Handwritten_Amharic_Digit_Recognition_Using_Deep_Learning_CNN_4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mukerem/ACPC2020/blob/main/Handwritten_Amharic_Digit_Recognition_Using_Deep_Learning_CNN_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Mount Google drive**"
      ],
      "metadata": {
        "id": "4CvYsgCsuSa7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29kuFJREaKYO",
        "outputId": "1f8e616d-8bc7-43c6-a43f-b7dcc820f9b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CmIQsuCTbSBd"
      },
      "outputs": [],
      "source": [
        "!unzip '/content/gdrive/MyDrive/PreProcessV3.zip' -d \"/content/gdrive/MyDrive/\" "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ceCPcifFah1-"
      },
      "source": [
        "# **Load library**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UcKozwcMZzr6"
      },
      "outputs": [],
      "source": [
        "import keras.backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import warnings\n",
        "\n",
        "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n",
        "from keras.models import Model\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "config = tf.compat.v1.ConfigProto( device_count = {'GPU': 1 , 'CPU': 56} ) \n",
        "sess = tf.compat.v1.Session(config=config) \n",
        "K.set_session(sess)\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaJv2RwyRfCN",
        "outputId": "e7b3a533-6ca4-47a9-b885-05a6d69b59ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/PreProcessV3\n"
          ]
        }
      ],
      "source": [
        "%cd /content/gdrive/MyDrive/PreProcessV3/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "i0pycJHOZS1n"
      },
      "outputs": [],
      "source": [
        "batch_size = 32\n",
        "img_height = 32\n",
        "img_width = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "QPzXdMv3cE5M"
      },
      "outputs": [],
      "source": [
        "train_data_dir = \"/content/gdrive/MyDrive/PreProcessV3/train/\"\n",
        "test_data_dir = \"/content/gdrive/MyDrive/PreProcessV3/test/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sV3b0cetmmDq"
      },
      "source": [
        "#**Load data**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9pKCR0DcEtS",
        "outputId": "abbfdcc0-862c-42f9-b2bd-1ee6c13fe5b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 46755 files belonging to 20 classes.\n",
            "Using 41566 files for training.\n"
          ]
        }
      ],
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  train_data_dir,\n",
        "  validation_split=0.111,\n",
        "  subset=\"training\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  color_mode=\"grayscale\",\n",
        "  batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53UWblhpdZ95",
        "outputId": "c5469a7d-a07b-4c50-a83b-92d4d265ff73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 46755 files belonging to 20 classes.\n",
            "Using 5189 files for validation.\n"
          ]
        }
      ],
      "source": [
        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  train_data_dir,\n",
        "  validation_split=0.111,\n",
        "  subset=\"validation\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  color_mode=\"grayscale\",\n",
        "  batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  test_data_dir,\n",
        "  image_size=(img_height, img_width),\n",
        "  color_mode=\"grayscale\",\n",
        "  batch_size=batch_size)"
      ],
      "metadata": {
        "id": "Vyz2AkrKK21F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ba30964-3957-4f50-fc74-2480fd26d5a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5197 files belonging to 20 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "CzBg_kpbeIVB"
      },
      "outputs": [],
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 1**"
      ],
      "metadata": {
        "id": "6bII9AAru-Bv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(),\n",
        "            layers.Dense(128, activation = 'relu'),\n",
        "            layers.Dense(20, activation = 'softmax'),\n",
        "])"
      ],
      "metadata": {
        "id": "JueMh78yvd3a"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = keras.optimizers.Adam(lr = 1e-3),\n",
        "        metrics = ['acc'],\n",
        ")"
      ],
      "metadata": {
        "id": "yDcgRfdaviQ8"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 2, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ],
      "metadata": {
        "id": "P9sz5RlcvkNL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier1 = model1.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdtaBPdLvnXj",
        "outputId": "05257732-f370-42c8-d1a4-7a0d745495b4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 61s 38ms/step - loss: 1.3781 - acc: 0.5833 - val_loss: 0.4569 - val_acc: 0.8772 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.4515 - acc: 0.8815 - val_loss: 0.3306 - val_acc: 0.9175 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.3329 - acc: 0.9153 - val_loss: 0.2940 - val_acc: 0.9252 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.2756 - acc: 0.9287 - val_loss: 0.2677 - val_acc: 0.9341 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.2476 - acc: 0.9345 - val_loss: 0.2780 - val_acc: 0.9370 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.2302 - acc: 0.9389\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.2306 - acc: 0.9388 - val_loss: 0.2832 - val_acc: 0.9318 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.1571 - acc: 0.9579 - val_loss: 0.2460 - val_acc: 0.9505 - lr: 5.0000e-04\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.1330 - acc: 0.9630 - val_loss: 0.2412 - val_acc: 0.9495 - lr: 5.0000e-04\n",
            "Epoch 9/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.1225 - acc: 0.9655 - val_loss: 0.2413 - val_acc: 0.9505 - lr: 5.0000e-04\n",
            "Epoch 10/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.1101 - acc: 0.9682\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.1101 - acc: 0.9682 - val_loss: 0.2507 - val_acc: 0.9484 - lr: 5.0000e-04\n",
            "Epoch 11/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0782 - acc: 0.9768 - val_loss: 0.2468 - val_acc: 0.9557 - lr: 2.5000e-04\n",
