9LaY2dEfER3mBybWYs9BpOhCUi6i2GkbZMacDtW6Tw8d5yoPwY8P5y4MCrwNfXAiNvlLtCGiDJJj3uvDoNd16dBqdLxJFLdd5ek8PFdThX2YhzlY149ZOL0KgUuCYjxnuXzphEIyfCEhH5gWGkIxnXAT/aDRx8Dfjol0DVKeCNbwOjbwLmPwPEjpC7QhpASoWArLQhyEobgmVzR6O+2Y59Z1tXhC2pa8bHZ6UelGe3f4XEKC1mjYrHNRkxmJJmwsj4SCi48BoRUacYRjqjUAI53wcmfAvY/b/A/s3A6feAsx8C05cAsx4FdIF7Tg6Fjmi9GjdNGoqbJg2FKIo4V9nonQj72flqlJut+FvBJfyt4BIAIFKrwlUp0ZicasIU95YYxcXXiIg8GEa6ozdJQzTZDwDvrwDO7ZQmuh7eCsx9Cpj8PUDBuyzClSAIGJkQiZEJkXjwuky02J04cLEWe89W4nBRHY6V1KPB6sC+c9XYd67ae97QaB2mpJq8AWXSsGhEaPnXkYjCE//166n4McC9bwNndkihpOYc8M+Hgf1/BG56HkibJneFFAR0aiWuGxWH60bFAQAcThfOVDTgSHEdDru30+UWlNa3oLS+dUKsQgBGJxq9PSeTU00YnWjkc3WIKCwwjPhDEIDR84HhNwD7/wDs+l+g9DDwSi4w6XZg7mogepjcVVIQUSkVGDc0CuOGRuGua9IAAI1WB46V1ONwcZ03pJTWt+CrMgu+KrPgL18UAwAMGiUmDYv2BpQpaSYkRek4OZaIBh2Gkd5QaYAZS4Gr7gR2rgEOvQEcewv46j/AdXnAjEe4tDx1KkKrwrXDY3Ht8NY1bMrNLd6ek8NFdTh6qQ6NNic+v1CDzy+0Pj8pwaj1BpMpKSZMSomGUcfbzokotDGM9EVkAnDrb4GrF0m3Ahd/Bvz3aekunNxfAuNv5dLy1COJUTrMn5CE+ROSAABOl4hzlQ04XFSHw5ekgHKq3IIKixU7TpRjx4lyANIfr1EJkZic4g4oqSaMSTRytVgiCikMI4GQPBV48H1pkbT8J4H6IuCt+91Ly68FkibJXSGFGKVCwOhEI0YnGnHH1akAgCabA8cvm6WA4u5FKalrxunyBpwub8Bb7rt3dGqFd3jHM0F2mEnP4R0iCloMI4EiCMCk7wJjbpLutvEuLT+bS8tTQBg0KlydEYOrM2K8+yosLThSXO+de3KkuA4WqwNfXKzFFxdrvcfFRWoxJbU1oFyVYkK0nsM7RBQcGEYCjUvL0wBKMOowb7wO88YnAgBcLhHnqxrdPSe1OFJcj5OlZlQ1WPHhyQp8eLLCe+6I+AhMTjVhaqoJU1KHYEySERoVh3eIaOAxjPQX79LyPwDefxwoa7u0/LPAyLlyV0iDkELRuu7Jd7NTAAAtdieOX67H4eJ6b0gprmn2Lmn/zsESAIBGpcDE5ChMSR2CyanRmJo6BElG/hNBRP2P/9L0t4yZwA+vXFr+O1xangaMTq1EdnoMstNbh3eqGqw46p4Ye8g9vGNuceBgUR0OFtV5jxtiUCNercAB8SuMSYrCyIRIjEqIRGykVobvhIgGK4aRgeCztPzz0holnqXlr/0fYPZjXFqeBlRcpBZfG5uIr42VhndEUcSFqkYccQeUw8V1OFFqRm2THbVQ4PRnRT7nx0RoMDI+EiMTIzEyPhKjEqXeGK6DQkS9wTAykPQmaYgm+wHggxVSGNn3a+DIX7i0PMlKEAQMj4/E8PhIfGuqNLxjdThxrLgWb3+4D4ak4Thf1YQzFQ24VNuMmkYb9jfWYP/FGp/rRGpV3mGiUd6PRqQM0fNhgUTUKYYROcSPlpaWP