{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NeuronalNwetworksWithKeras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "VG8KMnw1NSqD",
        "colab_type": "code",
        "outputId": "9ddd0a6e-0e61-4982-c323-67e490cfebf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from numpy.random import seed\n",
        "import tensorflow as tf\n",
        "from keras import backend as k\n",
        "import os"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "PBDb1J6CozWX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Obteniendo las imagenes"
      ]
    },
    {
      "metadata": {
        "id": "rRRTa3oYDmiJ",
        "colab_type": "code",
        "outputId": "a19d1635-2224-47e0-9a79-43db9c423d9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "cell_type": "code",
      "source": [
        "!wget https://github.com/vincent1bt/tesla-cars-dataset/archive/master.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-11-02 00:18:15--  https://github.com/vincent1bt/tesla-cars-dataset/archive/master.zip\n",
            "Resolving github.com (github.com)... 192.30.255.113, 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://codeload.github.com/vincent1bt/tesla-cars-dataset/zip/master [following]\n",
            "--2018-11-02 00:18:16--  https://codeload.github.com/vincent1bt/tesla-cars-dataset/zip/master\n",
            "Resolving codeload.github.com (codeload.github.com)... 192.30.255.121, 192.30.255.120\n",
            "Connecting to codeload.github.com (codeload.github.com)|192.30.255.121|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘master.zip’\n",
            "\n",
            "master.zip              [ <=>                ] 188.48M  22.5MB/s    in 8.8s    \n",
            "\n",
            "2018-11-02 00:18:25 (21.4 MB/s) - ‘master.zip’ saved [197632877]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rCk125NXYbKv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!unzip -qq master.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AVuElBkyYppO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p validation_images/tesla_model_3 && mkdir validation_images/tesla_model_s && mkdir validation_images/tesla_model_x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dk3JLzQ3h10y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "validation_set_size = 30"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WwfPNiZYh-CA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def move_images(from_path, to_path):\n",
        "  files = os.listdir(from_path)\n",
        "  folder_size = len(files)\n",
        "  first_index = folder_size - validation_set_size\n",
        "  files_to_move = files[first_index:]\n",
        "  \n",
        "  for file_name in files_to_move:\n",
        "    source_file_name = from_path + file_name\n",
        "    destination_file_name = to_path + file_name\n",
        "    shutil.move(source_file_name, destination_file_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FmCYCsArj8r2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "move_images(\"./tesla-cars-dataset-master/tesla-model-3/\", \"./validation_images/tesla_model_3/\")\n",
        "move_images(\"./tesla-cars-dataset-master/tesla-model-s/\", \"./validation_images/tesla_model_s/\")\n",
        "move_images(\"./tesla-cars-dataset-master/tesla-model-x/\", \"./validation_images/tesla_model_x/\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4_ekhtoAnDKm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mv tesla-cars-dataset-master training_images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yfi7Cv4_nkCv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mv training_images/tesla-model-3 training_images/tesla_model_3\n",
        "!mv training_images/tesla-model-s training_images/tesla_model_s\n",
        "!mv training_images/tesla-model-x training_images/tesla_model_x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zWfuyZkHnQYy",
        "colab_type": "code",
        "outputId": "503b1e62-6110-4b95-f231-b6fbf7bd2c1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls training_images"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tesla_model_3  tesla_model_s  tesla_model_x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4xM6h6EAo-G2",
        "colab_type": "code",
        "outputId": "d6c8feee-9d50-4656-ccf7-3d6e2af7acdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "master.zip  sample_data  training_images  validation_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9l1wFVY3o2u0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Preprocesar las imagenes"
      ]
    },
    {
      "metadata": {
        "id": "qZJS4I2uccxc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "img_height = 64\n",
        "img_width = 64\n",
        "\n",
        "def load_images(paths):\n",
        "  X = []\n",
        "  y = []\n",
        "  \n",
        "  for path in paths:\n",
        "    images_paths = os.listdir(path)\n",
        "    \n",
        "    for image_path in images_paths:\n",
        "      complete_path = path + image_path\n",
        "      image = load_img(complete_path, target_size=(img_height, img_width))\n",
        "      image_array = img_to_array(image)\n",
        "      X.append(image_array)\n",
        "      label = paths.index(path)\n",
        "      y.append(label)\n",
        "  \n",
        "  return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7Ye6KPhIgldA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "training_paths = [\"training_images/tesla_model_3/\", \"training_images/tesla_model_s/\", \"training_images/tesla_model_x/\"]\n",
        "validation_paths = [\"validation_images/tesla_model_3/\", \"validation_images/tesla_model_s/\", \"validation_images/tesla_model_x/\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CNV33bBlcnvz",
        "colab_type": "code",
        "outputId": "04a47fbd-cfcc-440f-bab5-b6f8ec9ccd66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "X_train, y_train = load_images(training_paths)\n",
        "X_val, y_val = load_images(validation_paths)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/Image.py:872: UserWarning: Palette images with Transparency   expressed in bytes should be converted to RGBA images\n",
            "  'to RGBA images')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "8F1mc8ZZdouT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = np.array(X_train)\n",
        "X_val = np.array(X_val)\n",
        "\n",
        "y_train = np.array(y_train)\n",
        "y_val = np.array(y_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CGiGfX7jjmwc",
        "colab_type": "code",