            "Epoch 12/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0629 - acc: 0.9813\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0629 - acc: 0.9812 - val_loss: 0.2604 - val_acc: 0.9566 - lr: 2.5000e-04\n",
            "Epoch 13/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0517 - acc: 0.9839 - val_loss: 0.2676 - val_acc: 0.9566 - lr: 1.2500e-04\n",
            "Epoch 14/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0433 - acc: 0.9866\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0433 - acc: 0.9866 - val_loss: 0.2745 - val_acc: 0.9549 - lr: 1.2500e-04\n",
            "Epoch 15/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0367 - acc: 0.9880 - val_loss: 0.2828 - val_acc: 0.9574 - lr: 6.2500e-05\n",
            "Epoch 16/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0326 - acc: 0.9894\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0327 - acc: 0.9893 - val_loss: 0.2868 - val_acc: 0.9578 - lr: 6.2500e-05\n",
            "Epoch 17/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0302 - acc: 0.9901 - val_loss: 0.2915 - val_acc: 0.9578 - lr: 3.1250e-05\n",
            "Epoch 18/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0286 - acc: 0.9910\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0287 - acc: 0.9909 - val_loss: 0.2867 - val_acc: 0.9582 - lr: 3.1250e-05\n",
            "Epoch 19/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0262 - acc: 0.9909 - val_loss: 0.2910 - val_acc: 0.9578 - lr: 1.5625e-05\n",
            "Epoch 20/30\n",
            "1291/1299 [============================>.] - ETA: 0s - loss: 0.0269 - acc: 0.9911\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0269 - acc: 0.9911 - val_loss: 0.2907 - val_acc: 0.9568 - lr: 1.5625e-05\n",
            "Epoch 21/30\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0241 - acc: 0.9919 - val_loss: 0.2933 - val_acc: 0.9574 - lr: 7.8125e-06\n",
            "Epoch 22/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0246 - acc: 0.9917\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0246 - acc: 0.9917 - val_loss: 0.2940 - val_acc: 0.9568 - lr: 7.8125e-06\n",
            "Epoch 23/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0236 - acc: 0.9922 - val_loss: 0.2933 - val_acc: 0.9570 - lr: 3.9063e-06\n",
            "Epoch 24/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0234 - acc: 0.9919\n",
            "Epoch 24: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0235 - acc: 0.9918 - val_loss: 0.2941 - val_acc: 0.9572 - lr: 3.9063e-06\n",
            "Epoch 25/30\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0245 - acc: 0.9917 - val_loss: 0.2940 - val_acc: 0.9566 - lr: 1.9531e-06\n",
            "Epoch 26/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0247 - acc: 0.9920\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 7s 5ms/step - loss: 0.0247 - acc: 0.9920 - val_loss: 0.2942 - val_acc: 0.9572 - lr: 1.9531e-06\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0236 - acc: 0.9918 - val_loss: 0.2941 - val_acc: 0.9572 - lr: 9.7656e-07\n",
            "Epoch 28/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0232 - acc: 0.9923\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0232 - acc: 0.9923 - val_loss: 0.2944 - val_acc: 0.9570 - lr: 9.7656e-07\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0236 - acc: 0.9918 - val_loss: 0.2947 - val_acc: 0.9570 - lr: 4.8828e-07\n",
            "Epoch 30/30\n",
            "1290/1299 [============================>.] - ETA: 0s - loss: 0.0232 - acc: 0.9921\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07.\n",
            "1299/1299 [==============================] - 7s 6ms/step - loss: 0.0232 - acc: 0.9920 - val_loss: 0.2946 - val_acc: 0.9570 - lr: 4.8828e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bkn8e1xRkpn",
        "outputId": "56ee2e95-43ec-4f8b-8e7b-07da9a5fd0ae"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 28ms/step - loss: 0.2946 - acc: 0.9565\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.29461410641670227, 0.9565133452415466]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier1.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier1.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier1.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('overall-loss-1-02946.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "5iosI-0Rx1Wb",
        "outputId": "dcb206da-3216-4c24-f9b5-2595b55e84a8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier1.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier1.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier1.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('overall-accuracy-1-9565.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "zYbeZpOPx7eL",
        "outputId": "3678fe61-244f-4c47-ff19-1dec6e386c22"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 2**"
      ],
      "metadata": {
        "id": "FINgvX2a46UV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu'),\n",
        "            layers.MaxPooling2D(),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(),\n",
        "            layers.Dense(128, activation = 'relu'),\n",
        "            layers.Dense(20, activation = 'softmax'),\n",
        "])"
      ],
      "metadata": {
        "id": "sHPIPN_f4_Po"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = keras.optimizers.Adam(lr = 1e-3),\n",
        "        metrics = ['acc'],\n",
        ")"
      ],
      "metadata": {
        "id": "NpWhJq2D4_Pq"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 2, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ],
      "metadata": {
        "id": "gaNB9fEt4_Pr"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier2 = model2.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55790dc0-7cc9-464b-eba1-db83692fa245",