71DCiXVZ7m0PAUdrUqJySnRKEkQseDrY6BWSxOvm2wOnK9sxNmKBpypsLg/NqCwugkNVof3tmPfaykwwtOD0qYnJT02AmquiUIU9hhG5DQ6Fxg+Rxq22f08l5ankGDQqDBxWDQmDov22W9zuHCxuhFnyht8gsr5qkZYHS6cKDXjRKnZ5xyVQkBmXIS3J2WEuydleHwEdGrlQH5bRCQjhhG5tV1a/qNfAgdfb7O0/E+lr3FpeQoBGpXCu1BbW06XiOIaaYjHE1A8W5PN6d7fgPfanCMIQFqMwSegeIZ/Ivl0Y6JBh3+rg0VkAvDN3wA5i6RbgIs+Bf77jBROuLQ8hTClQkBGXAQy4iK866EA0poopeYWqRel3IJzlQ04Uy4Fk/pmOwqrm1BY3eSzNgoAJEfrfAKKZ+hnSIRmoL81IgoQhpFgkzwF+P577ZeWT78OuOk5Li1Pg4ZCIWCYSY9hJj2uHx3v3S+KIqoabO7eEwvOVDR456VUWqy4XN+Cy/Ut2Humyud6cZGaNpNnjd6hn3ijlnf4EAU5hpFg5F1afoF7afkNQOHH0tLyWfcDX3sCiIiTu0qifiEIAuKNWsQbtZg+wvcRCvVNdpytdE+adfeinK1oQEldM6oabKhqqMFn533v8DHqVN47e0bEu7eESKQO0fOBgkRBgmEkmGkMwA0rgKn3tC4tX/AqcPwdLi1PYSnaoG63gBsANFqlO3za3t1zrqIBF6sbYelgMTcAUCsFZMRGtIaUhAiMcN/ezHkpRAOLf+NCQadLy78iPRWYS8tTmIvQqjApJRqTUnzv8LE6nLhQJd2GfK6iEecqG9x3+DSgxe7yTp69UlKUzhtOWntTIrioG1E/YRgJJZ6l5Q+9DuxcA1Sddi8t/3Vg/rNcWp7oClqVEmOTojA2yXeFY5dLxOV69/N5KhpwrtKzNaLSYkWZuQVl5hZ8crba57wIjRLD4yMxIl4KKiPdd/ukxxqgVfFWZKLeYhgJNQqltILr+NvaLC3/PnB2p7Rmyej5wKhcYEi6zIUSBS+FQkDKEANShhh8Js8CQH2zHefdwcTTk3KuUlrUrdHmxLGSehwrqfe9nvtWZM98FE9YGcG7fIh6hGEkVHW0tPzZfGkDgPhxUjAZPR9IuQZQsqmJeiJar8bUtCGYmjbEZ7/N4UJRTVNrL4p72OdcRQMsVgcuVjfhYnUTdn7leytyTITGJ5yMSIjAyHgjhg3RQ8kl8okAMIyEPs/S8hUnpR6S0zuA4s+AypPS9skGQGeS5pWMni99NMR0d1UiuoJGpfDeOtyWKIqobLC6e1Bah33OVzaipE56jk9Now1fXKxtd73hcZ6QEuHuUYlEZlwEIjiBlsIM/8QPFgnjpO26nwJNNcC5j4DTH0g9Jc21wJd/kzZBAaRc7R7OmQ8kTuBiakR9IAgCEow6JBh1mDHC95Z7z3N8zrUZ9jnnXiLf5nDhqzILviqztLumZ2G3EW3mp4xIiMQQHW9FpsGJYWQwMsRI65RM+i7gcgKXvpCCyekPgIrjQPHn0rZzDRCVIj0jZ9R8IHO2dDsxEQVEZ8/xcbpElNQ2t5k42zrsU91o63Rht0itCkNUSnxgOYL0uEikxRiQHmNAaowBySYO+1DoYhgZ7BRKIO1aaZv7FFBXDJzZIQWTC7sB8yXpFuEDrwAqnRRIRuVKPSemNLmrJxqUlAoBabEGpMUacMPYBJ+v1TbacL6qzZwUd69KYXUjGqwONFgFFH9ZDqDc5zyVQkDKED1SYwxIjzUgLcazRSAt1sC1Uyio8U9nuDGlAlcvkjZ7M3BhrzTX5MwOoN4dVM7sALY/CiSMdweTr0tDO5wES9TvhkRokB3RfmE3q8OJc2VmvJO/FwnDx+NSXQuKappQVNOESzXNsDld3km0e8+0v25MhKZNQJGCkOfzpCgdFOxVIRnxt0s4U+ulIZrRuYAotk6CPbNDGsapOCFtPpNgvw6MvJGTYIkGmFalxKjESEyKEbFgRjrU6tbVl50uEeXmFhRWN6HYHVAK3R+La5q8k2hrGm04XFzX7toapQIpMXqfYZ/02AikxRiQGqOHQcNfFdS/+CeMJIIAJI6Xtll50iTYszuBMx9Itw23mwR7TeutwwnjOQmWSEZKhYBkkx7JJn275/kAgLnFjmJ3MCmsbvL2qBTVNKGkVupVOV/ZiPOVjR1eP96o9e1VcfespMcY+CBCCgiGEeqYIQa46nZpczqkSbBnPpBuHa44Lt0+XPwZsHM1EJ3aOs8kc7bU40JEQSNKp8aE5GhMSI5u9zWH04XS+tYhH5/elepGmFscqLRYUWmxoqCwtt35OrUCqUOkeSqp7qDimbOSMsQAnZor01L3GEaoe0oVkD5d2uauck+CdQeTC7uluSYHXpY2lQ7IvL71Dh1TqtzVE1EXVEoFUt1DMzM7+Hp9k9097NPoHfbx9K5crmvu8hk/gPScnzTv0E9rr0qKSY/YSC3vACIADCPUG6ZU6YnBVz8E2JqAi3tbbx02X5KCypkPAPw/IGGCe16KexKsgv9LIgol0QY1JhnaP4QQAOxOF0pqm32GfYqq3fNVqhvRaHN6n/Oz/2JNu/OVCgHxkVokRmmREKVDYpQWSVE69+fS60SjDiaDmkNBgxzDCPWNxtA6d0QUpQmvnmByab80pFNxHPj4JUA/pHUS7IivcRIsUYhTKxXIiItARlxEu6+JoojaJjsKq9v3qBTVNKHc3AKnS/SGFaC+/Ru4aVQKJBiloJIYpUNClNYnrCRE6ZAUrePtyyGMLUeBIwjSiq6JE3wnwZ5+v3US7LG3pE1QAKnTWleCTRjHSbBEg4ggCIiJ0CAmQtPuOT+ANFelutGGsvoWlJtbUG6xosLs/txsRbm5BRUWK2oabbA5XLhU24xLtc1dvmeERnlFWNFJISba3dNilL7GeSzBh2GE+k+nk2A/kHpQij6Vtg9XSZNgR8+HMPxGqB0djz0T0eChUiq8gaErVocTFWYrKiytIaXcLAWXMnd4qTBbYbE60Ghz4nxVI85XdXxXkEe0Xi31qrQdDoqSlvT3fB5v1EKt5PL7A4VhhAZGu0mwRW1Wgt0jTYL94k9QffEnLAAgXnwaSJoIJE5097ZMBGJHcuE1ojCjVSm9E2y70mh1oMLiCStSQGkbVsotLSirb4HV4UJ9sx31zXacLu/8Pz6CAMRGaNuEFq07rLQGlliDEi4x0N9xeOK/7CQPU1oHk2Dfh3j2Iwh1FyFYLgOWy1Jg8VBqgYSxQOIkKaB4wgrnnhCFvQitCplaFTI7mL/iIYoizC0O93BQ27Di7nWxuIOLuQUOl4iqBiuqGqw4ftnc6TUFKLHm6H8xJEKDGIMGJoMGMRFqDDFoMCRCgyGGtp9Lr00GDe8iugLDCMmvzSRYh92OHe++jflTUqCq/goo+xIoPy5t9kag9Ii0tWVM9g0n7EUhog4IgoBovRrRejVGJRo7Pc7lElHTZGvtVfEJK61BpqrBClEUUNtkR22THefR9fBQax3S2i8xERqYDGqfECN9bB9iTAb1oB424r/WFHQcSj3E1GnA8Otad7pcQN3FNuHkS2mrvSj1oFguA2fzW49XaoH4MUDSpNZhnsSJQET71SmJiNpSKATERWoRF6nFhOTOj2tuseKtd99H9vRZsNhE1Dba3MHEhtpGG2qabKhrsqOm0ebdZ25xQBThHSryh1Gn8ulx6SjEmAxqd5iRPteqQmOyLsMIhQaFAogZLm3jv9m6v8UsPVOn/FhrD0r5ccDWAJQdlba2jEN9w0mSpxdFDSIif6iUCkRrgNGJRp9nBXXF4XShrtnuDS41jTbUNfkGl7omm/ujHTVNNtQ32yGKgKXFAUuLA0U1TT2uMUKjbB0iatvj0kGISYsxwKiT599ChhEKbbooIG2atHm4XEBdobv35DhQ5g4qtRcAS6m0nf2w9XilBogf2zpZ1jPcExE38N8PEQ1qKqXC2+vSU06XiPrm1h6X2iZPmHGHmEa7O8y0hpjaJhtcItBoc6LR1v1t0QDw4u2T8d3slL58e73GMEKDj0IBxGRK27hbWvdbLVIviieceMJKZ70okUnt56LEjWIvChENKKWidc0WxPfsHJdLhKXFgZomW4chRtrXNsTYERup6d9vpAsMIxQ+tEYg9Rpp8/D2oniGeNxBpeY80FAmbed2th6vUEu9KEkTfYd7Inv4LwQR0QBQKAREG9SINqiRic7vMAoWDCMU3nx6Ub7Rut/a4DsXxTNx1mZx7zvme53IxPZzUWKG8wnGREQ9wDBC1BFtJJB6tbR5iGJrL0rZl6139NRcABrKpe3cR77XMSYDQzKksDMkAxiS2fraEMsl8ImIwDBC1HOC4A4UGcDYm1v3WxuAyq/az0WxmltvOy7a1/56GqP7euntw4opjXNTiChsMIwQ9ZU2EkjJkTYPUZQeFFh7QVoLpcb90fPaXNL5kA8gPUgwOsW3J6VtWNGbBuAbIyIaGL0KIxs3bsQLL7yA0tJSTJgwARs2bMCsWbM6PPadd97Bpk2bcPjwYVitVkyYMAGrVq3C/Pnz+1Q4UVATBGmBtYhY35DiYW+Rns/TWVhxuL9eVwRc2N3+fP2Q9sM+ntdRyYAiNBY6IiICehFGtm3bhmXLlmHjxo2YOXMm/vCHP+Cmm27CiRMnkJaW1u74PXv2YN68eXj22WdhMpnw6quv4pZbbsHnn3+OqVOnBuSbIAo5ah0QP1rariSKgKXMN5y0DSyNFUBzrbRdPtT+fKVGGubpMKxkAJrgn1lPROHF7zCyfv16LFq0CA899BAAYMOGDfjggw+wadMmrF27tt3xGzZs8Hn97LPP4p///CfeffddhhGijggCEDVU2tKnt/+6tUGaSHtlb0rNBaknxWkDqs9KW0ciEjqeUDskQ7oriJNqiWiA+RVGbDYbCgoKsHz5cp/9ubm52Levgwl6HXC5XLBYLIiJ6fxJq1arFVar1fvabJaemGi322G3+7eWf1c81wrkNalv2CY9oNACMaOl7UouJ2C5DMHdmyLUFUKovQDUFUKovQihpU7qWWmsAIo/b3e6qNIDQ9IhmtIhDsmAaExFYn01HJdTgdgMaa0Wkg3/fgQXtkf3evqz8SuMVFVVwel0IjEx0Wd/YmIiysrKenSNdevWobGxEXfccUenx6xduxarV69ut3/Hjh0wGAz+lNwj+fn53R9EA4ptEgix0qbLApIAJAFqRyMMtgpEWCsRYS2HwVaJCGsFDLYKGGzVEBzNQOVXECq/AgAoAVwLAOdfAgDYlBFo0sShSROHZvfHtq/tSgN7VgYA/34EF7ZH55qaevYcnV5NYBWu+MdGFMV2+zqydetWrFq1Cv/85z+RkJDQ6XErVqxAXl6e97XZbEZqaipyc3MRFRXVm5I7ZLfbkZ+fj3nz5vX4IUfUv9gm8nE4bUD9JakHpe4iUHsRYs15NFw6iWixHkJLHTTORmiaG2FqLuzwGqLWCESnQnRv8H5MgRidxrVV+oh/P4IL26N7npGN7vgVRuLi4qBUKtv1glRUVLTrLbnStm3bsGjRIrz11luYO3dul8dqtVpote0fIqRWq/ulwfvrutR7bBMZqNWAbgyQOMa7y263Y/f27ViwYAHUzmagvhioK5bmptS77/bxvG6qgmC1ABUnIFSc6OQ9DEB0KmBKlSbZRrs/eraIBGlVXOoS/34EF7ZH53r6c/ErjGg0GmRnZyM/Px/f+ta3vPvz8/Nx6623dnre1q1b8eCDD2Lr1q24+eabOz2OiIKYLgrQTZCWve+IrQmov9RxUKkvlp6WbG8Cqk5JW0eUmivCiieouF8bh/K2ZaJByO9hmry8PCxcuBA5OTmYPn06Nm/ejKKiIixevBiANMRSUlKC1157DYAURO677z786le/wrXXXuvtVdHr9YiOjg7gt0JEstIYOr9dGQAc1jZhpbh9WDGXSHcC1ZyTto4oVEDUsNaeFG/Pivtj1DCuXEsUgvwOI3feeSeqq6uxZs0alJaWYuLEidi+fTvS09MBAKWlpSgqKvIe/4c//AEOhwMPP/wwHn74Ye/++++/H1u2bOn7d0BEoUGlBWJHSFtHnHbAfLmDsFIova6/BLgc0uu6juesQFBIvScdBZXoVGlBOK6zQhR0ejWBdcmSJViyZEmHX7syYOzatas3b0FE4Uaplp7TMyS946+7nNJicN6wUujbs1JXDDitUg+LuQTApx1fRxct9aBEJbu3Dj7XsdeWaCDx2TREFBoUSiB6mLShg8XgXC6gsbLzOSv1lwBbA9BSL22dTbIFpIcYdhVWopKlJfl5ZxBRQDCMENHgoFAAxkRpS72642NazNJQkLnE/bGDz1vqpIcYdjXRFgBU+m4CyzAgIo6BhagHGEaIKHzooqQtYWznx9gaAXNpm5DSQXBpqgIczV1PtgWku4O66l2JGgZExPMOIQp7DCNERG1pIoC4kdLWGXsLYLncee+K+TLQUCHdHeR50GFnFCpp0m1XvSyRSYCS/1zT4MU/3URE/lLrgJjh0tYZhw1oKOt6WMhSKt0hVF8sbZ0RFEBkIpTGoZjW4ITyb38FNHpApXNvWumjWtdmX2df03b8dZWWQ0okG4YRIqL+oNK0rofSGadDemihT0hpE1bqS6QeGJcDsJRCYSlFEgCYj/RTzZ5wovcNKeorXnf59SuCTmfnao2cBExeDCNERHJRqlqHZJDT8TGeu4TMJXDUFuPo/r2YPH4UlKIDcLRIQ0aOFmlROUdLm80K2Jvb7LdK81w8rz3nQWx9L8+5qB+Abx7SnJrIxNbNmCgNSUUmAMakNl9L4GJ2gxzDCBFRMGtzl5CYMAnF54BJ2QugDMSzUERRWmzuyhDTLuQ0+4adTr92ZQDq5ppOW/dDVAAAQXrIotEdVCKT3MHFE2LaBBdtZN9/LjTgGEaIiMKVIEjDSSoNgMA9Eb1H7C3SEJWlXJpb01De5vMKaYG7hnLpc9Ep3cHUVAWUd3NdTWTXPS2ej/oYPpQxiDCMEBHRwFPrup9TA0jDVE3VUkixlLsDStvPy1uDi71JWtiupqHrW64B6S4mzxCQt6elbXDx9MIkusMa9SeGESIiCl4KBRAZL21Jk7o+1mrpILBc2dNSLoUbl6PNowO6oY/x7WlxDxEJ+jjEWc5DuDwUiIiRJuVqjdKkXU7M9QvDCBERDQ6eMNDVGjGAdNt1Y4Xv0FC7nhb31112oLlG2ipP+lxGBWAmAJx9zvf6glKau6KNaq1Ja5SGkLTGK/Z3tK/NsWEycZdhhIiIwotKA0SnSFtXXC6gubbToSGXpRSNFYWIVAOCrRGwmgGI0hwXzzOQ+lyr/org0suAozYEdW8NwwgREVFHFAogIlbaEse3+7LTbsdH27djwYIFUKvVUnixN0nDRZ7NZvF9bTUD1oYr9rn329rsd7RIb+JolrbGir59L4JCegCk9sqtTXC56g4geWrf3qeXGEaIiIgCQaFw/3KPBDC0b9dy2Nzh5MrwYnaHnA72WS0dBB1Pb40LsNZLW2dSchhGiIiIyE2lAVQxgCGmb9cRxSt6a8wdhBb3vvhxgam9FxhGiIiIBitBkB7+qImQblkOUlzxhYiIiGTFMEJERESyYhghIiIiWTGMEBERkawYRoiIiEhWDCNEREQkK4YRIiIikhXDCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlkxjBAREZGsGEaIiIhIViHx1F5RFAEAZrM5oNe12+1oamqC2WyGWq0O6LWpd9gmwYXtEVzYHsGF7dE9z+9tz+/xzoREGLFYLACA1NRUmSshIiIif1ksFkRHR3f6dUHsLq4EAZfLhcuXL8NoNEIQhIBd12w2IzU1FcXFxYiKigrYdan32CbBhe0RXNgewYXt0T1RFGGxWJCcnAyFovOZISHRM6JQKJCSktJv14+KiuIfpCDDNgkubI/gwvYILmyPrnXVI+LBCaxEREQkK4YRIiIiklVYhxGtVounnnoKWq1W7lLIjW0SXNgewYXtEVzYHoETEhNYiYiIaPAK654RIiIikh/DCBEREcmKYYSIiIhkxTBCREREsmIYISIiIlmFdRjZuHEjMjMzodPpkJ2djb1798pdUlhau3Ytrr76ahiNRiQkJOC2227DqVOn5C6L3NauXQtBELBs2TK5SwlrJSUluPfeexEbGwuDwYApU6agoKBA7rLCksPhwBNPPIHMzEzo9XoMHz4ca9asgcvlkru0kBW2YWTbtm1YtmwZVq5ciUOHDmHWrFm46aabUFRUJHdpYWf37t14+OGH8dlnnyE/Px8OhwO5ublobGyUu7Sw98UXX2Dz5s246qqr5C4lrNXW1mLmzJlQq9V47733cOLECaxbtw4mk0nu0sLS//7v/+L3v/89fvvb3+LkyZN4/vnn8cILL+A3v/mN3KWFrLBdZ2TatGnIysrCpk2bvPvGjRuH2267DWvXrpWxMqqsrERCQgJ2796N2bNny11O2GpoaEBWVhY2btyIp59+GlOmTMGGDRvkLissLV++HJ988gl7b4PEN77xDSQmJuLll1/27vvOd74Dg8GA119/XcbKQldY9ozYbDYUFBQgNzfXZ39ubi727dsnU1XkUV9fDwCIiYmRuZLw9vDDD+Pmm2/G3Llz5S4l7P3rX/9CTk4Obr/9diQkJGDq1Kn44x//KHdZYeu6667Dzp07cfr0aQDAkSNH8PHHH2PBggUyVxa6QuKpvYFWVVUFp9OJxMREn/2JiYkoKyuTqSoCpMdN5+Xl4brrrsPEiRPlLids/eUvf8HBgwfxxRdfyF0KATh//jw2bdqEvLw8/PznP8f+/fvx4x//GFqtFvfdd5/c5YWdxx9/HPX19Rg7diyUSiWcTieeeeYZ3H333XKXFrLCMox4CILg81oUxXb7aGA98sgjOHr0KD7++GO5SwlbxcXF+MlPfoIdO3ZAp9PJXQ4BcLlcyMnJwbPPPgsAmDp1Ko4fP45NmzYxjMhg27ZteOONN/Dmm29iwoQJOHz4MJYtW4bk5GTcf//9cpcXksIyjMTFxUGpVLbrBamoqGjXW0IDZ+nSpfjXv/6FPXv2ICUlRe5ywlZBQQEqKiqQnZ3t3ed0OrFnzx789re/hdVqhVKplLHC8DN06FCMHz/eZ9+4cePw9ttvy1RReHvsscewfPly3HXXXQCASZMmobCwEGvXrmUY6aWwnDOi0WiQnZ2N/Px8n/35+fmYMWOGTFWFL1EU8cgjj+Cdd97BRx99hMzMTLlLCms33ngjjh07hsOHD3u3nJwc3HPPPTh8+DCDiAxmzpzZ7nb306dPIz09XaaKwltTUxMUCt9fn0qlkrf29kFY9owAQF5eHhYuXIicnBxMnz4dmzdvRlFRERYvXix3aWHn4Ycfxptvvol//vOfMBqN3h6r6Oho6PV6masLP0ajsd18nYiICMTGxnIej0x++tOfYsaMGXj22Wdxxx13YP/+/di8eTM2b94sd2lh6ZZbbsEzzzyDtLQ0TJgwAYcOHcL69evx4IMPyl1a6BLD2O9+9zsxPT1d1Gg0YlZWlrh79265SwpLADrcXn31VblLI7frr79e/MlPfiJ3GWHt3XffFSdOnChqtVpx7Nix4ubNm+UuKWyZzWbxJz/5iZiWlibqdDpx+PDh4sqVK0Wr1Sp3aSErbNcZISIiouAQlnNGiIiIKHgwjBAREZGsGEaIiIhIVgwjREREJCuGESIiIpIVwwgRERHJimGEiIiIZMUwQkRERLJiGCEiIiJZMYwQERGRrBhGiIiISFb/H1jfZeXB+3syAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(history.history['loss'], label='Training')\n",
    "pl.plot(history.history['val_loss'], label='Testing')\n",
    "pl.legend()\n",
    "pl.grid()\n",
    "\n",
    "score = model.evaluate(X_test_hog, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axdxLHYMbahu"
   },
   "source": [
    "Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "Mhm1wjNfbahv",
    "outputId": "8366db2a-2162-4c03-d1d3-de0a4114c3cb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-score 0: 0.9678735339112697\n",
      "F-score 1: 0.9796819787985865\n",
      "F-score 2: 0.942495126705653\n",
      "F-score 3: 0.9159622078567877\n",
      "F-score 4: 0.9475806451612903\n",
      "F-score 5: 0.9231619679380874\n",
      "F-score 6: 0.9517819706498952\n",
      "F-score 7: 0.9287469287469287\n",
      "F-score 8: 0.9080694586312563\n",
      "F-score 9: 0.9147670961347869\n"
     ]
    }
   ],
   "source": [
    "pred = np.argmax(model.predict(X_test_hog), axis=-1)\n",
    "cm = me.confusion_matrix(y_test, pred)\n",
    "\n",
    "plot_confusion_matrix(cm, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
   ]
  }
 ],
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