digits_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OEv4PvkIbatp"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.list_physical_devices()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atF4abDRgMQh",
        "outputId": "fec63a81-35b5-4f99-9526-7f1a03f47682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.config.list_physical_devices('GPU'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mu3OTVGDbnPX",
        "outputId": "874a7f8f-93ab-4849-c6e1-a9cdeb59a8f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzWVLDYJbrye",
        "outputId": "0c5ee39c-b751-4199-89dc-70edde506ea0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import mnist"
      ],
      "metadata": {
        "id": "SStiR5c1b1MJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zqw3DmJqb_cc",
        "outputId": "ab801687-77a9-44ab-d1e1-79b556b0d1d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLIr5dv2cpXo",
        "outputId": "560761c2-ff41-4271-bf25-c8291eb670f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWDJhSjjcrHX",
        "outputId": "e091eafe-af58-4045-d994-a7002d2fcc3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
              "         18,  18,  18, 126, 136, 175,  26, 166, 255, 247, 127,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  30,  36,  94, 154, 170,\n",
              "        253, 253, 253, 253, 253, 225, 172, 253, 242, 195,  64,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,  49, 238, 253, 253, 253, 253,\n",
              "        253, 253, 253, 253, 251,  93,  82,  82,  56,  39,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,  18, 219, 253, 253, 253, 253,\n",
              "        253, 198, 182, 247, 241,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  80, 156, 107, 253, 253,\n",
              "        205,  11,   0,  43, 154,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  14,   1, 154, 253,\n",
              "         90,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 139, 253,\n",
              "        190,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 190,\n",
              "        253,  70,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  35,\n",
              "        241, 225, 160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n",
              "        148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n",
              "        253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  23,  66, 213, 253, 253,\n",
              "        253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,  18, 171, 219, 253, 253, 253, 253,\n",
              "        195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n",
              "         11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0, 136, 253, 253, 253, 212, 135, 132,  16,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "DU33KtKBc3H6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[1], cmap = 'Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "eYflnTcIcxoc",
        "outputId": "2c761b02-7eb9-4d24-9498-6fce3867a502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fada0172390>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cenVo5lsewoW",
        "outputId": "5f318952-0954-43cc-bc08-b464275d31cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = [0,1,2,3,4,5,6,7,8,9]"
      ],
      "metadata": {
        "id": "U4EtRnV9c13E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.min(), x_train.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0rhppjYf3Us",
        "outputId": "2fb6a77e-b0b4-4b28-de2a-b5b61fa23999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train/255.0\n",
        "x_test = x_test/255.0"
      ],
      "metadata": {
        "id": "96tDeO8vgRu-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape = (28,28)),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001),\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20))\n",
        "\n",
        "history = model.fit(x_train, y_train, epochs = 10,\n",
        "          validation_data = (x_test,y_test),\n",
        "          callbacks = [lr_scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLafYFvhdzGo",
        "outputId": "93d58b13-a976-479d-ca74-d24882d6ec47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 14s 6ms/step - loss: 0.5554 - accuracy: 0.8279 - val_loss: 0.2714 - val_accuracy: 0.9195 - lr: 1.0000e-04\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.2028 - accuracy: 0.9396 - val_loss: 0.2044 - val_accuracy: 0.9372 - lr: 1.1220e-04\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1515 - accuracy: 0.9542 - val_loss: 0.1382 - val_accuracy: 0.9575 - lr: 1.2589e-04\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1263 - accuracy: 0.9621 - val_loss: 0.1373 - val_accuracy: 0.9593 - lr: 1.4125e-04\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1048 - accuracy: 0.9677 - val_loss: 0.1371 - val_accuracy: 0.9582 - lr: 1.5849e-04\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0922 - accuracy: 0.9720 - val_loss: 0.1069 - val_accuracy: 0.9673 - lr: 1.7783e-04\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0814 - accuracy: 0.9743 - val_loss: 0.1070 - val_accuracy: 0.9672 - lr: 1.9953e-04\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0728 - accuracy: 0.9777 - val_loss: 0.1195 - val_accuracy: 0.9650 - lr: 2.2387e-04\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0668 - accuracy: 0.9788 - val_loss: 0.1081 - val_accuracy: 0.9687 - lr: 2.5119e-04\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0630 - accuracy: 0.9810 - val_loss: 0.1325 - val_accuracy: 0.9598 - lr: 2.8184e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vZCGkgEWed3F",
        "outputId": "84872928-343c-4a77-b3a0-80cf17dd977c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1325 - accuracy: 0.9598\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.13246501982212067, 0.9598000049591064]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0VjGcu8Yg1ry",
        "outputId": "902f6b17-f7b5-4be8-b457-e3e5fb51e7b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fad3828bd90>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,10))\n",