        "id": "0LKUEi1-4_Ps"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 10s 7ms/step - loss: 1.7214 - acc: 0.4528 - val_loss: 0.6870 - val_acc: 0.8092 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.5546 - acc: 0.8501 - val_loss: 0.4149 - val_acc: 0.8900 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.4086 - acc: 0.8953 - val_loss: 0.3498 - val_acc: 0.9152 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.3436 - acc: 0.9135 - val_loss: 0.3183 - val_acc: 0.9206 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.3109 - acc: 0.9221 - val_loss: 0.3177 - val_acc: 0.9210 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2840 - acc: 0.9285 - val_loss: 0.3096 - val_acc: 0.9264 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2686 - acc: 0.9345 - val_loss: 0.2923 - val_acc: 0.9318 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2582 - acc: 0.9375 - val_loss: 0.3162 - val_acc: 0.9272 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.2415 - acc: 0.9412\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2426 - acc: 0.9411 - val_loss: 0.3267 - val_acc: 0.9268 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1732 - acc: 0.9575 - val_loss: 0.2571 - val_acc: 0.9420 - lr: 5.0000e-04\n",
            "Epoch 11/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1517 - acc: 0.9630 - val_loss: 0.2777 - val_acc: 0.9422 - lr: 5.0000e-04\n",
            "Epoch 12/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.1400 - acc: 0.9657\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1402 - acc: 0.9657 - val_loss: 0.2740 - val_acc: 0.9414 - lr: 5.0000e-04\n",
            "Epoch 13/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1087 - acc: 0.9721 - val_loss: 0.2740 - val_acc: 0.9457 - lr: 2.5000e-04\n",
            "Epoch 14/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0922 - acc: 0.9767\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0925 - acc: 0.9766 - val_loss: 0.2765 - val_acc: 0.9468 - lr: 2.5000e-04\n",
            "Epoch 15/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0772 - acc: 0.9804 - val_loss: 0.2767 - val_acc: 0.9482 - lr: 1.2500e-04\n",
            "Epoch 16/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0712 - acc: 0.9818\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0713 - acc: 0.9818 - val_loss: 0.2833 - val_acc: 0.9487 - lr: 1.2500e-04\n",
            "Epoch 17/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0633 - acc: 0.9832 - val_loss: 0.2907 - val_acc: 0.9489 - lr: 6.2500e-05\n",
            "Epoch 18/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0589 - acc: 0.9845\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0591 - acc: 0.9844 - val_loss: 0.3055 - val_acc: 0.9482 - lr: 6.2500e-05\n",
            "Epoch 19/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0539 - acc: 0.9856 - val_loss: 0.3086 - val_acc: 0.9491 - lr: 3.1250e-05\n",
            "Epoch 20/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0523 - acc: 0.9862\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0524 - acc: 0.9861 - val_loss: 0.3095 - val_acc: 0.9499 - lr: 3.1250e-05\n",
            "Epoch 21/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0506 - acc: 0.9866 - val_loss: 0.3109 - val_acc: 0.9493 - lr: 1.5625e-05\n",
            "Epoch 22/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0493 - acc: 0.9864\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0495 - acc: 0.9863 - val_loss: 0.3120 - val_acc: 0.9489 - lr: 1.5625e-05\n",
            "Epoch 23/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0474 - acc: 0.9873 - val_loss: 0.3126 - val_acc: 0.9493 - lr: 7.8125e-06\n",
            "Epoch 24/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0481 - acc: 0.9868\n",
            "Epoch 24: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0481 - acc: 0.9868 - val_loss: 0.3150 - val_acc: 0.9487 - lr: 7.8125e-06\n",
            "Epoch 25/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0467 - acc: 0.9870 - val_loss: 0.3164 - val_acc: 0.9487 - lr: 3.9063e-06\n",
            "Epoch 26/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0464 - acc: 0.9873\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0466 - acc: 0.9872 - val_loss: 0.3183 - val_acc: 0.9487 - lr: 3.9063e-06\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0489 - acc: 0.9866 - val_loss: 0.3178 - val_acc: 0.9485 - lr: 1.9531e-06\n",
            "Epoch 28/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0456 - acc: 0.9874\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0456 - acc: 0.9874 - val_loss: 0.3183 - val_acc: 0.9489 - lr: 1.9531e-06\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0471 - acc: 0.9869 - val_loss: 0.3182 - val_acc: 0.9489 - lr: 9.7656e-07\n",
            "Epoch 30/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0481 - acc: 0.9867\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0481 - acc: 0.9867 - val_loss: 0.3181 - val_acc: 0.9489 - lr: 9.7656e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a-OpXGQT7s7",
        "outputId": "0d303091-cbba-43d2-9e1d-7c60679db5a9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 29ms/step - loss: 0.2928 - acc: 0.9471\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.29278141260147095, 0.9470848441123962]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier2.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier2.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier2.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('overall-loss-2-02928.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "8758c796-963e-41d8-9897-1cb37ccae825",
        "id": "K4HqTahk5E70"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier2.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier2.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier2.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('overall-accuracy-2-9471.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "d4bd5619-d564-4a9a-a298-a094b8183fed",
        "id": "oVdULdCD5E72"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 3**"
      ],
      "metadata": {
        "id": "iUn28buH7bWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv1\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv2\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool1\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv3\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv4\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool2\"),\n",
        "            layers.Dropout(0.2, input_shape=(2,)),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv5\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv6\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv7\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool3\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv8\"),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu', name=\"Conv9\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool4\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(name=\"Flatten1\"),\n",
