Fashion_MNIST_with_neural_network.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO2TZzwyLEgGqOmduJg5lM2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thayyilakul/Tensorflow-ML/blob/main/Fashion_MNIST_with_neural_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFUm9r5W4zFo",
        "outputId": "1bcb064c-7922-46ea-a982-a99c1187fd9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.17.1\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMZ1RHdO5BD1",
        "outputId": "aa985d62-5fb6-4b26-ccfa-a8c4b2761e1f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJ7E3s6f5W-0",
        "outputId": "c9b22a64-ea57-4fe5-c926-6e7a522cc37c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Training sample data: \\n{train_data[0]}')\n",
        "print(f'Test sample data: {train_labels[0]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aA8M4omRGQ8b",
        "outputId": "5a009ae2-0bee-40e6-bfc0-23af69064d96"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training sample data: \n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            "Test sample data: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data[0].shape, train_labels[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgLHbXCSGTo4",
        "outputId": "20c8b813-afa9-43fd-e4ef-79d535599939"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), ())"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(train_data[7])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "2p8Bq-VJG7Ua",
        "outputId": "872a302b-6a85-4399-b2cf-a5f015616927"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x78bd095be770>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels[7]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlhpzOGgHEm6",
        "outputId": "cba00e4d-eabe-4ab7-d6a7-f9568cffce2b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']\n",
        "len(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrb4rZHWHI1d",
        "outputId": "ab822262-b7b1-4b0b-a2e1-280463b5f384"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names[train_labels[7]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "K2OcoGjaHc28",
        "outputId": "4f102d6d-cc49-4de0-95ba-7369bc522d66"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pullover'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(train_data[17], cmap = plt.cm.binary)\n",
        "plt.title(class_names[train_labels[17]]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "7AbT4PBBHg6Z",
        "outputId": "0ae076ea-1464-41ad-d93a-e186e851d45e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "plt.figure(figsize = (10, 5))\n",
        "for i in range(10):\n",
        "  plt.subplot(2, 5, i + 1)\n",
        "  random_index = random.choice(range(len(train_data)))\n",
        "  plt.imshow(train_data[random_index], cmap = plt.cm.binary)\n",
        "  plt.title(class_names[train_labels[random_index]])\n",
        "  plt.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "ffE2fyLWHrsR",
        "outputId": "b71a57b9-8f4c-47af-fec7-5969003ec97c"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model_0 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape = (28, 28)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax'),\n",
        "])\n",
        "\n",
        "model_0.compile(loss = 'sparse_categorical_crossentropy',\n",
        "                optimizer = 'adam',\n",
        "                metrics = ['accuracy'])\n",
        "\n",
        "non_norm_hist = model_0.fit(train_data, train_labels, epochs = 10, validation_data = (test_data, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Rezct97IBzh",
        "outputId": "7f850b4b-c4ec-4328-a210-bc772b560858"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.1146 - loss: 3.6160 - val_accuracy: 0.1816 - val_loss: 2.1117\n",
            "Epoch 2/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.1729 - loss: 2.1051 - val_accuracy: 0.2086 - val_loss: 2.0037\n",
            "Epoch 3/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.2213 - loss: 1.9047 - val_accuracy: 0.2474 - val_loss: 1.7401\n",
            "Epoch 4/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.2553 - loss: 1.7242 - val_accuracy: 0.2582 - val_loss: 1.7318\n",
            "Epoch 5/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.2775 - loss: 1.6814 - val_accuracy: 0.2862 - val_loss: 1.6670\n",
            "Epoch 6/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.2909 - loss: 1.6573 - val_accuracy: 0.3007 - val_loss: 1.6427\n",
            "Epoch 7/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.3015 - loss: 1.6397 - val_accuracy: 0.2962 - val_loss: 1.6313\n",
            "Epoch 8/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.3164 - loss: 1.6086 - val_accuracy: 0.3292 - val_loss: 1.5358\n",
            "Epoch 9/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3320 - loss: 1.5398 - val_accuracy: 0.3299 - val_loss: 1.5204\n",
            "Epoch 10/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3331 - loss: 1.5259 - val_accuracy: 0.3283 - val_loss: 1.5145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "V8JWwkeLK1O7",
        "outputId": "849986f6-c25e-4d5f-f9e6-bb73ca3c1592"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)                   │           \u001b[38;5;34m3,140\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)                   │              \u001b[38;5;34m20\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │              \u001b[38;5;34m50\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,140</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,632\u001b[0m (37.63 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,632</span> (37.63 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,210\u001b[0m (12.54 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,210</span> (12.54 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m6,422\u001b[0m (25.09 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,422</span> (25.09 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.min(), train_data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONlBY2QYLd-8",
        "outputId": "ff00a012-e615-4cf6-eb7a-e5c34c896a8f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data / 255.0\n",
        "test_data = test_data / 255.0\n",
        "\n",
