mnist-digits/mnist-digit-classification-with-a-fully-connected-neural-network.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST Digit Classification with a Fully-Connected Neural Network",
      "provenance": [],
      "authorship_tag": "ABX9TyOtUl0jm0CM4G+U6wQaGTlq"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq7MqgGKBfOq"
      },
      "source": [
        "# MNIST Digit Classification with a Fully-Connected Neural Network\n",
        "*By Carlos Santiago Bañón*\n",
        "\n",
        "**Year:** 2020\n",
        "\n",
        "**Technologies:** Python, NumPy, Matplotlib, TensforFlow, Keras\n",
        "\n",
        "**Discipline(s):** Computer Vision, Deep Learning\n",
        "\n",
        "**Keywords:** `classification`, `computer-vision`, `deep-learning`, `fully-connected`, `mnist`, `multi-class-classification`, `neural-network`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSmvrCh8CqZq"
      },
      "source": [
        "This notebook presents an MNIST digit classifier built with a fully-connected neural network in TensorFlow and Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uzlnsvf1DGpk"
      },
      "source": [
        "## 1. Import Statements\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8DhknOWDIc4"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yuvx7u4qDQ0p"
      },
      "source": [
        "## 2. Data Preprocessing\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qI70NJ8SN4An"
      },
      "source": [
        "The first step is to preprocess our data. Here, we load the MNIST digit dataset from the Keras datasets library, split it into training and test sets, reshape the matrices, and encode the labels categorically."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsaUq1mDDVLT"
      },
      "source": [
        "# Load the MNIST dataset.\n",
        "mnist = tf.keras.datasets.mnist\n",
        "train_data, test_data = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWuEpda6EBMN"
      },
      "source": [
        "# Divide the data into features and labels.\n",
        "train_images, train_labels = train_data\n",
        "test_images, test_labels = test_data"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrAabZnyDinP"
      },
      "source": [
        "# Reshape and normalize the images.\n",
        "X_train = train_images.reshape((60000, 784))\n",
        "X_train = X_train.astype('float32') / 255\n",
        "X_test = test_images.reshape((10000, 784))\n",
        "X_test = X_test.astype('float32') / 255"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHUWJlyBE1P0"
      },
      "source": [
        "# Reshape the labels and encode them categorically.\n",
        "y_train = tf.keras.utils.to_categorical(train_labels)\n",
        "y_test = tf.keras.utils.to_categorical(test_labels)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVEOuuLPOJVH"
      },
      "source": [
        "Further, the following are the shapes of each matrix, as well as a visualization of a random MNIST digit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PpPi0qpNI_L",
        "outputId": "644ecbd9-6925-47fa-a5d1-33f68b2d2d96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Show the shapes of the data.\n",
        "print(\"Training Images:\", X_train.shape)\n",
        "print(\"Testing Images:\", X_test.shape)\n",