        "            layers.Dense(128, activation = 'relu', name=\"Dense1\"),\n",
        "            layers.Dense(20, activation = 'softmax', name=\"Dense2\"),\n",
        "])"
      ],
      "metadata": {
        "id": "YK75YNyy7bWX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = keras.optimizers.Adam(lr = 1e-3),\n",
        "        metrics = ['acc'],\n",
        ")"
      ],
      "metadata": {
        "id": "9msINIhc7bWZ"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 2, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ],
      "metadata": {
        "id": "SqRMa6du7bWZ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier3 = model3.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c93155da-ac76-4c84-be5a-dbfd95444f37",
        "id": "kOb2EjNR7bWa"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 9s 6ms/step - loss: 1.4306 - acc: 0.5575 - val_loss: 0.6336 - val_acc: 0.8214 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.5214 - acc: 0.8596 - val_loss: 0.4035 - val_acc: 0.8963 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.4019 - acc: 0.8939 - val_loss: 0.3496 - val_acc: 0.9106 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.3447 - acc: 0.9095 - val_loss: 0.3142 - val_acc: 0.9212 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.3148 - acc: 0.9186 - val_loss: 0.3166 - val_acc: 0.9204 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2920 - acc: 0.9240 - val_loss: 0.2919 - val_acc: 0.9260 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2817 - acc: 0.9270 - val_loss: 0.2853 - val_acc: 0.9285 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2625 - acc: 0.9323 - val_loss: 0.2771 - val_acc: 0.9322 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2536 - acc: 0.9345 - val_loss: 0.2811 - val_acc: 0.9299 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.2537 - acc: 0.9345\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.2537 - acc: 0.9346 - val_loss: 0.2857 - val_acc: 0.9327 - lr: 0.0010\n",
            "Epoch 11/30\n",
            "1299/1299 [==============================] - 8s 7ms/step - loss: 0.1844 - acc: 0.9507 - val_loss: 0.2379 - val_acc: 0.9495 - lr: 5.0000e-04\n",
            "Epoch 12/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1518 - acc: 0.9592 - val_loss: 0.2564 - val_acc: 0.9462 - lr: 5.0000e-04\n",
            "Epoch 13/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.1465 - acc: 0.9597\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1469 - acc: 0.9596 - val_loss: 0.2538 - val_acc: 0.9468 - lr: 5.0000e-04\n",
            "Epoch 14/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.1123 - acc: 0.9696 - val_loss: 0.2363 - val_acc: 0.9505 - lr: 2.5000e-04\n",
            "Epoch 15/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0980 - acc: 0.9721 - val_loss: 0.2783 - val_acc: 0.9439 - lr: 2.5000e-04\n",
            "Epoch 16/30\n",
            "1291/1299 [============================>.] - ETA: 0s - loss: 0.0892 - acc: 0.9743\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0891 - acc: 0.9743 - val_loss: 0.2904 - val_acc: 0.9466 - lr: 2.5000e-04\n",
            "Epoch 17/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0718 - acc: 0.9786 - val_loss: 0.2811 - val_acc: 0.9501 - lr: 1.2500e-04\n",
            "Epoch 18/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0711 - acc: 0.9790\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0710 - acc: 0.9790 - val_loss: 0.2860 - val_acc: 0.9489 - lr: 1.2500e-04\n",
            "Epoch 19/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0591 - acc: 0.9816 - val_loss: 0.2848 - val_acc: 0.9526 - lr: 6.2500e-05\n",
            "Epoch 20/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0536 - acc: 0.9835\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0538 - acc: 0.9835 - val_loss: 0.2986 - val_acc: 0.9528 - lr: 6.2500e-05\n",
            "Epoch 21/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0499 - acc: 0.9842 - val_loss: 0.3015 - val_acc: 0.9514 - lr: 3.1250e-05\n",
            "Epoch 22/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0465 - acc: 0.9852\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0465 - acc: 0.9852 - val_loss: 0.3021 - val_acc: 0.9520 - lr: 3.1250e-05\n",
            "Epoch 23/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0452 - acc: 0.9853 - val_loss: 0.3024 - val_acc: 0.9526 - lr: 1.5625e-05\n",
            "Epoch 24/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0460 - acc: 0.9851\n",
            "Epoch 24: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0461 - acc: 0.9851 - val_loss: 0.2999 - val_acc: 0.9514 - lr: 1.5625e-05\n",
            "Epoch 25/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0435 - acc: 0.9860 - val_loss: 0.3014 - val_acc: 0.9511 - lr: 7.8125e-06\n",
            "Epoch 26/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0443 - acc: 0.9863\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0444 - acc: 0.9863 - val_loss: 0.3006 - val_acc: 0.9509 - lr: 7.8125e-06\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0436 - acc: 0.9857 - val_loss: 0.3012 - val_acc: 0.9512 - lr: 3.9063e-06\n",
            "Epoch 28/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.0427 - acc: 0.9861\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0427 - acc: 0.9861 - val_loss: 0.3028 - val_acc: 0.9514 - lr: 3.9063e-06\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0435 - acc: 0.9857 - val_loss: 0.3023 - val_acc: 0.9516 - lr: 1.9531e-06\n",
            "Epoch 30/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0422 - acc: 0.9862\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 8s 6ms/step - loss: 0.0423 - acc: 0.9862 - val_loss: 0.3024 - val_acc: 0.9520 - lr: 1.9531e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oESmw2DWdFT",
        "outputId": "35bffbf1-0b2d-4d98-abb0-04a6feb391ff"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 28ms/step - loss: 0.2908 - acc: 0.9498\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.29078152775764465, 0.9497787356376648]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier3.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier3.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier3.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('overall-loss-3-02908.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "348710a3-1316-47fe-efc2-a311aca82f66",