        "train_data.min(), train_data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1agVBB_wMGB1",
        "outputId": "8ef1bc19-2ba3-4fcc-bb11-2c6e90a393e0"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model_1 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape = (28, 28)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax'),\n",
        "])\n",
        "\n",
        "model_1.compile(loss = 'sparse_categorical_crossentropy',\n",
        "                optimizer = 'adam',\n",
        "                metrics = ['accuracy'])\n",
        "\n",
        "norm_hist = model_1.fit(train_data, train_labels, epochs = 10, validation_data = (test_data, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkNmC2OPMb8b",
        "outputId": "bdc63109-b130-4bd7-f103-8510dc2549c7"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.3461 - loss: 1.6821 - val_accuracy: 0.5559 - val_loss: 1.1564\n",
            "Epoch 2/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6035 - loss: 1.1035 - val_accuracy: 0.6470 - val_loss: 1.0025\n",
            "Epoch 3/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.6586 - loss: 0.9834 - val_accuracy: 0.6680 - val_loss: 0.9497\n",
            "Epoch 4/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6749 - loss: 0.9357 - val_accuracy: 0.6818 - val_loss: 0.9155\n",
            "Epoch 5/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6810 - loss: 0.9078 - val_accuracy: 0.6889 - val_loss: 0.8967\n",
            "Epoch 6/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6876 - loss: 0.8891 - val_accuracy: 0.6907 - val_loss: 0.8851\n",
            "Epoch 7/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.6913 - loss: 0.8759 - val_accuracy: 0.6956 - val_loss: 0.8756\n",
            "Epoch 8/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.6953 - loss: 0.8649 - val_accuracy: 0.6976 - val_loss: 0.8695\n",
            "Epoch 9/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 4ms/step - accuracy: 0.6980 - loss: 0.8568 - val_accuracy: 0.7002 - val_loss: 0.8640\n",
            "Epoch 10/10\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.7015 - loss: 0.8495 - val_accuracy: 0.7014 - val_loss: 0.8608\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.DataFrame(non_norm_hist.history).plot(title = 'Non-normalized History')\n",
        "pd.DataFrame(norm_hist.history).plot(title = 'Normalized History')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "9n2ejP7LNBQ6",
        "outputId": "fdb6b1a7-74f0-4c8c-f350-3c62d209726d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Normalized History'}>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape = (28, 28)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax'),\n",
        "])\n",
        "\n",
        "model_2.compile(loss = 'sparse_categorical_crossentropy',\n",
        "                optimizer = 'adam',\n",
        "                metrics = ['accuracy'])\n",
        "\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10 ** (epoch/20))\n",
        "\n",
        "find_lr_history = model_2.fit(train_data, train_labels, epochs = 40, validation_data = (test_data, test_labels), callbacks = [lr_scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3mnddQINv7d",
        "outputId": "2e49639a-5a66-4ac1-88b3-f840ebc67b5e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.3908 - loss: 1.6178 - val_accuracy: 0.6701 - val_loss: 0.9554 - learning_rate: 0.0010\n",
            "Epoch 2/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6819 - loss: 0.8861 - val_accuracy: 0.7051 - val_loss: 0.7962 - learning_rate: 0.0011\n",
            "Epoch 3/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7082 - loss: 0.7708 - val_accuracy: 0.7092 - val_loss: 0.7612 - learning_rate: 0.0013\n",
            "Epoch 4/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7157 - loss: 0.7322 - val_accuracy: 0.7150 - val_loss: 0.7428 - learning_rate: 0.0014\n",
            "Epoch 5/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7219 - loss: 0.7107 - val_accuracy: 0.7168 - val_loss: 0.7335 - learning_rate: 0.0016\n",
            "Epoch 6/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step - accuracy: 0.7262 - loss: 0.6961 - val_accuracy: 0.7175 - val_loss: 0.7182 - learning_rate: 0.0018\n",
            "Epoch 7/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7290 - loss: 0.6863 - val_accuracy: 0.7195 - val_loss: 0.7141 - learning_rate: 0.0020\n",
            "Epoch 8/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7320 - loss: 0.6783 - val_accuracy: 0.7219 - val_loss: 0.7082 - learning_rate: 0.0022\n",
            "Epoch 9/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step - accuracy: 0.7336 - loss: 0.6721 - val_accuracy: 0.7222 - val_loss: 0.7051 - learning_rate: 0.0025\n",
            "Epoch 10/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7343 - loss: 0.6677 - val_accuracy: 0.7243 - val_loss: 0.7001 - learning_rate: 0.0028\n",
            "Epoch 11/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7364 - loss: 0.6648 - val_accuracy: 0.7271 - val_loss: 0.6985 - learning_rate: 0.0032\n",
            "Epoch 12/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7366 - loss: 0.6631 - val_accuracy: 0.7281 - val_loss: 0.6986 - learning_rate: 0.0035\n",
            "Epoch 13/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7372 - loss: 0.6641 - val_accuracy: 0.7248 - val_loss: 0.7130 - learning_rate: 0.0040\n",
            "Epoch 14/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7368 - loss: 0.6634 - val_accuracy: 0.7303 - val_loss: 0.6950 - learning_rate: 0.0045\n",
            "Epoch 15/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7366 - loss: 0.6635 - val_accuracy: 0.7280 - val_loss: 0.6973 - learning_rate: 0.0050\n",
            "Epoch 16/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - accuracy: 0.7367 - loss: 0.6650 - val_accuracy: 0.7246 - val_loss: 0.7038 - learning_rate: 0.0056\n",
            "Epoch 17/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7368 - loss: 0.6660 - val_accuracy: 0.7288 - val_loss: 0.6942 - learning_rate: 0.0063\n",
            "Epoch 18/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7358 - loss: 0.6682 - val_accuracy: 0.7298 - val_loss: 0.6979 - learning_rate: 0.0071\n",