        "print(\"Training Labels:\", y_train.shape)\n",
        "print(\"Test Labels:\", y_test.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Images: (60000, 784)\n",
            "Testing Images: (10000, 784)\n",
            "Training Labels: (60000, 10)\n",
            "Test Labels: (10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSKjBaeNNc1m",
        "outputId": "6020122d-3261-412f-ed41-2c3449cd7c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Show a sample MNIST digit.\n",
        "plt.imshow(train_images[10])\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5ubfhUsFLR8"
      },
      "source": [
        "## 3. Neural Network\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gPoq2dRFtrH"
      },
      "source": [
        "### 3.1. Define the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jU5sDVjhOVgF"
      },
      "source": [
        "We then have to define our neural network. Here, we define a sequential model with two fully-connected layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri1rSpp3FSDS"
      },
      "source": [
        "# Define the sequential model.\n",
        "model = tf.keras.models.Sequential()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CNJ9gHWFWQv"
      },
      "source": [
        "# Add two fully-connected layers to the network.\n",
        "model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(28 * 28,)))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdMaLgtqFeV6",
        "outputId": "0d151a66-e466-4f9f-c581-6b7f95d11e59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Show the model.\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 512)               401920    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYactRmuOhCW"
      },
      "source": [
        "Once our model is defined, we can compile it using the Adam optimizer and the categorical cross-entropy loss function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57j-R0L4Fmmf"
      },
      "source": [
        "# Compile the model.\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRVabAG8F3IL"
      },
      "source": [
        "### 3.2. Train the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IuRzsr2Osga"
      },
      "source": [
        "We then train the model on 10 epochs, using a batch size of 256."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yl1f9mvZF5Pc",
        "outputId": "c3f72d50-525d-4904-d3f9-4188d3e381f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Define the parameters.\n",
        "num_epochs = 10\n",
        "batch_size = 256\n",
        "\n",
        "# Train the model.\n",
        "history = model.fit(X_train, \n",
        "                      y_train, \n",
        "                      epochs=num_epochs, \n",
        "                      batch_size=batch_size, \n",
        "                      validation_data=(X_test, y_test))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.3303 - accuracy: 0.9073 - val_loss: 0.1642 - val_accuracy: 0.9520\n",
            "Epoch 2/10\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.1390 - accuracy: 0.9602 - val_loss: 0.1128 - val_accuracy: 0.9656\n",
            "Epoch 3/10\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.0939 - accuracy: 0.9735 - val_loss: 0.0883 - val_accuracy: 0.9728\n",