        "id": "xyI8dOHl7bWc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier3.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier3.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier3.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('overall-accuracy-3-9498.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "074847b6-8889-47fd-da2f-ed458200afde",
        "id": "VSS4bsbp7bWd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 4**"
      ],
      "metadata": {
        "id": "rJoWmaFK-U-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model4 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv1\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv2\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv3\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool1\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv4\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv5\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv6\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool2\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu', name=\"Conv8\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool3\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(name=\"Flatten1\"),\n",
        "            layers.Dense(128, activation = 'relu', name=\"Dense1\"),\n",
        "            layers.Dense(20, activation = 'softmax', name=\"Dense2\"),\n",
        "])"
      ],
      "metadata": {
        "id": "L_fZZyBk-U-c"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model4.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = keras.optimizers.Adam(lr = 1e-3),\n",
        "        metrics = ['acc'],\n",
        ")"
      ],
      "metadata": {
        "id": "n_nF5zo6-U-d"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 2, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ],
      "metadata": {
        "id": "9ACEz6n2-U-e"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier4 = model4.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94a46f6c-0c2a-40e5-9cab-190bd28f6b0b",
        "id": "YKe4Cb1D-U-f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 10s 7ms/step - loss: 1.2800 - acc: 0.6176 - val_loss: 0.4857 - val_acc: 0.8745 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.4271 - acc: 0.8888 - val_loss: 0.3266 - val_acc: 0.9189 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.3155 - acc: 0.9197 - val_loss: 0.2860 - val_acc: 0.9308 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2668 - acc: 0.9308 - val_loss: 0.2608 - val_acc: 0.9351 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2379 - acc: 0.9386 - val_loss: 0.2779 - val_acc: 0.9352 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2143 - acc: 0.9443 - val_loss: 0.2594 - val_acc: 0.9379 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1991 - acc: 0.9467 - val_loss: 0.2581 - val_acc: 0.9399 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1958 - acc: 0.9489 - val_loss: 0.2495 - val_acc: 0.9414 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1806 - acc: 0.9515 - val_loss: 0.2581 - val_acc: 0.9351 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.1811 - acc: 0.9508\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1811 - acc: 0.9508 - val_loss: 0.2494 - val_acc: 0.9416 - lr: 0.0010\n",
            "Epoch 11/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1205 - acc: 0.9664 - val_loss: 0.2371 - val_acc: 0.9516 - lr: 5.0000e-04\n",
            "Epoch 12/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0955 - acc: 0.9732 - val_loss: 0.2553 - val_acc: 0.9505 - lr: 5.0000e-04\n",
            "Epoch 13/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0896 - acc: 0.9737\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0896 - acc: 0.9737 - val_loss: 0.2463 - val_acc: 0.9524 - lr: 5.0000e-04\n",
            "Epoch 14/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0642 - acc: 0.9808 - val_loss: 0.2635 - val_acc: 0.9532 - lr: 2.5000e-04\n",
            "Epoch 15/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0531 - acc: 0.9842\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0530 - acc: 0.9842 - val_loss: 0.2608 - val_acc: 0.9547 - lr: 2.5000e-04\n",
            "Epoch 16/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0414 - acc: 0.9865 - val_loss: 0.2730 - val_acc: 0.9553 - lr: 1.2500e-04\n",
            "Epoch 17/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0371 - acc: 0.9886\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0371 - acc: 0.9886 - val_loss: 0.2806 - val_acc: 0.9553 - lr: 1.2500e-04\n",
            "Epoch 18/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0307 - acc: 0.9900 - val_loss: 0.2834 - val_acc: 0.9564 - lr: 6.2500e-05\n",
            "Epoch 19/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0271 - acc: 0.9911\n",
            "Epoch 19: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0271 - acc: 0.9911 - val_loss: 0.2804 - val_acc: 0.9553 - lr: 6.2500e-05\n",
            "Epoch 20/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0267 - acc: 0.9912 - val_loss: 0.2765 - val_acc: 0.9566 - lr: 3.1250e-05\n",
            "Epoch 21/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0218 - acc: 0.9928\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0219 - acc: 0.9928 - val_loss: 0.2882 - val_acc: 0.9557 - lr: 3.1250e-05\n",
            "Epoch 22/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0213 - acc: 0.9928 - val_loss: 0.2923 - val_acc: 0.9551 - lr: 1.5625e-05\n",
            "Epoch 23/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0212 - acc: 0.9928\n",
            "Epoch 23: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0212 - acc: 0.9928 - val_loss: 0.2945 - val_acc: 0.9557 - lr: 1.5625e-05\n",
            "Epoch 24/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0203 - acc: 0.9935 - val_loss: 0.2953 - val_acc: 0.9557 - lr: 7.8125e-06\n",
            "Epoch 25/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0201 - acc: 0.9932\n",
            "Epoch 25: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0202 - acc: 0.9932 - val_loss: 0.2950 - val_acc: 0.9564 - lr: 7.8125e-06\n",