            "Epoch 19/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.7343 - loss: 0.6714 - val_accuracy: 0.7218 - val_loss: 0.7134 - learning_rate: 0.0079\n",
            "Epoch 20/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7321 - loss: 0.6742 - val_accuracy: 0.7257 - val_loss: 0.7059 - learning_rate: 0.0089\n",
            "Epoch 21/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7306 - loss: 0.6786 - val_accuracy: 0.7210 - val_loss: 0.7160 - learning_rate: 0.0100\n",
            "Epoch 22/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7293 - loss: 0.6823 - val_accuracy: 0.7231 - val_loss: 0.7154 - learning_rate: 0.0112\n",
            "Epoch 23/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7279 - loss: 0.6874 - val_accuracy: 0.7161 - val_loss: 0.7325 - learning_rate: 0.0126\n",
            "Epoch 24/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step - accuracy: 0.7245 - loss: 0.6937 - val_accuracy: 0.7126 - val_loss: 0.7444 - learning_rate: 0.0141\n",
            "Epoch 25/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.7230 - loss: 0.7010 - val_accuracy: 0.7188 - val_loss: 0.7343 - learning_rate: 0.0158\n",
            "Epoch 26/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7204 - loss: 0.7104 - val_accuracy: 0.7141 - val_loss: 0.7443 - learning_rate: 0.0178\n",
            "Epoch 27/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7150 - loss: 0.7162 - val_accuracy: 0.7109 - val_loss: 0.7481 - learning_rate: 0.0200\n",
            "Epoch 28/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7133 - loss: 0.7202 - val_accuracy: 0.7124 - val_loss: 0.7582 - learning_rate: 0.0224\n",
            "Epoch 29/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - accuracy: 0.7070 - loss: 0.7385 - val_accuracy: 0.7114 - val_loss: 0.7606 - learning_rate: 0.0251\n",
            "Epoch 30/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7082 - loss: 0.7348 - val_accuracy: 0.7048 - val_loss: 0.7800 - learning_rate: 0.0282\n",
            "Epoch 31/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7010 - loss: 0.7530 - val_accuracy: 0.7048 - val_loss: 0.8163 - learning_rate: 0.0316\n",
            "Epoch 32/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.7004 - loss: 0.7580 - val_accuracy: 0.7066 - val_loss: 0.7753 - learning_rate: 0.0355\n",
            "Epoch 33/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.6893 - loss: 0.7979 - val_accuracy: 0.6311 - val_loss: 0.9040 - learning_rate: 0.0398\n",
            "Epoch 34/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6873 - loss: 0.8120 - val_accuracy: 0.7022 - val_loss: 0.7847 - learning_rate: 0.0447\n",
            "Epoch 35/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.6779 - loss: 0.8413 - val_accuracy: 0.7016 - val_loss: 0.7880 - learning_rate: 0.0501\n",
            "Epoch 36/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6576 - loss: 0.8875 - val_accuracy: 0.6374 - val_loss: 0.8758 - learning_rate: 0.0562\n",
            "Epoch 37/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.4694 - loss: 1.3999 - val_accuracy: 0.3763 - val_loss: 1.3200 - learning_rate: 0.0631\n",
            "Epoch 38/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3527 - loss: 1.3800 - val_accuracy: 0.3816 - val_loss: 1.3510 - learning_rate: 0.0708\n",
            "Epoch 39/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.3592 - loss: 1.3860 - val_accuracy: 0.3793 - val_loss: 1.3294 - learning_rate: 0.0794\n",
            "Epoch 40/40\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.3624 - loss: 1.3849 - val_accuracy: 0.3782 - val_loss: 1.3073 - learning_rate: 0.0891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "lrs = 1e-3*(10**(np.arange(40)/10))\n",
        "plt.semilogx(lrs, find_lr_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": 476
        },
        "id": "ezhK7IY1PIio",
        "outputId": "517662af-9905-4b91-ab98-716571544e2d"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "model_3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Input(shape = (28, 28)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(4, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax'),\n",
        "])\n",
        "\n",
        "model_3.compile(loss = 'sparse_categorical_crossentropy',\n",
        "                optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001),\n",
        "                metrics = ['accuracy'])\n",
        "\n",
        "history = model_3.fit(train_data, train_labels, epochs = 20, validation_data = (test_data, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5spt1ZkRgNG",
        "outputId": "41a65960-fca4-48a8-aadb-4d6334d67c23"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.4343 - loss: 1.5302 - val_accuracy: 0.7603 - val_loss: 0.7123\n",
            "Epoch 2/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7807 - loss: 0.6606 - val_accuracy: 0.7884 - val_loss: 0.6227\n",
            "Epoch 3/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7994 - loss: 0.5923 - val_accuracy: 0.7979 - val_loss: 0.5932\n",
            "Epoch 4/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8091 - loss: 0.5644 - val_accuracy: 0.8030 - val_loss: 0.5788\n",
            "Epoch 5/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8138 - loss: 0.5476 - val_accuracy: 0.8061 - val_loss: 0.5707\n",
            "Epoch 6/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8170 - loss: 0.5363 - val_accuracy: 0.8081 - val_loss: 0.5645\n",
            "Epoch 7/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8190 - loss: 0.5279 - val_accuracy: 0.8093 - val_loss: 0.5595\n",
            "Epoch 8/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8220 - loss: 0.5213 - val_accuracy: 0.8092 - val_loss: 0.5561\n",
            "Epoch 9/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 2ms/step - accuracy: 0.8241 - loss: 0.5156 - val_accuracy: 0.8126 - val_loss: 0.5522\n",
            "Epoch 10/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8259 - loss: 0.5106 - val_accuracy: 0.8139 - val_loss: 0.5503\n",
            "Epoch 11/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8273 - loss: 0.5064 - val_accuracy: 0.8140 - val_loss: 0.5492\n",
            "Epoch 12/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8289 - loss: 0.5029 - val_accuracy: 0.8145 - val_loss: 0.5482\n",