            "Epoch 4/10\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.0683 - accuracy: 0.9810 - val_loss: 0.0774 - val_accuracy: 0.9752\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.0525 - accuracy: 0.9859 - val_loss: 0.0737 - val_accuracy: 0.9775\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.0401 - accuracy: 0.9890 - val_loss: 0.0700 - val_accuracy: 0.9784\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.0319 - accuracy: 0.9917 - val_loss: 0.0624 - val_accuracy: 0.9804\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.0249 - accuracy: 0.9938 - val_loss: 0.0629 - val_accuracy: 0.9797\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.0197 - accuracy: 0.9953 - val_loss: 0.0644 - val_accuracy: 0.9787\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.0149 - accuracy: 0.9969 - val_loss: 0.0602 - val_accuracy: 0.9815\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhyG0-sPHOAv"
      },
      "source": [
        "### 3.3. Display the Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TFadfX_Oyg_"
      },
      "source": [
        "Finally, we display the metrics. We begin by displaying the model's accuracy and loss based on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFoIAPdSYXqh",
        "outputId": "f42e5658-2d61-41cb-e4e7-53395906e28c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy:', test_accuracy)\n",
        "print('Test Loss:', test_loss)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0602 - accuracy: 0.9815\n",
            "Test Accuracy: 0.9815000295639038\n",
            "Test Loss: 0.06018499284982681\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3p2c0-RO2M8"
      },
      "source": [
        "Then, we save the metric values for each epoch to plot the loss and accuracy curves for our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-OdhC_0KtUx"
      },
      "source": [
        "# Save the metrics.\n",
        "metrics = history.history"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZztnSHgjZPDN"
      },
      "source": [
        "Finally, once we have our metric history, we can plot the curves."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfosMMiqHQB0"
      },
      "source": [
        "# Save the loss values.\n",
        "training_loss_list = metrics['loss']\n",
        "test_loss_list = metrics['val_loss']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKAZlyOmHV6f",
        "outputId": "c4dfefb7-c1ba-4afc-9753-2b776f236221",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Plot the training and test loss.\n",
        "x = np.arange(0, num_epochs, 1)\n",
        "plt.title('Training and Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.plot(x, training_loss_list, label='Training Loss')\n",
        "plt.plot(x, test_loss_list, label='Test Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwzIUzuuI0ZP"
      },
      "source": [
        "train_accuracy_list = metrics['accuracy']\n",
        "test_accuracy_list = metrics['val_accuracy']"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHj-j2HpI2Lb",
        "outputId": "a854646a-1ff0-4766-9a5d-6058d2309757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.title('Training and Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.plot(x, train_accuracy_list, label='Training Accuracy')\n",