        "lrs = 1e-4*(10**(np.arange(10)/20))\n",
        "plt.semilogx(lrs, history.history['loss'])\n",
        "plt.xlabel('Learning rate')\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Finding the ideal learning rate\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "bRaJRHdyg9KR",
        "outputId": "fc853b26-bdff-4621-d4cc-69813f4e1ccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Finding the ideal learning rate')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape = (28,28)),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(100, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model_2.compile(loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate = 0.00005),\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "history_2 = model_2.fit(x_train, y_train, epochs = 10,\n",
        "          validation_data = (x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZC7dwnooh-ax",
        "outputId": "3650e238-4fae-4681-ff15-11a14132028d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.7694 - accuracy: 0.7612 - val_loss: 0.3473 - val_accuracy: 0.9005\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2807 - accuracy: 0.9186 - val_loss: 0.2534 - val_accuracy: 0.9254\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2086 - accuracy: 0.9381 - val_loss: 0.1893 - val_accuracy: 0.9432\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.1749 - accuracy: 0.9481 - val_loss: 0.1771 - val_accuracy: 0.9478\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1508 - accuracy: 0.9548 - val_loss: 0.1667 - val_accuracy: 0.9482\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1338 - accuracy: 0.9593 - val_loss: 0.1416 - val_accuracy: 0.9589\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1205 - accuracy: 0.9635 - val_loss: 0.1312 - val_accuracy: 0.9599\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1091 - accuracy: 0.9675 - val_loss: 0.1323 - val_accuracy: 0.9606\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0987 - accuracy: 0.9701 - val_loss: 0.1356 - val_accuracy: 0.9602\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0914 - accuracy: 0.9722 - val_loss: 0.1257 - val_accuracy: 0.9610\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0HHHbC0kE2r",
        "outputId": "06f084c9-5fba-4726-96e1-8ce1aa81734b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.1257 - accuracy: 0.9610\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.1256665587425232, 0.9610000252723694]"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model_2.predict(x_test)\n",
        "y_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCRxmPAKkTOT",
        "outputId": "53c1ecdc-5a07-41a3-8d81-b5588628f135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.2713550e-06, 1.0791451e-04, 1.5383112e-04, ..., 9.9775118e-01,\n",
              "        5.0515217e-07, 1.6923746e-04],\n",
              "       [2.8447030e-06, 1.4033830e-03, 9.9735707e-01, ..., 1.7584312e-05,\n",
              "        3.5870591e-05, 2.4489299e-07],\n",
              "       [1.1674885e-04, 9.9153894e-01, 3.6168902e-03, ..., 2.8157949e-03,\n",
              "        2.1967600e-04, 1.7027828e-04],\n",
              "       ...,\n",
              "       [1.8027137e-07, 1.6056950e-08, 1.7384908e-05, ..., 4.5318516e-06,\n",
              "        2.9612684e-07, 1.6242849e-04],\n",
              "       [5.5967626e-09, 3.1509155e-09, 8.0850149e-10, ..., 6.6783135e-10,\n",
              "        9.5005788e-07, 9.3537764e-09],\n",
              "       [1.2633164e-08, 2.5597847e-11, 1.6517966e-07, ..., 6.6553967e-12,\n",
              "        4.1288986e-09, 2.3463553e-13]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob[0].argmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PH1Re3bblEvu",
        "outputId": "713bb86c-1786-4113-fc42-d30e02a59ff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_prob.argmax(axis=1)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReLsSk9WlSTU",
        "outputId": "bf8fa625-422a-4585-93f5-df24a05d3750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7, 2, 1, ..., 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixuqeqq6nwM8",
        "outputId": "a19780d2-c0e9-4070-e8a3-15ce6b1e4655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_test[1], cmap = 'Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "302wZKQJlhsg",
        "outputId": "9ba5bc04-c8cd-4851-c0e0-91789cc36621"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7facf006b4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def probability(x):\n",
        "  return np.round(y_prob[x].max()*100,2)\n",
        "\n",
        "def actual(x):\n",
        "  return class_names[y_test[x]]\n",
        "\n",
        "def prediction(x):\n",
        "  return class_names[y_pred[x]]"
      ],
      "metadata": {
        "id": "Cx43rKxuJtDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (18,18))\n",
        "\n",
        "for i in range (25):\n",
        "  plt.subplot(5,5,i+1)\n",
        "  rand_num = np.random.randint(0,len(x_test)-1)\n",
        "  plt.imshow(x_test[rand_num], cmap = 'Blues')\n",
        "  plt.title(f'Actual: {actual(rand_num)}, Pred: {prediction(rand_num)} with {probability(rand_num)}%',y = 0, color = 'green' if actual(rand_num) == prediction(rand_num) else 'red')\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gmiOTI9AlqRM",
        "outputId": "09a98571-a26d-45a5-fd87-99937c20be87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "3ldeDdJVnfvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_true = y_test,\n",
        "                      y_pred = y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lo62Mnl2pGf8",
        "outputId": "2f9b153e-97b3-4adc-9e0f-5e52eb399dff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 967,    0,    1,    3,    1,    3,    3,    1,    1,    0],\n",
              "       [   0, 1115,    3,    4,    0,    1,    4,    2,    6,    0],\n",
              "       [   6,    1,  997,   12,    3,    1,    3,    4,    3,    2],\n",
              "       [   0,    0,    3,  987,    0,    9,    0,    3,    5,    3],\n",