            "Epoch 26/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0192 - acc: 0.9936 - val_loss: 0.2946 - val_acc: 0.9564 - lr: 3.9063e-06\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0187 - acc: 0.9936\n",
            "Epoch 27: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0187 - acc: 0.9936 - val_loss: 0.2963 - val_acc: 0.9564 - lr: 3.9063e-06\n",
            "Epoch 28/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0201 - acc: 0.9935 - val_loss: 0.2952 - val_acc: 0.9561 - lr: 1.9531e-06\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0197 - acc: 0.9935\n",
            "Epoch 29: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0197 - acc: 0.9935 - val_loss: 0.2953 - val_acc: 0.9561 - lr: 1.9531e-06\n",
            "Epoch 30/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0194 - acc: 0.9936 - val_loss: 0.2951 - val_acc: 0.9563 - lr: 9.7656e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model4.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UZxvaGQYF5W",
        "outputId": "bc851125-d590-43ae-989c-62731cbb4c00"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 29ms/step - loss: 0.3032 - acc: 0.9542\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3032074570655823, 0.9542043209075928]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier4.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier4.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier4.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('overall-loss-4-03032.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "8b38ad14-709d-49e3-f13f-e96c80a9c15c",
        "id": "_9NqDpXs-U-g"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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s5qwwGlRxAyzX7tYKliIiIlIy9enTh7feeqvY24qXEm8/aBVfAYDlms9bREREilFMTEzOIyQkhKioqJzX2cugF9acOXMYNmxYsbctisTEROLj44v9vCWFEm8/yF5IZ6XqvEVERKQYpaSk5Dzq1KnDzJkzc14PHjw4p11GRkYAo5RsSrz9IHvpePV4i4iIiD9k9xw/9dRTVK9eneuuu45Dhw7Rt29fqlatSsWKFenbty/bt2/POSYhIYHXXnsNgDfffJMLL7yQu+++m4oVK1K/fn3mzJlzWm1/++03unXrRmxsLL169eK2227jmmuuKfI1rVmzhoSEBCpUqEDz5s2ZMWNGzr7PPvuMZs2aERsbS61atRg7diwA+/fvp2/fvlSoUIFKlSrRtWtXsrKyivzexUWJtx80qxGHMbBuTzKp6RpgKSIiIr63e/duDh48yJYtW5g4cSJZWVlcd911bNmyha1btxIVFcXtt99+wuMXLVrEueeey/79+7n33nu54YYbsNYWue2gQYPo0KEDBw4cYNSoUbz99ttFvpb09HQuvfRSevfuzd69e3nhhRcYPHgwa9euBeCGG27glVdeITk5mZUrV9KzZ08Axo0bR3x8PPv27WPPnj2MHj0aY0yR37+4hAXsncuQ6LPCaFQ1hvV7U/h1dzLn1a4Q6JBERETkDNS7b7Zf3mfzk5ec9rEhISE8/PDDnHXWWQBERUVxxRVX5Ox/4IEH6NGjxwmPr1u3LjfddBMAw4YNY8SIEezZs4fq1asXum1aWhqLFy/mq6++IiIiggsvvJB+/foV+VoWLlxISkoK9913HyEhIfTs2ZO+ffvyzjvvMGrUKMLDw1m9ejWtW7emYsWKVKxYkeTkZMLDw9m1axdbtmyhUaNGdO3atcjvXZzU4+0nms9bRERE/Klq1apERkbmvD527Bg333wzdevWJS4ujm7dunH48GEyMwv+Nj53gl2uXDnA1ZQXpe3OnTupVKlSzjaA2rVrF/ladu7cSe3atQkJ8aaudevWZceOHQB89NFHfPbZZ9StW5fu3buzYMECAO655x4aNWpE7969adCgAU8++WSR37s4qcfbT1rGl+fjZTtYsf0wUDfQ4YiIiMgZOJOeaH/JX1Ixbtw41q5dy6JFi6hevTo///wzbdq0OWH5SHGoUaMGBw8e5NixYznJ97Zt24p8npo1a7Jt2zaysrJyku+tW7fSuHFjANq3b8/06dNJT0/nxRdfZMCAAaxevZrY2FjGjRvHuHHjckpQ2rdvz0UXXVR8F1kE6vH2E2+P95EARyIiIiJlUXJyMlFRUVSoUIGDBw/y8MMP+/w969atS7t27Rg1ahRpaWksWLCAmTNnnvK41NTUPI8OHTpQrlw5xowZQ3p6OomJicycOZOrrrqKtLQ0pk6dSlJSEuHh4cTFxeUk57NmzWLDhg1YaylfvjyhoaF5es39TYm3nzSrGUeIBliKiIhIgNx1110cP36cKlWq0LFjRy6++GK/vO/UqVNZsGABlStX5sEHH2TgwIE5decF2bFjB1FRUXke27ZtY+bMmcyZM4cqVaowYsQIJk+eTJMmTQB4++23qVevHnFxcUyYMCFnDvP169fTq1cvYmJi6NSpEyNGjDhpXbuvGV9+vVCStGvXzi5ZsiSgMfR+9lvW7UnhkxGdaVOnYkBj8afExEQSEhICHYYUge5Z8NE9Cy66X8Fn2bJltGnTJtBhlAoDBw6kSZMmPu9xT05OJjY21mfnX7NmDU2bNv3DdmPMUmttu4KOUY+3H7Ws5WYz0QBLERERKSsWL17Mxo0bycrK4vPPP2f69On0798/0GEFhN8Sb2NMJWPMJ8aYo8aYLcaYQadoH2GMWWOM2Z5v+3nGmKXGmGOen+f5NvLi07JWHAArtJCOiIiIlBG7d+8mISGBmJgY7rjjDsaPH19mvz3w56wmLwFpQDXgPGC2MeYXa+2qE7S/B9gH5HxHYIyJAKYDzwEvAzcD040x51hr03wZfHFoGa8ebxERESlbLr30Ui699NJAh1Ei+KXH2xgTDVwBjLTWplhrvwdmAENO0L4+cA3wRL5dCbgPC89Za3+31v4XMEBPX8VenJrVcAMs1+9N4XiaBliKiIiIlCX+KjVpDGRYa9fl2vYL0PwE7V8A7geO59veHFhu844IXX6S85QoURGhNK4WS2aWZfUuTSsoIiISTMrKhBRyaqf7Z8FfpSYxQP5MM4lcZSTZjDF/BUKttZ8YYxIKOE/+Oo0Cz+M513BgOEC1atVITEwscuDFrUro7wB8kriE5LrhAY7GP1JSUkrE714KT/cs+OieBRfdr+ATExPDoUOHCA8vG/93lwaZmZkkJyf75Nzp6ekcP368yH+P/ZV4pwBx+bbFAXl+G56SlDHAX87kPNmstROBieCmEywJUzdtPWsz3+9YxfGos0lIaB3ocPxC02YFH92z4KN7Flx0v4LPDz/8wNGjR6lVq1ZAF2CRwvPVdIJZWVns2LGD+Ph4zj777CId66/Eex0Q5hkEud6zrTWQf2DlOUA94DvPMqcRQHljzG6go6f9P40xJle5SSvcwM2g0MKzguVKDbAUEREJGmlpaVhrWbt2baBDkUJKTU0lMjLSJ+eOjo6mSpUqRT7OL4m3tfaoMeZj4BFjzI24WU0uAzrna7oSqJ3rdWfgRaAtboaTnUAmcIcxZgJwk6fd1z4Mv1g1qxFHaIhh/d5kjqVlUC7CnxPLiIiIyOmqU6dOoEOQIkhMTCxx0xb687uSEUAUsBd4B7jVWrvKGNPVGJMCYK3NsNbuzn4AB4Esz+tMz5SB/YGhwGHgeqB/MEwlmC0yPJRzzo4hy8IaDbAUERERKTP81t1qrT2IS5rzb/8ON2iyoGMSgfh825YB5/sgRL9pFV+eX3cns3x7EufXrRTocERERETEDzQ6IABaeuq8tZCOiIiISNmhxDsAclaw1NLxIiIiImWGEu8AaFI9lrAQw8Z9KRz9PSPQ4YiIiIiIHyjxDoDIcLeCZZZFK1iKiIiIlBFKvAMkp85b5SYiIiIiZYIS7wBpGa8BliIiIiJliRLvANHMJiIiIiJlixLvAGlSI5bwUDfAMkUDLEVERERKPSXeAXJWmBtgaS2s3qkBliIiIiKlnRLvAGrlqfNevv1wgCMREREREV9T4h1ALTx13itV5y0iIiJS6inxDqBWtTwrWCrxFhERESn1lHgHUOPqMYSHGjbtP6oBliIiIiKlnBLvADorLJQm1eOwFlap11tERESkVFPiHWAtNJ+3iIiISJmgxDvAWmkFSxEREZEyQYl3gOWsYLldibeIiIhIaabEO8AaV4slIjSETfuPkpyaHuhwRERERMRHlHgHWERYCE1qxAKwcodWsBQREREprZR4lwAttZCOiIiISKmnxLsEyE68lyvxFhERESm1lHiXAC3j1eMtIiIiUtop8S4BGleLJSIshN/2H+WIBliKiIiIlEpKvEuA8NAQmtaIA9TrLSIiIlJaKfEuIVrWcom35vMWERERKZ2UeJcQrWpVALSCpYiIiEhppcS7hGhRS0vHi4iIiJRmSrxLiHOqxXBWWAhbDhwj6ZgGWIqIiIiUNkq8S4g8Ayx3qtdbREREpLRR4l2CtIpXuYmIiIhIaaXEuwTJqfPWzCYiIiIipY4S7xJEPd4iIiIipZcS7xKkUdUYIsND2HrwGIePpQU6HBEREREpRkq8S5Cw0BCa5axgeSTA0YiIiIhIcVLiXcK09NR5L99xOMCRiIiIiEhxUuJdwrSMdytYrlSdt4iIiEiposS7hMnp8dbMJiIiIiKlihLvEqZh1WiiwkPZfug4h45qgKWIiIhIaaHEu4QJCw2hWU03wFLTCoqIiIiUHn5LvI0xlYwxnxhjjhpjthhjBp2g3d+NMZuMMUeMMTuNMc8aY8Jy7d9sjDlujEnxPL7w1zX4S3a5iRJvERERkdLDnz3eLwFpQDVgMDDeGNO8gHYzgLbW2jigBdAauCNfm0uttTGeR29fBh0ILbWCpYiIiEip45fE2xgTDVwBjLTWplhrv8cl2EPyt7XWbrTWZs+lZ4AsoJE/4iwptIKliIiISOljrLW+fxNj2gDzrbXlcm27G+hurb20gPaDgAlALLAf6GWt/cWzbzMQhfvQsAy4J3tfAecZDgwHqFat2vnvvvtucV6Wz2RZy61zj/F7JrzQsxyxESbQIZ2RlJQUYmJiAh2GFIHuWfDRPQsuul/BR/cs+ATqnvXo0WOptbZdQfvCCtroAzFA/qUYk3CJ9R9Ya6cB04wx5wBDgT25dg8GfsL1ht8J/M8Y0yRXL3nu80wEJgK0a9fOJiQknOFl+E+rtT+wePMh4uq1oHvjqoEO54wkJiYSTL970T0LRrpnwUX3K/jongWfknjP/FXjnQLE5dsWBySf7CBr7XpgFfByrm3zrbXHrbXHrLVPAIeBrsUcb8C1yKnz1gqWIiIiIqWBvxLvdUCYpwc7W2tcUn0qYUDDk+y3uN7vUkV13iIiIiKli18Sb2vtUeBj4BFjTLQxpgtwGfB2/rbGmBuNMWd7njcD/g185XldxxjTxRgTYYyJNMbcA1QB5vvjOvxJM5uIiIiIlC7+nE5wBG5Q5F7gHeBWa+0