        "plt.plot(x, test_accuracy_list, label='Test Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+9a+58sorueeeezjrrLM6/Pzh4eH885//ZPbs2URGRjJx4sQD9qmoqOCdd97hkUceaVwXGRnJiSeeyBtvvMHf//53rrvuOp544gkCAwN5+OGHmTJlCr/73e+YPn06gYGBHHvssSxevJhrr72WOXPmMGHChMb3bM7s2bN55JFHOOqoo0hJSWHy5MkAxMfH8+ijj3L++efj8Xjo168f7733HgDnnnsuV199tVUvdTOqTvfO7QXlZOVXsH1vhbNc4DwXVez/IyOxVxiD+0YwdkAvZqTE7/9LP2rfr/3AALtx8nB1mzmpU1NTtemEQd9//z1HHXWUnyLqPMrKyoiKikJV+eUvf8nIkSO59dZb/R1Wu61du5Zbb72Vjz76qMV97N+8c1JVckurycrfVwrwfi6r3lcdGiAwoHc4ybGRDImNIDk2ksENz30jCA+x0mNHEpF1qtpsn3orQfQAjz32GP/+97+pqanh2GOP5frrr/d3SO1233338fDDD1vbQydW71F2F1funwDchLB9bzlVtft6+wQFCIP6RjC4bwSpQ/owJDaS5LgIhsRGktQnnNAgSwKdgZUgTLdi/+a+VVvvYWdh5QElgO0F5ezYW7lfl8+QwAD3l79z4d/3HMmA3mHdpqdPV2clCGNMu3g8ys6iSjJzSsncU0pGTimZOSVszSunzqsnUHhwIENiIxjZL5pTxyQ0VgsNiY0ksVeYtQN0cZYgjOnh9pbXOIkgp6QxGWzKKd2vd9DA3uGMToxm1ugEhsU7pYDk2Ajio0NtFN1uzBKEMT1EZU09m3NL3WSwr2SQV1rduE/viGBSEqK58PgkRiVGMzoxmlEJ0V3irl/T8SxBGNPN1HuUrIJyNuU0VA05ySCroJyGJsfQoABGJkRx8sh4Jwm4yaCflQiMF0sQPnQ4w32Dc7dxSEgIU6dObXGfuXPnkpOT03ijmek5GrqONpQIMnJKydxTwuY9ZVTXOY3FIpAcG0lKQjTnThjQmAySYyOtfcC0yhKED7U23Hdr0tPTiYqKajFBFBUVsW7dOqKioti6dSvDhg3rkLibsuG5/a+sum5f1VBOiZsMSve7iSw+OpTRidFcPnkIKYnRjE7sxYh+UXbfgDlk9r/+CFu3bh2/+tWvKCsrIy4ujsWLF9O/f38eeOABHnnkEYKCghgzZgz33XcfjzzyCIGBgTz99NM8+OCDnHTSSfud6+WXX+acc84hISGB5557jjvuuAOALVu2cMMNN5CXl0dgYCAvvvgiw4cP57//+795+umnCQgI4Mwzz+S+++5jxowZ3H///aSmppKfn09qaipZWVksXryYl19+mbKyMurr63nrrbeYM2cOhYWF1NbWcs899zBnzhyAA4b9/uc//8n48ePZtGkTwcHBlJSUMGHChMbX5uAqa+r5bncx32Y3PIrYmr+veigyJJBRidGceXQiKQnRpCT2IiUxmr6RBy+RGtNePSdBvH075Kzv2HMmjoMz72vz7qrKTTfdxGuvvUZ8fDzPP/88v/vd71i0aBH33Xcf27ZtIzQ0lKKiInr37s0NN9xw0FLH0qVLufPOO0lISOCCCy5oTBDz58/n9ttv57zzzqOqqgqPx8Pbb7/Na6+9xmeffUZERESbh+f+9ttv6du3L3V1dbzyyiv06tWL/Px8Jk+ezLnnnst33313wLDf0dHRzJgxg7feeou5c+fy3HPPcf7551tyaEZ1XT0Zu0v5dmcx67OL+Da7mM25ZY2DyiX0CmXcwN7MOWYgY/o7iSCpT7i1E5gjouckiE6gurqaDRs2cNpppwFQX19P//79ARg/fjzz589n7ty5zJ07t9Vz7dmzh82bN3PiiSciIgQHB7NhwwaGDBnCzp07Oe+88wAICwsD4P333+fqq68mIiICaNvw3KeddlrjfqrKHXfcwapVqwgICGDnzp3s2bOHlStXNjvs9zXXXMNf/vIX5s6dy5NPPsljjz3Wnq+qW6qt97B5TxnrdxbxTXYx67OLycgpaZxFrG9kCOOTYjh9TALjknozPimGhF5hfo7a9GQ9J0G045e+r6gqY8eOZc2aNQdse+utt1i1ahVvvPEG9957L+vXH7y088ILL1BYWNg4b0JJSQlLly7l9ttvb1dM3sNzNx1u23ugvWeeeYa8vDzWrVtHcHAwycnJBx2ee9q0aWRlZZGenk59fT1HH310u+Lq6uo9yrb8Mr7ZUcz6nU410cZdJY2Nx9FhQYxPiuFnJw5jQlIM45JiGNjbSgamc+k5CaITCA0NJS8vjzVr1jBlyhRqa2vZtGkTRx11FDt27GDmzJmceOKJPPfcc5SVlREdHU1JSfOz4C1dupR33nmHKVOmALBt2zZOPfVU7r33XpKSknj11VeZO3cu1dXV1NfXc9ppp3H33Xczf/78xiom7+G5J02axLJly1qMvbi4mH79+hEcHExaWhrbt28HaHHYb4ArrriCyy67jD/84Q8d/E12LqrK9oKKxmqib7KL2bizuPFGs4iQQI4eEMPlk4cwLimG8Um9GdI3ggDrRWQ6OUsQR1BAQADLli3j5ptvpri4mLq6OhYuXMioUaNYsGABxcXFqCo333wzvXv35pxzzuHCCy/ktdde26+ROisri+3btzcOkQ0wdOhQYmJi+Oyzz3jqqae4/vrrufPOOwkODubFF19k9uzZfP3116SmphISEsJPfvIT/vSnP3Hbbbdx8cUX8+ijjx50uPH58+dzzjnnMG7cOFJTUxk9ejQAY8eObXbY74Zjfv/73x90mtOuRlXZVVzFtzuK3ITglA4aZiALCQpg7IBeXHh8EuOSejMhKYZh8VHWpdR0STZYn/GZZcuW8dprr/HUU08dsffs6H/zveU1fLm9kG/daqL12cUUlDuz5AUFCKP7RzM+qTfjBzrVRKMSonvcrGOma7PB+swRd9NNN/H222+zfPlyf4fSbtmFFazYuIcVG3NYm7UXjzpzFIxKiGbW6H6Md6uJUhKjCQu2ewxM92UJwvjEgw8+6O8Q2kxV2bSnjBUbc1ixMYeNu5x2n5SEaG6cOYKTRsUzdkAvIkLsv4vpWbr9X7yqWs+QHqI91aUej/LVjiLedZNCVkEFAMcN7s1vzxzNGWMTSY5rfrpUY3qKbp0gwsLCKCgoIDY21pJEN6eqFBQUNN730ZyaOg+fbi1gxcYc3vtuD7ml