        "outputId": "cc30d95d-9559-4a5c-ca58-a2f17d00690a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape, X_val.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((356, 64, 64, 3), (90, 64, 64, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "cJVb71UXjyBO",
        "colab_type": "code",
        "outputId": "e748c7c7-e827-4d6a-e696-8a56533729c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "y_train.shape, y_val.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((356,), (90,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "B7Zsgzv6kQ8a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train)\n",
        "y_val = to_categorical(y_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cBsUi05AmLkt",
        "colab_type": "code",
        "outputId": "5a88be9c-db47-4f63-f6fe-62669e9dd017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "y_train.shape, y_val.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((356, 3), (90, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "bCQu0AT_ncvG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train.astype('float32') / 255\n",
        "X_val = X_val.astype('float32') / 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VeAglh04nvQW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "second_shape = 64 * 64 * 3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h7YdB5lNmO61",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], second_shape)\n",
        "X_val = X_val.reshape(X_val.shape[0], second_shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tcgvqw9rnSH0",
        "colab_type": "code",
        "outputId": "661d1364-76e1-4ef7-dcaf-f8ecb276bef2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape, X_val.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((356, 12288), (90, 12288))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "odB2c5FYnfre",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units=64, input_shape=(12288,)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(units=32))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(units=3, activation=\"softmax\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BNZDRUbyptiu",
        "colab_type": "code",
        "outputId": "d12592a4-63da-4d62-9b3a-635dbe20fd84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 64)                786496    \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 3)                 99        \n",
            "=================================================================\n",
            "Total params: 788,675\n",
            "Trainable params: 788,675\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QZpBEdBGpWiq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tpdfFessqOUL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs = 10\n",
        "batch_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aqojMybrpol7",
        "colab_type": "code",
        "outputId": "01fef2b6-ea13-46c4-cca6-de28f1db7d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "cell_type": "code",
      "source": [
        "model_train = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_val, y_val))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 356 samples, validate on 90 samples\n",
            "Epoch 1/10\n",
            "356/356 [==============================] - 0s 312us/step - loss: 0.7978 - acc: 0.6545 - val_loss: 1.1649 - val_acc: 0.3667\n",
            "Epoch 2/10\n",
            "356/356 [==============================] - 0s 278us/step - loss: 0.8469 - acc: 0.6011 - val_loss: 1.3855 - val_acc: 0.4222\n",
            "Epoch 3/10\n",
            "356/356 [==============================] - 0s 274us/step - loss: 0.9369 - acc: 0.5787 - val_loss: 1.4823 - val_acc: 0.3556\n",
            "Epoch 4/10\n",
            "356/356 [==============================] - 0s 256us/step - loss: 0.9227 - acc: 0.5843 - val_loss: 1.2405 - val_acc: 0.3889\n",
            "Epoch 5/10\n",
            "356/356 [==============================] - 0s 282us/step - loss: 0.9933 - acc: 0.5478 - val_loss: 1.3135 - val_acc: 0.4444\n",
            "Epoch 6/10\n",
            "356/356 [==============================] - 0s 279us/step - loss: 0.9345 - acc: 0.5955 - val_loss: 1.5068 - val_acc: 0.4000\n",
            "Epoch 7/10\n",
            "356/356 [==============================] - 0s 266us/step - loss: 0.9140 - acc: 0.5787 - val_loss: 1.1604 - val_acc: 0.4778\n",
            "Epoch 8/10\n",
            "356/356 [==============================] - 0s 301us/step - loss: 0.8785 - acc: 0.6096 - val_loss: 1.1919 - val_acc: 0.4222\n",
            "Epoch 9/10\n",
            "356/356 [==============================] - 0s 271us/step - loss: 0.6228 - acc: 0.7809 - val_loss: 1.1673 - val_acc: 0.4333\n",
            "Epoch 10/10\n",
            "356/356 [==============================] - 0s 320us/step - loss: 0.5851 - acc: 0.8230 - val_loss: 1.1123 - val_acc: 0.4111\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Uc0272H74XmL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8fb8db5f-de13-435d-b588-3c20f3829069"
      },
      "cell_type": "code",
      "source": [
        "np.arange(epochs, step=1)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "yUUL9hBHqiM2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_loss_and_accuracy(model_train):\n",
        "  accuracy = model_train.history['acc']\n",
        "  val_accuracy = model_train.history['val_acc']\n",
        "  loss = model_train.history['loss']\n",
        "  val_loss = model_train.history['val_loss']\n",
        "  epochs = range(len(accuracy))\n",
        "  plt.plot(epochs, accuracy, 'b', label='Training accuracy')\n",
        "  plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')\n",
        "  plt.ylim(ymin=0)\n",
        "  plt.ylim(ymax=1)\n",
        "  plt.xlabel('Epochs ', fontsize=16)\n",
        "  plt.ylabel('Accuracity', fontsize=16)\n",
        "  plt.title('Training and validation accuracy', fontsize = 20)\n",
        "  plt.legend()\n",
        "  plt.figure()\n",
        "  plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "  plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "  plt.xlabel('Epochs ', fontsize=16)\n",
        "  plt.ylabel('Loss', fontsize=16)\n",
        "  plt.title('Training and validation loss', fontsize= 20)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jLH5fnmwrGbI",
        "colab_type": "code",
        "outputId": "397f45fc-e5c0-43ce-8611-d269737b827e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        }
      },
      "cell_type": "code",
      "source": [