            "Epoch 13/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8297 - loss: 0.4998 - val_accuracy: 0.8149 - val_loss: 0.5477\n",
            "Epoch 14/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8304 - loss: 0.4970 - val_accuracy: 0.8151 - val_loss: 0.5473\n",
            "Epoch 15/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8314 - loss: 0.4945 - val_accuracy: 0.8150 - val_loss: 0.5470\n",
            "Epoch 16/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - accuracy: 0.8321 - loss: 0.4924 - val_accuracy: 0.8160 - val_loss: 0.5465\n",
            "Epoch 17/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8323 - loss: 0.4904 - val_accuracy: 0.8152 - val_loss: 0.5472\n",
            "Epoch 18/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8333 - loss: 0.4887 - val_accuracy: 0.8157 - val_loss: 0.5467\n",
            "Epoch 19/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8338 - loss: 0.4871 - val_accuracy: 0.8159 - val_loss: 0.5462\n",
            "Epoch 20/20\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.8339 - loss: 0.4856 - val_accuracy: 0.8152 - val_loss: 0.5467\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def make_confusion_matrix(y_true, y_pred, classes = None, figsize = (10, 10), text_size = 15):\n",
        "  cm = confusion_matrix(y_true, y_preds)\n",
        "  cm_norm = cm.astype('float') / cm.sum(axis = 1)[:, np.newaxis]\n",
        "  n_classes = cm.shape[0]\n",
        "\n",
        "  fig, ax = plt.subplots(figsize = figsize)\n",
        "  cax = ax.matshow(cm, cmap = plt.cm.Blues)\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  ax.set(title=\"Confusion Matrix\", xlabel=\"Predicted label\", ylabel=\"True label\", xticks=np.arange(n_classes), yticks=np.arange(n_classes), xticklabels=labels, yticklabels=labels)\n",
        "\n",
        "  ax.xaxis.set_label_position('bottom')\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f'{cm[i, j]} ({cm_norm[i, j]*100:.1f})*',\n",
        "             horizontalalignment = 'center',\n",
        "             color = 'white' if cm[i, j] > threshold else 'black',\n",
        "             size = text_size)"
      ],
      "metadata": {
        "id": "R61k0grDS0qw"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs = model_3.predict(test_data)\n",
        "y_probs[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoGlAMr7U3ld",
        "outputId": "0c669eba-102b-4279-e230-3ffcbe188402"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.98984087e-05, 5.44679649e-11, 1.99913325e-06, 8.15401719e-08,\n",
              "        1.27884192e-09, 2.96213806e-01, 1.34244074e-05, 1.03466555e-01,\n",
              "        7.01169120e-05, 6.00204110e-01],\n",
              "       [2.44177831e-03, 1.40778520e-05, 9.19000506e-01, 9.20367347e-06,\n",
              "        1.58075728e-02, 1.31574274e-18, 6.27254546e-02, 0.00000000e+00,\n",
              "        1.24722328e-06, 0.00000000e+00],\n",
              "       [7.05901766e-05, 9.99392629e-01, 2.89738120e-04, 1.56845825e-04,\n",
              "        8.28017583e-05, 3.43382380e-14, 7.26383496e-06, 5.38329834e-33,\n",
              "        1.01929845e-11, 2.57274063e-23],\n",
              "       [3.82111684e-05, 9.98086452e-01, 4.23170286e-06, 1.85903593e-03,\n",
              "        1.11080008e-05, 3.60964026e-13, 8.23803305e-07, 6.03808307e-32,\n",
              "        2.08020580e-12, 1.40456392e-19],\n",
              "       [2.10007489e-01, 5.11482067e-04, 1.70610428e-01, 5.80396578e-02,\n",
              "        5.17487824e-02, 4.40724887e-11, 5.08571982e-01, 2.73567525e-35,\n",
              "        5.10087411e-04, 5.48101065e-29]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_probs[0].argmax(), class_names[y_probs[0].argmax()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O30KTS5dVe0R",
        "outputId": "69427af8-bf35-49fa-bf8d-9afe482e2c80"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 'Ankle boot')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = y_probs.argmax(axis = 1)\n",
        "y_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HcgU3YarVrcs",
        "outputId": "39357cab-9cf3-4690-ff4f-decb16214324"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_true = test_labels, y_pred = y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Doi-MEzeVxVC",
        "outputId": "8b1fcaf7-9832-4898-9b13-eb65dfe4d562"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[730,  14,  15,  85,  10,   5, 128,   0,  13,   0],\n",
              "       [  2, 956,  10,  24,   5,   0,   2,   0,   1,   0],\n",
              "       [ 20,  13, 702,  14, 174,   1,  75,   0,   1,   0],\n",
              "       [ 31,  31,   4, 836,  45,   2,  47,   0,   4,   0],\n",
              "       [  1,   4,  91,  29, 819,   0,  53,   0,   3,   0],\n",
              "       [  0,   0,   0,   0,   0, 916,   0,  41,  10,  33],\n",
              "       [138,  10, 124,  56, 184,   2, 470,   0,  16,   0],\n",
              "       [  0,   0,   0,   0,   0,  41,   0, 927,   3,  29],\n",
              "       [  0,   0,   2,  14,  15,  15,  61,   4, 889,   0],\n",
              "       [  1,   0,   0,   0,   0,  36,   0,  55,   1, 907]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_true = test_labels, y_pred = y_preds, classes = class_names, figsize = (15, 15), text_size = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MLr_38c8V4NF",
        "outputId": "8470abed-a0a1-473d-d012-4c903b084c5c"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_random_image(model, images, true_labels, classes):\n",
        "  i = random.randint(0, len(images))\n",
        "  target_image = images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1, 28, 28))\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label = classes[true_labels[i]]\n",
        "\n",
        "  plt.imshow(target_image, cmap = plt.cm.binary)\n",
        "\n",
        "  if pred_label == true_label:\n",
        "    color = 'green'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel('Pred : {} {:2.0f}% (True: {})'.format(pred_label, 100*tf.reduce_max(pred_probs), true_label), color = color)"
      ],
      "metadata": {
        "id": "ofV2lbcsWFyg"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_random_image(model_3, test_data, test_labels, class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "DOKM0tDcXeQR",