1QQFCFOGx3LNScM4fUwC/ey+A2MadesEkZSURHZ2Nnl5ef4OxRwBYWFhJCUl7beuoqaODzPzWLExhw8ycimtqiM8OJAZKfHMPjqRGSn9iAm3O7yNaU63ThDBwcGNN5KZnqOwvIb3v9/Dio17+GhzHtV1HvpEBHPG2ETOGJvISSPjrHHZmDbo1gnC9By7iiobG5m/yCqk3qMMiAlj3qTBnDE2kYnJfWwOZGPayRKE6bK25JbyzoYcVmzcw/qdxQCM7BfFz6cP54yxiRw9sJe1PRlzGCxBmC7D41G+yS5ixcY9vPtdDlvzygE4ZlBvfjN7NGeMTWBYfJSfozSm+7AEYTq12noPn23d29jzKKekiqAAYfKwWK6emsxpYxJJjLGeR6YHKst1pjDYswGCwuCE6zv8LXyaIERkNvB3IBB4XFXva7J9CLAIiAf2AgtUNdvd9hfgLCAAeA+4RbvLuCDmoFSVT7fu5cW1O/ggI5fiylrCggOYPiqeX49N4ZTRCcREWM8j00PU1UD+JtizEfash5wNTlIo9+qdOWRa10oQIhIIPAScBmQDX4jI66r6nddu9wNLVPXfIjIL+DNwuYhMBaYB4939PgamA+m+itf4n6ry8ZZ8HvhgM19kFRITHswpR/XjjLGJnDwy3qbO9DePByryISAIQiIhMMSZ9Np0nPL8faWCPRudZJCXAR53atnAUOg3GkaeDglHQ+LRznNE6/O7HApfliAmAVtUdSuAiDwHzAG8E8QY4FfuchrwqrusQBgQAggQDOzxYazGj1SV9Mw8/v7BZr7eUUT/mDDunjOWi1MHWXfUI62yEAqzoHA7FG3ft1yYBcU7oL5m374NiSI40nlu7tHStoOtD+oBU6fW10LBFrc00FAq2AhlOfv2iUp0EsCIU/Ylg9gREHjkSs++TBADgR1er7OBE5rs8w1wPk411HlAtIjEquoaEUkDduMkiH+o6vc+jNX4gary/ve5PPDBZtbvLGZg73D+dN44Ljh+IKFBlhh8orbKudAXZu17FG13k8B2qC7ef//wPtB7iDO97lFnQ8wg8NRDTRnUVkBNubNc07Bc7lR9FGZ5bSsHT13bYwwIbiWhREBINITFQHhv5zms94HLwRGdo4RTsdctFWx0SgY5651SQUOyDQh2SgXDZ+5fKoiM82/c+L+R+jbgHyJyFbAK2AnUi8gI4Cig4bbY90TkJFX9yPtgEbkOuA5g8ODBRyxoc3g8HmXFxhweWLmF73eXMLhvBH+5YDznHTfQhso+XB4PlO4+8Nd/w+vS3fvvHxQGvQdDn2QYdAL0GeIs9x7iLIfFdExcdTUHSSoHSTa15fuWy/bsW64pg+rmJ9NqFBDcQhLp7bW+heXQGAho599ifR3s/eHAKqLSXfv2ieznJIBhN+xLBnGjjmipoD18mSB2AoO8Xie56xqp6i6cEgQiEgVcoKpFInIt8Kmqlrnb3gamAB81Of5R4FFw5oPw0ecwHaTeoyxfv5sHV25m054yhsZF8j8XTWDOMQPsJrb2qCxscuH3Wi76cf9qIAR6DXQu+sNnuRf+5H2JILJf+y+EhyIoBIL6Ah1YV+6ph6pi91EElUXOc1Vx88sN1WcN+2r9QU4uENoLwmMOTCLeCcdT55YK3LaCOnca3oAgiEuBoSftXyqI6tdxn/8I8GWC+AIYKSJDcRLDpcBl3juISBywV1U9wG9xejQB/AhcKyJ/xqlimg78zYexGh+qq/fwxre7+MfKLfyQV86IflH8/dJjOHv8gJ4705qq86u5qsT5JVxV4lTv7Pfa+7kYirOdJFDVQjVQwtEw+iyvEkAyxCRBUKg/PqHvBQQ6jbOH0kCr6pRE2pRY3Nf5m/ct11XuO1dEnJMAJl7jVSpI6RZtKT5LEKpaJyI3AitwurkuUtWNInI3sFZVXwdmAH8WEcWpYvqle/gyYBawHqfB+h1VfcNXsRrfqK338OpXO3kobQtZBRWMTozmocuO48yjE7v2fMwNF5cDLuQHu8A3s/2gv2ABCYDQaKe6I6wX9Brg22qgnkQEQqOcR0xS6/s3VVvlJAoRiIzvHG0dPtCtpxw1/lFT5+HlL7N5KH0LO/ZWMnZAL26aNZLTxyR0/sRQWQQFPzg9TAo2O89luU0u8KVtvLj3ci7sDRf4xtfNPcccuD4kqtteeEznYVOOmiOiuq6eF9Zm80j6D+wsqmRCUgx3nTOWWaP7da4xkeqqnbrogi1OtUHBln0P75uPJMD5ld5rgPMrM2xMKxd3r0QQEmkXd9PlWYIwh62qtp7nPv+RRz7cSk5JFccN7s295x3N9FHx/ksMqlCya18poOCHfcmgaDuoZ9++kfEQOxJGzYa4kU5f89iRTjVON6hHNuZQWYIwh6yipo5nP/uRf63aSl5pNZOS+/I/F09g6vAjOINfc1VCDQmhtmLffsEREDscBhwD4y5ykkDcCOg73OmVYow5gCUI027l1XU89el2Hlu1lYLyGqYOj+XBeccyeVisb96wvVVCsSMg+SQnIcS6JYLo/kemO6cx3YglCNNmpVW1LFmzncc/2kphRS0nj4rn5lkjSE3ugL7tqs44NPmb3Mdm57nFKqERTpVQ7Ih91UJ9krtvl05j/MAShGlVcUUtT67exqKPt1FSVces0f24adYIjh3cp/0nq69zSgNNE0H+JqfPeYOgcOeiP+AYGHfhvpJArFUJGXOkWIIwLSosr2HRJ9tY/EkWpdV1nDYmgZtnjWRcUhv63VeVOG0C3gkgf7PTNtAwMiVAVIIz1MDR5zvPcSOd515JViVkjJ9ZgjAHKCir5rGPtvHUmizKa+r5ybhEbpw5kjEDeu2/oyqU7DywJJC/ef8xfyQQ+g5zLvyjZkN8irMcO8JKA8Z0YpYgzH6e+Ww797z5PVV19Zw9fgA3zhxBSmww7N0KGzObJIItzjchqVoAABo1SURBVGBqDUJjnBLAsJn7SgJxo6y7qDFdlCUIAzgjrP73Oxm8tOor/qP/Ji4YXEGfiix4ftOBjcQxg50EcNzU/RNBVD+7OcyYbsQShKGqtp4/PfMOI7csYk34KoILa6A0zGkYHnAsjL9kXyKIHe7cJWyM6fYsQfRwxdu+4pvn7+LOylVIcAABx1wGk2+A+NHOaJnGmB7LEkRPtX0NFSv/Ssz2DzhOw/hx1JUMO+fXzrhDxhiDJYieRRU2rYCP/w92fEo10TwplzLtst9wzKhh/o7OGNPJWILoCerrYOPLTmLI/Y6K8P7cX38Vq3v9hH/99ESGxFqbgjHmQJYgurPaSvjqaVj9ABT9iMYfxcrRd3P918