        "plot_loss_and_accuracy(model_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wrpc88NPj4xhkZIjmjH5uk2t41qxZY7GfPXv2MGLEiFTtT4KCgrh9+zaTJ0/Gzc2NJk2a\nWB2byBypchfPzMHBgYCAALZv387QoUOpV68e165dY8WKFUybNo2+ffty6NAhQkNDLQa8yAm1atXC\n39+fn3/+mY8++og6depw69YtVq9eTYsWLVINYpOWwMBAjh49yqBBg+jYsSNRUVGsWLGC9u3bo1ar\n+eOPP/jmm29o3LhxhvoMe3l50aZNG9auXUufPn1o0qQJCQkJ/Pjjj5QoUYKrV69ava9WrVqxatUq\n5s2bR+HChalVq5bF882bN2fDhg2MHj2anj17YjQaWbt2LS+//DJ58+Zly5YtzJo1i5YtWz61/39G\nYy9atCiVK1fmyJEjTJw4kUqVKnH27Fk2btzIlClT6Nu3Lzt37sTX15fAwMCUxlzLly8nISEhzd4M\nAJ06dWLTpk3MmTOHe/fuUblyZcLCwli7di3R0dHMmjUr5cr2WWX0HIKDg1NiCw8Pp2LFipw9e5bv\nv/+el19+OaXXhbXrNWzYkPnz5zNlyhTCwsJwd3fn119/5Z9//rF6WOWMfm4DAwNZs2YNq1evJikp\niVq1anH9+nUWL15MsWLF6Natm8X+W7duzbRp0/jjjz9o27YtLi4uWVL2In1yhS6yxLhx42jdujWH\nDh1i/PjxHDt2jLlz5/Laa6/Rv39/DAYDU6dOJTIyMkfjVKlUfP311zRv3pz9+/cTEhKSEmvygCXJ\nV4Tp6dChAx988AEPHz7ks88+IzQ0lD59+tCzZ0/69++Pl5cX8+fP58yZMxmOb+zYsfTs2TNlTP1l\ny5YREBDAkCFDMrSfGjVqULhwYR4+fEiLFi1SndMbb7zB2LFjAQgJCWHJkiU0b96cUaNG0aNHD4oV\nK8by5cs5ePBglseuUqmYOXMmDRo0YNOmTUyaNImrV6+yZMkS6tevT/v27bl//z4zZszAaDRSo0YN\n2rRpw82bN5k3b166XdocHR1ZunQpXbt2Zf/+/YwePZqvv/4aHx8flixZQkBAQIbK8Ekyeg4uLi4s\nW7aMjh07smPHDkaOHMmWLVvo2rUrixYtSvmhYe16lSpVYvbs2Xh5eTFz5kymT5+OTqfjm2++yVDD\n0ox8blUqFfPnz6d///4cO3aMTz/9lBUrVtC8eXNWr16dqkuch4cHDRo0ALC626V4NipFsWJMSSFe\nAJMnT+a7777jm2++4Y033sjpcITI1UwmEy1atMDZ2Zn169fndDgvBLlCFy+UhIQEPvroI0aMGGGx\nXK/Xs337dhwcHDI9rasQ4j+rVq3i8uXL6XblFFnP5vfQz58/T//+/enWrRvBwcEWzx08eJAZM2ag\n0WioV68e77//vq3DE3YuuUVyaGgokZGRNG7cmISEBNauXcudO3fo1auXzLEtRCbFxcWxb98+/vzz\nT5YtW0adOnVSjcooso9Nq9zj4uLo27cvJUqUwM/PL1VCb968OQsXLqRQoUIEBwczfvx4m825LF4c\nBoOBRYsWsXHjRu7cuYPJZKJUqVIEBQXRqVMnGclKiEy6c+dOSmPQRo0aMWbMmJReEyL72TShG41G\njEYj//vf//Dw8LBI6Ddu3OCTTz5h5cqVACxYsACdTpdqekQhhBBCpGbTe+harTbdISzv379vUdXp\n6emZqQEuhBBCiBdRrm4UZzRmfphNIYQQwp48NwPLFCxYkAcPHqQ8vnv3LgULFnziNuHhcVkag5eX\nO/fvR2fpPkXapKxtQ8rZNqScbUPK2czLK+2JpJ6bK/SiRYsSExPDzZs3MRqN/PTTT9StWzenwxJC\nCCFyBZteoZ86dYopU6Zw69YttFotO3bsoGHDhhQtWpSAgADGjRvHRx99BJhbvCfPYSyEEEKIJ8vV\nI8VlddWLVOfYjpS1bUg524aUs21IOZs991XuQgghhMg8SehCCCGEHZCELoQQQtgBSehCCCGEHXhu\n+qELIYTIHebM+ZK//z5LWNhDEhIS8Pb2IU+evEyaNPWp227duhlXVzfeeKNBms/PmjWdtm074O3t\nk9Vh2z1p5f4IaUFpO1LWtiHlbBsvajlv3bqZy5cvMWDAIJsc70Ut58el18pdrtCFEEJkid9/P8YP\nPywjLi6OAQMG88cfx9m3bw8mk4lXX61Ljx59WLhwAfny5aNkydKEhq5GpVJz7doV6tdvRI8efRgw\noA9DhnzCTz/tITY2huvXr3Hr1k0GDvyI1q2bsWzZYnbv3om3tw9Go5EOHTpTrVqNlBiOHv2Nb7/9\nGgcHB9zd3Rk/fjIODg7MnDmNM2dOodFo+PjjEZQqVSbVsoiICEJDVzNx4hcAtGjRiB9/3MOAAX0o\nVao0AMHB3ZgwYQxgnnBs1KjP8PEpyvbtP7J27SpUKhUdOnQmKiqKBw/u07v3ewAMGtSfAQMGU6ZM\n2Wwrf0noQgiRi40b58TmzVn7Vd6qlZFx4/SZ2vbSpYusXBmKo6Mjf/xxnHnzvkWtVtOu3Zu0b9/J\nYt0zZ06zYsU6TCYTbdu2okePPhbP37t3l2nTZnP48EE2blxHvXq1CQ1dw8qV64iNjaVDhzZ06NDZ\nYpvo6GjGjp2It7cPEyaM4bffDuHk5MS9e3f55pvFnDjxO3v27OLhw4epllWv/kq651WqVGneeiuI\ns2dP0717b6pVq8GWLRsJDV1Dz559WLz4W5YsWUliooGQkLGMHDmWAQP60Lv3e8TExBAVFZmtyRwk\noQshhMhCZcqUxdHREQBnZ2cGDOiDRqMhIiKCqKgoi3X9/MqlOwMnQOXKLwPmuT5iYmK4fv06pUqV\nxsnJGScnZ8qXr5hqm3z58jFlykSSkpK4ffsW1au/Qnh4GJUqVQHg5Zer8fLL1Vi+fEmqZb//fizd\nWMqX9wfA0zM/M2dOY+HCBURHR+HnV56rV69QvHiJlLgmT54BQNGixfn773Ncv36VBg0aW1uEmSYJ\nXQghcrFx4/SZvprODg4ODgD8888dVq1azqJFy9HpdHTp0i7VuhqN5on7evR5RVFQFAW1+r/OWSpV\n6m0+/3wCU6fOpESJksyYMQUAtVqDopgs1ktrmeqxHRqNxkfOy5wuFy5cQK1atXnrrSB++mk3Bw/+\nmua+AJo1a8FPP+3mn3/u0Lfv+08816wg3daEEEJkuYiICDw8PNDpdPz99zn++ecfDAbDM+3Tx8eH\ny5cvYTQaCQ8P59y5s6nWiY2NoVChwkRHR/P778cxGAyUL18h5er7/PlzTJ8+Jc1lrq6uPHxonvXz\n4sULxMWlntEzIiICH5+iKIrCr7/ux2Aw8NJLJbh+/RpxcXHo9XoGDeqPoii8+mpdTp78nZiYaIoU\n8X6mc7eGXKELIYTIcmXL+uLiouO993pQqdLLvPlmG6ZPn0LlylUyvc8CBQoQENCM3r278tJLJalQ\noWKqq/w2bdry3ns9KVasOJ07d2XRom+YP38RL71Ukv79ewHw0UfDKV26DL/8st9iWcmSpXB2dqFf\nvx5UqlSFwoVTJ+E332zDl19OpXBhb4KC2vPFFyH89ddJevbsx6BB/QFo374TKpUKBwcHXnqpJH5+\n5TN9zhkh3dYeIV0ibEfK2jaknG1Dytk2vLzcWbJkBQEBzdBoNHTt2oEZM+ZQsGChnA4tTXq9nvff\n783MmfNwc3PLsv1KtzUhhBC53sOHD+nT510cHBxp0qTZc5vMT536i6lTJ9GpU5csTeZPIlfoj5Bf\n2bYjZW0bUs62IeVsG1LOZjJ9qhBCCGHHJKELIYQQdkASuhBCCGEHJKELIYQQdkASuhBCiAzp27d7\nqkFdvv56LitXLktz/d9/P8aoUZ8AMHz4kFTPr1u3ioULF6R7vIsXL3D9+jUAxo4dgV6fkNnQ7Zok\ndCGEEBkSENCUvXt3WSzbt28vjRs3eeq2yeOcZ8T+/Xu5ceM6AJ999jlOTumP//4ik37oQgghMqRR\noya8915P+vcfCMC5c2fx8vLCy6tgmtOXPip5StJjx44we/Z0PD3zkz9/gZTpUENCxnH//j3i4+Pp\n0aMPhQsXYePGUPbv30upUkX54IOBLF26ipiYaD7/fDwGgwG1Ws3w4aNRqVSEhIzD29uHixcv4Ovr\nx/Dhoy2Ov3PnNtauXYVGo6ZEidIMG/YpRqORiRPHcvfuHRwdnRg16jM8PDxTLTt69LeU+d/j4uLo\n2rU9a9dupkOHt6lduy4eHh7UqfM6M2ZMQavVolarmTBhMnny5GX58iXs27cHlUpNv34DOHz4IMWL\nF6dly7cACA5uy1df/Y+8efNl+nWRhC6EELmY67hROG3ekKX71Ld6i9hxE9N93sPDE29vH86cOUWF\nCv7s3buLgIBmQNrTl+p0ulT7WLBgLqNHT6BsWV+GDh2It7cP0dFR1KxZm8DAlty6dZPRo4ezaNEy\natV6lfr1G1G5cuWU7b/99mtatnyTRo2a8NNPu1m06Bt69uzL33+f5bPPJuHh4cnbbzcnOjoad/f/\n+m3Hx8czffoc3N3def/93ly6dJEzZ06RP39+xo0LYffuHfz6689otdpUy5ycnNIsD6PRSO3adahd\nuw5Hjx5m8OCP8fUtx7fffs3OnduoVasO+/btYcGCxdy+fYtlyxbTrl1H5sz5kpYt3+LKlct4e/s8\nUzIHSehCCCEyISCgGXv27KJCBX8OHPiZ+fMXAWlPX5pWQr9z5w5ly/oC5ulL9Xo97u55OHv2NJs2\nhaJSqYmKikz3+H//fZZ+/QYAUK1aDRYv/hYAH59i5M9fAIACBbyIjY2xSOh58uRhxIiPALh27QqR\nkRH8/fc5atQwz4XeuHFTAKZNm5xq2datm9ONp0IF81SuHh75mT9/Dnp9Ag8e3CcgoBnnz/9NhQr+\nqNVqihYtllJrEBMTTXh4OL/+uj/lB9GzkIQuhBC5WOy4iU+8ms4ub7zRgKVLFxEQ0JRixYqTJ08e\nIO3pS9Py6DSoyQOW7tq1naioKL766luioqLo1avLEyJQpWxnMBhRqcz7e3yylkcHQzUYDMyY8QWL\nF68gf/4CfPLJoH+3UWMyWQ6amtayR6dXfXRqVQCt1jxt7KxZ0+jc+V1q167DihXfEx8fl+a+wPyj\naP/+vRw7dpQpUzLetuBx0ihOCCFEhul0rpQuXZalS7+zuLpMa/rStBQo4MX161dRFIU//jgOmKcm\nLVLEG7Vazf79e1O2ValUJCUlWWz/6PSnJ04cp1y5p89oFhcXi0ajIX/+Aty9+w/nzp3FaDRSrlwF\nfv/9KAAHDvzC0qWL0lym0/03veqff55I8xiRkebpVRMTEzl8+ABGoxE/v/L89ddJjEYjYWEPGTFi\nKGC+8t+6dTMFCuTH2fnZG/rJFboQQohMCQhoxsSJYxk7dkLKsrSmL+3Tp3+qbfv06c+oUcMoXLhI\nygQr9es3ZPjwIZw5c4oWLVpTsGBBvvvuf1SpUpWZM6fi7V0gZftevfrx+ecT2Lx5A1qtAyNGjE51\n1fy4vHnz8cortejVqytlypSlU6cuzJ49g0WLlnHs2BEGDOiDRqNl1Khx5MvnkWqZTqdj6dJFDBjQ\nhzp1XkupFXjUO++0Z8SIofj4+PDOO+358ssvaNgwgKZNmzNgQB8URaFv3/cB8PTMj4uLjsaNn726\nHWRyFgsy8L/tSFnbhpSzbUg524a9lXNERAQfffQB//vfEotbEE8j06cKIYQQz4mff97HwoUL+OCD\nwRlK5k8iCV0IIYSwsXr16lOvXv0s3ac0ihNCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5IQhdC\nCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5I\nQhdCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQwg5IQhdCCCHsgCR0IYQQ\nwg5IQhdCCCHsgNbWB5w0aRInT55EpVIxcuRIKleunPLc8uXL2bRpE2q1Gn9/fz799FNbhyeEEELk\nSja9Qj9y5AjXrl1j1apVhISEEBISkvJcTEwMCxcuZPny5axcuZJLly5x4sQJW4YnhBBC5Fo2TeiH\nDh2icePGAJQuXZrIyEhiYmIAcHBwwMHBgbi4OIxGI/Hx8eTNm9eW4QkhhBC5lk0T+oMHD/Dw8Eh5\n7Onpyf379wFwcnLi/fffp3HjxjRo0IAqVapQsmRJW4YnhBBC5Fo2v4f+KEVRUv4fExPDggUL2L59\nO25ubrz77rucO3eOcuXKpbs5FlaMAAAgAElEQVS9h4cOrVaTpTF5ebln6f5E+qSsbUPK2TaknG1D\nyjl9Nk3oBQsW5MGDBymP7927h5eXFwCXLl2iWLFieHp6AlCjRg1OnTr1xIQeHh6XpfF5eblz/350\nlu5TpE3K2jaknG1Dytk2pJzN0vtRY9Mq97p167Jjxw4ATp8+TcGCBXFzcwPAx8eHS5cukZCQAMCp\nU6coUaKELcMTQgghci2bXqFXq1aNihUr0qFDB1QqFWPHjiU0NBR3d3cCAgLo2bMnXbt2RaPRULVq\nVWrUqGHL8IQQQohcS6U8eiM7l8nqqhepzrEdKWvbkHK2DSln25ByNnsuqtyFEEIIkT0koQshhBB2\nQBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQsh\nhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQck\noQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEII\nYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6\nEEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2QBK6EEIIYQckoQshhBB2\nQBK6EEIIYQckoQshhBB2QJvTAQghhLBPigIGAyQkQHy8ivh489/kxwkJEBdn+fjR9eLjISEh+S94\ne0O/fipKlFBy+tSeS5LQhRDiBRQTY06mlklTRVyc5eNHk+t/STf9dR9fnpSkytK4ly93ZcCARAYO\nTMTFJUt3netJQhdCiBdIWBj07u3CL79k3de/SqXg4gI6nYKzM3h4KDg7K7i4mB8nL3dx4d/l5r86\nneVjFxdwcUn/8bFj7gwerDB9uhNr1jgwfryewEAjqqz9zZBrSUIXQogXxOXLKjp10nH5sprq1ZMo\nWtT0xGT6pOWPJmNHR2ySVDt0gFq1Ypk+3YkFCxzo1s2Fhg2NhIQkULq0VMNLQhdCPBdiYmDjRgeK\nFDHRsGFSTodjdw4d0tCtmwvh4So+/FDPiBGJqHNhs2g3Nxg7Vk+nTgZGjHBi714tb7zhynvvJTJo\nUCKurjkdYc7JhS+nEMKenDqlZuhQJypVcmPwYGc6dNAxaZIjSZLTs8yaNVqCglyIjoaZM+P59NPc\nmcwfVbasiTVr4lm4MB4vL4VZs5yoW9eVTZu0KC/oxXouf0mFELlRQgKsXq2leXMdDRu6snSpI/ny\nKQwZoqdECRMzZzoRHOxCZGROR5q7KQp88YUj77/vgosL/PBDPJ06GXM6rCyjUkGrVkZ+/TWWwYP1\nPHigolcvF4KCXDh//sVLby/eGQshcszlyyrGjXPi5ZddGTDAhePH1TRsaGTp0jiOHo1l+PBEdu6M\npUEDI3v2aGnSxJVz5+RrKjP0enj/fWemTXOieHETW7fGUa+efVZ7uLrCiBGJ/PxzLI0aGfnlFy31\n6+sYN86JmJicjs52rPqkDBs2jN9++y27YxFC2CGjEbZu1dKunQu1a7sxb54jKhUMGKDnt99i+eGH\neJo1S0L7b4uefPlgxYp4Bg7Uc+WKmsBAHVu2SHOfjAgLg7ZtXVi71oHq1ZPYti0OX19TToeV7UqV\nUlixIp6lS+Pw9laYN8+RV191Zd26F6MaXqUoTz/NVq1aceHCBXx8fHj77bd56623KFq0aKYOOGnS\nJE6ePIlKpWLkyJFUrlw55bk7d+4wZMgQDAYDFSpUYPz48U/c1/370ZmKIT1eXu5Zvk+RNilr28jJ\ncv7nHxXff+/AsmUO3LljvnaoVctIt24GWrY04uT09H1s2qRl4EBn4uJUDB6sZ9iw5/Pe7/P0fr58\nWUXHjjquXFHz5psGZs9OsJv+2hkp5/h4mDPHkTlzHNHrVbz6qpHPP9dToULu/2Hj5eWe5nKrPhqb\nN29m27ZtBAUFsWfPHpo0aUKXLl3YsGED8fHxVgdx5MgRrl27xqpVqwgJCSEkJMTi+cmTJ9OjRw/W\nrl2LRqPh9u3bVu9bCJHzTCbYv19D9+7OVK3qytSpTkRHq+jePZF9+2LZvDmed96xLpkDtG5tZOvW\nOF56ycSXX8p99ac5dEhDYKArV66oGTRIz4IF9pPMM8rFBT75JJFffomlWTMDhw5padRIx6hRTnb7\nHrLqCv1x169fZ8eOHWzZsoWbN28SGBhIcHAw5cqVe+J2s2bNwtvbm7Zt2wLQrFkz1q5di5ubGyaT\niXr16rF//340Go1VccgVeu4lZW0btirn8HD44QcHlixx5PJl83VCxYpJdOtm4J13DLi5Pfv++/Z1\nYd8+LaVKmViyJB4/v+fnSut5eD+vWaNl0CBnFAWmT0+gY0f7afyW7FnKefduDZ9+6syVK2oKFDAx\nZoyedu2Mz2WNz9Okd4WOkgnR0dHK+vXrlb59+yoVK1ZUmjRpopQvX14ZOnSoEhMTk+52o0aNUnbt\n2pXyuGPHjsrly5cVRVGU+/fvK02aNFEmTJigdOjQQZk2bdpT4zAYjJkJXwiRBUwmRTl8WFHefVdR\nnJ0VBRTFyUlRunRRlIMHzc9nJaNRUYYNMx/HzU1RQkOzdv+5lcmkKGPHmsslXz5F2bMnpyN6fsXH\nK0pIiKK4uJjLq04dRfn995yOKutY3dLEZDLxyy+/sHHjRvbu3YuTkxOtW7dm3bp1+Pn5ceHCBYYM\nGcLYsWOZNm2aVftUHqkcUBSFu3fv0rVrV3x8fOjTpw/79u2jfv366W4fHh5nbfhWeR5+Zb8opKxt\nIzvKOTYWQkMdWLzYgb/+MtemlSxp4t13E+nQwYCnp3m9Bw+y9LAAfPQRlCmj5cMPnWnTRsWQIXo+\n+STn76vn1PtZr4dBg5xZt86B4sVNrFgRj6+vifv3bR6KTWRFOffuDYGBKsaMcWLLFgdq1FB4910D\nw4fr8fDIokCzWXpX6FYl9JCQELZu3UpYWBg1a9Zk4sSJNGnSBEdHx5R1ypYtS0hICN26dUt3PwUL\nFuTBI5/ye/fu4eXlBYCHhwfe3t4UL14cgFdffZULFy48MaELIWzn3Dk1ixc7sGaNA9HRKjQahebN\nDXTrZqBevSSbJdU33zRSpkwc777rwowZTpw6pWHevHjy5LHN8Z8XDx+q6NbNmd9+01KjRhJLlpgH\nWBFPV7SowqJFCezbZ2DkSCe++86RTZu0fPppIp06GXL8B2JmWRX29u3bCQoKYufOnSxZsoSWLVta\nJPNkJUqUoEWLFunup27duuzYsQOA06dPU7BgQdz+vbmm1WopVqwYV69eTXm+ZMmSGT0fIUQW0ush\nNFRL69Yu1KvnyqJFjri6Kgwdquf332NZvDiB+vVtl8yTVaxoYteuWOrXN7Jzp5amTV1fqIFELl1S\n0by5jt9+0/LWWwbWrYuTZJ4J9esnsW9fHGPGJBAfr2LIEGcCA3X88UfufC9Z1Sjuk08+YcyYMSnJ\n91FXrlzhyy+/ZPbs2VYdcNq0aRw7dgyVSsXYsWM5c+YM7u7uBAQEcO3aNYYPH46iKPj6+jJu3DjU\nT/imkEZxuZeUtW1ktpyvX1exdKkDK1Y48OCB+TNYr565y1nTpkYcHLI60sxJSoKQEEfmznXCzU1h\n7twEmje3fWMwW76fHx2TfdAgPcOH5/wtB1vJznK+c0fFZ585ERrqgEqlEBxsYOTIRPLnf/5+KKVX\n5W5VQi9fvjwHDhzAM/nm2CN2797N4MGD+euvv549ygyShJ57SVnbRkbKOSkJ9uzRsHixI3v2aFAU\nFR4eCh06GHj33URKlXr+vtiSbdhgbuEdF6fio4/0fPyxbZOcrd7Pq1drGTzYvluyP4ktyvnAAQ0j\nRjhx7pyGfPkURozQ07WrASs7X9lEphJ6uXLlUFkxJ16FChVYt25d5qPLJEnouZeUtW1YU85376pY\nscKB77934OZNcxasXj2Jbt0Sad3amGv6MZ86paZbNxeuX1fTtKmRr76y3X317H4/J4/JPn26E3nz\nKixaFM/rr9vnMK5PYqvvDYMBFi1y4IsvzOMoVKqUxOTJCbzyyvPRVTJTCf3BgwecOHGCAQMG0K9f\nP3Q6Xap18ubNS0BAQJpX79lNEnruJWVtG+mVs6LAwYMaFi924McftRiNKnQ6hXfeMTdyq1Tp+fji\nyqiwMHN/9f37tZQpk8SSJQmULZv955Kd72e9Hj780JnQUHNL9pUr421yTs8jW39v3L2rYvx4J9as\nMd9jat/ewOjRegoWzNnaqmeqcl+/fj0tWrRIsyFcTpKEnntJWdvG4+UcGQmrVzuwZIkD58+b6xDL\nlUvi3XcNtGtnwD2d8SpyE6MRQkKc+OorR9zcFObNM48Vn52y6/38eEv2pUvjKVDg+b31kd1y6nvj\n8GFzNfzp0xry5FEYNkxP9+6GlPkHbC3DCX3z5s00bdoUR0dHNm/e/NQDtGrV6tkizARJ6LmXlLVt\nJJfzyZPmLmfr1zsQF6fC0VGhZUtzI7datZKw4s5arrN+vfm+eny8iqFD9Qwdmn331bPj/XzpknlM\n9qtX1bz1lnlMdmfnLD1ErpOT3xtGIyxZ4sDkyU5ERqooXz6JyZP1vPqq7W99ZDihlytXjgMHDpA/\nf/6nDumqUqk4e/bss0eZQZLQcy8p6+wXFwd79rgzd24Sf/xhvhovXtxE164GOnY0vBDdnB69r96s\nmYGvvkrIllqIrH4/HzxobskeEfF8T0pja8/D98b9+yomTXJk+XJzjfU77xgYO1ZP4cK2+zxlOKHf\nunULb29vVCoVt27deuoBfHx8ni3CTJCEnntJWWevM2fUdOzowp07atRqhYAAcyO3Bg1s32c8p4WF\nQe/eLvzyS/bdV8/K9/OqVVqGDDG3ZJ8xI4EOHV6sluxP8jx9bxw/rmb4cGdOntTg6qrw8cd6evc2\n2KRL5zPdQ08WERFBvnz5Uh7funUrRxJ5MknouZeUdfY5dkxNp046IiJUfPQRdO4cQ9Gi9n81/iRG\nI0yc6MS8edlzXz0r3s+Pt2T/7rt4XnvtxWvJ/iTP2/dGUhIsX+5ASIgT4eEqfH2TmDRJT7162d9m\nIy1W/Va/f/8+7dq14/PPP7dY/vHHHxMUFMS9e/eePUIhxDPbt09DUJCO6GiYMyeeadN44ZM5gFYL\n48bp+frreJKSoGtXHVOnOmJ6ThqLJyTAe+85M326Ey+9ZGLr1jhJ5rmARgNduxo4dCiGd99N5MIF\nNUFBOnr1cubWLds3TLEqoU+ePBmTyUSXLl0slo8ePRpHR0cmT56cLcEJIay3ebOWzp1dSEqCRYsS\naN9eqmof16aNkS1b4ihWzMTUqU506+ZMdA5f8D18qCIoyIXQUAdeeSWJbdviXthuabmVpydMnapn\n5844qldPYtMmB+rWdWX2bEf0etvFYVVCP3ToEBMmTMDf399iefny5Rk9ejSHDx/OluCEENZZsUJL\n797OODrCypXxBAZKMk9PpUomdu6M4/XXjWzf7kCzZjouXsyZZv4XL6oIDNRx5IiWt982j8n+IndL\ny+2qVDHx449xzJoVj06nMHGiEwEBOmJjbXN8qxK6Xq9Pd0x1rVZLQkJClgYlhLDe/PkODBrkQr58\nCqGhUlVrjfz5FVatiue99xK5cEFD06au7Nhh27E9Dx7U0Ly5K1evqhkyRM/8+dItzR6o1dCxo5GD\nB2Pp1SsRV1dz+wibHNualWrXrs20adO4e/euxfLLly8zevRoatasmS3BCSHSpyjw+eeOjB3rTJEi\nJjZujKdqVamqtZZWC599pmf+/HiMRujSRce0aba5r75qlZa2bV2IjYXZs+NfqAlWXhT58sGkSXq2\nbYsjjXnNsoVV49yMGjWK7t2706BBA/Lly4eLiwsxMTFERUVRrFgxpk6dmt1xCiEeYTLBiBHmeZxL\nljSxZk0cxYtLVW1mvPOOEV/fOLp1c+GLL5z46y81c+dmT391RYEpUxyZMcPckn3x4njq1pUaFZE1\nrEroRYoUYdOmTezZs4fTp08TFRWFh4cHFStWpHHjxk+c4lQIkbUMBvjgA/PY3hUqJLFqVTyFCkky\nfxbJ99X79HFm2zYHAgPVLFkST+nSWVeuCQkwaJD5dXvpJRMrV8ZRpoy8biLrZKgfelrCwsIYP348\nM2fOzKqYrCb90HMvKevMiYszD5Kya5eWV15JYsWKOPLmTX99KeeMMRrhs8+cWLDAEXd3hfnz42nS\n5OlX0E8r5wcPzGOyHzlift2WLHmxx2TPLHk/m6XXD93qoeUvXrzI4cOHiYiIsFh+5swZDh069GzR\nCSGeKioKgoNdOHxYS8OGRhYujMfVNaejsi9aLUyYoKdKlSSGDHGmSxcXhg1LZNCgzN/jvnhRRadO\n5jHZ337bwKxZ0vhNZA+rEvrOnTsZMmQIiqJgMpnQarUYDAYAihcvzocffpitQQrxort/X0WHDi78\n9ZeGN980j0n+nE1+aFeCgv67rz55shN//mm+r57Rxk0HDmjo3t08JvuQIXo++UQav4nsY9Vba968\nefTr14+TJ0/i7OzM1q1bOXLkCKNGjaJkyZK8/fbb2R2nEC+smzdVtG6t46+/NHTpksjXX0syt4XK\nlc331V97zcjWreb+6pcuWd9ffdUqLe3aSUt2YTtWvb2uXr3K22+/jVarRaVSYTKZyJMnD8HBwTRs\n2JDRo0dnd5xCvJAuXlTRqpWOS5fUDBigZ9o0PRrbdpd+oRUooLB6dTx9+yZy/ry5v/quXU9+ARQF\nJk925IMPXHB1hdWr42WCFWETViV0jUaTMnhM3rx5uX37dspzr732GgcPHsye6IR4gf35p5pWrXTc\nuqVm1Cg9Y8Yk2uW85c+75PvqX30VT2KiuR3Dl186pjlYSEIC9OvnzIwZTpQoYWLr1ljpliZsxqqE\nXr16dSZPnkxkZCQVK1Zk7ty5PHjwAKPRyMaNG3GWFh5CZKlDhzS8/baOsDAV06YlMHBgYk6H9MJr\n29Y8DryPj8LnnzvRo4czMTH/Pf/ggYp33tGxfr0DNWsa2bZNuqUJ27IqoX/00UdcunSJqKgo+vbt\ny5kzZ3j99depVKkSc+bMoXPnztkdpxAvjF27NLRv70JCAixYkEDXroacDkn8K/m+et26Rn780YHA\nQB2XL6v4+28IDNRx9KiGNm0MrF0bT/78ksyFbVndDz15NZVKxe3bt9m7dy8Gg4HKlStTvXr1bA0y\nPdIPPfeSsk5baKiWAQOccXCA776Lp2HDZ6uulXLOHo/2V8+TR0GjUREeDkOG6Bk2TG6NZBd5P5s9\nUz/0YcOGMXz4cDw8PADw9vYmODg466ITQvDddw4MH+6EuzssXx5PrVpy7/V5lXxfvVKlJIYOdSYp\nyTz/vExZK3KSVQn94MGD3L17NyWhCyGyjqLAzJmOfP65EwUKmFi9Oh5/f5lkJTdo187IK6/E4u7u\nRoECksxFzrIqoU+YMIGpU6fSpk0bypcvj2saw1MVKlQoy4MTwt4pCowb58T8+Y4UK2aeZKVUKbn3\nmpuULKng5QX37+d0JOJFZ1VC79evH8ATu6edPXs2ayIS4gVhNMLQoU6sWOGIr28Sq1fH4+0tyVwI\nkTlWJfRJkyahklYeQmQZvd7cX/nHHx14+eUkVq6UVtFCiGdjVUJv06ZNdschxAsjJga6dXPh55+1\n1K1rZOnS+GyZe1sI8WKxKqFv2LDhqeu89dZbzxyMEPYuPBw6ddJx/LiGpk2NfPNNPC4uOR2VEMIe\nWJXQhw8fnubyR6vhJaEL8WR376po186Fs2c1BAWZp9F0cMjpqIQQ9sKqhP7rr7+mWhYXF8eJEycI\nDQ1l3LhxWR2XEHbl6lUVbdvquHZNTa9eiUycqJeZt4QQWcqqhF6gQIE0lxcvXhwPDw9GjRrFsmXL\nsjQwIezF2bNq2rVz4e5dNUOH6vn4YxlJTAiR9axK6E9SokQJTp06lRWxCGF3jh9X07GjjogIFRMn\nJtCnj4zLLoTIHlYl9Lt376a5PDIykoULF8oIckKkYf9+De++64JeD7Nny5zYQojsZVVCf+ONN9Lt\nh65WqxkzZkyWBiVEbrdli5Z+/czTCi9cmEDz5pLMhRDZK9MDy6hUKtzc3ChXrhxFixbNluCEyI1W\nrtQyeLAzLi6wdGk8r78uk6wIIbJfhgaWMZlMqB9pmqvX63FycsqeyITIhb7+2oExY5zx8FBYuTKO\natVkkhUhhG1Y1XEmJiaGQYMGpapa79mzJwMHDiQmJiZbghMit1AUmDzZkTFjnClc2MSmTZLMhRC2\nZVVCnz59OqdPn6ZJkyYWy3v37s3FixeZOnVqtgQnRG5gMsGIEU7MmOFEiRImNm+Ow89PkrkQwras\nSuh79+7liy++oF69ehbL33jjDSZOnMjevXuzJTghnncGA/Tv78yiRY6UL5/E5s1xvPSSTLIixItO\nde8ebh/2x+ON2qiio2xyTKsSelRUFHny5EnzOU9PT6lyFy+k+HjzJCuhoQ7UqJHExo1xFCokyVyI\nF5rBgMv8uXi+Wg2XlctQnJ1RNM885ItVrEroL7/8Mt9++y2JiYkWy2NiYpg2bRr+/v7ZEpwQz6uo\nKOjQwYVdu7TUr29kzZo48uXL6aiEEDnJYf9PeDSog9vYkaDVED1lBhFb94BOZ5PjW/WzYcSIEXTr\n1o3atWtTunRpXFxciImJ4eLFi7i4uPDdd99ld5xCPDcePFDRoYMLf/6poXVrA199lYB09hDixaW+\nfg23sZ/i9OMmFLWa+G49iR0+CsUzv03jsCqh+/r6smXLFkJDQzl9+jRRUVGUKlWKVq1aERQUhLtM\n5ixeELduqWjb1oWLFzUEBycydaoejSanoxJC5Ij4eHRzZ6Kb8yWqhAQMNWsT8/lUjJWq5Eg4Vlfs\ne3p60qNHD+mHLl5Yly6pCArSceuWmvffT2TMGL1MsiLEi0hRcNy6BbexI9Fcv0ZSocLEzpiA/p12\n5OSXgvRDF8IKf/2lplUrczIfNUrP2LGSzIV4EWnO/03etm+Rt3tn1HduEzdgEOGHjqMPap+jyRyk\nH7oQqdy7p+KnnzTMmeNIv37OvP66joAAHQ8fqpg6NYGBAxOfvhMhhF1RRUfhOmYkHvVfxfHnn0hs\n2Jjw/YeJHTMexe35uO1sVZX73r17mTlzJlWrVrVY/sYbb+Du7s6HH37IZ599li0BCpFdkpLg8mU1\np06pOX1azalTGk6dUnPvnuXvXDc3hVdeSaJfPwMtWsgkK0K8UEwmnFavxG3CWNT375H0UgliJkwm\nsWlgjl+RP86qhC790EVuFxMDZ86oOX1a828C13D2rJr4eMsPZNGiJpo1M1Chggl/fxP+/kkUL66g\ntqouSwhhT7Qn/8Bt+FAcjh9FcXEhdvgo4voPBGfnnA4tTVYl9OR+6J999hmOjo4py6Ufes65dUvF\nrl1aDh/W4Oam4O2t4O1twttbwcfHRJEiiq26Pj5XFAX++UfFqVP/XXGfPq3hyhUVivJf8nZwUPDz\nM1Gxojlp+/ubqFAhCQ+Px3YYH4/L19/iMn8Oqrg4FA9PTB4eKB4emDw9/33s+cj/PVKWKZ6eKO55\nnrtf8UKIJ1M9eIDr5+NxXrYElaKQ8GYbYsdOwFS0WE6H9kTP3A/d2dmZxYsXW33ASZMmcfLkSVQq\nFSNHjqRy5cqp1pk+fTonTpzg+++/t3q/9s5kgj/+ULNrl5YdO7ScPv30vlIeHuYk7+OjUKSI5V9v\nb3PSd3GxQfDZxGCACxfUKUk7ueo8LMzycjpfPoW6dZNSknfFiiZ8fU088ts0Nb0e52VL0M2chubu\nP5jc82AqVhxVeBjav8+iSkiwKkZFq0XJ52GR8E0e//7/kR8Eiqcnpnwe5r8enkjHdiFygNGI8+Jv\ncZ0yCXVkBMbyFYgJ+QLDa/Wevu1z4Jn6obdu3ZoWLVqwZcsWKlSo8NT9HDlyhGvXrrFq1SouXbrE\nyJEjWbVqlcU6Fy9e5OjRozg4OGTujOxITAzs26dl504tu3drePDAnKgcHRUaNjQSEGCkfn0jBoOK\n27dV3L6t5tYtFXfuqLh1S83t2yquXFFz+nT6V4j585sTe3KSf/RKP/nv85BbIiPh9GmNxb3uv/9W\nk5hoeW4lSpioU8eAv7+JihXNV97e3or1F8lGI86rVqCbPgXNzRsoOldiBw0l/r0BKB6e/60XF4c6\nPAxVWJj5b0Q46uT/J/8ND0Md9u9zD+6junAelWLd0LCKzhXTv8k9vVoBJeX5f38k5M2H3BsQInMc\nDv6K24iP0Z49jSlPXmJCphDfvTdobTNsa1bIUD/0Xr16Aeb+57t372bDhg1MmzYNlUpF9+7dn7qP\nQ4cO0bhxYwBKly5NZGQkMTExuLm5pawzefJkBg8ezNy5czN6Lnbh2jVVylX4wYMaDAZzJvLyMtG5\ncyIBAUnUq2fkkSIDFMqVA0hKtT9FMQ9Tevu2OcEnJ/rkx7dvq/5tGJZ+xitQ4PFEb3nlX6RI1iV9\nRYHr11UpV9ynTqk5c0bD9euWicrZWUm54k6+312hQhKZHuMoKQmn9WvRTf0c7ZXLKE5OxPUbQNwH\ng1G8vFKvr9Nh0unAp2gapZ4OkwlVZMRjST/cMvmHh//7Nwx1RDjaSxdRxcVatXtFrUbJl8+iBoDS\nJXHxLk5S6TIklSpNUrGXQH4s27+4ONQR4al+XD76nlNcXEhsGEDi62+Aq2tOR5xj1Ldv4TruU5w3\nhKKoVMR37krsyLFpf+6fcxn66XHs2DE2bNjAjh07SEhIoE6dOkyYMCElST/NgwcPqFixYspjT09P\n7t+/n5LQQ0NDqVmzJj4+Plbtz8NDh1abtcN0eXnZtvuB0QiHD8PmzbBlC5w5899z1apBy5bmf9Wr\nq1Grn1RHnL6CBaFMmfsbhvAAAB2hSURBVPSfVxSIiIAbN+DmTfNfy/+ruXAB/vwz/bIuVAiKFoVi\nxf77++j/vb1JVcWdJ487p0/DiRNw8uR/fyMjU8ffpAm8/LL5X5Uq4Our+ve1f8bXX1EgNBTGjDEX\nvoMD9O+PauRIdD4+ZHkzhEJ5gZcytk1CAoSFwcOH//17/PHDh6j+/acOC4OrV8zN+AGL335aLZQq\nBb6+ULas+W/yP29vucJ/Btny3ZGUZH6t03i903sf8PCh+T1jBZfFC823dxo0MH/RtGgBJUpk/Xlk\noSwr54QEmDEDQkIgLg5q1kQ1dy4ur7xCbr0T+dSEfuPGDTZs2MCmTZu4efMmVatWJSYmhhUrVqTq\nxpZRyiPVjxEREYSGhvLdd99x9+5dq7YPD497puM/zsvLnfv3o7N0n2mJjISffjJfhe/dqyU83Hx1\n7OKi0LRpEgEB5ur0IkX+K5+HD7M9LIoUMf975ZXUzykKhIdbXuknV+0n/z19WsXx42lf6atUCl5e\n5qp9Ly+F27e1nDunYDT+t75arVC6tImGDU0W97vTmsEsPPwZT1ZRcNy9A93kEBz+Oomi0ZDQqQtx\nQz7BVPzfhGuD94LVHNyhsDsULmHd+oqCKiqSAjEPiTz+J9pLF9Ek/7tyCfX586k3cXEhqWTplKt5\nY+kyJJUqQ1LpMiientK47wme+t2hKKhiY/6rkUnr9kx4+L9/H6mtiYywOgaTex5zzYxfeasabar/\n+Qen3Ttw3LUD7fbtsH07DBiA0a8ciQHNSAxoiuGVWs9VlXNWfUc77tyG26jhaK5ewVTAi5jPp6Fv\n38n8g/Z5+tynI70fNem+UqtXr2bDhg38/vvveHt78+abb9KmTRuKFCmCv7+/RWt3axUsWJAHDx6k\nPL537x5e/1ZrHD58mLCwMDp37kxiYiLXr19n0qRJjBw5MsPHeR5duqRi507z/fDDhzUkJZm/HL29\nTbRubaBJEyOvvZaUsUZq8fE4/HYIY8VK2V49pFKBpyd4epowd2pIu3o/LEyVUpWfunpfzZkzavR6\nFa6uULXqf0nb3z+JcuVMNmmZ7/DzPlw/n2DuiqJSkdCmLXEfDyepdNnsP7itqFTme+plipHoU5rH\nh8JRhT1Ec/lSSoLXXDL/X3v5Etozp1LtzpQ3H0mlS6ck+KRS/yX+52VQDZsxGFISc3JSxhiHy7Xb\nFu0pVI88r44IR5Vo3YBEiqOjOQkXKYKxQsWnNqBMbkeR0VspphIlMdZ+ldhR41DfvIHjrh047t6B\n4y/7zeOTz52JKV8+Ehs0Mif4ho1tPtlIVtNcvojrqOE47f5/e3ceHVWVr338W1VJZQaSSBgaGYy+\n4kWiINhKIqAkQQQnEBJsHGkVkbeFdmDQ7ogSLuDQKiDiEKfbQJBRaCQMEUUEkYYXGmy9BFoUURJC\ngFTGSlW9f5QUBAIEqJxKDs9nLdaicipVv+xVOU/2PnvvswKPzUbpI49R+tQYPI0aB7o0v7B4PDXP\n0mnfvj3t27dnzJgxXHfddb6vu1wuOnTowPz586sNn9fG5s2bmTp1Ku+99x47duxgwoQJzJ49+6Tn\n7d27l7Fjx55xlru/e9P+7KE7nfD11zZycoJYuTKI3bu9Q5kWi4fOnd2+XviVV7rPuuNj2b+fsPfe\nIuyDLKyFhXhsNip7pVA+aDCVqX3q7RpJODa8f+mlURQWGvuXcNDXG4iYPAH7l18AUHHLrZSMfgbX\nFWee0NlQnfVn2uPBuv/XY7353buw7f7t/z/8B4vTedK3uJo1Py7gjwv8tu3q92x9jwdL8ZHTXmeu\nNrfhaDg7at+ebt+chtqtbnBHx3ivZwdyNKS0FPu6L7Cv8Aa87ee9gHeORlWXa6lI6U1lys3e3xuD\n6zznc7TDQcTfXiTszWlYnE4qb+iJY+IUXJe393+RBjjrHnqfPn3Izc1l5MiR3HLLLdxxxx01LjE7\nG507d6ZDhw6kp6djsVjIyMhgwYIFREVFkZKScl6vXR8cPAirV3t74bm5QRQXez/sEREe+vb19sJ7\n9XIRF1e7mc4nsv1rG+EzpxOycB4WpxN3dDRl9w8laMtmQlYsJ2TFctyNm1Bxe3/KBw2mquu19W6Y\n1GKB6GhjL9UGbd1C+KQJhKxeCUBFciqlo5+h6qrzu2RkShYL7uYtcDdvgTPxhurHqqqw7v0J2+48\n7xD+0R7+7l0Eb/gK+/p11Z7usVpxt2qN65JLcMVfemwI/5J43Be3xq+3qauoOHko+1DRcYF8/FD2\nsdC2uGo3pdETFuYN3NZtqKphKDuydQsOB4VXX33QpIl/f0ajhIf/NuR+M3g82L7dgX1VDiErlhO0\naSPBGzdA5nhcv2tFZUpv77+kHtTLNbAeDyELPiZi/F+w/foLrlYX4xg/kcp+t9W7c6M/nLKHDlBc\nXMzSpUtZtGgRW7du9S1Ve+2115g/f36tlqrVpUD30D0e+P57629D6TY2bbLhdns/JK1bu0lN9fbC\nu3VznXtHxe3GvjKHsJnTfT3Lqksvo+zh4ZQPGszRMWrbv78l9OM5hMzLxvbrL97ntbuEikGDKR+Y\nfuy6cD1hxHwF27c7iJgykZBlSwCoTOpOyZi/UHXt7+v0fesTo+aFUF6