        "outputId": "e16c047d-ec64-41f1-fdf8-022f96eb78be"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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inL4/nj171nbNwYMHbc1f2kFcK/c7HACg2iKAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMCIMv9KblMqcuTsilqWk5GC9+/fb7vmu+++s10jSTt37rRd4+RrOWrVqmW7Jjc313aN5GykYCc1TkZ0djLatJPlSM5G+K5du7ajZdkVHBxcITWSHH2R5uHDh23X+Pv726753//+Z7tGkn788UfbNZmZmbbmZzRsAEClRgABAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjKu1gpC6Xy9agn1u2bLG9jPXr19uukZwNCunn52e7JiMjw3ZNQECA7RonvUlS/fr1bdfk5+fbrklLS7Nd43QQzrCwMNs1J06csF3jZLBPJ1JTUx3V+fjY/9/Uybpr2LCh7ZqaNWvarnEysK/kbOBOJ4PTOll3Tv6WJMnX19d2jd3+SvseyR4QAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhRaQcjPXHihK3B9pYvX257GYGBgbZrJGeD+TkRFBRku6ZGDfu/0uTkZNs1kpSXl2e7xslArk4GXQwODrZdIzkbtDI7O9t2jdvttl3jZNBYp9u4k9/TyZMnbdc4GVjUySC9W7dutV0jOfs9OXl/cDJ4rpPeJOnAgQO2azZv3mxr/pycnFLNxx4QAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhRaQcjnTdvnq2BCrdt22Z7GSEhIbZrJKl+/fq2a+rWrWu7JiwszHaNkwEKfXyc/R/iZJBQy7Js1xQUFNiuOXv2rO0aSTp69KjtGn9/f9s1mZmZtmucDPZ55MgR2zWSdOzYMds1hYWFtmu+//572zVOBjB1MpCrU04G6a1Tp47tGqd/ty1btrRdY3f9lfZvlj0gAIARBBAAwAhbARQbG6tu3bopMDBQoaGhGjJkiBITE73mycnJUUxMjOrXr686dero1ltvdXRYAwBQvdkKoPj4eMXExGjDhg1atWqV8vPz1b9/f68vr5owYYKWL1+u9957T/Hx8Tpy5IhuueWWMm8cAFC12boIYeXKlV73FyxYoNDQUG3ZskW9evVSenq65s6dq3feeUfXX3+9JGn+/Plq3769NmzYoCuvvLLsOgcAVGm/6hzQ+a8vPn812ZYtW5Sfn69+/fp55mnXrp2aNGmi9evXX/A5cnNzlZGR4XUDAFR/jgOosLBQ48eP19VXX60OHTpIklJSUuTv71/kkuOwsDClpKRc8HliY2MVHBzsuUVFRTltCQBQhTgOoJiYGO3YsUOLFy/+VQ1MmjRJ6enpntvBgwd/1fMBAKoGRx9EHTdunFasWKE1a9aocePGnsfDw8OVl5enU6dOee0FHT16VOHh4Rd8Lrfb7ejDkwCAqs3WHpBlWRo3bpyWLFmiL774Qs2bN/ea3qVLF/n5+SkuLs7zWGJiopKSktSzZ8+y6RgAUC3Y2gOKiYnRO++8o2XLlikwMNBzXic4OFgBAQEKDg7Wvffeq4kTJyokJERBQUF68MEH1bNnT66AAwB4sRVAs2fPliT17t3b6/H58+dr5MiRkqS//e1v8vHx0a233qrc3FxFR0frn//8Z5k0CwCoPlyWk9Ehy1FGRoaCg4P1ww8/KDAwsNR177//vu1lbd261XaNJCUnJ9uucTI45g8//GC7platWrZr7KznX7ssJwN3OuH0cv4DBw7Yrjl16pTtmpycHNs1DRs2tF3z849E2NG5c2fbNXfddZftmg8//NB2zdSpU23XDBo0yHaN5Ox3GxQUZLvGyfuD0wF3k5KSbNec/1xnaeXm5urFF19Uenr6RdcHY8EBAIwggAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEEEADAiEo7GnZJo6ia5GQk4z179tiuSUtLq5DlHD9+3HaNJOXl5dmuyc3NrZCasLAw2zWSiv3m3otp37697Zo2bdrYrvn5twz/ls2fP992zYkTJxwtKyIiwnZNzZo1bddU1CjxkrO/W7tfKJqZmam2bdsyGjYAoHIigAAARhBAAAAjCCAAgBEEEADACAIIAGAEAQQAMIIAAgAYQQABAIwggAAARhBAAAAjCCAAgBEMRgoAKFOlfR9nDwgAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjLAVQLGxserWrZsCAwMVGhqqIUOGKDEx0Wue3r17y+Vyed3uv//+Mm0aAFD12Qqg+Ph4xcTEaMOGDVq1apXy8/PVv39/ZWVlec03evRoJScne24zZswo06YBAFVfDTszr1y50uv+ggULFBoaqi1btqhXr16ex2vVqqXw8PCy6RAAUC39qnNA6enpkqSQkBCvx99++201aNBAHTp00KRJk5SdnV3sc+Tm5iojI8PrBgCo/mztAf1cYWGhxo8fr6uvvlodOnTwPH7HHXeoadOmioyM1Pbt2/X4448rMTFRH3744QWfJzY2VlOnTnXaBgCginJZlmU5KRw7dqw+/fRTrV27Vo0bNy52vi+++EJ9+/bV3r171bJlyyLTc3NzlZub67mfkZGhqKgopaenKygoyElrAACDMjIyFBwcXOL7uKM9oHHjxmnFihVas2bNRcNHknr06CFJxQaQ2+2W2+120gYAoAqzFUCWZenBBx/UkiVLtHr1ajVv3rzEmoSEBElSRESEowYBANWTrQCKiYnRO++8o2XLlikwMFApKSmSpODgYAUEBGjfvn165513dOONN6p+/fravn27JkyYoF69eqlTp07l8gIAAFWTrXNALpfrgo/Pnz9fI0eO1MGDBzV8+HDt2LFDWVlZioqK0tChQ/Xkk0+W+nxOaY8dAgAqp3I5B1RSVkVFRSk+Pt7OUwIAfqMYCw4AYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYEQN0w38kmVZkqSMjAzDnQAAnDj//n3+/bw4lS6AMjMzJUlRUVGGOwEA/BqZmZkKDg4udrrLKimiKlhhYaGOHDmiwMBAuVwur2kZGRmKiorSwYMHFRQUZKhD81gP57AezmE9nMN6OKcyrAfLspSZmanIyEj5+BR/pqfS7QH5+PiocePGF50nKCjoN72Bncd6OIf1cA7r4RzWwzmm18PF9nzO4yIEAIARBBAAwIgqFUBut1tTpkyR2+023YpRrIdzWA/nsB7OYT2cU5XWQ6W7CAEA8NtQpfaAAADVBwEEADCCAAIAGEEAAQCMqDIBNGvWLDVr1kw1a9ZUjx49tGnTJtMtVbinn35aLpfL69auXTvTbZW7NWvWaNCgQYqMjJTL5dLSpUu9pluWpaeeekoREREKCAhQv379tGfPHjPNlqOS1sPIkSOLbB8DBgww02w5iY2NVbdu3RQYGKjQ0FANGTJEiYmJXvPk5OQoJiZG9evXV506dXTrrbfq6NGjhjouH6VZD7179y6yPdx///2GOr6wKhFA7777riZOnKgpU6bo22+/VefOnRUdHa1jx46Zbq3CXXrppUpOTvbc1q5da7qlcpeVlaXOnTtr1qxZF5w+Y8YM/eMf/9CcOXO0ceNG1a5dW9HR0crJyangTstXSetBkgYMGOC1fSxatKgCOyx/8fHxiomJ0YYNG7Rq1Srl5+erf//+ysrK8swzYcIELV++XO+9957i4+N15MgR3XLLLQa7LnulWQ+SNHr0aK/tYcaMGYY6LoZVBXTv3t2KiYnx3C8oKLAiIyOt2NhYg11VvClTplidO3c23YZRkqwlS5Z47hcWFlrh4eHWCy+84Hns1KlTltvtthYtWmSgw4rxy/VgWZY1YsQIa/DgwUb6MeXYsWOWJCs+Pt6yrHO/ez8/P+u9997zzLNr1y5LkrV+/XpTbZa7X64Hy7Ks6667znrooYfMNVUKlX4PKC8vT1u2bFG/fv08j/n4+Khfv35av369wc7M2LNnjyIjI9WiRQvdeeedSkpKMt2SUfv371dKSorX9hEcHKwePXr8JreP1atXKzQ0VG3bttXYsWOVlpZmuqVylZ6eLkkKCQmRJG3ZskX5+fle20O7du3UpEmTar09/HI9nPf222+rQYMG6tChgyZNmqTs7GwT7RWr0g1G+kupqakqKChQWFiY1+NhYWHavXu3oa7M6NGjhxYsWKC2bdsqOTlZU6dO1bXXXqsdO3YoMDDQdHtGpKSkSNIFt4/z034rBgwYoFtuuUXNmzfXvn379H//938aOHCg1q9fL19fX9PtlbnCwkKNHz9eV199tTp06CDp3Pbg7++vunXres1bnbeHC60HSbrjjjvUtGlTRUZGavv27Xr88ceVmJioDz/80GC33ip9AOEnAwcO9PzcqVMn9ejRQ02bNtV//vMf3XvvvQY7Q2UwbNgwz88dO3ZUp06d1LJlS61evVp9+/Y12Fn5iImJ0Y4dO34T50Evprj1MGbMGM/PHTt2VEREhPr27at9+/apZcuWFd3mBVX6Q3ANGjSQr69vkatYjh49qvDwcENdVQ5169ZVmzZttHfvXtOtGHN+G2D7KKpFixZq0KBBtdw+xo0bpxUrVujLL7/0+vqW8PBw5eXl6dSpU17zV9ftobj1cCE9evSQpEq1PVT6APL391eXLl0UFxfneaywsFBxcXHq2bOnwc7MO336tPbt26eIiAjTrRjTvHlzhYeHe20fGRkZ2rhx429++zh06JDS0tKq1fZhWZbGjRunJUuW6IsvvlDz5s29pnfp0kV+fn5e20NiYqKSkpKq1fZQ0nq4kISEBEmqXNuD6asgSmPx4sWW2+22FixYYO3cudMaM2aMVbduXSslJcV0axXq4YcftlavXm3t37/fWrdundWvXz+rQYMG1rFjx0y3Vq4yMzOtrVu3Wlu3brUkWS+//LK1detW63//+59lWZY1ffp0q27dutayZcus7du3W4MHD7aaN29unTlzxnDnZeti6yEzM9N65JFHrPXr11v79++3Pv/8c+uKK66wWrdubeXk5JhuvcyMHTvWCg4OtlavXm0lJyd7btnZ2Z557r//fqtJkybWF198YW3evNnq2bOn1bNnT4Ndl72S1sPevXutadOmWZs3b7b2799vLVu2zGrRooXVq1cvw517qxIBZFmW9corr1hNmjSx/P39re7du1sbNmww3VKFu+2226yIiAjL39/fatSokXXbbbdZe/fuNd1Wufvyyy8tSUVuI0aMsCzr3KXYkydPtsLCwiy322317dvXSkxMNNt0ObjYesjOzrb69+9vNWzY0PLz87OaNm1qjR49utr9k3ah1y/Jmj9/vmeeM2fOWA888IBVr149q1atWtbQoUOt5ORkc02Xg5LWQ1JSktWrVy8rJCTEcrvdVqtWraxHH33USk9PN9v4L/B1DAAAIyr9OSAAQPVEAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAwggCqgkYuHakhi4eYbkOSdODUAbmmupSQklDsPK6pLi3dvdTxMlYfWC3XVJdO5Zxy/BzV3eQvJmvM8jElz1iNlLRdNZvZTDM3zHT8/KXZtsvTsPeH6aWvXzKy7IpCAJWRkUtHyjXVJddUl/yf8Verf7TStPhpOlt41nRrJWr3aju5n3Ur5XT1/L6U0lqQsEB1p9ct1byzNs1S+1ntFfBcgNq+2lZvbHvDa/r3x77Xrf+5Vc1mNpNrquuCb4Rvb39bUX+LUr3n62niZxO9ph04dUBtXmmjjNyMEntJOZ2iv2/8u/5y7V8kybMdFnd7evXTpXqN5S36rWj5TvPVN4e/Md2KUcX9g/Vkryf13FfPKT0n3UxjFYDvAypDA1oN0PzB85V7Nlef7PlEMZ/EyM/HT5OunVRk3ryCPPn7+hvo0tvapLU6c/aMfn/J77UwYaEev+Zx0y1VerO/ma1JcZP02qDX1K1RN206vEmjl49WvZr1NKjtIElSdn62WtRtoT9c8gdN+GxCkedIzU7Vfcvv04LBC9SiXgvd9M5Nur759bq5zc