              "       [   0,    0,    4,    0,  936,    1,   12,    1,    2,   26],\n",
              "       [   5,    0,    0,    8,    1,  863,    8,    0,    4,    3],\n",
              "       [   4,    2,    1,    1,    3,   15,  928,    0,    4,    0],\n",
              "       [   0,    6,   18,    8,    7,    0,    0,  960,    3,   26],\n",
              "       [   1,    0,    5,   27,    2,   27,    5,    2,  895,   10],\n",
              "       [   4,    4,    1,   12,    9,    8,    1,    5,    3,  962]])"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,10))\n",
        "fig, ax = plt.subplots(figsize=(9,9))\n",
        "ConfusionMatrixDisplay(cm).plot(cmap = 'Blues',ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "qWNjwHplpM5W",
        "outputId": "c0b35b11-8388-40a5-df62-e817ac6584b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fad0ef3b690>"
            ]
          },
          "metadata": {},
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg0AAAH9CAYAAACZYk8QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU1f3/8ddJQoBACIFAIGwBIewgIcpWkEWUTRCpfosKaLG4IdX+VEBFBKlWWxUrVutGQURRwOJCVVREdiFYVlEWBdkDBAhbkpk5vz9mSEFIMgFm7o15Px+PeSQzc+8979ybOzn53HPvNdZaRERERAoT4XQAERERKR7UaRAREZGgqNMgIiIiQVGnQURERIKiToOIiIgERZ0GERERCUqU0wFERESKi8gKdaz1nAhpG/ZExqfW2h4hbeQ8qdMgIiISJOs5QemGN4S0jZP/fTEhpA1cAHUaREREgmbAlNwj+yX3JxcREZEiUaVBREQkWAYwxukUjlGlQURERIKiSoOIiEhRaEyDiIiISMFUaRARESkKjWkQERERKZgqDSIiIkHTdRpERERECqVKg4iISFGU4DEN6jSIiIgEy6DDEyIiIiKFUaVBREQkaKZEH55QpUFERESCokqDiIhIUWhMg4iIiEjBVGkQEREpCo1pEBERESmYKg0iIiJB02WkRURERAqlSoOIiEiwDBrTICIiIlIYVRpERESKQmMaRERERAqmSoOIiEjQdPaEiIiISKFUaRARESmKCJ09ISIiIlIgVRpERESCZdCYBhEREZHCqNIgIiJSFCX4ipDqNIiIiARNp1yKiIiIFEqVBhERkaIowYcnVGkQERGRoKjSICIiUhQa0yAiIiJSMFUaREREgmWMxjSIiIiIFEaVBhERkaIowWMaXNVpMKVjrYmp7HSMfLWql+B0BBERyce2bT+xf//+knvsIAzc1WmIqUzprmOcjpGvxTOGOh1BRETy0aFNWnga0pgGERERkYK5qtIgIiLibrr3hIiIiBQTxpg3jDH7jDHrTnutkjFmnjFmU+BrfOB1Y4z5uzFmszFmjTEm9bR5hgSm32SMGRJM2+o0iIiIFMWpazWE6lG4fwE9fvHaKOALa20D4IvAc4CeQIPAYxjwkv9HMJWAsUAb4HJg7KmORkHUaRARESlGrLVfAwd/8XI/YErg+ynAtae9PtX6LQMqGmOqA1cD86y1B621mcA8zu6InEVjGkRERIJlCMeYhgRjzMrTnr9irX2lkHkSrbW7A9/vARID39cAfj5tuh2B1/J7vUDqNIiIiLjLfmvteZ8/aq21xhh7MQOdosMTIiIiQQucPRHKx/nZGzjsQODrvsDrO4Fap01XM/Bafq8XSJ0GERGR4u8D4NQZEEOAOae9PjhwFkVb4HDgMManwFXGmPjAAMirAq8VSIcnREREisLhK0IaY94GOuMf+7AD/1kQfwHeNcYMBbYBNwQmnwv0AjYDx4FbAay1B40xjwMrAtONt9b+cnDlWdRpEBERKQqHL+5krR2Yz1vdzjGtBe7OZzlvAG8UpW0dnhAREZGgqNIgIiJSFLphlYiIiEjBinWl4faeTRnSrSEAU7/8npfnrgfgDz2acNtVjfH6LPO+/Zmxb63g+t9cwj3XNM+bt2ntSlwx6t+s21bouI+L7vMlGxj9zEy8Ph+D+rXnvluuCnuGggwfP41PF60jIT6WpTMedjrOWU5m59J72ESycz14PV76dmvF6Nt7Ox0rj9bfhXF7PnD/NnZ7PnD/52C+TMm+YVVIOw3GmB7A80Ak8Jq19i8Xa9mNa8UzpFtDuj00hxyPj5kPXc2n6dupkVCeXmm16fjg++R4fCRUKAPAe4u28N6iLQA0qRXPtPuvdKTD4PX6eODpd3l/0nCSEivSdchf6dmpOY3qVQ97lvwM7NOWP9xwBXeMnep0lHMqHR3FnJdGUD6mNLkeLz1ve5Yr2zfhsuZ1nY4GaP1dKLfnA/dvY7fnKw6fg3JuIesuGWMigRfx3yyjCTDQGNPkYi0/pUYcKzft40SOF6/PsnjDHq5pk8zvuzdi4pw15Hh8AOw/cvKseQd0qMfsJVsvVpQiSV//E/VqJZBcM4HoUlFc1z2VuQvWOJIlPx1S6xNfIcbpGPkyxlA+pjQAuR4vuR4vxkXHGLX+Lozb84H7t7Hb8xWHz8ECOX/DKseEssZyObDZWrvVWpsDvIP/xhkXxXc/Z9KuUTXiy5embHQk3VvVokblctSvHke7RonMm3ANH43tRatLEs6at3+7esxyqNOwO+MwNRL/dyOxpMR4dmccdiRLceb1+uh445OkXDWKzm0akdYs2elIxYrb15/b88mF0edg8RXKTkNQN8Mwxgwzxqw0xqy02VlBL/yHnYd5/oM1zH64BzMf6sG6nw7g9VmiIiOIL1+a7o98yKPTvmHyvV3PmK91/SqcyPHw3c+Z5/ljiRtERkawcPpo1n88gVXrt7Fh8y6nIxUrbl9/bs8nJZsxJqQPN3N8NIe19hVrbZq1Ns2Uji3SvNPm/0CX0XPo/djHHDqWzZbdR9h54BgffrMNgFVb9uPzWSrHlsmb57r29Zi12JkqA0D1KnHs3Pu/DsuuvZlUrxLnWJ7iLi42ho6tU/hi6QanoxRLbl9/bs8n50efg8VXKDsN53UzjKI4NcixZuVy9Lk8mfcWbWHuim10bOIfTHNJ9QpER0VwIMs/rsEYuLZdXccOTQCkNqnDlu0ZbNu5n5xcD7PnraJnpxaO5SmO9mdmcTjrOAAnTuYw/5uNNEhOLGQuOcXt68/t+eTCFefPQUPJrjSE8uyJFUADY0xd/J2F3wE3XswGpv6pG/GxpfF4fTzwxhKOHM9h2vwfmHRnR5b87TpyPF7u/MfXedO3b1yNnQeOsW1f8IdBLraoqEiefvAGBox4Ea/XclPftjS+xF0jhoc+PJnF6Zs4cOgoTXs/wqhhvRjUr73TsfLs2X+Eux57E6/Ph89n6X9lKj06Ni98xjDR+rswbs8H7t/Gbs9XHD4H5dyM/7LUIVq4Mb2AifhPuXzDWvvngqaPiE+2pbuOCVmeC5U5Y6jTEUREJB8d2qSRnr4ypP+qR1ZKtmW6jQ1lExyf+ft0a21aSBs5TyG9ToO1di7+O2yJiIhIMVesrwgpIiISXu4fdxBKjp89ISIiIsWDKg0iIiJFoEqDiIiISCFUaRARESkCVRpERERECqFKg4iISBGo0iAiIiJSCFUaREREgmUCjxJKnQYREZEgGV3cSURERKRwqjSIiIgUgSoNIiIiIoVQpUFERKQISnKlwVWdhlb1Elg8Y6jTMfIVf9lwpyMUKHPFJKcjiMgFstY6HaFAJfkPpris0yAiIuJ2JbnjpDENIiIiEhRVGkRERIJVwi/upEqDiIiIBEWVBhERkSLQmAYRERGRQqjSICIiEiTde0JEREQkCKo0iIiIFIEqDSIiIiKFUKVBRESkKEpuoUGVBhEREQmOKg0iIiLBMhrTICIiIlIoVRpERESKoCRXGn6VnYbPl2xg9DMz8fp8DOrXnvtuuSos7b4w5iau/k0z9mdm0f53TwDQr1srRg7rRcPkRLrd8jf++912AOLjyjHlL0Np1aQOb3+0jAf/+l7ecj58+Y8kJlTgZHYuANcNn8T+zKNh+RkATmbn0nvYRLJzPXg9Xvp2a8Xo23uHrf1geb0+ugx+mupV45jx3J1OxzmDU7+DwRg+fhqfLlpHQnwsS2c87HScfLl1++7Yk8mdj00l42AWBhjSvwN3DOzidKyzHM46zog/v83GLbvAGF545CYub1HX6Vh53LyPFEadhhAwxrwB9AH2WWubhaqdX/J6fTzw9Lu8P2k4SYkV6Trkr/Ts1JxG9aqHvO23P1rGq+8u4OVxg/Ne+27LLgY/+CrPjR54xrTZ2bk88fJHNL4kicaXnJ1t2JgpeR2McCsdHcWcl0ZQPqY0uR4vPW97livbN+Gy5u75wAF4+Z35pNRNJOvYSaejnMHJ38FgDOzTlj/ccAV3jJ3qdJQCuXX7RkVFMOHe62jZqBZZx07SZfBTdG7TyDXb95TRz8yiW9vGTPnLUHJyPZw4meN0pDxu30ckf6Ec0/AvoEcIl39O6et/ol6tBJJrJhBdKorruqcyd8GasLS95NstZB45fsZrP/y0l83b9p017fGTOSxbvZWTOblhyVYUxhjKx5QGINfjJdfjdV3PeufeTD5btJ7B/do7HeUsTv4OBqNDan3iK8Q4HaNAbt6+1RLiaNmoFgCx5cqQklyN3RmHHE51piNHT7Dk280M6tcOgOhSUcTFumebu30fKcipy0iH8uFmIes0WGu/Bg6Gavn52Z1xmBqJ8XnPkxLj2Z1xONwxLtiLj97M12+N4v6hYe93Af7/BDre+CQpV42ic5tGpDVLdiRHfh56dhbjRlxLRIT7drBfy++gk9y8fU+3fdcB1ny/g9ZNk52OcoZtuw6QEF+e4eOnccXNTzFiwnSOnch2OlYe7SPFl86ecKFhY/5Fh4FP0OsPz9Hu0kv4v16Xhz1DZGQEC6ePZv3HE1i1fhsbNu8Ke4b8fLJwLQnxsVzauLbTUSQEisv2PXo8m8EjX+PJPw2gQvmyTsc5g8fjY/X3O7h1QEcWTBtJTNloJk6Z53SsXw8T4oeLOd5pMMYMM8asNMaszNifccHLq14ljp17M/Oe79qbSfUqcRe83HA61eM+ejybmZ+upHXTOo5liYuNoWPrFL5YusGxDL+0fPVWPlm4lhZ9H2XoQ5NZuOIHho2Z4nSsPL+G30EnuX37gv+w3ZCRr3J9jzSu6Xqp03HOklS1IklVK+ZVCPt1vZQ13//sbKjTaB8pvhzvNFhrX7HWpllr06okVLng5aU2qcOW7Rls27mfnFwPs+etomenFhchaXhERkZQKa4cAFGREVz9m2Z8t2V3WDPsz8zicJZ/bMaJkznM/2YjDZITw5qhIGOH92P9xxNY88F4Xn/iVjpelsIrjw9xOlae4v476DS3b19rLfc8/hYpydW4+6ZuTsc5p8SECtSoWpFN2/YCsGDFDzSs655BhsV6Hwlc3Kmkjmn41Z1yGRUVydMP3sCAES/i9Vpu6tv2nGcnhMJrE26hQ+sGVK5YnnUfPc5fXplL5pFjPHX/9STEl2fGc3ew9oed/HbEiwCsnjOO2HJlKFUqil5XtGDAPS/y8+6DzHrhbkpFRRIRGcGCbzYy5d+Lw5L/lD37j3DXY2/i9fnw+Sz9r0ylR8fmYc1QnDn5OxiMoQ9PZnH6Jg4cOkrT3o8walgvBrlwwKFbLVu9lRlzv6FJ/SQ63vgkAGPu7stVHZo6nOxMTz1wPbePmUKOx0tyUmUmPXqz05HyuH0fkfwZa21oFmzM20BnIAHYC4y11r5e0DytW6fZxctXhiTPxRB/2XCnIxQoc8UkpyOIyAUK1WfyxeLm/4Q7tEkjPX1lSANGV61vq/72b6Fsgp0v9U+31qaFtJHzFLJKg7V2YOFTiYiISHHxqzs8ISIiEkpurraEmuMDIUVERKR4UKVBRESkKEpuoUGVBhEREQmOKg0iIiJFoDENIiIiIoVQpUFERCRIxeGqjaGkSoOIiIgERZUGERGRIlClQURERKQQqjSIiIgUQUmuNKjTICIiUhQlt8+gwxMiIiISHFUaREREikCHJ1zC4u57yWeumOR0hALFX/ui0xEKdWD2XU5HKFBERMn9MBB3KMl/kMT9XNVpEBERcTVTsjt2GtMgIiIiQVGlQUREJEgGKMGFBlUaREREJDiqNIiIiARNN6wSERERKZQqDSIiIkVQggsNqjSIiIhIcFRpEBERKQKNaRAREREphDoNIiIiwTL+MQ2hfAQVw5j7jDHrjTHrjDFvG2PKGGPqGmOWG2M2G2NmGGOiA9OWDjzfHHg/+Xx/fHUaREREihFjTA1gBJBmrW0GRAK/A54CnrPW1gcygaGBWYYCmYHXnwtMd17UaRAREQmSwX9ju1A+ghQFlDXGRAExwG6gKzAz8P4U4NrA9/0Czwm8382c58AMdRpERESKEWvtTuBvwHb8nYXDQDpwyFrrCUy2A6gR+L4G8HNgXk9g+srn07Y6DSIiIkUQhjENCcaYlac9hp3ZvonHXz2oCyQB5YAe4fjZf5WnXB7OOs6IP7/Nxi27wBheeOQmLm9R1+lYeYaPn8ani9aREB/L0hkPO5bj9j4tGNK9CRjD1HnrefnDNTRLrswzd3SmfNlSbN93hGHPziPrRC7Xd0rhnv6t8uZtWqcyV/y/d1n34/6wZL3n8bf4bLF/nS1++yEAxv7933yyaC3RpaJIrpHApDE3ERcbE5Y8BTmZnUvvYRPJzvXg9Xjp260Vo2/v7XSsPG75/cuP29ffKV6vjy6Dn6Z61ThmPHen03HO4PZ1uGNPJnc+NpWMg1kYYEj/DtwxsIvTsdxkv7U2rYD3rwR+tNZmABhjZgMdgIrGmKhANaEmsDMw/U6gFrAjcDgjDjhwPsFCVmkwxtQyxsw3xmwIjPD8Y6ja+qXRz8yiW9vGLH9vDAvfGkXDuonhajooA/u0Zebf73Y0Q+PalRjSvQndHphJx3vf4eq0ZOpWi+P5u7sw7s2ldPjjO3y07Me8jsJ7X/9Ap/tm0Om+GdwxcR7b9h0JW4cBYGCfNrw78a4zXut8eUMWT3+IhW+N5pLaVXluyryw5SlI6ego5rw0gkXTR/P19NF8sXQDK9b+6HSsPG74/SuI29ffKS+/M58Ul322nOL2dRgVFcGEe69j2buP8Nnk+3lt5tds3Lrb6VhBM8aE9BGE7UBbY0xMYGxCN2ADMB/4bWCaIcCcwPcfBJ4TeP9La609n589lIcnPMD/s9Y2AdoCdxtjmoSwPQCOHD3Bkm83M6hfOwCiS0W54r/P03VIrU98BWczpdSMZ+WmvZzI8eD1WRav38U17epRP6kiS9bvAuCr1T9zTbtLzpp3QMcUZi/cFNa87Vudvc66tG1MVFQkAGnNktm971BYM+XHGEP5mNIA5Hq85Hq8rroYjBt+/wri9vUHsHNvJp8tWs/gfu2djnJObl+H1RLiaNmoFgCx5cqQklyN3Rnu2H8L5YJTLq21y/EPaFwFrMX/t/wVYCTwJ2PMZvxjFl4PzPI6UDnw+p+AUef744fs8IS1djf+ARpYa7OMMd/hH4yxIVRtAmzbdYCE+PIMHz+NdZt20bJRLZ78fwMoV7Z0KJstdr7bfpBHbmpLfGxpTmZ76Z5ah/9u2cfGnw/Sq01d5i7/kX7tL6FGQvmz5u3/m/rc9MRcB1Lnb/qHy7j2ylSnY+Txen10HvQUP+7IYOj1nUhrlux0pGLF7evvoWdnMW7EtRw9ftLpKPly+zo8ZfuuA6z5fgetmyY7HaVYsdaOBcb+4uWtwOXnmPYkcP3FaDcsAyEDF5JoBSwPdVsej4/V3+/g1gEdWTBtJDFlo5nokrK1m/ywI5Pn31/F7Mf6MnPsNaz7cT9en2X4C18ytGcz5j9zPeXLRpOb6ztjvtYNEjmR7eG77QcdSn62ZyZ/SmRkBNf3KOgQYHhFRkawcPpo1n88gVXrt7Fh8y6nIxUrbl5/nyxcS0J8LJc2ru10lAK5eR2ecvR4NoNHvsaTfxpAhfJlnY4TFIMrDk84JuQDIY0x5YFZwL3W2iPneH8YMAygVu0L3wmTqlYkqWrFvF51v66XMnGqOg3nMu3z75j2+XcAjLm5LbsOHGXTzkMMeOxDAC5JiuOq1nXOmOe6jvWZFeZDEwWZ/tEyPlu0jvdfvMeVO1tcbAwdW6fwxdINNKmf5HScYseN62/56q18snAt85asJzs7l6xjJxk2ZgqvPD6k8Jkd4MZ1CP7DJkNGvsr1PdK4puulTseRIIW00mCMKYW/w/CWtXb2uaax1r5irU2z1qYlJFS54DYTEypQo2pFNm3bC8CCFT/QsG71C17ur1FCnL9nXzOhPH3a1uO9r3/Ie80YuP/6NCZ/uj5vemPg2g7u6TR8sXQDL7z5BW/9bRgxZaKdjpNnf2YWh7OOA3DiZA7zv9lIg2R3DphzI7evv7HD+7H+4wms+WA8rz9xKx0vS3Fdh8Ht69Bayz2Pv0VKcjXuvqmb03GKKLRVBjf+83O6kFUaAiM6Xwe+s9Y+G6p2zuWpB67n9jFTyPF4SU6qzKRHbw5n84Ua+vBkFqdv4sChozTt/QijhvVikAMDqqaO7EF8bBk8Hh8PvPI1R47lcHufFtzWszkAHy3bwltffJc3ffumSezcf5Rte88qGIXcHx6ZzOJVmzlw6CjN+oxh1LBeTJzyGdk5Hgbc8yLgHwz5zKjfhT3bL+3Zf4S7HnsTr8+Hz2fpf2UqPTo2dzpWHrf8/uXH7euvOHD7Oly2eisz5n5Dk/pJdLzxSQDG3N2Xqzo0dTiZFMac51kXhS/YmN8AC/GP7Dx1YPwha22+I+hSW6fZxctWhCTPxeD2HmD8tS86HaFQB2bfVfhEDirCJVxFxGU6tEkjPX1lSHfimKSGNmXYP0LZBKvHXZleyHUaHBPKsycW4R8zIiIiIr8Cv8orQoqIiISK26vOoaR7T4iIiEhQVGkQEREJVpBXbfy1UqVBREREgqJKg4iISJBOXRGypFKlQURERIKiSoOIiEgRlOBCgyoNIiIiEhxVGkRERIpAYxpERERECqFKg4iISBGU4EKDKg0iIiISHFUaREREgmU0pkFERESkUKo0iIiIBMl/RUinUzjHVZ2Gkn55zguV+e+7nY5QqPgBLzsdoUCZs+5wOkKBfD7rdIQCRURo/71Q1rp7G+sz2pTodaDDEyIiIhIUV1UaRERE3K4EFxpUaRAREZHgqNIgIiJSBBrTICIiIlIIVRpERESCZTSmQURERKRQqjSIiIgEqaRfT0iVBhEREQmKKg0iIiJFoEqDiIiISCFUaRARESmCElxoUKVBREREgqNKg4iISBFoTIOIiIhIIX6VlYbPl2xg9DMz8fp8DOrXnvtuucrpSGdwe76T2bn0HjaR7FwPXo+Xvt1aMfr23mHPcXvv5gy5sjEYmDrvO17+eC3Nkivz7O2dKFMqEo/Xx/2vLmLV5n3c068l13dsAEBUZAQpNSpS//dTOHQ0O+y5wf3b+J/vfMXUOUuw1jK4X3vuGNjF6UhncPv6c8s+UpCW/cZSPqY0kRERREVG8OXUB52OlKc4rL98lfArQoas02CMKQN8DZQOtDPTWjs2VO2d4vX6eODpd3l/0nCSEivSdchf6dmpOY3qVQ9100Fxez6A0tFRzHlpBOVjSpPr8dLztme5sn0TLmteN2wZGteKZ8iVjek2cjY5Hi8zx/Tm0/RtjBvUlqffXcnn3/5M99TajBvUlmvGfsALc1bzwpzVAPRIq8OdfVo41mFw+zb+bssups5ZwrzJ9xMdFcn19/6Dq37TjHq1qjgdDXD/+gN37CPB+OClEVSuWN7pGGcpLutPzhbKwxPZQFdrbUvgUqCHMaZtCNsDIH39T9SrlUByzQSiS0VxXfdU5i5YE+pmg+b2fOA/Xlc+pjQAuR4vuR5v2I/hpdSMZ+WmvZzI8eD1WRav38U1bephgdiy0QBUiIlmT+axs+Yd8Jv6zFq0Oax5T+f2bfzDT3tp3bQOMWWiiYqKpEOrBnz01WqnY+Vx+/oDd+wjxVlxXn8GgzGhfbhZyDoN1u9o4GmpwMOGqr1TdmccpkZifN7zpMR4dmccDnWzQXN7vlO8Xh8db3ySlKtG0blNI9KaJYe1/e+2H6Rd4+rEly9N2egouqfWpkZCOR56YzHjB7dl3T9vZvzgdox/a/kZ85WNjqLbpbX4YNnWsOY9ndu3caN61Vn23y0cPHyM4ydzmLdkPTv3ZjodK4/b198pTu8jhTHAgHtepMvgp/nX+4udjnMWt68/ObeQjmkwxkQC6UB94EVr7fJzTDMMGAZQq3btUMaRIoiMjGDh9NEczjrOzQ+8yobNu2hSPyls7f+w8xDP//u/zH60D8ezc1n30wG8Psvvr27KQ/9awofLfuTa9pfw97s603/cR3nz9Uirw/Lv9zh2aKI4aFi3GiMGd+e397xITNlomqXUJDJCY6KLyul9pDBzX72PpKoVyTiYxXXDJ5FSJ5H2qfWdjpXH7euvIC4vBoRUSD8prLVea+2lQE3gcmNMs3NM84q1Ns1am1Yl4cKPqVavEnfGf0279mZSvUrcBS/3YnF7vl+Ki42hY+sUvli6IextT/tiI10enEXvMR9w6Gg2W3YdZmDnFD5c9iMA/16yhdT6Vc+Y57rf1GfWQucOTUDx2MY3923Hl1Mf5KN/3kvF2LJcUtsd4xmgeKy/0zm5jxQkqWpFAKpUiqV355akb9jmcKJzc+v6K0iEMSF9uFlY/r2w1h4C5gM9Qt1WapM6bNmewbad+8nJ9TB73ip6dmoR6maD5vZ8APszszicdRyAEydzmP/NRhokJ4Y9R0KFMgDUTChPn7Z1eW/hJnZnHqdDU/9/I52a12Dr7v+VrSvERNOhSXXmrvgp7FlPVxy2ccbBLAB27DnIR1+t5rdXpzmc6H+Kw/pzyz6Sn2Mnssk6djLv+/nLN9L4EvcMJHX7+pP8hfLsiSpArrX2kDGmLNAdeCpU7Z0SFRXJ0w/ewIARL+L1Wm7q29ZVO4vb8wHs2X+Eux57E6/Ph89n6X9lKj06Ng97jqkPXE18bGk8Xh8PvLqII8dzuPelBTz5+w5ERRpO5ni59+UFedP3blOX+at3cDzbE/aspysO2/iWUa9x8PBxSkVF8PQDNxAXG+N0pDzFYf25ZR/JT8bBLAY98CoAHq+P316dxpXtmjic6n/cvv4K4/JiQEgZa0MzNtEY0wKYAkTir2i8a60dX9A8rVun2cXLV4Ykj7hD/ICXnY5QoMxZdzgdoUA+X8jHEl+QiIgS/Gl6kYTqM/licfPo/g5t0khPXxnSgBVqN7ZtR04OZRPMG94u3VrrnvLfaUJWabDWrgFahWr5IiIi4WaMuztOoaYh0yIiIhKUX+VlpEVEREKlJB+FU6VBREREgqJKg4iISBFoTIOIiIhIIVRpEBERKYISXGhQpUFERESCo0qDiIhIkAz+22OXVKo0iIiISFBUaRARESkCXadBREREpDqYm9AAACAASURBVBCqNIiIiATLGF2nQURERKQwqjSIiIgUQQkuNKjTIOGVOesOpyMUqNqQaU5HKNCeKTc7HaFAPp91OkKBIorBCDa3l749Xp/TEfLl7t++Xwd1GkRERIJkgAiXd+xCSZ0GERGRIijBfQYNhBQREZHgqNIgIiJSBG4fdxJKqjSIiIhIUFRpEBERCZIxGtMgIiIiUihVGkRERIqgJJ9yqUqDiIiIBEWVBhERkSIouXUGVRpEREQkSKo0iIiIFIGu0yAiIiJSCFUaREREguS/YZXTKZyjSoOIiIgE5VdZafh8yQZGPzMTr8/HoH7tue+Wq5yOdAa35wPwen10Gfw01avGMeO5O52OcxY3rMPbujfk5s4NMAamfbWZVz/byIPXtaRHak18Psv+rJP88dWl7D10AoD2jRIZf2NrSkVFcDArm/5Pzgt7ZoDh46fx6aJ1JMTHsnTGw45k+KV7Hn+Lzxb7My1++yEAxv7933yyaC3RpaJIrpHApDE3ERcb43BSPzeuw9Pt2JPJnY9NJeNgFgYY0r8Ddwzs4mimnXszuXvcm/5MxjDo2vbc/n+dAXj13QW8MWshkRERdG/flLH39HM0a4GMKdFjGkLeaTDGRAIrgZ3W2j6hbs/r9fHA0+/y/qThJCVWpOuQv9KzU3Ma1ase6qaD4vZ8p7z8znxS6iaSdeyk01HO4oZ12KhGHDd3bkDPcf8hx+Pj7fu7Mu+/O/jH3A08PXs1AEO7N+RP/Zozcso3VIgpxV8GX8bAv33JzoPHSYgtHbasvzSwT1v+cMMV3DF2qmMZfmlgnzbcdn0n7hr3Zt5rnS9vyJi7riEqKpLHJs3huSnzeGy4O/6YuHEdni4qKoIJ915Hy0a1yDp2ki6Dn6Jzm0aOfs5ERkYwbkR/WjaqxdFjJ+l2y1/pfHlDMg5m8cnXa/nqzZGUji5FxsEsxzJK4fI9PGGMecEY8/f8HkVo44/AdxceNTjp63+iXq0EkmsmEF0qiuu6pzJ3wZpwNV8ot+cD/38Eny1az+B+7Z2Ock5uWIcNkuJYtWU/J3K8eH2WpRv30TutNkdP5uZNE1P6f33y69rW5eP0n9l58DgA+7Oyw5r3dB1S6xNfwR3/sZ/SvtXZmbq0bUxUVCQAac2S2b3vkBPRzsmN6/B01RLiaNmoFgCx5cqQklyN3RnOrr/TM5UvV4aU5ER27zvM5NmLGDG4O6WjSwFQpVKskzGDcur+E6F6BJfBVDTGzDTGbDTGfGeMaWeMqWSMmWeM2RT4Gh+Y1gT+dm82xqwxxqSe789e0JiGlUB6AY9gfqiaQG/gtfMNWFS7Mw5TIzE+73lSYjy7Mw6Hq/lCuT0fwEPPzmLciGuJcOloHzesw407DtGmYVXiy0VTNjqSbi2TSKrk/yMyakBL0p/tz4B2dfOqDvWqxVIxJprZo7rz6bieXN+hbljzFnfTP1xGt3ZNnI5RLG3fdYA13++gddNkp6Pk2b7rAGt/2EnrZnXYsj2DZau3cPXvn6Hvnc/z7YZtTscrLp4HPrHWNgJa4v/nfBTwhbW2AfBF4DlAT6BB4DEMeOl8G8338IS1dsrpz40xMdba40Vc/kTgQSDfrqMxZhj+H4JatWsXcfFysX2ycC0J8bFc2rg2i9J/cDqOa23afYRJH6/nnQe7cTzbw/rtmXh9FoC/zFrNX2at5p4+Tfn9lQ356/triIqMoEVyJa5/6nPKREfx0ZirSd+8n617VYotzDOTPyUyMoLre6Q5HaXYOXo8m8EjX+PJPw2gQvmyTscB/JluHf06E+69jthyZfF6fWQePs4nr/+Jbzds57aHJ7Ny9lhXjxtwOpsxJg7oBNwCYK3NAXKMMf2AzoHJpgBfASOBfsBUa60FlgWqFNWttbuL2nahZ08ESh4bgI2B5y2NMf8IYr4+wD5rbYFVCWvtK9baNGttWpWEKsHmzlf1KnHs3JuZ93zX3kyqV4m74OVeLG7Pt3z1Vj5ZuJYWfR9l6EOTWbjiB4aNmVL4jGHklnX49tdbuHrsf+j/xDwOHcth654zOwCzl/xI7zR/R3jXweN8tW43x3O8HDyazbLv99G0dvy5Fiunmf7RMj5btI5/jh/i+Ad1cZPr8TJk5Ktc3yONa7pe6nQcwJ/p1tGv89ur0+jTpSUA1avG0adLC4wxpDatQ0SE4cChow4ndb26QAYw2RjzrTHmNWNMOSDxtI7AHiAx8H0N4OfT5t8ReK3IgjnlciJwNXAAwFq7Gn8PpzAdgL7GmJ+Ad4Cuxphp5xOyKFKb+Mtd23buJyfXw+x5q+jZqUWomw2a2/ONHd6P9R9PYM0H43n9iVvpeFkKrzw+xOlYZ3DLOjw1mLFGpRh6ta7F7GU/Ujfxf0W1Hqm12Lzbf9jk01U/c3mDKkRGGMpGR5J6SQKbdrnrsJTbfLF0Ay+8+QVv/W0YMWWinY5TrFhruefxt0hJrsbdN3VzOg7gz3Tvn6eTkpzInTd2zXu9V6cWLErfBMCW7fvIyfVSuWJ5p2IW6tR1GkL5ABKMMStPewz7RYwoIBV4yVrbCjjG/w5FABCoKtiL/fMHdfaEtfbnX/TyvUHMMxoYDWCM6Qzcb629+TwyFklUVCRPP3gDA0a8iNdrualvWxpf4p4zE9yerzhwyzp87Z4rqFQ+mlyvZfSbKzhyPJdnf9+O+tUr4LOWHfuP8eCU5YD/cMb8tbuZP6E3PgtvLdjMxp3OdBqGPjyZxembOHDoKE17P8KoYb0Y5PCg1z88MpnFqzZz4NBRmvUZw6hhvZg45TOyczwMuOdFwD8Y8plRv3M05yluXIenW7Z6KzPmfkOT+kl0vPFJAMbc3ZerOjR1LNPy1Vt59z8raHJJEp0HPQXAw3f24cZr2vLHCdPpeOOTlIqKZNKjN7u+qhSGfPuttQUdj9sB7LDWLg88n4m/07D31GEHY0x1YF/g/Z1ArdPmrxl4rciMvzNSwATGzASeBSYBbfCfDZFmrQ167z2t01DgKZetW6fZxctXBrtYkYuu2pCQF8MuyJ4pIe93XxCf76L/Y3NRuXVwb3Hi8fqcjpCvTu0vZ1X6ypBu5IR6Te01T7wTyib418AW6YV0GjDGLARus9Z+b4x5DCgXeOuAtfYvxphRQCVr7YPGmN7AcKAX/r/jf7fWXn4+2YKpNNyBf5RmDWAX8Clwd1EasdZ+hX9AhoiISLHmkq7nPcBbxphoYCtwK/4hB+8aY4YC24AbAtPOxd9h2AwcD0x7XgrtNFhr9wM3nW8DIiIicnFZa/8LnKsacdYglsD4hiL9s5+fYM6eqGeM+dAYk2GM2WeMmWOMqXcxGhcRESlOjIEIY0L6cLNgzp6YDrwLVAeSgPeAt0MZSkRERNwnmE5DjLX2TWutJ/CYBpQJdTARERE3csNlpJ2S75gGY0ylwLf/CYzCfAf/OZ//h39QhYiIiJQgBQ2ETMffSTjV77n9tPcsgWswiIiIlCRuv45EKBV07wndUUdERETyBHVFSGNMM6AJp41lsNa680byIiIiIVSCCw2FdxqMMWPx3zWrCf6xDD2BRYA6DSIiIiVIMJWG3+K/V/e31tpbjTGJgLuvtSsiIhICBvdfSyGUgjnl8oS11gd4jDEV8N8Ao1Yh84iIiMivTDCVhpXGmIrAq/jPqDgKLA1pKhERETcqBtdSCKVg7j1xV+Dbl40xnwAVrLVrQhtLRERE3KagizulFvSetXZVaCKJiIi4l67TcG7PFPCeBbpe5CxYwH8zLncqyb8oF4ubty/Anik3Ox2hQPX/+G+nIxRo08R+TkeQEIuKDGYonDP0CR16BV3cqUs4g4iIiBQH7u02hV5J/tlFRESkCIK6IqSIiIj4D4GU5EPVqjSIiIhIUIK5jLQBbgLqWWvHG2NqA9Wstd+EPJ2IiIjLRJTcQkNQlYZ/AO2AgYHnWcCLIUskIiLiYhEmtA83C2ZMQxtrbaox5lsAa22mMSY6xLlERETEZYLpNOQaYyLxX0YBY0wVwBfSVCIiIi5kjAZCFubvwPtAVWPMn/HfFvuJkKYSERER1wnm3hNvGWPSgW74zza51lr7XciTiYiIuJDbxx2EUjBnT9QGjgMfnv6atXZ7KIOJiIiIuwQzpuFj/OMZDFAGqAt8DzQNYS4RERFXKsFDGoI6PNH89OeBu1/elc/kIiIi8itV5MtIW2tXGWPahCKMiIiImxkgogSXGoIZ0/Cn055GAKnArpAlEhEREVcKptIQe9r3HvxjHGaFJs7F0bLfWMrHlCYyIoKoyAi+nPqg05HO8PmSDYx+ZiZen49B/dpz3y1XOR3pLG7P+I/pX/LmnKUYY2hSvzqTxtxMmdKlnI4FwPDx0/h00ToS4mNZOuNhR7Pc2vkSbmhbBwt8v+sII6evIsfj40+9G9Pz0hp4fZbpi39k6tdbubJZNe7t3RifD7w+HxPeX0v61oOO5Hbz9gX37x8AXq+PLoOfpnrVOGY8d6fTcc5wMjuX3sMmkp3rwevx0rdbK0bf3tvpWEEryTdtKrDTELioU6y19v7zWbgx5if8l532Ah5rbdr5LOd8fPDSCCpXLB+u5oLm9fp44Ol3eX/ScJISK9J1yF/p2ak5jepVdzpaHrdn3LXvEK/MWMDSGQ9Ttkw0t45+g9nz0rmxT1unowEwsE9b/nDDFdwxdqqjORLjyjC4Uz16PPkF2bk+/n7LZfRJrYkxUL1iWa564nOshUrl/Rd4XfJDBp+v2wNAw6QK/P2Wy7j6iS/Cntvt29ft+8cpL78zn5S6iWQdO+l0lLOUjo5izksjKB9TmlyPl563PcuV7ZtwWfO6TkeTQuTbYTLGRFlrvUCHC2yji7X20nB2GNwsff1P1KuVQHLNBKJLRXFd91TmLljjdKwzFIeMHq+Pk9m5eDxeTpzMoVpCnNOR8nRIrU98hRinYwAQFWEoUyqSyAhDmehI9h0+wY0d6jLp0++x1j/NwaM5ABzP8ebNFxMdmfe+E9y8fYvD/rFzbyafLVrP4H7tnY5yTsYYyseUBiDX4yXX4y1WV1n0XxUydA83K6jS8A3+8Qv/NcZ8ALwHHDv1prV2doiznTcDDLjnRYwxDOnfgVv6X2i/5+LZnXGYGonxec+TEuNJX/eTc4HOwe0Zk6pWZPjN3WjR91HKlI6mS5tGdG3b2OlYrrP38Elem7+Zrx+7muxcLws37mPR9xk8N6QcvVrV4KoW1Tl4NIfxs9ewLcO/a3dvUZ37+zShcvnS/OGVpY7kdvv2dfv+AfDQs7MYN+Jajh53X5XhFK/XR+dBT/HjjgyGXt+JtGbJTkeSIARzaKYMcADoCvQBrgl8DYYFPjPGpBtjhp1rAmPMMGPMSmPMyv37M4JcbMHmvnofX705kncn3snr733NklWbL8pyxR0OHTnOfxas4dt/P8aGuRM4fiKbd/+zwulYrlOhbCmubFadLuM+o/2YT4iJjqJfWk2ioyLI8fjo/8wCZiz9ib8MbJU3z7w1u7n6iS+48/Xl3NvLmT/U2r4X5pOFa0mIj+XSxrWdjlKgyMgIFk4fzfqPJ7Bq/TY2bC4e4+uNMUSE+OFmBXUaqgbOnFgHrA18XR/4ui7I5f/GWpsK9ATuNsZ0+uUE1tpXrLVp1tq0hIQqRUufj6SqFQGoUimW3p1bkr5h20VZ7sVQvUocO/dm5j3ftTeT6lXcU3oF92f86pvvqZ1UmYT4WEpFRdKnS0u+WbPV6Viu06FhFXYcPM7BYzl4fJZP1+witW4l9hw6waer/R/Qn63ZTaOks7ftii0HqFW5HPHlwn9DW7dvX7fvH8tXb+WThWtp0fdRhj40mYUrfmDYmClOx8pXXGwMHVun8MXSDU5HkSAU1GmIBMoHHrGnfX/qUShr7c7A1334b3p1+YWEDcaxE9l5A3+Onchm/vKNNL7EPQOUUpvUYcv2DLbt3E9OrofZ81bRs1MLp2Odwe0Za1aLZ+W6nzh+MgdrLV+v+IGU5GpOx3KdXZknuLROPGVKRQLQPqUKm/cc5fO1u2nbIAGANvUT+DHjKAB1Esrlzdu0ZhzRURFkHssJe263b1+37x9jh/dj/ccTWPPBeF5/4lY6XpbCK48PcTrWGfZnZnE46zgAJ07mMP+bjTRITnQ4VfA0puHcdltrx5/vgo0x5YAIa21W4PurgPNeXrAyDmYx6IFXAf9gqt9encaV7ZqEutmgRUVF8vSDNzBgxIt4vZab+rZ1VacG3J8xrVkyfbtdSpdBTxEZGUmLhjUZ0t89A76GPjyZxembOHDoKE17P8KoYb0Y5MCAtNXbMvlk9S7mPNAZr8+yYcdhZiz5idLRkTw7qDW3dr6E49leHnr7WwCubplE/8tqkeu1ZOd6+eMUZw4JuH37un3/KA727D/CXY+9idfnw+ez9L8ylR4dmxc+ozjO2HyGSBtjvrXWtjrnm8Es2Jh6+KsL4O+cTLfW/rmgeVJbp9nFy9x77LI4je51q/x+39zC7du4/h//7XSEAm2a2M/pCAVy+/aVC9OhTRrp6StDupGTUprbYZNCex7AuKtT0t16xmFBlYZuF7Jga+1WoOWFLENERETcI99Og7XWmUvBiYiIuJTuPSEiIiJBK8F9hhJ9CW0REREpAlUaREREgmUgQpUGERERkYKp0iAiIlIEhpJbalClQURERIKiSoOIiEiQ/KdcOp3COao0iIiISFBUaRARESkCVRpERERECqFKg4iISBGU5BufqdIgIiIiQVGlQUREJEgl/ewJV3UaDCW77FMSaPtemO+f6+d0hAJVv/UtpyMUaM+/bnY6gkix5qpOg4iIiKsZ3eVSREREpFCqNIiIiBRBRAkuNajSICIiIkFRpUFERCRIJf3sCVUaREREJCiqNIiIiBRBCR7SoE6DiIhI8AwRlNxegw5PiIiISFBUaRAREQmS/8rFTqdwjioNIiIiEhRVGkRERIJldMqliIiISKFUaRARESkCXUZaREREpBC/2kqD1+ujy+CnqV41jhnP3el0nDw79mRy52NTyTiYhQGG9O/AHQO7OB3rDMPHT+PTRetIiI9l6YyHnY5zFrfnO5mdS+9hE8nO9eD1eOnbrRWjb+/taKYRj7/FZ4v962zR2w8B8NSrc3lzzhISKpYH4OE7r6F7h6ZhzXVb94bcfEUDjIFpCzbz6mcbefT/Uul+aQ1yPT5+2pfFva8v5cjxXKIiDc/e2pbmdSoRGRnBe4u38sLH68Oa95TPl2xg9DMz8fp8DOrXnvtuucqRHAVx62fgKcVhHZ6Lzp4IIWNMRWPMTGPMRmPMd8aYdqFs73QvvzOflLqJ4WouaFFREUy49zqWvfsIn02+n9dmfs3GrbudjnWGgX3aMvPvdzsdI19uz1c6Ooo5L41g0fTRfD19NF8s3cCKtT86mul3fdowY+JdZ71+x++68NW0UXw1bVTYOwyNasRx8xUN6Dn+P3Qd8zHdW9YguWp5FqzbTeeHP6LrmI/ZuieLEb2bAXDNZXWILhVJlzEfc/VjcxncpQG1EsqFNTP4/xg/8PS7vPf8XSx79xFmfZbuun0Y3PsZCMVnHcrZQn144nngE2ttI6Al8F2I2wNg595MPlu0nsH92oejuSKplhBHy0a1AIgtV4aU5GrszjjkcKozdUitT3yFGKdj5Mvt+YwxlI8pDUCux0uux4tx+F+T9q3ct84aJMWxaut+TuR48fosS7/fR+/WtVmwfjdenwUgfct+qlfy57YWYkpHERlhKFMqkhyPj6wTuWHPnb7+J+rVSiC5ZgLRpaK4rnsqcxesCXuOgrj5MxCKxzosSIQxIX24Wcg6DcaYOKAT8DqAtTbHWhuWv44PPTuLcSOuJcLl58Vs33WANd/voHXTZKejyEXm9froeOOTpFw1is5tGpHWLNnpSOf0+syv6XTTk4x4/C0OHTke1rY37jhEm5SqxJeLpmx0JN1aJJFU+cyOzcBOl/Dlml0AfLRyG8ezPayZOID0Z6/jpf9s4NCxnLBmBtidcZgaifF5z5MS49mdcTjsOQri9s/A4rAO5dxCWWmoC2QAk40x3xpjXjPGnFVLNMYMM8asNMaszNifccGNfrJwLQnxsVzauPYFLyuUjh7PZvDI13jyTwOoUL6s03HkIouMjGDh9NGs/3gCq9ZvY8PmXU5HOsut1/2GlbPG8tWbI0lMqMCjz78f1vY37T7CpLnreeeBbkz/f11Zvz0zr8IA8MdrmuHx+pi11H9op1XdBLw+S8v7ZnH5/e9zR48m1K5SPqyZi4Pi8hlYnBkT2oebhbLTEAWkAi9Za1sBx4BRv5zIWvuKtTbNWptWJaHKBTe6fPVWPlm4lhZ9H2XoQ5NZuOIHho2ZcsHLvZhyPV6GjHyV63ukcU3XS52OIyEUFxtDx9YpfLF0g9NRzlK1cgUiIyOIiIhgUL/2rNqwLewZ3v56C1c/9h/6PzmPQ8dy2LonC4D/+009ureswd3/XJw37XXtkpm/dhcer2V/VjYrNu3j0uRKYc9cvUocO/dm5j3ftTeT6lXiwp4jP8XhM9Dt61DyF8pOww5gh7V2eeD5TPydiJAaO7wf6z+ewJoPxvP6E7fS8bIUXnl8SKibDZq1lnsef4uU5GrcfVM3p+NICOzPzOJwlr/Uf+JkDvO/2UiDZPcNSNuz/3/l4I8XrKZRvephz5AQ6x/7UaNSDL3SajF72Y90aV6du3s2YcjzX3Eix5s37c4Dx/hN42oAxERH0vqSBDbtPhL2zKlN6rBlewbbdu4nJ9fD7Hmr6NmpRdhz5Mftn4Hg/nVYEIP/D2coH24WslMurbV7jDE/G2MaWmu/B7oB7vt3K8yWrd7KjLnf0KR+Eh1vfBKAMXf35aowj1wvyNCHJ7M4fRMHDh2lae9HGDWsF4NcNKDK7fn27D/CXY+9idfnw+ez9L8ylR4dmzua6Q+PTGbxqs0cPHSU5n3GMHJYLxanb2Ldph0YY6hVvRLPjPpd2HO9NvwKKpWPJtdrGT11BUeO5/LEzZcTHRXBjAf8ner0LfsZOeUb3vjiB56/rR0L/twHA7yzaCvf7Qj/IOKoqEiefvAGBox4Ea/XclPftjS+JPwdruJM67D4Mtbawqc634UbcynwGhANbAVutdZm5jd969ZpdvHylSHLI1LcnX7M341q/P4tpyMUaM+/bnY6goRQhzZppKevDOmogLpNWtjHpn4cyia45bLa6dbatJA2cp5CenEna+1/AVf+4CIiIlI0bj98IiIi4iomxI+gMhgTGTgz8aPA87rGmOXGmM3GmBnGmOjA66UDzzcH3k++kJ9dnQYREZHi54+cecHEp4DnrLX1gUxgaOD1oUBm4PXnAtOdN3UaREREgmRw/oqQxpiaQG/8YwYx/kvOdsV/liLAFODawPf9As8JvN/NXMAlatVpEBERKYIwHJ5IOHXRw8Bj2C8iTAQeBHyB55WBQ9ZaT+D5DqBG4PsawM8AgfcPB6Y/L7/au1yKiIgUU/vzO3vCGNMH2GetTTfGdA5vLHUaREREisThSz13APoaY3oBZYAK+G8OWdEYExWoJtQEdgam3wnUAnYYY6KAOODA+TauwxMiIiLFhLV2tLW2prU2Gfgd8KW19iZgPvDbwGRDgDmB7z8IPCfw/pf2Ai7QpEqDiIhI0Izjt7rPx0jgHWPMBOBbAneYDnx90xizGTiIv6Nx3tRpEBERKYastV8BXwW+3wpcfo5pTgLXX6w21WkQEREJ0qkbVpVUJflnFxERkSJQpUFERKQIXDqmISxUaRAREZGgqNIgIiJSBCW3zqBOQ5FcwKmtYeH1uTsfQGRESd7dLpzb19+ef93sdIQCVR442ekIhTrw9q1ORyiQx+srfCKHuP8TsPhTp0FERCRYRmMaRERERAqlSoOIiEiQdJ0GERERkSCo0iAiIlIEGtMgIiIiUghVGkRERIqg5NYZVGkQERGRIKnSICIiUgQleEiDOg0iIiLB8p9yWXJ7DTo8ISIiIkFRpUFERKQISvLhCVUaREREJCiqNIiIiATNYDSmQURERKRgv8pKw+dLNjD6mZl4fT4G9WvPfbdc5XSkMxzOOs6IP7/Nxi27wBheeOQmLm9R19FMIya8xbzF60mIj2Xh9NEArP1hBw88NYOTOR6iIiN4+oEbSG1ax9Gcp/xj+pe8OWcpxhia1K/OpDE3U6Z0KadjAbBp216GPjQ57/lPuw4welgv7hzYxcFUZ3L7PuKWfMN6NGFw1xSMgalf/sA//7MBgD9c3Zih3RvhtZbPvt3BuOkrAbi3X3Nu6pyCz2cZNWUZ89fsciT3yexceg+bSHauB6/HS99urRh9e29Hspyyc28md497k4yDWRhjGHRte27/v84AvPruAt6YtZDIiAi6t2/K2Hv6OZq1MCV5TEPIOg3GmIbAjNNeqgc8aq2dGKo2AbxeHw88/S7vTxpOUmJFug75Kz07NadRveqhbLZIRj8zi25tGzPlL0PJyfVw4mSO05H4Xe82DP1tJ4aPn5b32vhJc7h/aE+ubN+EeUvWM27SHOa8NMLBlH679h3ilRkLWDrjYcqWiebW0W8we146N/Zp63Q0ABrUSeTrt0YB/t/Hpr0foU/nlg6n+h+37yNuydeoZkUGd02h+yMfkuPx8d6oq/hs1c/UqFyOnq1r02nUHHI8PhIqlAGgYY04+rerR4cH3qdafAyzH76ay++bjc/asOYGKB0dxZyXRlA+pjS5Hi89b3uWK9s34bLmzv1zEhkZwbgR/WnZqBZHj52k2y1/pfPlDck4mMUnX6/lqzdHUjq6FBkHsxzLKIUL2eEJa+331tpLrbWXAq2B48D7oWrvlPT1P1GvVgLJNROILhXFdd1TmbtgTaibDdqRoydY8u1mBvVrB0B0qSjiYmMcTgXtW9UnvsIvchhD1rGTAGQdPUm1KnEOJDs3j9fHyexcPB4vJ07mUC3BPdlOt2DF9yTXTKBW9UpOxUwG5wAAIABJREFUR8nj9n3ELflSalQkfXMGJ3K8eH2Wxd/toc/ldbi1eyOe/2ANOR4fAPuP+PeRnmm1eX/pVnI8PrZnHOXHPVmk1k8Ie27w31CpfExpAHI9XnI9XsdvslQtIY6WjWoBUL5cGVKSE9m97zCTZy9ixODulI72VwqrVIp1MmahTl2nIZQPNwvXmIZuwBZr7bZQN7Q74zA1EuPzniclxrM743Comw3atl0HSIgvz/Dx07ji5qcYMWE6x05kOx3rnP5873WMmzSHln0fZewL/+aRO69xOhIASVUr8v/bu+/4qMp8j+OfXyb0GkMvAqI0kZasIgqXIiiIsNgWXMtFV1ZXbHtXBXvbu1fvFte17FURRcGuK6uIIIo0qVFpShNFitJraJn87h8zxNAnksk5Id83r3kxc6Y835xpz/ye55wz+PJutOpzL8173U3liuXo2r550LEO6e3xWVzUIyPoGPsJ+3skLPm+/n4T7ZvVJK1iGcqVjtC9TT3qplegca3KtG9Wk3EP9Wb0vT1pe1KsY1A7rQKrNuzIu//qjTuonRbcD4JoNJeOl/2JJj2G0PmMZmS2bBhYlgOtWL2BeYtXkdGyActWrGP6l8s49+q/0Of6v/P5wqR/TcgxKKpOQ3/glSJqK9RycnL5ctFKBl7UkU9fvoPy5Urz2Ivjg451SMPfnsJDN/fjy9EP8tDN/bjlj6OCjgTA5q3ZfPDpXD7/1/0sHPMw2Tt38/oHs4KOdZA9e3MYO2kefbu1DTqK/AyLV2/h8dHzeHNoD14f0oP5320kmuukRlJIq1iGHve8x/0jZzHs5s5BRz2kSCSFyaOGsuD9h8la8B0LlwYzv+JA27N3M3DoMB6+5UIqVShHNJrLpi3ZjB32e+4f/Et+c9dwPIAhnYRZbE5DMk9hlvROg5mVBvoAbxzm+kFmNtvMZq9bv+6Y26tdvQqrftyUd3n1j5uoHaKyep0aValTo2per79v1zbMXfR9sKEO47UxM+ndJTYW37dbW7JC8gtg4sxFnFgnnWpplSiVGqF3l9bMnPtN0LEO8tG0hbRqVp8a6ZWDjrKfsL9HwpRv5MQldLvr31zw4Ads3rGbZWu2snrjDt6bGXsvZC1bT6476ZXKsGbTDuqmV8i7b50TKrBmU3YgufOrUqk8HTOaMOGzhUFHYW9OlIFDh3HxuZl5ny21a1Shd5dWmBntTm1ASoqxYfP2gJPK4RRFpaEnkOXuPx7qSnd/xt0z3T2zerXqx9xYuxaxctd3q9azZ28Ob4/PomenVsf8uIWlZrXK1K1RlSXfxVbHp7MW07RROCagHahWtSpMy1oKwOTZizmp/rE/P4WhXq00Zs//luxde3B3Js1aTJOGtYKOdZC3xs0J3dAEhP89EqZ8+yY51k2vQO9fNODNqd8wZvYKzm4Re882rlWZ0qkRNmzbzQdzvqffmSdROjWFE6tX5KRalclauj6Q3Os3bWPLtliHZeeuPXwy82tOaVgzkCz7uDu3/HEUTRrW5PrLuuYt79WpFVPmLAFg2Yq17NkbJb1qxaBiJqQkVxqKYpPLARTh0ERqaoRHb7+Ui256kmjU+XWf9jRvHK4v5Uduu4Tf3vMie3KiNKyTzhP3Xh50JAbd8wJTs5aycfN2Wl1wD7df24u/Du3PXX97i2g0lzKlS/HXof2DjglAZsuG9OnWhi5XPEIkEqFV03pc1a9D0LH2s2PnbibO+Jq/hWSd5Rf290iY8r1waxdOqFiWvdFcbh8+na3Zexj5yRL+cd3ZTHn0l+zJyeWGpycDsGjlZt6dvpxpf+5HNOrcPvyzQLacAPhh/VZ+d/9LRHNzyc11+p3TjvM6nhZIln1mfPkNr38wixaN69D5ikcAuOv63lx2QXtufngUHS/7E6VSIzxx7+WBT9qUw7Nkjh2ZWQVgBXCSux91JlNGRqZPnTE7aXmOVajH2YBobrjzAURS9GFwLPRhemzSBww/+o0CtuGVgUFHOKKcaG7QEQ6rU4fTyZozO6lvkiYt2/iTb3yUzCbo0aL6HHfPTGojP1NSKw3uvgNIT2YbIiIiUjSOyz1CioiIJIMBJblgqmNPiIiISEJUaRARESkAHeVSRERE5ChUaRARESmAkrwRkyoNIiIikhBVGkRERAqgJM9pUKdBREQkQdrkUkRERCQBqjSIiIgkzEr08IQqDSIiIpIQVRpEREQSVQwOX51MqjSIiIhIQlRpEBERKYASXGhQpUFEREQSo0pDAVjIB7IixaALmBP1oCMcUanUcK/EvTm5QUc4otRIuN8jG14ZGHSEo2pw3RtBRzii5U9dHHSEQMX20xDu13kyhfsTUkREREJDlQYREZECKLl1BlUaREREJEGqNIiIiBRECS41qNIgIiIiCVGlQUREpAB07AkRERGRo1ClQUREpABK8G4aVGkQERGRxKjSICIiUgAluNCgSoOIiIgkRpUGERGRgijBpQZ1GkRERBJkaJNLERERkaM6LisNgx98mQ+nzKdaWiU+e+2uoOMc5KNpCxn6lzeJ5uZyRd8O3PqfPYKOdJDWfe+jYvkyRFJSSI2k8PGI2wPNs+rHTQx+8CXWbdyGmXFF3w4M+lVnrr17OEtXrAVg67adVK5Ujk9G3BFo1pU/bOL6+0fEsgJX9TuL6wZ0CTRTcVp/EL7XX35hen6v6XYK/c9qhDt8vXoLt704i4zG6dx1UWtKRVKYt2ITt780m2iu075JdZ69/iy+X78DgLGfr+TxMV8VWdYbHxrJuKmxz+Wpr9wJwKYtO7jm7uF8v3oj9eucwPN/vJqqlcsXWaafxUr2JpdJ7TSY2a3AbwAH5gED3X1XMtsEGNC7Pdde+h9cd9+IZDdVYNFoLrc9+jrvPDGYOjWr0vWq/6Vnp9NodlLtoKMdZPTTN5FetWLQMQBIjaTwwE39aNW0Ptt37OKcgf/Lf5zelGcfHph3m3sff4fKFcoGmDImNTWFh2+5kNbN6rNtxy66XPkInc9oFuhzXJzW3z5hev3lF5bnt2bVsgzscgrdHhjL7r25PHlte/qefiK39j6Vyx77lOVrt/P7C07l4vYNeG3atwDMWrKOq5+aWqQ59xnQ+wx+c0knfvfAS3nL/j5iPJ0ym3DLVT147MVxPDZiPPcP7htIPklM0oYnzKwucBOQ6e4tgQjQP1nt5XdWu5NJC2lvdc6CbzmpfjUa1qtG6VKpXNi9HWM+nRt0rNCrWa0KrZrWB6BihbI0aViTNeu25F3v7oye8DkX9sgIKmKeWtWq0LpZLGulCmVp0rAWa9ZtDjRTcVp/YRem5zeSYpQtFSGSYpQrFSF7dw57o7ksX7sdgMlf/UjPdvUCyXagDm0P/lweM2ke/c8/A4D+559RbD4LLcmnMEv2nIZUoJyZpQLlgdVJbi/01qzbQt2aaXmX69RM2+/DOywMuOjGJ+ly5aO88E4wv0wOZ8WaDcxbvIqMUxvkLZv+xTKqn1CJk+rXCDDZwVas3sDcRSvJOLVh0FHyFIf1F+bXX35BPr8/bt7FMx8t4rP/7s2sRy5g2669vDdnJZEU47QTY58xvdrVo3baT1/U7U5K54O7u/Pi4LM5pXblIs98oHUbt1GrWhUAaqZXZt3GbQEnkqNJ2vCEu68ysz8DK4CdwDh3H5es9qRwjXn2VurUqMq6jdu4cPATNGlQkw7tTg46Ftuzd3P10GE8dMuFVKpQLm/52+Pn0K97uH4lb8/ezZV3PMeffn8RlSuWO/odikBxWX9hff3lF/TzW7l8KXq0qsvZd7/P1uy9PDXoTPqdfiI3Pjedey9pTelSESYv/IForgMwf8UmOtz1Ptm7o3RpWYtnr+9A53vHFnnuwzGz4jNXoLjkTIJkDk+kAX2BRkAdoIKZXX6I2w0ys9lmNnvd+nXJihMatatXYdWPm/Iur/5xE7WrVwkw0aHVqVEVgOonVOL8zq2Zs/C7gBPB3pwoV985jIvOzaR359Z5y3Nyorw/cS6/PKdtgOn2tzcnylV3PMsl52VyQdc2QccBitf6C+PrL78wPL9nN6vJ9xt2sHH7HnJynbGfryKjcTpZyzdyyV8m0vd/JjBjyXqWr439et++K4fs3VEAPpn/A6mRFNIqlA4k+z7VT6jED+tjldYf1m+hWlqlQPPI0SVzeOIcYLm7r3P3vcDbQIcDb+Tuz7h7prtnVq9WPYlxwqFdiwYsW7GO71atZ8/eHN4en0XPTq2CjrWfHTt3s23Hrrzzn8z4muaNg52o6e7c8sdRNGlQk+sHdN3vukmzFnFKgxrUqZF2mHsXLXfnxodG0qRhLW74dbeg4wDFa/2F8fWXX1ie39Ubs2nb6ATKlooAcFazGixds430SmUAKJ2awvXnNmXkpGUAVK9cJu++rRumkWLGph17ij54Pj07nsar788A4NX3Z9Cr02mB5kmMJf1fmCVz64kVQHszK09seKIbMDuJ7eW55q7hTJ2zhA2bt3Pq+XczZFAvruh7UH8lEKmpER69/VIuuulJolHn133ah+oDEWLjjFfc9iwAOdFcLj43k3PObBFophlzv+GNsbNo3rgOXa58BIC7ruvNOR1O5Z2PskJVWp/+5Te8NmYmLU6uQ8fL/gTAPTf0ocdZpwaWqTitvzC+/vILy/P7xbcbGZO1kvfvOodo1Fnw/WZGTfmGP/RpSbfTamNmvDxpGdMWxSq4vdrV4/JOjcnJdXbtiXLjc9OLNO+1dw9natZSNmzeTsve9zBkUC9uvqo7V9/5PCNHT6de7TSe/+PVRZpJCs7cPXkPbvYA8CsgB/gc+I277z7c7TMyMn3qjCLpVxyXkvlcFpacaLgzlkoN9/7O9ubkBh3hiFIj4f6VZMVg0LzBdW8EHeGIlj91cdARDuvsM39B1pzZSX2SW7Rq56Pe+zSZTdC2QeU57p6Z1EZ+pqTup8Hd7wPuS2YbIiIiUjSOyz1CioiIJENx2JdCMoW7FisiIiKhoUqDiIhIQZTgUoMqDSIiIpIQVRpEREQKIOz7UkgmVRpEREQkIao0iIiIFEAx2N1H0qjSICIiIglRp0FERKQALMmno7ZvVt/MPjGzhWa2wMxuji8/wczGm9mS+P9p8eVmZo+b2VIzm2tm7X7u365Og4iISKKS3WNIbOgjB/gvd28BtAduMLMWwBBggrufAkyIXwboCZwSPw0Cnv6Zf706DSIiIsWJu69x96z4+W3AV0BdoC/wYvxmLwK/jJ/vC4zwmOlAVTP7WUdK1ERIERGRAiiCTS6rmVn+ozc+4+7PHDKLWUOgLTADqOnua+JX/QDUjJ+vC3yf724r48vWUEDqNIiIiITL+kSOcmlmFYG3gFvcfWv+o7i6u5tZoR9WWJ0GERGRBBnh2OTSzEoR6zCMdPe344t/NLPa7r4mPvywNr58FVA/393rxZcVmOY0iIiIFCMWKykMA75y97/mu2o0cFX8/FXAu/mWXxnfiqI9sCXfMEaBqNJwHLEwdH+PIhLybmpubqFX8wpVaiTcz3HYX4Pu4X5+Ab775yVBRzii9AHDg45wWLuWry+SdkLwKj8LuAKYZ2ZfxJfdCfwP8LqZXQN8B1wav24M0AtYCmQDA39uw+o0iIiIFCPuPoXD9126HeL2DtxQGG2r0yAiIlIQISg1BCXkxWIREREJC1UaRERECkCHxhYRERE5ClUaRERECiDkGwkllSoNIiIikhBVGkRERAqgBBcaVGkQERGRxKjSICIiUhAluNSgSoOIiIgkRJUGERGRBBnaT4OIiIjIUanSICIikigr2ftpUKdBRESkAEpwn+H47TREo7l0ufJRateowmt/uz7oOAcJc77BD77Mh1PmUy2tEp+9dlfQcQC48aGRjJsayzT1lTsBuO/xfzF2yjxKl0qlYd1qPHHPr6lSqXzASWP+79WJjHh3Gu7OlX07cN2ALkFH2s9Toz7mpXc/w8xocXJtnrjncsqWKRV0rDxhfA0eqHXf+6hYvgyRlBRSIyl8POL2oCPl2bV7L+cPeozde3OI5kTp060tQ397fpHnGHReC67s2gQzGPHxYv7vg4UAXHtuc67p3oyoO+M+X8kDo2bT+bQ63NM/g9KpEfbkRLl/1GwmL1hT5JnlyJI6p8HMbjaz+Wa2wMxuSWZbB/rnq5/QpFHNomyyQMKcb0Dv9rz5eKEcer3QDOh9Bq8/9rv9lnU+vSlTR93J5JFDaXxiDf724viA0u3vq2WrGfHuNMYP/wOTXh7Ch1Pn883364KOlWf12s0889qnfPzibUx79U6iUeft8XOCjrWfML4GD2X00zcxaeSQUHUYAMqUTuXdp29iyqihTBo1lAmfLWTWvOVFmqFZvapc2bUJ3e/+N53ueJdz29anUc1KnN2iFj0zTqTTkHc567Z/8eR78wHYsG0Xv/7zR3S841/c8PRknv5dxyLNWyCW5FOIJa3TYGYtgWuB04HWQG8zOzlZ7eW36sdNjJuygCv7diiK5gos7PnOancyaZXD8Yt9nw5tD87UpX1zUlMjAGS2bMiatZuDiHaQxd/+SMapDShftjSpqRHOansK7038MuhY+8mJ5rJr915ycqLs3LWHWtWqBB1pP2F8DRYnZkbF8mUA2JsTZW9OFCvigfgmdasyZ+k6du6JEs11pn71A71Pb8DA7s34++i57MnJBWD91l0AzPt2Iz9s2gnA1ys3U7Z0KqVTNVc/bJL5jDQHZrh7trvnAJ8CFyaxvTx3/vUtHrjpl6SkhLPLFvZ8xdGof0+n25ktgo4BQLOTajP9i2Vs3LKD7F17GD9tAat+3BR0rDx1alRl8OXdaNXnXpr3upvKFcvRtX3zoGMVOwZcdOOTdLnyUV54Z2rQcQ4SjebS8bI/0aTHEDqf0YzMlg2LtP2vv99E+2Y1SatYhnKlI3RvU4+66RVoXKsy7ZvVZNxDvRl9b0/anlTtoPtecHoD5i7fkNexCBdL+r8wS2anYT7Q0czSzaw80Auof+CNzGyQmc02s9nr1h97CXfs5HlUS6tEm+YnHvNjJUPY8xVHfxn+IZFICpeclxl0FACaNqrFTVd25+Ibn+TSm5+iZZN6RFLC84tp89ZsPvh0Lp//634WjnmY7J27ef2DWUHHKnbGPHsrE1+6g9cfu55hb0xiWtbSoCPtJxJJYfKooSx4/2GyFnzHwqWri7T9xau38Pjoebw5tAevD+nB/O82Es11UiMppFUsQ4973uP+kbMYdnPn/e7XtF5V7rssk98/N61I80pikjYR0t2/MrNHgHHADuALIHqI2z0DPAOQkZHpx9rujC+/YezkeYyftoDdu/eybccuBt3zIs88dNWxPnShCHu+4mbUe9MZN2U+7zx5Y5GXX4/k8j5ncnmfMwF46KnR1KlRNeBEP5k4cxEn1kmnWlolAHp3ac3Mud9wac9fBJyseNn3nFY/oRLnd27NnIXf0aFdkYzAFkiVSuXpmNGECZ8tpMXJdYq07ZETlzBy4hIA7v5VO1ZvzOaUOlV4b+Z3AGQtW0+uO+mVyrBh227qnFCeEb/vyu+emsy3a7cVadaCCNFHTZFL6s8fdx/m7hnu3gnYBCxOZnsA9w3uy4L3H2bu6AcZ9t8D6fiLJqH6Qg57vuJkwmcL+cdLExj550GUL1s66Dj7Wbcx9oG38oeNvDfxSy4+NxxVEIB6tdKYPf9bsnftwd2ZNGsxTRrWCjpWsbJj52627diVd/6TGV/TvHHtgFP9ZP2mbWzZlg3Azl17+GTm15zSsOgnXlerXBaAuukV6P2LBrw59RvGzF7B2S