Obc8Px/Xsjwv8/JPnxHjs\ndlxt2/kC3hV/rGfvCQmpHsonLgM8YSWA9eDB81oJcGJvuaalgWcKK8PaOcAsBwux567CvnI59tzV\nvmv8ntBQKm/oQWWyN+DragOWs2ln2/Z/ETnuKewbvvKuWhkxktL/Owoz7Lh1qh76aQP9eLt27WLh\nwoV88skn5Ofn07lzZ26//XZSUlKIiYk58wvUgUAEekUFfPWVjRUrvEPpR5dTWa3e/b5TUlykplZx\n+eVnP5ReTUkJoXP+TtjbMwjavQuAyu43UjZsOJU3pZy6i+tyEfzFGkLnziZk2RIsZWXe770+kYq0\nu6m49Xbv7mUBVpcnQNuunYS/+N+ELJyPxePB2eVaSsb+BecNPerk/eqz+hA0luIj2P6z+1jYH71u\nn5eH9cjhM7/AKZy0Vj8m+oRNfGKqX2eOifFeK62D4aH60M6Gq6oi+Juvfdfeg77797FDV3SgMvVm\nKpJ7U9Wlq99GKmrTzpaig0RMmkDoB1lY3G4q+vTD8fxE3G3a+qWG+uC8A/0ot9vNunXrWLhwIatX\nr8blctG1a1fee+89vxR6NowK9Px8C6tWeUN8zZogSku9Sd2o0bENXnr1qsIff9dY9/1M2LtvEfrR\ne1gPHcJjt1M+YBBlDw/H1eHstti1OIqxL1lM6NzZ2NetBbxDhxV9+lE+aDDOHjcGbEiwLk6A1h/3\nEP7yZEKzZ2Fxu3EmXE3p2Ge9fwCZcHitNup10Hg8WAoLfQF/dCY+Vc4Gt5tevW5ng1h/3IN9ZQ4h\nK5cTvG4tlooKANwxMVTemExl6s1U3tgLT5MT91euvdO2s8tF6EfvE/Hfz2MtKqLq0su8u7zd2Ouc\n36++8lugH+/4IfkTd3wzQl0FuscD27dbfb3wzZuPhV58vHdCW2pqFb//vctve3QEbfknYTOnE/LJ\nIixVVbgvuoiy+/9I2f1/xBMXd96vb/3pR0LnZROSPcvX43c1a07FXWmUDxps+MQwf54Arb/sI/xv\nLxL69w+xOJ1Utb+CkqefobLvrRdskB+loDGG2vkEJSXY137u673bftkHgMdmw3ntdb6hedfl7c/q\nd/RU7Rz09QYixz1F8L+24o6IpPTJMZQ9NOzkzS9Mok4CPdD8+Qvk8cCWLVHMmlXJypVB/PKLd1gu\nKMjDdde5fCEeH+/H5nK5sC9bSvjM6d6JJkDVFf9F2SOPUd5/YN3MVvd4CPrnN94h+UXzsR7yXgNz\ndryKikHplPcfZMgOSf44AVoKCgh//RXC3n8HS0UFVe0uofTpcVTcMaBhTkaqAwoaY6idT8Pjwbb9\nX9417yuWE7R5k28LZFfrNlQmp1KRejPObjec8Zx3Yjtb9/9KxPi/EDrP26EsHzSYkr+Mx92sed39\nPPWAAv0McnNtpKd7J0tER3vo1csb4DfeWEVjPy9RtBQfIXTWR4S9/Sa2H/cAUNErhbJhI3B272lc\nr7KiAvuK5YTOnYV99UosVVWGLYE7nxOgpegg4W9MJeztGVhKS3G1upjSJ8d4JwnWo00w6gMFjTHU\nzrVnOXAA++oV2FetwJ67CmvxEQA84eFUdu95bGJdi5Ynfa+vnSsrCXtrBuEvT8Za4sCZcDWOiS9e\nMBNeFehnUFoKS5ZE0aZNKV27uuqkg2f9cQ9hb79J6N8/xOooxhMWRvnAwZQ9/Ciu/3O5/9/wLFgK\nCghdNI+QuXMI3roFoE6XwJ3LCdBSfISwmW8QNmMa1uIjuJo1p3TUU5T/4d56dT21PlHQGEPtfI6c\nToI3bvAOza9cTtDOYzsYOq9MoDK1N5XJvanqdA3YbDRtGsWh7IVEPjuaoLyduGNiKBmX4T0HXECj\ncgr0WqiTX0qPh6BvNhL+5jTsy5ZgcbtxNWtO+dCHKbv3gXq585IRS+DOqq1LSgjLepvwaX/DWlSE\nOzaW0j89Qdn9Q+vn2td6REFjDLWzf1j/s9u3HW3wV1/6dtdzx8ZS2SuV0PIS+OQTPFYr5fcPpWT0\nM9XvgniBUKDXgl9/KZ1OQpYuJmzmdII3/9P7pY5XUfbIcO813oYwWeNUS+C6JVExaPB5LYGrVVuX\nlxP20XuEv/oy1oJ83I2bUPbYnyj94zBOuN2cnIKCxhhq5zrgcGD/Yo13O9qVOdj2/wp4l+A6Jr54\n1qt+zESBXgt+mah1qIjQjz4g7N2Z2Pb9jMdiobL3LZQNewzn9YkNdta1v5fAnbatnU5CZ/8P4a9M\nwbbvZ9wRkZQ9Mtx7T/LGTfzx41wwFDTGUDvXMbeboO3biA5yU3BFpwZ7HvUXBXotnM8vpW13HmFv\nzSB0ziwspSV4wiMoH/wHSh96FPcl8X6tM9D8sQSuxrZ2uQiZl03ES5Ow7fkBT1gYZQ8+TOmIkXhi\n69+liYZAQWMMtbMx1M5eCvRaOJcbWQR/9aV3W9acT7F4PLh+14qyoY9Qfs995u9NnscSuGpt7XYT\nsmQR4VMmErTzf/HY7ZTd+wBljz9h+uUndU0nQGOonY2hdvZSoNdCrT8slZWELJxH2Mw3CN6+DQDn\nNV0oe+QxKvredta3MTSFo0vgPp6NfdWK6kvg0u723ujhuCVwTZtGUZB/BHvOp0RMmkDQt9uP3ZN8\n1FN1thf0hUYnQGOonY2hdvZSoNfCmT4slsJCwj54l9Cst7Hl78djtVLZ9zZKhz1GVdcLY/1jbVgO\nHCB04cc1L4FLG0xVl2tp+v824BwzluAtm/FYLFTclUbJE6NNd3ki0HQCNIba2RhqZy8Fei2c6sNi\n+9/vCZv5BqEfz8ZSXo47qhHlf7iXsj8+Uu/uYlbf2L77t3dI/rglcO7YWKyFhQCU33YnpU+NbbD3\nJa7vdAI0htrZGGpnLwV6LVT7sHg8BK/JJXzmdOy5qwBwtW5L2cPDKB88pF7csaxBcbkIXvs5odmz\nsOeuxJqYyMFRY3B1TAh0ZaamE6Ax1M7GUDt7nSrQtU/micrLCZ2XTdhbb/huB+j8/fWUPvIYlX36\nXlC7EfmVzYaz5004e94EeD+QLv1iioj4jQL9KKcTxo8ndto0rAcO4AkKorz/QMqGPUbV1Z0DXZ2I\niMhpKdB/Y/88F557Dpo0ofRPf6bswYdwt/xdoMsSERGpFQX6bypvSoHPPqOwbXuIiAh0OSIiImfF\nGugC6g2rFXr2VJiLiEiDpEAXERExAQW6iIiICSjQRURETECBLiIiYgIKdBERERNQoIuIiJiAAl1E\nRMQEFOgiIiImoEAXERExAQW6iIiICSjQRURETECBLiIiYgIKdBERERNQoIuIiJiAAl1ERMQEFOgi\nIiImoEAXERExAQW6iIiICSjQRURETECBLiIiYgIKdBERERNQoIuIiJiAAl1ERMQEFOgiIiImoEAX\nERExAQW6iIiICSjQRURETECBLiIiYgIKdBERERNQoIuIiJhAkNFvOHHiRLZu3YrFYmHcuHEkJCT4\njm3YsIFXXnkFq9VKu3btyMzMxGrV3xwiIiJnYmhabty4kT179pCdnU1mZiaZmZnVjv/1r3/l9ddf\nZ86cOZSUlLB27VojyxMREWmwDA309evXk5ycDEB8fDyHDx/G4XD4ji9YsIDmzZsDEBMTQ1FRkZHl\niYiINFiGBvqBAweIjo72PY6JiaGgoMD3ODIyEoD8/HzWrVtHjx49jCxPRESkwTL8GvrxPB7PSV8r\nLCxk2LBhZGRkVAv/mkRHhxMUZPNrTU2bRvn19eTU1NbGUDsbQ+1sDLXzqRka6HFxcRw4cMD3OD8/\nn6ZNm/oeOxwOHnroIUaOHElSUtIZX6+oqNSv9TVtGkVBQbFfX1NqprY2htrZGGpnY6idvU71R42h\nQ+6JiYnk5OQAsGPHDuLi4nzD7ACTJk3ivvvuo3v37kaWJSIi0uAZ2kPv3LkzHTp0ID09HYvFQkZG\nBgsWLCAqKoqkpCQWLVrEnj17mDdvHgD9+vUjLS3NyBJFREQaJMOvoT/55JPVHrdv3973/+3btxtd\njoiIiClo1xYRERETUKCLiIiYgAJdRETEBBToIiIiJqBAFxERMQEFuoiIiAko0EVERExAgS4iImIC\nCnQRERETUKCLiIiYgAJdRETEBBToIiIiJqBAFxERMQEFuoiIiAko0EVERExAgS4iImICCnQRERET\nUKCLiIiYgAJdRETEBBToIiIiJqBAFxERMQEFuoiIiAko0EVERExAgS4iImICCnQRERETUKCLiIiY\ngAJdRETEBBToIiIiJqBAFxERMQEFuoiIiAko0EVERExAgS4iImICCnQRERETUKCLiIiYgAJdRETE\nBBToIiIiJqBAFxERMQEFuoiIiAko0EVERExAgS4iImICCnQRERETUKCLiIiYgAJdRETEBBToIiIi\nJqBAFxERMQEFuoiIiAko0EVERExAgS4iImICCnQRERETUKCLiIiYgAJdRETEBAwP9IkTJ5KWlkZ6\nejrbtm2rduyrr77irrvuIi0tjenTpxtdmoiISINlaKBv3LiRPXv2kJ2dTWZmJpmZmdWOT5gwgalT\npzJ79mzWrVtHXl6ekeWJiIg0WIYG+vr160lOTgYgPj6ew4cP43A4APjpp59o3LgxLVq0wGq10qNH\nD9avX29keSIiIg2WoYF+4MABoqOjfY9jYmIoKCgAoKCggJiYmBqPiYiIyOkFBfLNPR7PeX1/06ZR\nfqqkbl9Taqa2Noba2RhqZ2OonU/N0B56XFwcBw4c8D3Oz8+nadOmNR7bv38/cXFxRpYnIiLSYBka\n6ImJieTk5ACwY8cO4uLiiIyMBKBVq1Y4HA727t1LVVUVn332GYmJiUaWJyIi0mBZPOc77n2WXnrp\nJTZt2oTFYiEjI4Nvv/2WqKgoUlJS+Oabb3jppZcASE1NZejQoUaWJiIi0mAZHugiIiLif9opTkRE\nxAQU6CIiIiagQP/N6bakFf+ZMmUKaWlpDBgwgBUrVgS6HFMrLy8nOTmZBQsWBLoUU/vkk0+47bbb\n6N+/P2vWrAl0OaZUUlLCiBEjuOeee0hPT2ft2rWBLqleCug69Pri+C1pd+3axbhx48jOzg50Waaz\nYcMGdu7cSXZ2NkVFRdx5552kpqYGuizTmjFjBo0bNw50GaZWVFTE9OnTmT9/PqWlpUydOpWePXsG\nuizTWbhwIe3ateOJJ55g//793HfffSxfvjzQZdU7CnROvSXt0SV14h9du3YlISEBgEaNGlFWVobL\n5cJmswW4MvPZtWsXeXl5Cpc6tn79eq6//noiIyOJjIzkhRdeCHRJphQdHc33338PwJEjR6rtOCrH\naMid029JK/5js9kIDw8HYN68eXTv3l1hXkcmT57MmDFjAl2G6e3du5fy8nKGDRvG3XffrftP1JG+\nffuyb98+UlJSGDJkCKNHjw50SfWSeug10Eq+urVq1SrmzZtHVlZWoEsxpUWLFnH11Vdz8cUXB7qU\nC8KhQ4eYNm0a+/bt49577+Wzzz7DYrEEuixTWbx4MS1btuTdd9/lu+++Y9y4cZobUgMFOqffklb8\na+3atbz55pu88847REVpT+a6sGbNGn766SfWrFnDr7/+it1up3nz5nTr1i3QpZlObGwsnTp1Iigo\niNatWxMREcHBgweJjY0NdGmmsnnzZpKSkgBo3749+fn5ulxXAw25c/otacV/iouLmTJlCjNnzqRJ\nkyaBLse0Xn31VebPn8/cuXMZOHAgw4cPV5jXkaSkJDZs2IDb7aaoqIjS0lJd360Dbdq0YevWrQD8\n/PPPREREKMxroB460LlzZzp06EB6erpvS1rxv2XLllFUVMTIkSN9X5s8eTItW7YMYFUi565Zs2b0\n7t2bQYMGAfDss89itaqf5G9paWmMGzeOIUOGUFVVxXPPPRfokuolbf0qIiJiAvpTUkRExAQU6CIi\nIiagQBcRETEBBbqIiIgJKNBFRERMQMvWREzinnvuYePGjac8npaWxvPPP29YPWPGjGH79u0sXbrU\nsPcUuZAp0EVMpEuXLrz66qs1HgsLCzO4GhExkgJdxESCg4O1bbHIBUrX0EUuMAsWLODyyy9n27Zt\n3HXXXXTs2JEePXqQnZ1d7XnLly/njjvuoGPHjnTp0oVHH32UH374odpzZs2aRe/evUlISODWW29l\n8eLFJ73f+vXr6devH1deeSV9+vRhy5YtvmM7duzggQce4Nprr6VTp04MGDCA3NzcOvm5RcxOgS5y\ngXrhhRcYNWoUixYtokePHmRkZLBt2zYAPv/8cx5//HGSk5NZvHgxWVlZFBYWcv/991NWVgbA/Pnz\nmTRpEsOGDWPp0qWkpaUxevRo1qxZ43uPQ4cO8eGHHzJ58mTmzp1LUFAQTz/9NOC9q+Gjjz5KTEwM\ns2fPZvHixXTv3p0RI0awd+9ew9tDpKHTkLuIiWzcuJFOnTrVeOwf//hHtX3zBw4cSGJiIuDdg3zJ\nkiV8+umnJCQk8MEHH9CpUydGjBjhe/7kyZO5+eabyc3NpW/fvmRlZdGvXz/uvPNOAIYMGcIvv/xC\nQUGB73sKCwt57rnnaNasme89MzMzOXToEC6Xi/3795OcnEx8fDwAjz/+OElJSbp5j8g5UKCLmEhC\nQgKTJ0+u8VhcXFy1x1dddZXv/3a7ncsuu4x9+/YBsH37dgYMGFDt+e3atSMqKoodO3bQq1cv8vLy\nuPvuu6s956mnnqr2+KKLLvKFOUBMTAwAJSUltGzZkoSEBMaPH09eXh5JSUkkJCRwzTXXnOVPLSKg\nQBcxldDQUNq0aVOr5554P/rw8HCKi4sBcDgcREREnPQ9EREROBwODh8+DJx55nxoaGi1xxaLBfAO\nt1ssFt555x2ysrJYunQp06ZNIzY2luHDhzNkyJBa/QwicoyuoYtcoI5eCz+qpKSERo0aAd6wdzgc\nJ32Pw+EgKiqK6OhoLBZLjc85G40bN2bUqFHk5OSQk5ND7969eeGFF/jiiy/O63VFLkQKdJEL1KZN\nm3z/r6ysJC8vj3bt2gFw5ZVXsnnz5mrP37lzJw6Hg44dO/qG6E98zoQJE065Dv5E+/fvZ9myZb7H\nbdu2JSMjg8jISPLy8s71xxK5YGnIXcREnE5ntUlpx7PZbL5r2ADZ2dm0aNGCVq1a8f7771NeXk6/\nfv0AGDp0KA8++CCvvPIK/fv3p7CwkMzMTNq2bctNN90EwAMPPMAzzzzDnDlzSEpK4ssvv2TWrFm8\n/vrrtarV4XDwxBNPsGvXLvr160dwcDCrV6+mtLRU19FFzoECXcRENm3aRFJSUo3HLrroItatW+d7\n/Oc//5np06ezfft2YmNjmThxom+2ebdu3XjttdeYPn06WVlZhIeHk5iYyOjRo7Hb7QD079+fI0eO\n8Pbbb5OZmUnr1q2ZMGECycnJtao1Pj6eadOmMWPGDLKysgDvxLuXX3652oQ9Eakdi8fj8QS6CBEx\nzoIFCxg7diyff/45zZs3D3Q5IuInuoYuIiJiAgp0ERERE9CQu4iIiAmohy4iImICCnQRERETUKCL\niIiYgAJdRETEBBToIiIiJqBAFxERMYH/D5TnTOnCm8YVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf73e6e1d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HDBjCf/87ia1bN2FnZ8+YMR+j0+lMjj2z4x0dnZg/fzabN29ArVbTo0cvihUr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\n66dLvxMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf73e49898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R2tBff6IrIEZ",
        "colab_type": "code",
        "outputId": "4c91cc33-4e64-4bcb-e9ee-00986be7449f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "validation_acc = model_train.history['val_acc'][-1] * 100\n",
        "training_acc = model_train.history['acc'][-1] * 100\n",
        "print(\"Validation accuracy: {}%\\nTraining Accuracy: {}%\".format(validation_acc, training_acc))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation accuracy: 41.11111111111111%\n",
            "Training Accuracy: 82.30337078651685%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MguAJAUY0Hvc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Segundo modelo"
      ]
    },
    {
      "metadata": {
        "id": "TJIVyUAY0Xgt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import regularizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xZgKT49YikRg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "second_model = Sequential()\n",
        "second_model.add(Dense(units=16, input_shape=(12288,)))\n",
        "second_model.add(Activation('relu'))\n",
        "second_model.add(Dense(units=32))\n",
        "second_model.add(Activation('relu'))\n",
        "second_model.add(Dense(units=64))\n",
        "second_model.add(Activation('relu'))\n",
        "second_model.add(Dense(units=3, activation=\"softmax\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J3sFKfRm2B0_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "second_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R0c7yIVE3BCQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs = 10\n",
        "batch_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6L504AyK2nI5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "1730726a-2f04-44d1-a4e6-21d5a345abca"
      },
      "cell_type": "code",
      "source": [
        "second_model_train = second_model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_val, y_val))"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 356 samples, validate on 90 samples\n",
            "Epoch 1/10\n",
            "356/356 [==============================] - 2s 4ms/step - loss: 1.4237 - acc: 0.3792 - val_loss: 1.1118 - val_acc: 0.4111\n",
            "Epoch 2/10\n",
            "356/356 [==============================] - 0s 263us/step - loss: 1.1014 - acc: 0.3343 - val_loss: 1.1263 - val_acc: 0.3556\n",
            "Epoch 3/10\n",
            "356/356 [==============================] - 0s 263us/step - loss: 1.1044 - acc: 0.3624 - val_loss: 1.1032 - val_acc: 0.3667\n",
            "Epoch 4/10\n",
            "356/356 [==============================] - 0s 259us/step - loss: 1.0761 - acc: 0.4073 - val_loss: 1.1082 - val_acc: 0.3111\n",
            "Epoch 5/10\n",
            "356/356 [==============================] - 0s 267us/step - loss: 1.0311 - acc: 0.4803 - val_loss: 1.0865 - val_acc: 0.4333\n",
            "Epoch 6/10\n",
            "356/356 [==============================] - 0s 287us/step - loss: 0.9946 - acc: 0.5225 - val_loss: 1.0990 - val_acc: 0.3889\n",
            "Epoch 7/10\n",
            "356/356 [==============================] - 0s 292us/step - loss: 0.9904 - acc: 0.5056 - val_loss: 1.0881 - val_acc: 0.3778\n",
            "Epoch 8/10\n",
            "356/356 [==============================] - 0s 282us/step - loss: 0.9337 - acc: 0.5421 - val_loss: 1.1477 - val_acc: 0.4444\n",
            "Epoch 9/10\n",
            "356/356 [==============================] - 0s 279us/step - loss: 0.9095 - acc: 0.5702 - val_loss: 1.2298 - val_acc: 0.3667\n",
            "Epoch 10/10\n",
            "356/356 [==============================] - 0s 278us/step - loss: 0.9554 - acc: 0.5281 - val_loss: 1.0509 - val_acc: 0.4667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6TPqNvBk2rxL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "47dbcf48-b668-4386-88e4-6b3c9cb94a72"
      },
      "cell_type": "code",
      "source": [
        "plot_loss_and_accuracy(second_model_train)"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jI3jy5An79+81fSFKD0noQgiRg0WNnfDS2nRmqV+/IUuWLCQwsClFixYjd+7c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\njxhRfxZ0z553N3SrKtPDbe8eeb791En5tT08UT1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REREJgIFOREQkAAb6DbfbkpYcZ82a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\n6ERERAJgoBMREQng/wG1bGyCe9G50gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf5be67438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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znZycmTVrJqtWrUCtVtOzZx+KFy+RYVtuyeNTH/LowJalS7W8+aYLH32UyOuv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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf5f29eb38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "FtDSLtM426kU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "cacf35cd-85ec-4085-f839-4f8dea032daf"
      },
      "cell_type": "code",
      "source": [
        "validation_acc = second_model_train.history['val_acc'][-1] * 100\n",
        "training_acc = second_model_train.history['acc'][-1] * 100\n",
        "print(\"Validation accuracy: {}%\\nTraining Accuracy: {}%\".format(validation_acc, training_acc))"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation accuracy: 46.66666673289405%\n",