2SpAc+fkDT+01XkDuoxH5e//Z1XRV1lZrWbSpJSn442TPt3R3v6qnVTylxXKLnsTr+dTw/W5alAqtANXwq9q0gKT1JXx/8WuO6jdO8rfPUrVG3Cl1+VdAhtINahrTUW9vfUkz3GNPtlAv2gMqQ29et8Drhalq3qcZ2G6t+Lfrpox8+kvTTYbPn1jynyJci1fbVtpKkg+kH9cf3/qi60+sq5PkQDV48WAdOHfA8Z0FhgSZ+NlF1p9dV/Rn19diqx2Sp7Ibvm7t1ru7ocIfu6nSX5iXMKzK92cxm+utXf9U9y+5RYGygmvytif695d/FPl9BYYHuWXaP2r3aTknpF/668JJec3HWJa1Tp9mdVPPZmrry9Su149gOr+kf7PxAl/7zUrmfdavZzGZFDl+cPHNSdy+5W/Wer6daz9XSwLcHak/aHknn/gsdtWyU0nPTS9xTeHP7m/pTlz/ptg63qUW9FhrWYZjGXDFGz6973jNPt0bd9EL/FzSswzC5fd1FnuPHkz8q2B2s2zrcpm6NuqlP8z7adXyXJGnRd4vk5+unW9rfUuI6kaTFOxZrUJtBnvvhdcI9t+CawXLJ5bm/O3W3AmMD9emeT9Xl313kftattUlrL3hYd/zK8eq9oLfnfqFVqNivYtX8780V8FyAOs/prPd3vl+qHn9p/tb5urnNzRrbbawW7VikM/lnvKb3XtBbf/70z3ps1WMKeT5E4S+Gl7jnNuXLKYp4KULbj26/4PRTOad030f3qeELDRUUG6TrF16vbSnbSux1d+puXTX3KtV8tqY6/LOD4g/Ee02PPxCv7q91l/tZtyJeitATnz/hdeQj92yu/vzpnxX6QqhqPltT18y7xrPXd+DUAfVZ2EeSVO/5enJNdWnk0pGe2kFtBmnx94tL7LGqIoDKUYBfgPIK8jz34/bHKTEtUavuWqUVt69QfkG+ot+KVqB/oL4a9ZXW3bNOdfzraMBbAzx1L61/SQsSFmje4HlaO2qtTpw5oSW7lpS4bNdUlxYkLLjoPJm5mXrv+/c0vNNw3dDiBqXnpOur/31VZL6X1r+krpFdtfVPW/VAtwc09uOxSkxNLDJf7tlc/eG9PyghJUFfjfpKTYKbFJmnNK+5OI+uelQv9X9J34z+Rg1rN9SgRYOUX5AvSdpyZIv++P4fNezSYfpu7Hd6uvfTmvzlZK91MHLZSG0+slkfDftI6+9dL8uydOM7Nyq/IF9XRV2lmdEzFeQOUvLDyUp+OFmPXPXIBfvILchVzRo1vR4L8AvQpsObPP2UpHVIa2XnZ2tr8ladOHNC3xz+Rp3COunkmZOa/OVkvTrw1VI9z4kzJ7Tz+E51jexaqvnPeyLuCU3vO127YnapU1inUtXEfhWrN7a/oTk3zdH3D3yvCVdO0PAPh3u9ITeb2azEoLAsS/MT5mt4x+Fq16CdWoW0umCQLdy2ULX9amvjfRs144YZmhY/Tav2rbrg8z34yYN6Y/sb+mrUV8W+nj+89wcdyzqmT+/8VFvGbNEVEVeo7xt9deLMiYv2++iqR/Vwz4e19U9b1bNxTw1aNEhp2WmSpMMZh3XjOzeqW2Q3bbt/m2bfNFtzt87Vs2ue9dQ/tuoxfbDrAy0cslDf/ulbtQpppei3onXizAlFBUXpgz9+IElKHJeo5IeT9fcBf/fUdm/UXZsOb1Lu2dyL9lhVEUDlwLIsff7j5/ps72e6vtn1nsdr+9XW6797XZeGXqpLQy/Vu9+/q0KrUK//7nV1DOuo9g3ba/7g+UpKT9LqA6slSTM3zNSkaybplva3qH3D9ppz8xwF1wwusYe29dsq2H3x+RbvWKzW9Vvr0tBL5evjq2Edhmnu1rlF5rux9Y16oNsDahXSSo9f/bga1GqgLw986TXP6bzTuumdm3Q8+7i+HPGlGtZueMFlluY1F2fKdVN0Q8sb1DGsoxYOWaijp49qye5zYfzyhpfVt3lfTb5ustrUb6ORl43UuO7j9MLXL0iS9qTt0UeJH+n1372ua5teq87hnfX2LW/rcMZhLd29VP6+/kX2Fn5+qOrnoltG6/Wtr2vLkS2yLEubj2zW69++rvzCfKVmp170NZxXL6CeFg5ZqLuX3q3ur3XX3Z3vVnSraD3y30c0rvs47T+1X5f/63J1+GeHi+5lJKUnyZKlyMDIUi33vGm9p+mGljeoZUhLhQSElDh/7tlc/XXtXzXvd/MU3SpaLeq10MjLRmp4p+H615Z/eeZrGdJSDWo1uOhzff7j58rOz1Z0q2hJ0vBOwy+43XUK66Qpvaeodf3Wurvz3eoa2VVx++O85jlbeFbDlwxX3P44rR21Vq1CWl1wmWuT1mrT4U167w/vqWtkV7Wu31ov9n9RdWvWLXEvbly3cbr1klvVvmF7zb55toJrBnv6/ec3/1RUUJRevfFVtWvQTkPaDdHU3lP10vqXVGgVKisvS7M3z9YLN7ygga0H6pKGl+i1Qa8pwC9Ac7+dK18fX8/6D60d6tlrPS8yMFJ5BXnV9vws54DK0IofVqjOX+sovzBfhVah7uh4h57u/bRnesewjl7nfbalbNPeE3sVGBvo9Tw5Z3O078Q+pTdKV/LpZPVo3MMzrYZPDXWN7KqSvkVj97jdJfY7L2Gehncc7rk/vNNwXbfgOr0y8BUFun/qqVPoT/9Rulzn3qCPZR3zeq7bP7hdjYMa64u7v1CAX0CxyyzpNesiX1XfM+qnb7QMCQhR2wZtPYetdh3fpcFtB3vNf3XU1Zq5YaYKCgu0K3WXavjUUI9GP63L+rXqn3uO1F3FL/QCJvearJTTKbpy7pWyLEthdcI0ovMIzfh6hnxcpf+fbmj7oRrafqjnfvyBeG0/tl2v3PiKWv2jlRbdukjhdcLV/fXu6tW0l0JrhxZ5jvOHrn65R1YSu3tMe0/sVXZ+tm548wavx/MK8nR5xOWe+3F3x/2ytIh5CfN026W3ec473d7hdj266lHtO7FPLUN+2gB+vt1JUkRgRJHtbsJnE+T2dWvDfRsuGnzbUrbpdN5p1Z9R3+vxM2fPnNvuLuLn2935v7/z28yu1F3qGdVTLpfLM8/VUVfrdN5pHco4pFM5p5RfmK+ro672TPfz9VP3Rt1Ltd0F1Dj3t5Sdn13ivFURAVSG+jTvo9k3zZa/r78iAyOLnNit7Vfb6/7pvNPqEtlFb9/ydpHnaljrwnsQZWXn8Z3acGiDNh3epMc//+nCgwKrQIt3LNboLqM9j/n5+nnVuuRSoVXo9diNrW7UW9+9pfWH1uv65terOCZfc1kJ8AvQvMHz9K+b/6WjWUcVUSdC/97ybwX6Bxa751eS3LO5euCTB/Tm0De198RenS08q+uaXSdJalO/jTYe2ui5wOHnzr/pnsw5aWvZtf29t0Ufl0+Rc4s/P5x4Ou+0JOnjOz5Wo6BGXvNd6BxXcc4fQs4vzNfszbM9jxdYBZq3dZ6e6/uc57HSbHc3tLhBi3Ys0md7P9Odne4sdrmn804rok6EVo9cXWRa3Zp1S91/RTt/eNDpdlXZEUBlqLZf7WIPAVzIFRFX6N3v31Vo7dBir3aKqBOhjYc2qlfTXpLOHXLYcuTc8etfY+63c9WraS/NunGW1+Pzt87X3K1zvQKoNMZ2G6sOoR30u0W/08d3fOx58/yl0rzm4mw4tMFzXunkmZP6Ie0HtW/YXpLUvmF7rTu4zmv+dQfXqU39NvL18VX7Bu11tvCsNh7eqKuirpIkpWWnKTE1UZc0vESS5O/rrwKroNT9+Pn6qXFQY0nS4u8X6+Y2N9vaA/q5Z9c8qwEtB+iKiCu0NXmr10ns/IL8YvtqGdJSQe4g7Ty+U23qt3G0bOlc+P/yoo6Eowny8zkXApc0vERuX7eS0pOK/d2Wxtvb31bjoMZaOmyp1+P/3fdfvbT+JU3rM02+Pr6lfr7ftf2dBrUZpDs+vMNzGPlCroi4QimnU1TDp4aa1W1mq+cNhzYU+fsb132cJKl9g/b6YNcHsizLsxe07uA6BfoHqnFQY9UPqC9/X3+tO7jOc5VifkG+vjn8jcZfOV6SPEdFCgqL/o53HNuhxkGNSzysWVVxDsigOzvdqQa1Gmjw4sH66n9faf/J/Vp9YLX+/OmfdSjjkCTpoR4Pafq66Vq6e6l2p+7WAx8/UKoPZLZ7tV2xFyvkF+Trze1v6vYOt6tDaAev231X3KeNhzfq+2Pf2349D/Z4UM9e/6xuXnSz1iatdfyaizMtfprifozTjmM7NHLZSDWo1UBD2g2RJD3c82HF7Y/TM/HP6Ie0H7QwYaFe3fSq50KC1vVba3DbwRq9fLTWJq3VtpRtGr5kuBoFNfIcumtWt5lO551W3I9xSs1OLfawxw9pP+it7W9pT9oebTq8ScPeH6Ydx3bor33/6pknryBPCSkJSkhJUF5Bng5nHFZCSoL2nthb5Pl2Ht+pd79/V9P6TJMktWvQTj4uH839dq4+/uFj7U7drW6RF75M2cflo34t+hW7vkvr+ubXa/ORzXpj2xvak7ZHU76c4hVIge5APXLVI5rw2QQtTFiofSf26dvkb/XKxle0MGGhZ76+b/TVq5uKv4Bi7ta5+v0lvy+y3d17+b1KzU7Vyr0rbfc+tP1QvTn0TY1aNqrY8zn9WvRTz6ieGrJ4iP677786cOqAvj74tf4S9xdtPrL5os8/65tZWrJriXan7lbMxzE6mXNS91x+jyTpgW4P6GDGQT346YPanbpby3Yv05TVUzSx50T5uHxU27+2xnYdq0dXPaqVe1dq5/GdGr18tLLzs3Xv5fdKkpoGN5VLLq34YYWOZx337G1K0ldJX6l/i/6210lVwR6QQbX8amnNqDV6/PPHdct/blFmbqYaBTVS3+Z9PXsHD1/1sJJPJ2vE0hHycfnonsvu0dD2Q0v8cFpiWqLScy88z0eJHyntTJqGthtaZFr7hu3VvkF7zd06Vy9Hv2z7NY2/crwKrULd+PaNWjl8pWdvw85rLs70ftP10MqHtOfEHl0WfpmW377c89/jFRFX6D+//4+eWv2UnlnzjCICIzStzzSNvGykp37+4Pl6aOVDuvmdm5VXkKdeTXvpkzs+8RzquSrqKt3f5X7d9v5tSjuTpinXTfE6h3deQWGBXlr/khJTE+Xn66c+zfro63u+9vrP+kjmEV3+r5/Ojby4/kW9uP5FXdf0Oq/DQJZlaczyMXo5+mXPYbEAvwAtGLJAMZ/EKPdsrl698dUih71+7r7L79Po5aM14wZ756B+LrpVtCb3mqzHVj2mnLM5uufye3R3p7v13bHvPPM80+cZNazVULFrY/XjyR9Vt2ZdXRFxhf7v2v/zzLPvxL5iL8TYcmSLth3dptcGvVZkWnDNYPVt3ldzt87VTW1ust3/7y/5vQqtQt215C75uHyKXMLucrn0yR2f6C9f/EWjlo3S8azjCq8Trl5NeymsdthFn3t63+mavm66ElIS1CqklT4a9pFnj6RRUCN9cscnenTVo+o8p7NCAkJ07+X36sleT/5U32+6p7fM3Ex1jeyqz4Z/pnoB9TzPMbX3VD0R94RGLRuluzvfrQVDFijnbI6W7l6qlcPth3JV4bJKOpsNoFKzLEs9Xu+hCVdO0O0dbzfdDsrI7G9ma8nuJfrvXf813Uq54RAcUMW5XC79e9C/q8SwTyg9P18/vTLwFdNtlCv2gAAARrAHBAAwggACABhBAAEAjCCAAABGEEAAACMIIACAEQQQAMAIAggAYAQBBAAw4v8BcN5rZTGjqIUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFSNrt5DXmRz",
        "outputId": "fac1d61f-6255-4b3e-b3ce-a1dbe00394c8"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<Flatten name=flatten_7, built=True>,\n",
              " <Dense name=dense_19, built=True>,\n",
              " <Dense name=dense_20, built=True>,\n",
              " <Dense name=dense_21, built=True>]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.layers[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gk6ye4jzX2AW",