1i66pxrcqUTo2wYdtuKpcvzSu3d+ehV+Ywc/HaIs8qiUnqJpdmVsPd15rZicTmM7RPZntSOK65azhT5yxhw+btnHr+3QwZ1IsrAp60ee3dw5matZQNm7fTsvc9DBnUi8deHMfuPTlcdOOTQGwy5F+G9A805z7/OeQ5Nm7JplRqCo/edmloNgWF2Hrq060NXa54hEgkQqum9biqX7gm5YbxNZjfuo3buOK2Z4HYpNKLz83knJDMqQH4Yf1Wfnf/S0Rzc8nNdfqd047zOp5W5DleuLULJ1Qsy95oLrcPn87W7D2M/GQJ/7jubKY8+kv25ORyw9OTgdhmmI1qVuIPF7bmDxe2BuDiP43LmygZFsVgA4ekMvdjHhE4/IObTQbSgb3A7919wpFun5GR6VNnzE5aHglebm7yXm8lQdjLomEaIjqUZH7eFZawr8P0AcODjnBYuyY8QHTjt0ldga3aZPjoj5I78bVR9XJz3D085cl8klppcPcQb2grIiLyM4S7X5dU4ZnSLSIiIqF23O5GWkREJBnCvi+FZFKlQURERBKiSoOIiEgBhHyualKp0iAiIiIJUaVBRESkAEpwoUGVBhEREUmMKg0iIiKJMs1pEBERETkqVRpEREQKpOSWGlRpEBERkYSo0iAiIpIgo2TPaVCnQUREpABKcJ9BwxMiIiKSmFBVGrKy5qwvV8q+K8SHrAasL8THK2zKd2yU79iFPaPyHZuSlq9BIT7WYWl4IiTcvXphPp6ZzXb3zMJ8zMKkfMdG+Y5d2DMq37FRPilsoeo0iIiIhJ0OjS0iIiJyFMd7peGZoAMchfIdG+U7dmHPqHzHRvmSoeQWGjB3DzqDiIhIsdC6bYZ/+On0pLZRu0rpOWGd63G8VxpEREQKVQkuNGhOg4iIiCTmuOw0mNl5ZrbIzJaa2ZCg8xzIzJ43s7VmNj/oLIdiZvXN7BMzW2hmC8zs5qAz5WdmZc1sppl9Gc/3QNCZDsXMImb2uZm9F3SWA5nZt2Y2z8y+MLPZQec5kJlVNbM3zexrM/vKzM4MOlN+ZtY0vu72nbaa2S1B58rPzG6Nvz/mm9krZlY26Ez5mdnN8WwLwrbujsQs+acwO+46DWYWAZ4EegItgAFm1iLYVAd5ATgv6BBHkAP8l7u3ANoDN4RsHe4Gurp7a6ANcJ6ZtQ8406HcDHwVdIgj6OLubUI6dvp3YKy7NwNaE7L16O6L4uuuDZABZAPvBBwrj5nVBW4CMt29JRAB+geb6idm1hK4Fjid2PPb28xODjaVJOK46zQQexEudfdv3H0P8CrQN+BM+3H3ScDGoHMcjruvcfes+PltxD6w6wab6icesz1+sVT8FKoZvWZWDzgfeC7oLMWNmVUBOgHDANx9j7tvDjbVEXUDlrl7Ye7NtjCkAuXMLBUoD6wOOE9+zYEZ7p7t7jnAp8CFAWdKmCX5X5gdj52GusD3+S6vJERfeMWNmTUE2gIzgk2yv3jp/wtgLTDe3UOVD3gMuB3IDTrIYTgwzszmmNmgoMMcoBGwDhgeH955zswqBB3qCPoDrwQdIj93XwX8GVgBrAG2uPu4YFPtZz7Q0czSzaw80AuoH3AmScDx2GmQQmJmFYG3gFvcfWvQefJz92i8NFwPOD1e7gwFM+sNrHX3OUFnOYKz3b0dsWG8G8ysU9CB8kkF2gFPu3tbYAcQurlJAGZWGugDvBF0lvzMLI1YhbURUAeoYGaXB5vqJ+7+FfAIMA4YC3wBRAMNVRCW5FOIHY+dhlXs32OtF18mBWBmpYh1GEa6+9tB5zmceNn6E8I1R+QsoI+ZfUtseKyrmb0cbKT9xX+J4u5riY3Fnx5sov2sBFbmqx69SawTEUY9gSx3/zHoIAc4B1ju7uvcfS/wNtAh4Ez7cfdh7p7h7p2ATcDioDPJ0R2PnYZZwClm1ij+K6A/MDrgTMWKmRmx8eSv3P2vQec5kJlVN7Oq8fPlgO7A18Gm+om7D3X3eu7ekNjr72N3D82vPDOrYGaV9p0HehArF4eCu/8AfG9mTeOLugELA4x0JAMI2dBE3AqgvZmVj7+fuxGyyaRmViP+/4nE5jOMCjZR4kpwoeH427mTu+eY2WDgQ2Izhp939wUBx9qPmb0CdAaqmdlK4D53HxZsqv2cBVwBzIvPGwC4093HBJgpv9rAi/EtZVKA1909dJs1hlhN4J3YdwmpwCh3HxtspIPcCIyMd/y/AQYGnOcg8Q5Xd+C3QWc5kLvPMLM3gSxiW0N9Tvh22fyWmaUDe4EbQj7ZVeK0G2kREZEEtWmX4RMmJ3fedbWKpUK7G+njcXhCREREkuC4G54QERFJnvDvSyGZ1GkQERFJkBH+XT0nk4YnREREJCHqNIiIiEhC1GkQOQwzi8aPYDjfzN6I7+725z7WC2Z2cfz8c0c6AJiZdTazAu+IJ37kymqJLj/gNtuPdP0hbn+/mf2hoBlFpHhTp0Hk8HbGj2TYEtgDXJf/yviBgArM3X/j7kfaWVFnQrb3PhH5iQ6NLSJHMxk4OV4FmGxmo4GF8QNn/a+ZzTKzuWb2W4jtVdPMnjCzRWb2EVBj3wOZ2UQzy4yfP8/MsszsSzObED9A2HXArfEqR8f4HjDfircxy8zOit833czGmdkCM3uOBHYmZ2b/ih+kasGBB6oys7/Fl08ws+rxZY3NbGz8PpPNrFlhrEwRKZ609YTIUcQrCj2JHVgHYsdBaOnuy+NfvFvc/RdmVgaYambjiB0ZtCnQgtgeGBcCzx/wuNWBZ4FO8cc6wd03mtk/ge3u/uf47UYBf3P3KfFd7n5I7NDC9wFT3P1BMzsfuCaBP+fqeBvlgFlm9pa7bwAqALPd/VYzuzf+2IOJ7UXwOndfYmZnAE8BXX/GahQ5bmiTSxE5lHL5dqM9mdjxODoAM919eXx5D6DVvvkKQBXgFKAT8Iq7R4HVZvbxIR6/PTBp32O5+8bD5DgHaGE/1S0rx49A2onYPvtx9/fNbFMCf9NNZtYvfr5+POsGYofwfi2+/GXg7XgbHYA38rVdJoE2ROQ4pU6DyOHtjB9+O0/8y3NH/kXAje7+4QG361WIOVKA9u6+6xBZEmZmnYl1QM5092wzmwiUPczNPd7u5gPXgUiJVgzmHSST5jSIHJsPgevjhxLHzJrED2Q0CfhVfM5DbaDLIe47HeiN+dCAAAACLUlEQVRkZo3i9z0hvnwbUCnf7cYRO4AT8dvt+xKfBFwWX9YTSDtK1irApniHoRmxSsc+KcC+asllxIY9tgLLzeySeBtmZq2P0oaIHMfUaRA5Ns8Rm6+QZWbzgf8jVsF7B1gSv24E8NmBd3T3dcAgYkMBX/LT8MC/gX77JkICNwGZ8YmWC/lpK44HiHU6FhAbplhxlKxjgVQz+wr4H2Kdln12AKfH/4auwIPx5b8GronnWwD0TWCdiBy3kn1Y7LAXMXSUSxERkQS1y8j0T6fOTGoblctFQnuUS81pEBERKYiwlwOSSMMTIiIikhBVGkRERAqgJO+nQZUGERERSYgqDSIiIgWg/TSIiIiIHIUqDSIiIgVQggsNqjSIiIhIYlRpEBERKYgSXGpQpUFERKQALMn/Espgdp6ZLTKzpWY2JMl/ch51GkRERIoRM4sATwI9gRbAADNrURRta3hCREQkQUYoNrk8HVjq7t8AmNmrxA4mtzDZDavSICIiUrzUBb7Pd3llfFnSqdIgIiKSoKysOR+WK2XVktxMWTObne/yM+7+TJLbTIg6DSIiIgly9/OCzgCsAurnu1wvvizpNDwhIiJSvMwCTjGzRmZWGugPjC6KhlVpEBERKUbcPcfMBgMfAhHgeXdfUBRtm7sXRTsiIiJSzGl4QkRERBKiToOIiIgkRJ0GERERSYg6DSIiIpIQdRpEREQkIeo0iIiISELUaRAREZGEqNMgIiIiCfl/lDuSgEju858AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.layers"
      ],
      "metadata": {
        "id": "7fjKGOv-pUcM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62a4b6d9-7676-4208-a74b-86cdbbd7eb2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.core.flatten.Flatten at 0x7fad38361c90>,\n",
              " <keras.layers.core.flatten.Flatten at 0x7fad3834fe10>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3847ec90>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3847a2d0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3840e910>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3836e310>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3830c890>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3830ccd0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad384b4850>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3831d090>,\n",
              " <keras.layers.core.dense.Dense at 0x7fad3831d550>]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the weights of the first hidden layer\n",
        "\n",
        "weights, biases = model_2.layers[1].get_weights()\n",
        "weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8bTUl5OVbpN",
        "outputId": "8636b17b-3c91-41c7-a756-690471b48f01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.02711498, -0.00972034, -0.02424062, ..., -0.06670354,\n",
              "        -0.06818554,  0.0205607 ],\n",
              "       [-0.02133671,  0.002291  ,  0.02031962, ...,  0.0396647 ,\n",
              "         0.02099904, -0.00442758],\n",
              "       [ 0.05204089,  0.0794735 ,  0.04844743, ..., -0.03063007,\n",
              "         0.04273382, -0.07629031],\n",
              "       ...,\n",
              "       [-0.06943287, -0.01702297,  0.06385626, ...,  0.06231274,\n",
              "         0.04954486,  0.06814063],\n",
              "       [-0.04320041,  0.00517786, -0.05265555, ..., -0.04418077,\n",
              "         0.00621913, -0.0463354 ],\n",
              "       [ 0.0026977 ,  0.0814071 ,  0.08104731, ...,  0.01354251,\n",
              "         0.0667464 , -0.02558881]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    }
  ]
}