            "Training Accuracy: 52.80898876404494%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UWMSqecXBpLx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "second_model.save('tesla_model_46_52.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JclpBKGXGMEr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Cargar imagenes externas "
      ]
    },
    {
      "metadata": {
        "id": "Kycwl2AOGPNR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "093c6e19-5105-42a2-a989-2d9fb7866c8d"
      },
      "cell_type": "code",
      "source": [
        "! wget https://static.urbantecno.com/2018/08/Tesla-Model-3-4-720x550.jpg"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-11-02 01:46:08--  https://static.urbantecno.com/2018/08/Tesla-Model-3-4-720x550.jpg\n",
            "Resolving static.urbantecno.com (static.urbantecno.com)... 198.50.181.89\n",
            "Connecting to static.urbantecno.com (static.urbantecno.com)|198.50.181.89|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 65769 (64K) [image/jpeg]\n",
            "Saving to: ‘Tesla-Model-3-4-720x550.jpg’\n",
            "\n",
            "Tesla-Model-3-4-720 100%[===================>]  64.23K  --.-KB/s    in 0.08s   \n",
            "\n",
            "2018-11-02 01:46:09 (854 KB/s) - ‘Tesla-Model-3-4-720x550.jpg’ saved [65769/65769]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hErLjk43GVVB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "f99ed4ad-56ac-4ca0-f2a4-696ef58b870b"
      },
      "cell_type": "code",
      "source": [
        "! wget https://www.autonavigator.hu/wp-content/uploads/2014/01/109102_source-2.jpg"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-11-02 01:46:39--  https://www.autonavigator.hu/wp-content/uploads/2014/01/109102_source-2.jpg\n",
            "Resolving www.autonavigator.hu (www.autonavigator.hu)... 194.33.69.21\n",
            "Connecting to www.autonavigator.hu (www.autonavigator.hu)|194.33.69.21|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1028573 (1004K) [image/jpeg]\n",
            "Saving to: ‘109102_source-2.jpg’\n",
            "\n",
            "109102_source-2.jpg 100%[===================>]   1004K  1.20MB/s    in 0.8s    \n",
            "\n",
            "2018-11-02 01:46:41 (1.20 MB/s) - ‘109102_source-2.jpg’ saved [1028573/1028573]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "F59Xs9e9Gc9K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "ef0e5511-d4ae-421c-fe49-e2a97cd33dd8"
      },
      "cell_type": "code",
      "source": [
        "! wget https://upload.wikimedia.org/wikipedia/commons/9/92/2017_Tesla_Model_X_100D_Front.jpg"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-11-02 01:46:48--  https://upload.wikimedia.org/wikipedia/commons/9/92/2017_Tesla_Model_X_100D_Front.jpg\n",
            "Resolving upload.wikimedia.org (upload.wikimedia.org)... 198.35.26.112, 2620:0:863:ed1a::2:b\n",
            "Connecting to upload.wikimedia.org (upload.wikimedia.org)|198.35.26.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4768386 (4.5M) [image/jpeg]\n",
            "Saving to: ‘2017_Tesla_Model_X_100D_Front.jpg’\n",
            "\n",
            "2017_Tesla_Model_X_ 100%[===================>]   4.55M  19.3MB/s    in 0.2s    \n",
            "\n",
            "2018-11-02 01:46:49 (19.3 MB/s) - ‘2017_Tesla_Model_X_100D_Front.jpg’ saved [4768386/4768386]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S2fox-e6GfNZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "be9a123a-2cfd-4fa7-9819-1bbd224f315e"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "109102_source-2.jpg\t\t   Tesla-Model-3-4-720x550.jpg\n",
            "2017_Tesla_Model_X_100D_Front.jpg  tesla_model_46_56.h5\n",
            "master.zip\t\t\t   training_images\n",
            "sample_data\t\t\t   validation_images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-BiITWwiGhWC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = []\n",
        "\n",
        "image = load_img(\"./Tesla-Model-3-4-720x550.jpg\", target_size=(img_height, img_width))\n",
        "image_array = img_to_array(image)\n",
        "X.append(image_array)\n",
        "\n",
        "image = load_img(\"./109102_source-2.jpg\", target_size=(img_height, img_width))\n",
        "image_array = img_to_array(image)\n",
        "X.append(image_array)\n",
        "\n",
        "image = load_img(\"./2017_Tesla_Model_X_100D_Front.jpg\", target_size=(img_height, img_width))\n",
        "image_array = img_to_array(image)\n",
        "X.append(image_array)\n",
        "\n",
        "X_test = np.array(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OqbygitXGzyc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2ae3b554-a7f2-4855-e6fa-933181a72999"
      },
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "metadata": {
        "id": "ZvceAn2dHT9H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = X_test.astype('float32') / 255\n",
        "X_test = X_test.reshape(X_test.shape[0], second_shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T2_IHV2nHgiY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a808939a-7960-46d1-8ede-c9d1ef36d483"
      },
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 12288)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "metadata": {
        "id": "Ap3Acct5IbMH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_true = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YDJXSZqEHhKm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "187448a2-7df1-41b8-c1cf-a17c1f2ac432"
      },
      "cell_type": "code",
      "source": [
        "y_pred = second_model.predict(X_test, batch_size=None, verbose=1, steps=None)"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r3/3 [==============================] - 0s 3ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xHCEjxeVIAhz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "40075398-9bda-4882-abdf-1955d1fa712c"
      },
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.22741655, 0.4156091 , 0.35697436],\n",
              "       [0.583717  , 0.3267301 , 0.08955295],\n",
              "       [0.35106906, 0.5099873 , 0.13894367]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "metadata": {
        "id": "5ieAgSZaITZk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d53b1f62-785d-4c0d-aadd-d249c827b266"
      },
      "cell_type": "code",
      "source": [
        "np.argmax(y_true, axis=1)"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "metadata": {
        "id": "8WLPm3V-JGgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6770ce2b-bfbc-4e50-d92c-7e6786672f86"
      },
      "cell_type": "code",
      "source": [
        "np.argmax(y_pred, axis=1)"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "metadata": {
        "id": "-38b1PIjNWB2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}