        "outputId": "1e66a924-129b-4c5e-dc45-ae887d27cdf3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Dense name=dense_19, built=True>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights, biases = model_3.layers[1].get_weights()\n",
        "weights, biases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PG1SPFpOX7T-",
        "outputId": "75d428e9-2600-41c0-8207-edbf00aee1de"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[-0.4881415 , -0.69750524,  0.5366517 ,  0.3589799 ],\n",
              "        [-0.8368572 ,  0.2012945 ,  0.73366225, -0.01228958],\n",
              "        [-0.91797084, -0.5965198 ,  0.24622114,  0.84535885],\n",
              "        ...,\n",
              "        [-0.14449054, -0.31361997, -0.14273123,  0.3895555 ],\n",
              "        [-0.29094112, -0.86818135, -0.11119629, -0.32514244],\n",
              "        [-0.09030859, -0.40539408,  0.60484254,  0.1529983 ]],\n",
              "       dtype=float32),\n",
              " array([-0.18815012,  1.325256  ,  0.4065829 ,  1.2900332 ], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights.shape, biases.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COvFTONwYE0e",
        "outputId": "56ec1c20-549b-4760-b720-8ea7feaff7ac"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((784, 4), (4,))"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "xcIOjn8sYJuJ",
        "outputId": "66fa9e12-e8a0-4d70-eb43-d0bd254e7738"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_7 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)                   │           \u001b[38;5;34m3,140\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)                   │              \u001b[38;5;34m20\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │              \u001b[38;5;34m50\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,140</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m9,632\u001b[0m (37.63 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">9,632</span> (37.63 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,210\u001b[0m (12.54 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,210</span> (12.54 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m6,422\u001b[0m (25.09 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,422</span> (25.09 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model_3, show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jJ_k5pziYxaA",
        "outputId": "458f4a2c-0904-4047-da1e-0ce1d5db0731"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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RNL/fn376KdP3rXT69GkZN25czuty5crJ6tWrZd68eVKhQoUCl3d1dZWRI0fKr7/+anKzuW+//VYWL15cLGPOT2k7npa2zwPHsHd8QGygXxx3SxcSG05o1apVOXfhrV+/fk69Ukq+++476dq1q1SpUkXc3NzE399fnnjiCXnttdfk/PnzFvucN29eTp916tTJqY+Li5N//vOf0qpVK6lWrZp4eHhItWrV5Mknn5SFCxdKUlKSxT5nz56d02e5cuWs+mwff/yx5jK5nxYTHR2dUz9mzBiTuxIX5WyN0WiUr7/+WoYOHSpPPPGEBAQEiJubm5QvX16Cg4PlySeflClTpsjvv/9uVX8PM7pZWVmyYsUK6dq1q9SpU0c8PT2lYsWK0rhxY3n99dfljz/+sKq/+/fvy/bt22XkyJHStGlTCQwMFHd3d/H29pYaNWpIt27dZO7cuXLr1q18+ymOfZ3XtWvXZNasWdK5c2epUaOGlC9fXvz8/OTRRx+Vnj17yrJly8yuW9WS+/tgMBis+ufPWt9++63ZtZL+/v7Sr18/q/to166dyeusrCyZNGmSXcaXV2RkpEyfPl3atm0rtWrVEi8vL/Hx8ZGQkBBp27atTJ8+3aqnKK1YscLsbt5du3bNeV8pJRs2bJCePXtKUFCQuLm5SeXKlaVNmzYye/ZsSUlJsXrMycnJEhERIQMHDsw5g+3p6SkhISHy9NNPy//8z/8U+H11pPHjx8vNmzdN6ubOnSv+/v45r2/duiV37941W/bZZ5/V7LNBgwbSvHlzs/rLly8XOJ4lS5aYTIUNCAiw+fsWFhYmPj4+JnVJSUmyZcsWm/opq2bOnGly2dA///lPGTZsmM39NGnSRNavX29yWdPMmTNNLm3JzZHHU+ID+8cHIvaNEYgPnCs+IDbIn55iA467pYwqRcLDw5WIWCxdunRx9BBVRESEyZj27dtn1mbNmjU571etWlUppVRCQoJq165dvp/P3d1drVmzRnO9S5YsyWkXGBiolFLqwIEDqkqVKvn2+cgjj6jIyEjNPj/66KOcdq6urlZ9/oULF2ouk3e7WCrR0dFWrSevqKgo9eijj1q1DhFRYWFhKjEx0aSPrVu3mrSJiYlR169fVy1atChwv6xduzbf8Z04cUI1bdrUqrF5e3ur5cuXW+yrOPb1Q5mZmWry5MnK3d29wHEGBgaqVatW5dtf7u+DiKidO3fm294WXbt2NRvTqFGjLLZfvny5WftPPvlE1axZ06z+p59+stjPvHnzzNpPmTLFYvuDBw+qDh06WP3dbN++vTpw4IDF/tatW2e2TOvWrZVSSt25c0c99dRT+fZfvXp1dezYsXy3bXZ2tpo/f77y9fUtcLx+fn75fl8dZefOnZrbNjs726RdbGys5uf66quvLPbdq1cvs/atWrXKdzwXL15U3t7eJsusWrVKHTlyxKyvoKCgfPt6/vnnzZbp27ev9RunmHTp0iXf70p4eLhDx3fx4kXl6uqaM56GDRuqrKysIvU5ZswYk88YERGh2c6Rx1Pig6LHB0oVX4xAfPCgOCo+IDZ4UEpjbFAWjrsF7QtH7wM728+MDSfk7u6e8/Pdu3fFaDRKp06dZP/+/fkuZzQaJTw8XPPxgrnPfqSmpsrVq1elR48eBWZMY2JipFevXnLu3DkbP4XzOHfunHTq1EkuXLhg9TIbN26Ufv36iVLKYhuDwSDdunUr8CyR0WiU4cOHy+nTpzXfP3/+vISGhsrRo0etGltaWpqMGjVKPv/8c833i2tfZ2VlSa9evWTu3LliNBoLHOedO3fkxRdflNmzZxfY1t7S09Pll19+Mavv0aOHTf2kpKTIrFmzzOonTJigec8FW3355ZfSoUMH2bdvn9XLREZGSmhoqKxevVrzfQ8PD7O65OTknP33888/59t/bGysdO7cWe7cuaP5fnZ2tgwaNEgmTZpk1Rmc5ORkGTVqlLz33nsFti0pKSkp8sorr5jUubq65jy/PreqVatqXoKQ343B8s4CERF57LHHLLZX//9u7GlpaTl1PXv2lBdeeEHu379vcTlLtL7ne/bs4bGEBdi0aZPJ9n7ttdc0LyuyxRtvvGHyndqwYUOR+isOxAf2jw9E7BMjEB/Ynz3iA2IDc3qLDTjulk4kNpyQm5tbzs/p6ekyZ84cOXz4sDz++OOyZs0auX79umRmZkpcXJxs27ZN/vKXv+S0z8jIkE8++cSsz9zBWUZGhkyePFkSEhKkXbt28t1338mNGzfEaDTKjRs3ZN26dfLoo4/mtE9ISCj0EwGsNXr0aFFKmV2PFhERYXKjvxYtWtjc91tvvZUz5dDd3V2mTZsm0dHRkpCQIFlZWZKSkiIXLlyQtWvXmkwv/Pnnn+Wbb76x2O+8efPk2LFj0qBBA/niiy/k2rVrYjQa5fbt27Jp0yZp1KhRTtusrCyZP3++Zj9jx441mZbZs2dP2bp1q8TGxkpGRoakpaXJkSNH5PXXXzeZ1jxhwgTN6aHFta+nTZsmu3btynldr149+fTTT+X06dOSlpYmqampcvz4cfnoo48kMDDQZLk9e/ZY3I7FITIy0mzKt6urqzz99NM29ZOQkCBDhw41+94dP35c8/pcW+zYsUNGjBhhVRCYV2Zmprzwwguye/dus/dyJ0YfSk5Olnnz5smBAwes6v/WrVvy/vvva7735ptvat5wsyDvvvuubN682eblisO0adPkypUrJnWvvvqqyd/Sh1xcXCQsLMys/quvvtIMYP/44w/Nf2QGDRpkcTyLFy82CSoDAgJk+fLl+X2EfHXq1MksQZOamipRUVGF7rMsyL0PDAaDxcuNbFG/fn2Tvx9RUVE2PzrWVrYeT4kP7B8fiNgnRiA+sD+Z6+rjAAAgAElEQVR7xAfEBub0Fhtw3C2lSn6WSPEpLZei5J7OaDAYlKenp+rSpYu6e/euZp9xcXEqICAgZ5latWqZtVm1apXZ9ujXr5/KzMzU7DMxMVHVr1/fpP3x48dN2thzqulD9+7dM1mnpWm71srOzlZeXl45/c2fP7/AZYYNG6aCgoJUixYt1IIFC3Lq804z9fDwUJ06dVJpaWma/dy5c0dVqlTJZBpfXn/88YfZPsnP7NmzTdprTV8tjn198eJFVa5cuZz3u3fvbvH7qJRSV69eVSEhITntGzdunO/nsrfc382HpVGjRvkuozXddOzYsUoppX755RfNaYnJyclm/Vgz3TQ+Pt7ku5G7DB06VB04cEClpKSo1NRUtX//fhUWFqbZNjg42Oz7t2PHDrN2Xl5eqkKFCsrFxUWNHz9eXbhwQaWnp6ujR4+q3r17a/YdGBho9p05efKkcnFxMWvbrFkztWPHDnX9+nWVmJioIiMjVffu3c3a1alTR2VkZBRml9rN0aNHTS43EBFVoUIFFRcXZ3GZmJgY5e/vb/Z5/v73v6ujR4+q9PR0lZycrH744Qf1+OOPm7Xr2LGj2SUuD2lNhc39ex0dHW3zlFillKpbt67ZcgsXLrR9g9mRs1+KEhgYmDOWhg0b2q3f8ePHm3xOrSn9jjyeEh8UPT5Qyv4xAvFB8bA1PiA2KH2xQVk67nIpCpyKUko8PT1lzZo1Ur58ec02gYGBJmcDL1++bHZTpLx8fHzks88+s3hTrwoVKsjcuXNN6rZt22bj6B0vMTHR5MZ/eR/JpOXLL7+UGzduSHR0tIwfP95iOy8vL1m3bp14eXlpvh8QECCDBw/OeR0bG2u2X2JjY6VDhw5Sv3598fPzk3/84x/5jm3cuHEmM3