kcmxzP0stT6RNp3U+NMc2zBNEdVRbBF4/Dpw9DRT4kTaL+jPv4w3dJPPtFNudMGMBfLxxPWLA1QhtjWmYJojspzYE1D8HaJ6GmFEacBifeSlniJH757Fd8uCmbX8wYzm2np3T+md2MMX5nCaI7KPjBqUb6+lnw1MHY8+HEhZA4jt3Flfz0X5+yaU8pfz5/HPMmDfZ3tMaYLsISRFe2+xun4fm71yAgGI5dAFNvcsY9Ar7bVcJPF39BaVUti66ayPRR8X4O2BjTlViC6GpUIetjJzH88IEz//G0W+CEn0N0QuNuH27K4xdPryM6LJgXb5h64EB7xhjTCksQXYXHA5nLncSwc60zac4p/wUTfwZh+w+/vfTzH/n9qxsYlRDNk1dNJDEmzE9BG2O6MksQnV19Lax/ET7+G+RnOlNqnvW/cMxlEBy+364ej3L/u5n8M/0Hpo+K56H5xxEVav/ExphDY1ePzqqmHL5cAqv/ASXZkHA0XPAEjJkLgQf+s1XV1vOfy77ljW92MW/SYP44ZyxBgTbhjjHm0FmC6Gwq9sLnj8Fnj0DlXhg8Fc75G4w4tcWhtAvLa7juqbV8kVXI7WeO5vqThyE27LYx5jBZgugsqksh/T7nHobachh1ptNVdfDkgx62vaCcq578gp1FlTw471jOmWCD7RljOoYliM6gdA88exHkrIdxF8G0hZAwptXD1m0v5Nola/Go8sw1JzAxue8RCNYY01NYgvC3/M3w9PlQng/znodRp7fpsOXrd7Pw+a8ZEBPGk1dPYmicDbhnjOlYliD8acfn8OzFIIFw1Zsw8PhWD1FVHvtoK39ansHxQ/rw2BWp9LUB94wxPmAJwl++fxNe+pkzQc+Clxrvfj6YunoPd72xkac//ZGzxvXnfy6eYAPuGWN8xhKEP3z+GLz9axhwHFz2PETGtXpIeXUdNz77JWmZeVw/fRi/OWO0DbhnjPEpSxBHkip88P+cu6FHnQkXLoKQiFYP21NSxU8Xf8H3u0u4Z+7RLJg85AgEa4zp6SxBHCl1NfD6jfDt83D81fCT+5u94a2pjJwSfvrkFxRV1vLElROZObrfEQjWGGMsQRwZVSXwwuWwNR1m/R5Ouq3Fm968fbw5n58/vY6I0EBeuH4KRw+MafUYY4zpKJYgfK1kNzxzEeR9D3P+CcfOb9NhL3yxgzteWc+IflEsumoiA3qHt36QMcZ0IJ8O1iMis0UkU0S2iMjtzWwfIiIfiMi3IpIuIkle2waLyLsi8r2IfCciyb6M1SfyMuGJ06Bwm9MY3YbkoKr8z7uZ/Pqlb5kyPJYXb5hiycEY4xetJggROUdE2p1IRCQQeAg4ExgDzBORprcH3w8sUdXxwN3An722LQH+qqpHAZOA3PbG4Ffb18ATp0NdNVz1ljOWUiuq6+q59fmveXDlFi6dOIhFV00kOiz4CARrjDEHasuF/xJgs4j8RURGt+Pck4AtqrpVVWuA54A5TfYZA6x0l9MatruJJEhV3wNQ1TJVrWjHe/vXd6/BkjnOnA3XvAcDjmn1kMqaeq544nNe/XoX/3lGCn8+fxzBNhqrMcaPWr0CqeoC4FjgB2CxiKwRketEJLqVQwcCO7xeZ7vrvH0DnO8unwdEi0gsMAooEpGXReQrEfmrWyLp/D59BF64EvpPgJ+9C32S23TYm9/u4rNte/nrheP55cwRNhqrMcbv2vQTVVVLgGU4pYD+OBfzL0XkpsN8/9uA6SLyFTAd2AnU4zSen+RunwgMA65qerCbqNaKyNq8vLzDDOUweTzw7u/hnd/A6LPgytchou2D56Vn5tEvOpQLj09qfWdjjDkC2tIGca6IvAKkA8HAJFU9E5gA/MdBDt0JDPJ6neSua6Squ1T1fFU9Fvidu64Ip7TxtVs9VQe8ChzX9A1U9VFVTVXV1Pj4+NY+iu/UVcPL18DqB2HitXDxkgNmezuY2noPqzbnMTOln5UcjDGdRlu6uV4A/J+qrvJeqaoVIvKzgxz3BTBSRIbiJIZLgcu8dxCROGCvqnqA3wKLvI7tLSLxqpoHzALWtuUDHXGVRfD8Asj6CE69yxmqu50X+XXbCymtqrOb4IwxnUpbqpjuAj5veCEi4Q1dTlX1g5YOcn/53wisAL4HXlDVjSJyt4ic6+42A8gUkU1AAnCve2w9TvXSByKyHhDgsfZ8sCOieCc8eSb8+Cmc9yiceGu7kwNAWmYuwYHCtBGxPgjSGGMOTVtKEC8CU71e17vrJrZ2oKouB5Y3WXen1/IynLaN5o59Dxjfhvj8Y8938MyFzl3SC5bBsBmHfKr0jDwmJve1Lq3GmE6lLSWIILebKgDucs+egGDbR7BoNnjq4advH1Zy2FlUSeaeUmamWPWSMaZzaUuCyPOqEkJE5gD5vgupk9vwkjMDXHQiXPM+JI47rNOlZzr3/80c7cdGdmOMaUZbqphuAJ4RkX/gtAXsAK7waVSd1ep/wLu/g8FT4dJn2tWNtSVpGbkM6hvO8PioDgjQGGM6TqsJQlV/ACaLSJT7usznUXU2Ho+TGD79J4yZ4zRIB4cd9mmrauv5ZEsBF6UmWfdWY0yn06bRXEXkLGAsENZwIVPVu30YV+dRWwWvXA/fvQon/BzO+BMEdMwQGJ9v20tlbb21PxhjOqVWE4SIPAJEADOBx4EL8er22q1VFsJz82H7J3D6vTD1xg49fVpmLqFBAUweZt1bjTGdT1t+Ck9V1SuAQlX9f8AUnLGSureiHU5Ppewv4IInOjw5gDO8xpThsYSHdI1hpowxPUtbEkSV+1whIgOAWpzxmLqvnPXOPA4lu2HByzDuwg5/i2355WzLL2eW3T1tjOmk2tIG8YaI9Ab+CnwJKJ3xruaOsjUdnlsAodHOPQ4JY33yNmkZTvfWGaMsQRhjOqeDJgh3oqAP3AH0XhKRN4EwVS0+ItEdad++AK/+AuJGwvxlENN0dPKOk5aZy/D4SAbHRvjsPYwx5nActIrJHUTvIa/X1d0yOajCx/8HL18LgyfD1W/7NDlU1NTx2da91nvJGNOptaUN4gMRuUC6a0d9Tz28/Wt4/y44+gJY8BKE9/bpW67eUkBNvcdGbzXGdGptSRDX4wzOVy0iJSJSKiIlPo7ryCnMgq+fhak3wfmPQ1Coz98yLTOXyJBAJiYf/p3YxhjjK225k7q1qUW7ttjh8ItPofeg1vftAKpKWkYuJ46MIyTI5pw2xnRebblR7uTm1jedQKhLO0LJAWDTnjJ2FVdx8ykjj9h7GmPMoWhLN9f/9FoOAyYB63BmeTPtlOaO3jrDGqiNMZ1cW6qYzvF+LSKDgL/5LKJuLi0jl6P69yIx5vAH+zPGGF86lErwbOCojg6kJyipqmXt9kJm2dwPxpguoC1tEA/i3D0NTkI5BueOatNOH23Kp96jdv+DMaZLaEsbxFqv5Tpgqap+4qN4urW0zFxiwoM5ZpBv77MwxpiO0JYEsQyoUtV6ABEJFJEIVa3wbWjdi8ejpGfmcfKoeIICrXurMabza9Od1EC41+tw4H3fhNN9bdxVQn5ZNTNTrP3BGNM1tCVBhHlPM+ou2whz7ZSWmYsITB9lCcIY0zW0JUGUi8hxDS9E5Hig0nchdU8rM3KZkNSb2CjfD+VhjDEdoS1tEAuBF0VkFyBAInCJT6PqZgrKqvkmu4iFp3T/ifiMMd1HW26U+0JERgMp7qpMVa31bVjdy6rNeajCTLv/wRjThbRaxSQivwQiVXWDqm4AokTkF74PrftIy8gjLiqUowfE+DsUY4xps7a0QVzrzigHgKoWAtf6LqTupd6jfLgpjxkp8QQEdM8pNYwx3VNbEkSg92RBIhIIhPgupO7lqx8LKa6stbunjTFdTlsaqd8BnheRf7mvrwfe9l1I3UtaZi6BAcKJI+P8HYoxxrRLWxLEb4DrgBvc19/i9GQybZCWkcfxQ/oQEx7s71CMMaZdWq1iUlUP8BmQhTMXxCzge9+G1T3kFFfx3e4Sq14yxnRJLZYgRGQUMM995APPA6jqzCMTWtf34SZncqBZoy1BGGO6noNVMWUAHwFnq+oWABG59YhE1U2szMhlQEwYoxKi/B2KMca028GqmM4HdgNpIvKYiJyCcye1aYOaOg8fb85nxuh+eHUCM8aYLqPFBKGqr6rqpcBoIA1nyI1+IvKwiJx+pALsqtZm7aW8pt7aH4wxXVZbGqnLVfVZd27qJOArnJ5NrRKR2SKSKSJbROT2ZrYPEZEPRORbEUkXkaQm23uJSLaI/KONn6fTSMvMJSQwgKnDY/0dijHGHJJ2zVyjqoWq+qiqntLavu4NdQ8BZwJjgHkiMqbJbvcDS1R1PHA38Ocm2/8IrGpPjJ1FWmYeJwzrS2RoW3oSG2NM5+PLqc0mAVtUdauq1gDPAXOa7DMGWOkup3lvd4cVTwDe9WGMPrFjbwVbcsuseskY06X5MkEMBHZ4vc5213n7BqcxHOA8IFpEYkUkAPgf4LaDvYGIXCcia0VkbV5eXgeFffjSMp3urTOte6sxpgvz9+TItwHTReQrYDqwE6gHfgEsV9Xsgx3sVnelqmpqfHznGUo7LSOX5NgIhsZF+jsUY4w5ZL6sIN8JDPJ6neSua6Squ3BLECISBVygqkUiMgU4yR1WPAoIEZEyVT2gobuzqaqtZ/UPBcybNNjfoRhjzGHxZYL4AhgpIkNxEsOlwGXeO4hIHLDXHc7jt8AiAFWd77XPVUBqV0gOAGu2FlBd57HqJWNMl+ezKiZVrQNuBFbgjN30gqpuFJG7ReRcd7cZQKaIbMJpkL7XV/EcKWkZuYQHB3LC0L7+DsUYYw6LT/tgqupyYHmTdXd6LS8DlrVyjsXAYh+E1+FUlZUZuUwbEUtYcKC/wzHGmMPi70bqbuWHvHKyCyuZYd1bjTHdgCWIDpTudm+dkdJ5elQZY8yhsgTRgdIycxmVEEVSnwh/h2KMMYfNEkQHKauu4/Nte633kjGm27AE0UE+3pxPbb3a8BrGmG7DEkQHSc/MJTo0iOOH9PF3KMYY0yEsQXQAVSUtM5eTRsURHGhfqTGme7CrWQf4fncpe0qqrXurMaZbsQTRAdKse6sxphuyBNEB0jJyGTcwhn7RYf4OxRhjOowliMNUVFHDlz8WMtNKD8aYbsYSxGFatTkfj8IMu//BGNPNWII4TOkZufSNDGFCUm9/h2KMMR3KEsRh8HiU9E15TB8VT2CA+DscY4zpUJYgDsM32UXsLa+x3kvGmG7JEsRhSMvMI0Dg5JGWIIwx3Y8liMOQnpnLsYP70CcyxN+hGGNMh7MEcYjySqv5NrvYurcaY7otSxCH6MNNeQA2vLcxptuyBHGI0jJy6Rcdypj+vfwdijHG+IQliENQW+9h1eY8Zqb0Q8S6txpjuidLEIfgy+2FlFbVMXO0tT8YY7ovSxCHIC0zj+BAYdqIOH+HYowxPmMJ4hCkZ+YyMbkv0WHB/g7FGGN8xhJEO+0sqiQjp9TmnjbGdHuWINop3Z0cyNofjDHdnSWIdkrLyCOpTzjD46P8HYoxxviUJYh2qK6r55Mt+da91RjTI1iCaIfPt+2lsrbeqpeMMT2CJYh2WJmRS2hQAFOGWfdWY0z3ZwmiHdIz85gyPJbwkEB/h2KMMT5nCaKNtuWXsy2/3Lq3GmN6DEsQbdTYvdUShDGmh7AE0UZpmXkMi49kcGyEv0MxxpgjwhJEG1TU1PHp1gJmWenBGNOD+DRBiMhsEckUkS0icnsz24eIyAci8q2IpItIkrv+GBFZIyIb3W2X+DLO1qzeUkBNnccmBzLG9Cg+SxAiEgg8BJwJjAHmiciYJrvdDyxR1fHA3cCf3fUVwBWqOhaYDfxNRHr7KtbWpGXmEhkSSGpyH3+FYIwxR5wvSxCTgC2qulVVa4DngDlN9hkDrHSX0xq2q+omVd3sLu8CcgG/3J2mqqRn5jFtRByhQda91RjTc/gyQQwEdni9znbXefsGON9dPg+IFpFY7x1EZBIQAvzQ9A1E5DoRWSsia/Py8joscG+bc8vYWVRp1UvGmB7H343UtwHTReQrYDqwE6hv2Cgi/YGngKtV1dP0YFV9VFVTVTU1Pt43BYyVGda91RjTMwX58Nw7gUFer5PcdY3c6qPzAUQkCrhAVYvc172At4DfqeqnPozzoNIycjmqfy8SY8L8FYIxxviFL0sQXwAjRWSoiIQAlwKve+8gInEi0hDDb4FF7voQ4BWcBuxlPozxoEqqalm7vZCZKTY4nzGm5/FZglDVOuBGYAXwPfCCqm4UkbtF5Fx3txlApohsAhKAe931FwMnA1eJyNfu4xhfxdqSjzfnU+9Ra38wxvRIvqxiQlWXA8ubrLvTa3kZcEAJQVWfBp72ZWxtkZaRS0x4MMcO8lsPW2OM8Rt/N1J3Wh6PkpaZx8mj4gkKtK/JGNPz2JWvBRt3lZBfVm3tD8aYHssSRAvSMnMRgZNHWYIwxvRMliBakJaZy/ik3sRFhfo7FGOM8QtLEM3YW17D1zuKrHrJGNOjWYJoxqpNeajCLOveaozpwSxBNGNlRi5xUSEcPSDG36EYY4zfWIJoot6jfLgpj+mj+hEQIP4Oxxhj/MYSRBNf7yikuLKWmaOt/cEY07NZgmgiLSOPwADhpJGWIIwxPZsliCbSMnM5fkgfYsKD/R2KMcb4lSUIL3tKqti4q8TmfjDGGCxB7Cc9050cyNofjDHGEoS3tIw8+seEkZIQ7e9QjDHG7yxBuGrqPHy8JZ8ZKf0Qse6txhhjCcK1dvteyqrr7O5pY4xxWYJwpWXkEhIYwNThsf4OxRhjOgVLEK60zDxOGNaXyFCfTrJnjDFdhiUIYMfeCrbkljHDurcaY0wjSxB4dW+14b2NMaaRJQic6qXk2AiGxUf5OxRjjOk0enyCqKqtZ/UP+Va9ZIwxTfT4BFFSWcvpYxI5Y2yiv0MxxphOpcd32enXK4wH5h3r7zCMMabT6fElCGOMMc2zBGGMMaZZliCMMcY0yxKEMcaYZlmCMMYY0yxLEMYYY5plCcIYY0yzLEEYY4xplqiqv2PoECKSB2w/jFPEAfkdFE5XZ9/F/uz72J99H/t0h+9iiKo2O1Jpt0kQh0tE1qpqqr/j6Azsu9iffR/7s+9jn+7+XVgVkzHGmGZZgjDGGNMsSxD7POrvADoR+y72Z9/H/uz72KdbfxfWBmGMMaZZVoIwxhjTLEsQxhhjmtXjE4SIzBaRTBHZIiK3+zsefxKRQSKSJiLfichGEbnF3zH5m4gEishXIvKmv2PxNxHpLSLLRCRDRL4XkSn+jsmfRORW9//JBhFZKiJh/o6po/XoBCEigcBDwJnAGGCeiIzxb1R+VQf8h6qOASYDv+zh3wfALcD3/g6ik/g78I6qjgYm0IO/FxEZCNwMpKrq0UAgcKl/o+p4PTpBAJOALaq6VVVrgOeAOX6OyW9Udbeqfukul+JcAAb6Nyr/EZEk4CzgcX/H4m8iEgOcDDwBoKo1qlrk36j8LggIF5EgIALY5ed4OlxPTxADgR1er7PpwRdEbyKSDBwLfObfSPzqb8CvAY+/A+kEhgJ5wJNuldvjIhLp76D8RVV3AvcDPwK7gWJVfde/UXW8np4gTDNEJAp4CVioqiX+jscfRORsIFdV1/k7lk4iCDgOeFhVjwXKgR7bZicifXBqG4YCA4BIEVng36g6Xk9PEDuBQV6vk9x1PZaIBOMkh2dU9WV/x+NH04BzRSQLp+pxlog87d+Q/CobyFbVhhLlMpyE0VOdCmxT1TxVrQVeBqb6OaYO19MTxBfASBEZKiIhOI1Mr/s5Jr8REcGpY/5eVf/X3/H4k6r+VlWTVDUZ5+9ipap2u1+IbaWqOcAOEUlxV50CfOfHkPztR2CyiES4/29OoRs22gf5OwB/UtU6EbkRWIHTC2GRqm70c1j+NA24HFgvIl+76+5Q1eV+jMl0HjcBz7g/prYCV/s5Hr9R1c9EZBnwJU7vv6/ohsNu2FAbxhhjmtXTq5iMMca0wBKEMcaYZlmCMMYY0yxLEMYYY5plCcIYY0yzLEEY0w4iUi8iX3s9OuxuYhFJFpENHXU+Yw5Xj74PwphDUKmqx/g7CGOOBCtBGNMBRCRLRP4iIutF5HMRGeGuTxaRlSLyrYh8ICKD3fUJIvKKiHzjPhqGaQgUkcfceQbeFZFwv30o0+NZgjCmfcKbVDFd4rWtWFXHAf/AGQkW4EHg36o6HngGeMBd/wDwoapOwBnTqOEO/pHAQ6o6FigCLvDx5zGmRXYntTHtICJlqhrVzPosYJaqbnUHPMxR1VgRyQf6q2qtu363qsaJSB6QpKrVXudIBt5T1ZHu698Awap6j+8/mTEHshKEMR1HW1huj2qv5XqsndD4kSUIYzrOJV7Pa9zl1eybinI+8JG7/AHwc2ic9zrmSAVpTFvZrxNj2ifca6RbcOZobujq2kdEvsUpBcxz192EMwvbf+LMyNYwAuotwKMi8jOcksLPcWYmM6bTsDYIYzqA2waRqqr5/o7FmI5iVUzGGGOaZSUIY4wxzbIShDHGmGZZgjDGGNMsSxDGGGOaZQnCGGNMsyxBGGOMadb/B67x3Us5NN5PAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2BDwaUigDgC"
      },
      "source": [
        "## 4. Make a Prediction\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vx34e_AWgLeE"
      },
      "source": [
        "Once our model is trained, we can use it to make predictions. To do this, we first use our test set to predict the classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SylCmq61gTnM"
      },
      "source": [
        "# Make predictions with the trained model.\n",
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMo9sEZpggPq"
      },
      "source": [
        "Finally, we can show a random test image with its corresponding prediction."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc6bx2stgfiY",
        "outputId": "9737b199-0a4d-40f0-c00f-2bf656f650b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Choose an index.\n",
        "index = 45\n",
        "\n",
        "# Show an image from the test set.\n",
        "plt.imshow(test_images[index])\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bmSdfcFgunA",
        "outputId": "f6ddf4a6-3cf4-4615-f080-d443f3b4c043",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\"Prediction:\", np.argmax(predictions[index]))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction: 5\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}