qsuUO8Pfb1woULJSsrS0REKleuLGvXrrX4fRQRqV69usnz4U+ePFmij+k7duyYWZ01+z6vh585NDTU7C7ZN2/elI8++qhQ41u6dKnExcWZ1b/33nvy1VdfSZs2bcTHx0e8vb2lbdu28s0332h+N65fvy5r1641qdN6TNndu3clKSlJPvnkE1mwYIHUrVtXPDw8pEmTJrJ582azG6GJPJgqfObMGZM6rceehoSEyM8//yzdu3fPuWyjXbt2smPHDunZs6dJ24sXLzp81sZrr71mNs106tSpJmcR86pRo4Zs27bNrM3mzZuladOm4unpKX5+ftKtWzezSwHbtWsnGzdu1NwvSmMq7IABA2TIkCGF+WgmtGafaP1e4IGsrCyTKdZaj+0trMaNG5u8vn79ut36Lg7EB5ZZGx+IFD1GID4oHvaID4gN9BsbcNwt3Uhs6MALL7wglSpVyrdN06ZNTV4XdMfpgQMH5hvIizyY8pj7Lr+RkZEFjNT5+Pn5mUy93L59u936Dg8PL3C/PPHEEyav4+PjTV536NBBfv31Vzl79qwkJSXJM888k29/Xl5e8sgjj+S81joA5mWPfZ37cZdDhw41eWqEJV27djUZ69atWwtcxl60rpdu0KBBkfqcO3euSdAo8iCgs+ZpF3lpTXd87LHH5O2337a4zJw5cyQgIMCs/ssvv7RqnS1atNAMgFxdXS3eATz3k5bu37+v+djTN954Q/z8/CyOOa/CTFW1l2+//VZ+/fVXk7qgoCCrptK3b99ejh8/LuPGjTN5jKcWg8Eg7dq1k2XLlskvv/wiFStW1GyXdyps5cqVJSIiouAPYgWt77u1T2gqi/JeN671u1ZYefuydI26syA+sI+ixgjEB8XD3vEBsYG+YgOOu6UbiQ0dKOhgJiJmB0+txxPmZs21hOXKlZNmzZrlvM7vcbLOytXVVZ566qmc1x9//LGMGzdOYmNji9x3p06dCmyTd7/Y45nWuc+EPDxrkJ+i7uvr16+bBAK52xWkTZs2OT8fP37c6uWKSuvGjsHBwUXqs379+jJ69GiTuvT0dJk6dapN/Vy5ckUuXbpkVv/cc8+ZXCOdl5eXl/Tq1cusPjo62qrvwQsvvGDxPa2zMiIPzmg+9Pvvv5u8fqhVq1YW+23YsKHZP/W5H6tWkpRS8u6775rVjx8/Pt+zi7ldvXpVkpKSCrxpoFJKrl27JidOnJA///xTs83FixfNvjtLly6VypUrWzWWglSvXt2s7urVq3bpuzTKO5vO0ln2wsj7GMCCZlQ6GvFB0eMDEcfECMQHBbN3fEBsoJ/YgONu6UdiQwdCQkIKbJP3bscFBd55zxJYUqtWrZyfY2JirFrG2cybN8/kYL9o0SKpWbOmtG/fXmbMmCF79uwxu5GUNWrWrFlgm7w3a8pvv9y8eVNWrlwp4eHh8uSTT0q9evUkKChIKlasKD4+PuLp6SnlypWTU6dO2TTOou7rvDdZHDFihNmz0C2V3DdXK8k759++fdusrqCz7NZ45513zJ6QsX79eptuDmVperA1N77TChrv3btn1R39cweReVWqVEkzcMp9k0OtgEvkQeBjaf+7uLiYzR67c+eO5lNDits333wjJ0+eNKmrUKGCjBkzpsBls7OzZeLEidKmTRtZvXq1VeP/888/ZdGiRdKoUSNZsmSJyXtaU2GHDh0q/fv3t/LTFEwrUHfEdteLvGeZtW68WFh5+7I0g8dZEB8UPT4QsW+MQHxgP8URHxAbmHO22IDjbtlAYkMH8p7tsQdrp9nm/kN97949uzzGqqQ1a9ZMdu/eLbVr186py87Olv3798sHH3wgnTp1kooVK0q3bt3ks88+szqgtdcZvYyMDBk/frzUqlVLRo4cKatWrZLIyEi5cOGC3Lp1SxITEyUtLU0yMjIK9Qiqou7rvJfPFJZWRr84ZGZmaj5eyx77KzAwUN566y2z+tzXWmtdx5qbVlAlIlKtWrUC128p+LJmH+UXuLm6umo+0tTWdVjLlkcr2kve68RFHgThlqbK5jZjxgxZsGCByT8d5cqVkxkzZsjZs2clIyNDkpKS5JdffpE+ffqYLGs0GmXs2LEm02wXLVpk8rjB4OBg+d///d/CfCyLtL7v9pgxVlpVrFjR5HfXmmn81sr7u1PQ1H9HIz4oenwgYp9jDvGBfRVXfEBsYB/FGRtw3C0bSGyUUd7e3la1y3s2oTCPn3IG7du3l/Pnz8tXX30lrVu3NjvApKeny65du2TUqFESEhIiH330UYkEaRkZGdKxY0f5+OOPi+0RgEXd17mz20VRUtOvLW1HT09Pu/T/2muvmc2iioqKknXr1omIWLwJ20OWnu9uzeUQltpY88x4rWfY55bfVFcR++6/5ORku/VljUOHDmmeDXvxxRcLXPb8+fOaSZEFCxbI+++/L/Xr1xd3d3fx8/OT0NBQ+f777zUf7Tpx4kTJysqSixcvyrRp00zeW758ud3P4mt9V5RSxf6oUb1ycXExueb/999/t1vfeW8el/vstzMiPiA+yIv4oGDEBkVXXLEBx92yg8RGGWXtL1nuKZgGg6HAP4DOzNXVVYYOHSpRUVFy/fp1WbVqlQwePNjs2rrExESZPn269O/fv1BnQGwxY8YM2b9/f85rNzc3GTFihKxfv17+85//yMWLFyU+Pl5SUlLk3r17kpWVJY0aNbJpHUXd176+vibtdu3aJUopm4s9p3YXRkGXZ1nLw8ND847nU6dOlfT09AIDJEszBKwJEC21KeiMij3k/R4UhTXBlj1p3RisSZMmZjdd1vLVV1+ZXafs7+9vdk11blo3Yrty5Yrs379ffvzxR7P92KtXL4tTdlu2bGnW182bN03afPDBB2Zt7PV9L0vat2+f83NsbKzF+6PYKvd09ICAAKun/zsK8QHxQV7EBwUjNii64ooNOO6WHSQ2yihrDyK5pwf6+voWOJWuICV9ptaSoKAgeeGFF2TdunVy8+ZNOXz4sEydOtXkOuvvv//ebndK1pKenm5yB+yKFSvKwYMH5fPPP5dnn31WmjdvLrVr1za5htbV1dXmYKqo+zrvtefOfkd/S2cuCnudtJbBgwdL69atTequXLkiCxYsKPCO8JZuUmXNDaYs3dTOXje+yo+lMxtHjhyxOYjVmtFQXIxGo2zatMmsfsCAAVYtf/ToUbO6+vXrm90FP+/7Wk6cOGHVOu1B6/uu938+i1toaKjJ61WrVhW5z7Nnz5rMFvrb3/5W4BlQaxXX8ZT4gPggL+ID6xAb/B9njw2KA8ddxyOxUUblfQa1JbnPWOWdPps7iLl//75VB1R7nQGzJ4PBIH/961/lo48+klOnTkm9evVy3tOagm4vJ06cMAkWpk+fXuAdxY1Go803aSvqvm7QoIHJvs57A0Zn4+rqqvlPZ0FPCrLVv/71L7O62bNnF/h78Ne//lWz/tChQwWuU6tNxYoVpU6dOgUuW1SPP/64Zr2z3zRw7969mv8wde/e3arltc4gaV2jnZula2rt/R3Mj9a67Pmkj9Jo4MCBJtto6dKlRf5nO+813CNGjNBs50zHU+KD/0N88ADxgfWIDR5w9tigOHDcdTwSG2XUvn37CmxjNBpNzlbmfT5z3sx3QZn67Oxs+fe//23DKEtetWrVTG4AFRMTU2xT465fv27yOr87Uz+0ZcsWm69pLeq+9vf3Nwnmtm3bZtP6HaFKlSpmdbdu3bLrOtq3b2921j8lJUUWL16c73I1a9bUfNLR2rVr8300W3x8vOzYscOsPjQ0tMhnSq3RqFEjzTNO1ny/HGnr1q1mdZUqVZLmzZtbtXzexzGKPLheN78g9eLFi5r1JXH27KG8f19E7PNkoNIsMDDQ5NGHt27dkjfeeKPQ/UVFRZmc1W/UqJHZzWUfcqbjKfGBNuKDB4gP8kds8ICzxwbFgeOu45HYKKPWrl1b4A1/Nm/ebHLmMffz3kXM76atNWU7t2+//VYuX75s0ziLeg3r4sWLJSwsTEJCQmTt2rVWLZP3cU32mjacV95+CwqQEhMTzZ6/bc30SXvs69zB+PHjx2Xnzp0FrjcjI0OaNm0qAwcOlM8//7zE7nouon0Xca1n1xfVnDlzzG6qlvuaaEtefvlls7qLFy/KzJkzNdtnZ2fLq6++qnk24JVXXrFytEVjMBikX79+ZvVLly61eCfzHTt2iI+Pj9SpU0fatGkjffr0MblLvIjIDz/8oHmN62+//WaXcWvtj+bNm1sd8GmdRUtKSpLVq1dbXGbZsmWa9S1btpTRo0fbNDU3OjrarJ+goCCTNm+//bZZG63ve/Xq1fP7qBCRadOmmRzbVq1aJe+//77N/Zw+fVoGDBiQc5NJg8Egc+bMsfi9c6bjKfGBZcQHxAfWIDZwrtiA427ZQWKjjLp165aMGzfO4o1u4uLiZMqUKTmvXV1dpVevXiZtGjZsaPJ66dKlFtd3+vRpGTt2bIE3T3J1dTV5XdSpbFFRUTkB01tvvWXxTGpuuZ+tXqNGDavvGm6r3I+XExGTx0Hmde3aNenWrZvEx8dLq1atcuqtmbprj339yiuvmARa4eHhcvbsWYvrNBqNMnLkSDl27Jhs3LhRXn755RK9OVjuM0gP5Tfewqpbt66MHTvW5uXGjBmjORPg/fffl5deekmOHTsmGRkZkpiYKLt375bOnTvLhg0bzNq3aNFCunXrVqixF8aECRPM/jFLTU2VJ598UlauXCk3b96UzMxMiYmJkUWLFsngwYMlLS1NLl26JAcPHpStW7eW6LWmWVlZcvr0abP6xo0bW93HgAEDzP4uiTz4nXjnnXfk7NmzYjQa5d69e3L48GF57rnn5OuvvzZr36hRI5tv7FcUWt/3Rx99tMTWr1c1atSQlStXmtS988478txzz1m8jj03pZR88cUXEhoaahLkvvnmm9KzZ0+LyznT8ZT4wDLiA+IDaxAbOHdsUFw47joBVYqEh4crEbFYunTp4ughqoiICJMx7du3z6zN1q1bTdpcunSpwH7zLvPf//7X5P1Vq1aZvD9o0CAlIio0NFR9//336ubNm8poNKrr16+rL7/8UtWqVcuk/bBhw8zWmZmZqapWrWrSbvjw4erw4cMqLS1NZWRkqDNnzqiZM2cqX19f5erqqj744IOctq6urpqfxcfHJ6dN1apV1f79+1V6erq6deuWunz5snUb+v+Ljo5WBoMhp7+AgAD1wQcfqOjoaJWYmKiysrJUamqqiomJUdu3b1d9+/Y1+TzTp08vtv2SnZ2tatSoYfL+2LFj1alTp9S9e/dUfHy8OnDggJo8eXLONomIiFBjxozJaW8wGNTatWvVvXv3VHJycrHta6WUmjJlikk7b29v9c4776jjx4+r1NRUlZycrM6cOaMiIiJU48aNTdqOGTNGs8+FCxeatNu5c6cNe9eyOXPmmP3+N2rUKN9lli9fbrbMK6+8UuC64uPjVcWKFfP92zNlyhSz5Xbu3Gny3bS1+Pr6qnPnzmn2q9X+9u3b+X6OwMBAs2UiIiLM2k2YMKHQY65Tp07O97Sg8Wr9bbTViRMnNPteuXKlTf28+uqrhf7MD8u2bdsK9Rmio6PN+goKCipwubp165ot9/HHHxdqDPbSpUuXfLdReHi4Q8eX28cff6xcXFzM/uYNHz5cbdy4UZ0/f14lJSWp9PR0FRMTo/bv36/ee+899cQTT5h9rqFDh6qsrKx81+fI4ynxQdHjA6XsGyMQHzhPfEBsUPpig4KUpuOur69vvtt++fLlDh2fne0nsVHCnCWxce7cOVWhQgWr/uDUqFFD3bhxQ3O98+fPt/oP1/Tp09VPP/1kctDV0qlTJ4t9TJw4seCNnMe0adMK9Yf2L3/5i0pLS7O4je2xX/J+H/IrgwYNUvfv31dffPGF5vt9+/ZVShXfvs7IyFDdu3e3eTs2b95cpaamavZZXIHLnj17zMbh6uqqEhMTLS5T2OBFKaUWLFiQ7zbQCl6UUuqLL75Q7u7uNm/TypUrq99++02zz+IOXoxGo+rVq5fNYw4KClInTpywerz2CF527dql2ff27dtt6icjI6PAf8rzK7Nnzy70ZyhMgBUXF6cZGFv6zpQUPSU2lFJq8+bNVv/t1Cqurq5q1qxZVq/PUcdT4gPb923e+EAp+8cIxAfOER8QG5S+2KAgpem4W9YSG1yKUkYFBwfLzp07C7ypzWOPPSY//PCDBAUFab4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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    }
  ]
}