qY0xXY0xKrnZdgBXGmKPAZ57H/Z59scB44BCwA7gY6GOtPeCna/Cb+lViKBcRys6kVPan/B7ocERERETkDPlrcCXW2oNA/wK2f4cbfJn9+rqTnGMV0MonAZYwoSGGFjXL8+Pmg6zYkUSPc88OdEgiIiIicga0ZHwJlj3AcqXKTURERESCnhLvEix7gOVyDbAUERERCXpKvEuwnB5vJd4iIiIiQU+JdwnWoEo00RGh7EpKZV+yBliKiIiIBDMl3iVYSIihuXq9RUREREoFJd4lXCtP4r1cAyxFREREgpoS7xKupVawFBERESkVlHiXcDkrWO44HOBIRERERORMKPEu4epVjibmrDD2HPmdvUdSAx2OiIiIiJwmJd4lXEiIoUWtOEDlJiIiIiLBTIl3EPCWmyjxFhEREQlWSryDQMv4CgCs0MwmIiIiIkFLiXcQUI+3iIiISPBT4h0E6lYqR2xkGHuTf2ePBliKiIiIBCUl3kEgJMTQoqan11vlJiIiIiJBSYm3L1kLs2bB77+f8alaeRbSWa5yExEREZGgpMTbl26+GS69FF588YxP1cJT571SibeIiIhIUFLi7Ut//av7+dhjcPDgGZ0qp8d7exLW2jONTERERET8TIm3L118MVx0ERw+7JLvM1CnUjniIsPYn/I7e46ceemKiIiIiPiXEm9fMgaeftr9fPFF2LTpDE5laJnT6324uCIUERERET9R4u1rbdrAkCGQng7//vcZnUp13iIiIiLBS4m3Pzz2GERGwvvvw6JFp32aVrXcCpaa2UREREQk+IQFOoAyoXZteOABN71gixanfZqWuXq8rbUYY4orQhERERHxMSXe/vLgg2d8itqVoigfFc7+lDR2H0mlRvmoYghMRERERPxBpSaBcOSIq/kuImNMTq/3cq1gKSIiIhJUlHj723vvQaNG8Morp3V49swmGmApIiIiElyUePtbRATs2wcPPwxJRU+es3u8F/12ZgvyiIiIiIh/KfH2t/79oUsX2L8fnnqqyId3aVSFchGh/PjbQfV6i4iIiAQRJd7+ZgyMHeueP/ssbNtWpMPLR4VzdYc6AEycd/oL8oiIiIiIfynxDoSOHWHAAEhNhZEji3z4DRfWJyzEMHvFLrYdPOaDAEVERESkuCnxDpQnnoDwcJg8GX7+uUiH1qwQRb/zapKZZXn9+998FKCIiIiIFCcl3oHSoAHcfjs0b+56votoeLcGALy7eCsHj6YVd3QiIiIiUsyUeAfSY4+53u6OHYt8aJPqcfQ4tyqp6Vm8vWCLD4ITERERkeKkxDuQypWD0NDTPvzm7g0BeGvBZo6nZRZTUCIiIiLiC0q8S4IdO+D662HSpCIddkH9SrSuXYGDR9P4YGnRZkcREREREf9S4l0SzJvnku4HHoCjRwt9mDGGWzy13q9+t4mMzCxfRSgiIiIiZ0iJd0kwcCC0bw+7dsG4cUU6tHfz6tSrXI5tB48zZ+VuHwUoIiIiImNI4akAACAASURBVGdKiXdJEBLiXVRnzBjYXfgEOjTEcJOn1/uVeRux1voiQhERERE5Q35LvI0xlYwxnxhjjhpjthhjBp2g3d+NMZuMMUeMMTuNMc8aY8Jy7a9njPnGGHPMGPOrMaaXv67Bp7p1g379XKnJf/5TpEOvaBtPlZgIVu44wg8bD/goQBERERE5E/7s8X4JSAOqAYOB8caY5gW0mwG0tdbGAS2A1sAdufa/AywDKgMPAB8aY6r6MnC/eeopN8vJa6/B6tWFPiwyPJRrO9cDYMK3G30UnIiIiIicCb8k3saYaOAKYKS1NsVa+z0uwR6Sv621dqO19nD2oUAW0MhznsZAW+A/1trj1tqPgBWecwe/Jk1g+HDIyoIvvyzSodd0rEu5iFC+W7+fVTuTfBSgiIiIiJwuf/V4NwYyrLXrcm37BSioxxtjzCBjzBFgP67H+xXPrubAJmttcmHOE5RGjYKlS+HOO4t0WIVyEVzVvg4AE+dt8kFgIiIiInImwk7dpFjEAEfybUsCYgtqbK2dBkwzxpwDDAX25DpP/u7cJKBWQecxxgwHhgNUq1aNxMTE04k9ME4j1uZhWYQamPnLTi6MO0TVciVj7GxKSkpw/e5F9ywI6Z4FF92v4KN7FnxK4j3zV+KdAsTl2xYHJBfQNoe1dr0xZhXwMnB5Uc9jrZ0ITARo166dTUhIKHLgAfXll5CeDn/5S6EPmX/kZz5etoNVGdUYlVAyvghITEwk6H73ZZzuWfDRPQsuul/BR/cs+JTEe+avxHsdEGaMOcdau96zrTWwqhDHhgENPc9XAQ2MMbG5yk1aA9OKNdqS4PPPoU8fqFMH1q6FyMhCHTa8ewM+XraD9xZv486LzqFidISPAxURERG/ycyE48chJsa9Tk2F996DAwe8j6QkiIpyba65Bjp0cG3XrYMVK9z2mBiIjc37MyoqcNdVRvgl8bbWHjXGfAw8Yoy5ETgPuAzonL+tZ/8Ma+1eY0wz4N/A/zznWWeM+Rn4jzHmQaAP0IrSMrgytz/9CVq1guXL4b//hXvvLdRhTarHkXBuVRLX7mPygi3c2escHwcqIiJymqyFjAyXPB4/DuHhULGi25eSAj/95Lanpnof2a+vvhqqVXNt33kHvvnGbU9Pd+tjhIS4mcLq1IFHHvG+59//7t43NNQ9stuFhsIll8AFF7h2K1a4TjDPvlqbNrmOsLAw97jmGrcPYP58OHLEuy883Pu8alWoW9e1S0tza3Vk7w8NddeZnTB37OhNqCdNgq+/zptQHzwIhw+7KYizSygyM+Haa0/8O+7Y0Zt4z5oF//xnwe0iI93vNlu/fi7W/Im5ta5j8Morvb+nJ590E0NY637mfrzyivc+Pf44fPedt132T2OgbVt4+mnX7uhRGDrUe1+yf1fZj+HDoV071/abb2DOHG+b7J8NGkDNmif+vQSIv3q8AUYAbwB7gQPArdbaVcaYrsAca63nTxpdgMeNMTHAPuADYGSu81wFvAkcArYCV1pr9/nnEvwoNNT9Afzzn90f1OuvhypVCnXozd0akrh2H28t2Mzwbg2Iigj1bawiIiLZsrJckrhrl0vc9uyBIbkmMRs6FH74wW0/dsy1z3bddfDGG+75xo3QvfuJ3+eCC7wJ3fz58OqrBbdr2TJv4v3iiy7ZL0i1at7Ee/HiPJ1ef+jGGjTIm3j/85+waFHB5xw2DN580z3/9Vdo3fpEV+Q+aLRp454vWABTphTcLj3d+7xcOfceFSpA5cruUb68+xCSnOxNUMElo5df7ranpLif2c/zf7P+88+wbVvB71+1qjfx3r0bpp2k8OCZZ/Ke83//K7hdaK5cJTUVPv74xOfs3dt7XQsXehP23Hr2hJEj/7g9wPyWeFtrDwL9C9j+HW7QZPbr605xns1AQjGHVzL17u0eX3wBjz4Kzz9fqMM6NqhE6/jy/LI9iQ+XbmNIp3q+jVNEREq/jAzYu9cl1Lt2QaNGbhpcgLlz4f773fY9e/ImhgD9+7teU4AdO1xSnS0szCV9kZF5Sx0qVHA9u9n7svdnPz/7bG/bgQNdgh0V5XqTs7JcT3BmpjtPbs88464lMzNvu6wsb88wQIsWLqH27NuxZQu1qlVzx2Zk5E0UO3VyPfXp6d792c/r1/e2CwmB+Pi8bWJivAlz7nMOGQKdO3v3ZT8qVszbzhhvYn8q/fu7R2F88YUrWcmdpKemumto1Srv72nKFBdH9jcN2Q9jvB+OwP0Zuf56777sn+D9pgPc7+SDD7z3JjPTe88yM13veLYePVyPe+79GRnuQ0YJZMrKEuPt2rWzS5YsCXQYRbd8OZx3nvtLtno1nFO40pHPVuxixNSfqFOpHF//szthoYGb4aQkDm6Qk9M9Cz66Z8ElKO6XtXDLLbBsGWzZAvv2uW3ZRo3yrrT82WeuTCNbhQpQo4b38dxz3m9t16xxCVf16hAd7RLvIBAU90zyCNQ9M8Ystda2K2hfcPxpL8tatXK1W5MmuZKTQn6q/XPz6tStXI4tB47x+ard9G1V8uqcREQkgLKy4Lff4JdfXAnAL7+43ujFi10vpDGuHnfNGtc+u/eyenWXTOfuye3UyZV71Kjh9p9skF7Tpr69LpESTIl3MHj0UfcPWSEHWAKEhhhu6tqABz9dySvfbuKSljUw2V/niIhI2WKt9yv9xER48EGXaKek/LHtrl3eQWnPPuuS6EaNXGnHiXqnK1Z0ZREiclJKvINBrVowenSRD7vy/Hie/XIdK3YksWDjATo3KtzgTBERCVLWul7rX37J+xg0CB56yLUJCXG90+A6dVq3diWNrVu7R+6a3D//2f/XIFKKKfEONsePw+bNhfqqLjI8lGs712Pcl+uYMG+TEm8RkdIqK8vNCDJ7tptRJL9ly7zPzz/fzSyRP8kWEZ9T4h1M1q2DXr0gIsINtIw49eI4QzrVZfy3G5m3bh+rdibRvGZ5PwQqIiJ+FeIZQH/ggCv7yO69zu7JbtbM2zY62s2YJSJ+F7ipLqTo6td383Vu3OjmIS2ECuUiuKp9HQAmztvky+hERMRfdu2C22/POyfy6NGug+bAAbeoyHPPucH5bdrAWWcFLFQR8VLiHUzCw2HMGPf8vvvcvKmFcEPX+oSGGGYt38W2g8d8GKCIiPjU/v1wzz1ujuKXXnKDJLOn+KtVy005q4H0IiWWEu9g06+fW+42Pd2tPvXzz6c8pFaFKPq1rklmluX173/zQ5AiIn6UlOTWPNhUir/VS0pyc2Y3aABjx7qFTC6/3E0xq0RbJGioxjsYjR0LO3fCe+9Bnz5uWdl69U56yPBuDfhk2Q7eW7yNOy86h4rRp64PFxEpcWbOdNPhbd7s5qDevBkOHXL7WrZ0CTi4XuA77oA6daBhQ+8jJuYEJy7B5s+HSy/1XufFF8Njj7lBkiISVJR4B6OQEHjrLbcsb2IifP21W4L1JJrWiKN746p8u24fby/cwh0XFW4FTBERn0tPhw0b8ibT2T83b4Yvv3SDBMHN2vHKK3mPj4qCunXzziO9d2/BY2HOPtsl4E8+6ZYjB7ciI7iVFUti73HLli6ubt1cwt21a6AjEpHTpMQ7WJ11Fnz6qUu8L7usUIfc3L0B367bx5s/bGZ4twZEhof6NkYRkdyOH4c5c+DHH12ifOutbvtvv+WddSO/337zJt5//asbaF6vnvdn1ap/TJgjIuD5591g9OzHpk0uId+7N2/bcePgqacgNjZv73jDhtCkiTdB94eMDNex8uqrrlOlXDmIi3NlhfHxJfODgYgUmhLvYFa+fN6ke/t2txjCCVYW69SgMq3iy7N8exIfLN3OkI51/RSoiJRZqanw+efw/vswYwYcPeq2d+vmTbzr1HErI9arlzehzv6Zf0GXwizqUrGiKzXJLTPTLS6zcaOb6SObte7f06Qkl+DmHjvTqpVbgCa73V13QePG0Lw5tGjhesmLQ1YWvPuuq+PesMFtmzIFhg93z2vXLp73EZGAUuJdWixdCn/5i+sNGj++wF4RYww3d2vIbdN+4tV5mxjUoQ6hIeo9EREfGT8e/vUvSE72bmvf3v1bld2DDRAZCevX+z6e0FCX5Nepk3f7U0+50pODB11SvmGDt5e8QQNvux074L//zXtstWreJPzWW10PeVFYC9Onw8iRsHKl23bOOfDwwzBwYNGvUURKNCXepcXx46635pVX3NeRDz5YYLOLW1SnTqVybD14jDkrd9G3VU0/ByoipVJampvi9OyzoV07t61GDZd0t20LAwa4R/36gY3zRIyBypXdo0OHgttERsIzz7gEedUq99izxz2+/jpvovz00/DVV96kvHlzV06Tf3Dn0KGuZxtcr/Z//gPDhp3wm0sRCW76m11aXHghTJsGV17pek5q1XLLB+cTGmK4qVsDRn66kle+3cQlLWtgfFEzuHu3+w8sPNx9hXrDDe4/Ja2WJlJ6pKe75PL99+GTT+DwYbj6avdvEbjZN9avd2UkpUGVKm4612xZWbBtmzcRb9HCu2/ePLe4Te4FbsCVzvTv7y0T7N/fDR594AFXVqKFbkRKNc3jXZpcfjm88IJ7ftNNbhBTAf7v/HgqR0ewYkcSCzYeKP445s51dZH//jcAZ3/zDbzxhpv68PnnvYs9iEhwWrAAbrzRjSnp0wcmTXJJd8uW3t5ucD3EpSXpLkhIiBskesklcO+9bhBktv/+130YefRRuOoq97sJD3eztGTPogLu3+1Nm+Bvf1PSLVIGqMe7tLntNleH+MQTrvc7MdHVVOYSGR7KsM71eObLdUyYt4nOjYppcFBmppvq6uGHXXL9yy+Qns7eHj1oBm7fXXfBihVuxTX9JyMSHDIyXClJuXLu9dy58Prr7nnTpu7brAED3HNx6td3j/79vduyp000xn0rCO559u9VREo99XiXRo8/7uoGjx/3LiaRz5COdYkKD2Xeun2s3nnkzN9z3z43YGrUKPf6oYfcTAbh4a5X6NFH3Yj9yEj3H/ZFF/1xSi8RKTmystwH9xEjXOna88979119tXcw4OrVri5ZSfephYe731NRB2CKSKmhxLs0MgZeew2++cbVVhegYnQEV3Vw01NNnLfxzN5v/nw3NdcXX7gayM8/d73eofnmCR84EL7/3v0nPn++64kvzUs8iwSrL790qyL26OFmJtm71829na1RI3jkETdgUERECk2Jd2kVHg7du3tfr1sHKSl5mtxwYX1CQwwzl+9i+6Fjp/9eTz/tyls6d4Zly04+gPL882HxYrjgAjf7Sq1ap/++IlK8Vqxwc2T37u3msq5Z043VWLYMPv440NGJiAQ9Jd5lwcKF0LGjq8FMT8/ZHF+xHJe2qkFmluX17387/fO//rrr/UpMdMn0qdSo4drOmOGt8z5+3H21LSKBs2GD++aqfHk3r/WGDTB6tJtzWysmioicMSXeZUHlyq7sY84cN11VrllFhndrCMC7P27j0NG0wp1v6VIYNMibxFeu7Oo9w8MLH1NkpDsO3KDM//s/V4qSvaqdiPjeoUNu5o1s/fvDs8+6hWP+9S+IigpcbCIipZAS77LgnHNg1iz3n+ibb7qBjx7NasbRrXFVjqdn8vbCLSc/j7Wu3rNzZ3jnnT+u4Ha61q2D776DDz9085Fv3Vo85xWRgqWmwtixblXGAQPc30Fwvdp33eX9UCwiIsVKiXdZccEFbpGL0FA3rd+ECTm7bunmlkR+84fNpKZnFnx8Sgpcc42b4SAtzS2NfNttxRNb06auHKZRI1dX2r49/PBD8ZxbRLyysuDtt+Hcc+Gee9zc2926qcxLRMRPlHiXJX37ehPu226DTz8FoFPDyrSsVZ6DR9MKrvVeudIlw9OmQXQ0TJ0KL7/sykWKS9OmsGgR9OrlZlDo0cP1zotI8cieqWToUPetUsuWrvxs7lxNbyci4idKvMuaG290c20b43q7AGMM/+jdGIBnvlzHwk25VrNcuRI6dIBff3VThy1Z4uq7faFSJZcI/O1vrlf9uus0k4JIcZk40X2jFB/vPtQuW+aWdNegSRERv1HiXRY99BD89BNce23Oph7nns0t3RuSmWW5fdpP7E5KdTuaNXOL3Qwd6nqkfd0zFhbmascnTnTTml16qW/fT6S02rLFfXDONnq0m6lk3ToYNuyP8+yLiIjPacn4ssgYaNXK+3r5cqhShbt7N2b59sP8sPEAt01awDu3dyciLMQNeoyI8G/P2E03ud757Pc8dAj273cDRUWCWb9+kJwMdev+8VG7tneKzdN18KBLsl94wS1stWCB+3t0zjluphIREQkYJd5l3cKFrme5fn3C5s3jv7E76JtylKW7YfSslYzq3+rME4HTlZ10Z2S4mReWLIH33jv5Aj0iJd3337sPkgW57z544gn3fNUqmDz5j8l5bGyBh4akpbnFrEaPzikjo1EjN0VnTIwPLkRERIpKiXdZ16gRVKsGv/wCrVtTZfNmXq55LgOvGcObC7fRpl5lLjsvwKtLpqW5xOHwYejTB555Bu64Q7WpUvKlpsLf/w5XXulKtsB92N2yxT02b/Y+37LFTe+XbfFiGDPmj+esWNEl4N9+C3FxbtuUKXS4+27Ys8e9vugid2zbtj69PBERKRol3mVdlSrwv/9Bp04uCQgLo+2/RvBQu1aMnLGK+z5aQZPqcZxbveBeNr8oVw4++gj+8x83FeJdd7mlrV96KXC98SKnsmmTS7iXLYPZs90qkBER0Lixe5xK27buz3vuxHzLFtdbnpqat+f76aeJ3LPHlZCNGeO+FdIHUxGREkeJt0D9+m5Kseeec7XVF1zANdby07bDfLJsB7dOWcr027sQG1mElSmLW0gIPPootGjhZjt5/XW34t6oUW4WFHA9hC+95HoBcz/Kl3c/e/XyJuopKe55UVbbFCmsGTPcgOSkJPf3K3ucRFG0apV3LAa4Raz27nU927kT6w4dWNOnD00ff1yDJkVESjAl3uK0aAGvvZbz0hjD6L+2ZM2uI/y6O5m7P/iFCdecjwl0L9rAga485sorXQ99RoZ337p18NZbJz42KcmbePfrB99841bzzJ+k9+kDd9/t08soFgcOuEVQrrzS1ekr4Qq8jAx44AFviUi/fm7qvooVi+f8xrjSsGrV8m5/9VX2JCbSVH8GRERKNCXeckJREaFMuOZ8Ln3xe/63ag+vzNvELd0bBjostwjI+vWu5y862ru9Y0d44w04csT7SEryPs89wMxal6geP+4e2bWxkLfONi3NrepXnIsFna6UFPjqK7jsMve6cmU3I82kSW5u5muvheuvdz2sEhiDBsEHH7g/W0884T7ABfrDqoiIlBhKvOWk6lWJ5pkB53HT5CWM+fxXWsWXp3PDKoEOy833XbNm3m0NG7pHYXzzjUu+jx/Pm6jv2JG3/vbDD+H222HwYJfUtmlTfNdQGNa6wXivv+5mdElJcbNdNGvmPhDcdhs8/jhs3OjqgR97DHr2hBtugMsvLxkfGMqSm2920/dNmwZduwY6GhERKWG0gI6c0p+aVWNEQkOyLPxt2jJ2JR0PdEjFwxg3cLN6dZdst2vnepObNvW2yZ767cUX3WC3tm3d84MHfRvbnj0wdqxbLbRzZ5d4p6S458nJrk1IiKt3X78eEhPhmmtcov311+6DwqpVvo0x0DIz3bzUFSvCkCHw3Xfug4o/ZWXB/Pne1xdd5O6Hkm4RESmAEm8plH/2PpcLG1XhwNE0Rkz9ibSMrECH5B8vv+xmpfjb31yCl/28Zk0YOdI375mW5lYIveceWLPG1fPee697Pn8+XHBB3vbGQPfu8PbbsGuXG2A6eHDeqeRuvRUmTHClN6XFo4+6WurDh2HKFOjWzX1Qee45338wAvce/fq5JHvuXO92fcsgIiIn4LfE2xhTyRjziTHmqDFmizFm0Ana3WOMWWmMSTbG/GaMuSff/s3GmOPGmBTP4wv/XEHZFhpieP6q86hZPpJlWw/z+OzVgQ7Jf847zy1jv3MnvPuum6otLQ3OPtvbZtcuN9jzdKxdCw8+6KaIAzf7xcCBLqmbPh22bYOnnnLJ+KlUqAAjRrhENLu2eO1al3TfeivUqOGWC583z/+9w8XtzjvdNwAffOAGNFav7j6c/P3vrjzIl5YscR9sZs92v/Pcg3xFREROwJ893i8BaUA1YDAw3hjTvIB2BhgKVAQuBm43xlyVr82l1toYz0PLGPpJ5ZizePma84kIDeGtBVv4dNmOQIfkX5GRLiH+3//gt99cApvt+efdoMw//QneecebRJ9ISoobCHrhhS6hfvxx+PRT7/7x413S3a/fmU95WLcuTJ0KPXq4mvbJk10P+bnnwpNPutr2YPHtt5Ce7p5XrOhKga680tW2b90KH3/sZni56SbvMYmJxdcLbq27N126uDm127eHn36Ciy8+83OLiEip55fE2xgTDVwBjLTWplhrvwdmAEPyt7XWjrHW/mStzbDWrgWmA138Eaec2nm1K/DQpc0AuO/j5fy6O4iStuJUt6531UCA3393PdVz57qZLWrUcL2uy5Z521gLP/zgBj5Wr+5+zp/vZma5/no3YDJbcc6EERnpYvr6a7eIy/33u1KZ9evhkUfy9nyX1F7wrCxXWpKQ4BZQypb79xQeDn/9K3z+ufuQkW3cONcLXrOmqwX//vvTu86UFFdHP2KE+8bjtttcXXm9eqd7VSIiUsb4q8e7MZBhrV2Xa9svQEE93jmMmzS6K5B/lNhUY8w+Y8wXxpjWxRuqnMrgC+pwedtapKZnccvbSzmSmh7okALv2We99dVt27q64+znDz7obXfrra6n++hR19v9xhuwe7cbPJl/sRRfaNjQ9a5v2QKzZrkSlvLl3b60NFdW8+9/E7lrl+9jKayUFBgwAB56yCXadeoULXG+8UbXC56W5kpwunZ189Y//3zResGTk910jtHR7luNF1/UyqkiIlIkxvqhh8sY0xX4wFpbPde2m4DB1tqEkxz3MNAf6GCt/d2zrQvwE64k5U7Po4m19nABxw8HhgNUq1bt/HfffbfYrqms+z3T8tjCVLYlZ9Hm7FD+1uYsQk7QS5uSkkJM7jm0y4CYDRuo/tlnVJs7l5WPPkpSa/f5sNrnnxO9dSu7Lr6Y43XqBDjKvCr/8AMtH3gAgKywMHb268eWoUNJz07MAyBy505ajBxJzKZNZERHs/rBBznYsePpnWvXLmrMnk31OXM4y5Nwb7rxRrYOHnzyA63N6VmPW7GCjLg4jtWte1ox+FJZ/HsWzHS/go/uWfAJ1D3r0aPHUmttu4L2+SvxbgPMt9aWy7Xtn0CCtfbSExxzO/BPoKu1dvtJzv0rcI+1dubJYmjXrp1dsmTJacUvBdty4Ch9X/ie5NQM7r34XEYkNCqwXWJiIgkJCf4NrqRITXW9osGwiIq1rvRlwgTstGkYa105zb//7QYyRkX5N56vvnI93QcPunr06dPdzzOVng4zZ7qVWt94w5X9gHuenOyWeq9Y0ZUP/f3vbhDtqFFn/r4+Vqb/ngUh3a/go3sWfAJ1z4wxJ0y8/VVqsg4IM8ack2tba/5YQgKAMeZ64D7gopMl3R4W1/stfla3cjTPDTwPgLH/W8v8DfsDHFEJFBkZHEk3uDgvvBCmTGHJq6+6AYNHjrjEu3cAxjC/9JJLui+5BBYtKp6kG1wt+OWXw2efeZPuzExX737XXa4WfNgw97sYP96V4+zcWTzvLSIiZZpfEm9r7VHgY+ARY0y0p1zkMuDt/G2NMYOB0cCfrLWb8u2rY4zpYoyJMMZEeqYarALMz38e8Y+Lmlbjbz0bucV13lnGzsOlZHGdMu5ow4YwZw58+aWr+77lFu9Of02d9+abbhGh6dO9dei+NG6c+4CRmupmflmyxA2c/O67P66SKiIichr8OZ3gCCAK2Au8A9xqrV1ljOlqjEnJ1e4xoDKwONdc3RM8+2KB8cAhYAduusE+1toDfrsK+YO7ejWm6zlVOOhZXOf3jMxAhyTFpVcvWLoUrr7au+2++1yC+vPPxfteO3e6GUN+/929jouDf/4TQkOL930KEhoKV1zhporcuNHN/DJihLv2dgV+WygiIlJkYf56I2vtQdxAyfzbvwNicr2uf5JzrAL8MPWDFIVbXKcNl77wPT9vO8xjs9bwaP8WgQ5LiktIrs/nx465FTL37nUztgwZ4qb5O9OBogsXuvKPXbsgNtaVdwRKgwZu5hcREZFipiXjpVhUio7g5cFtiQgN4e2FW/j4p1OV5ktQKlcOVq92tdBhYa4ko3Fj+Ne/3BSKp2PSJLegz65dbtn3u+8u3phFRERKCCXeUmxa167AqH5uavb7P1nBml1ldHGd0q5yZTdv+a+/wlVXudKQMWPcHOG7dxf+PBkZLoG//no3x/aIEW4BoqpVfRe7iIhIACnxlmJ1dYfa/N/58W5xnSlLSTquxXVKrQYN3EIyixa5nurOnb2zhMDJF7lJSXGL2jz/vJtlZOJEN4tJeLjv4xYREQkQJd5SrIwxPNq/Bc1rxrHlwDH++f7PZJXUZcileHToAImJLgnPtngxdOoE8+YVfEy5cm6u7GrV4Jtv4Kab/BKqiIhIICnxlmIXGR7K+MHnExcZxtw1e5m9Sb3epZ4xkHt1sDFjXE949+5w2WWwZo3bnj1jSUiImy5wyRLo0sXv4YqIiASCEm/xiTqVy/H8VW0A+Hh9OrOX7wpwROJXkybBww9DdDTMmAEtW7rSkq5d4bhnrveYGIiPD2ycIiIifqTEW3ymR5OzuefP52KBO99dxje/7g10SOIvMTHw0EOwYYN38Z0vvnDzYicmBjQ0ERGRQFHiLT41IqEhF9cLIyPLcsuUpSzYqLWOypTq1d2y6ytWwJ13upUw+/QJdFQiIiIBocRbfMoYw8BzIxh0QR1+z8jixrcWs2zroUCHJf7WtCk83EOWogAAIABJREFU9xz07BnoSERERAJGibf4nDGGxy5rwV/b1OJoWibD3viR1Ts1x7eIiIiULUq8xS9CQgxPX9mKPzevxpHUDIa8voiN+1ICHZaIiIiI3xQ68TbG9DDG1Pc8r2GMecsYM8kYU/1Ux4oAhIWG8N+r29CtcVUOHE3jmtcWse3gsUCHJSIiIuIXRenxfhnI9DwfB4QDWcDE4g5KSq+zwkJ55Zrz6VCvEruSUhn82iL2HEkNdFgiIiIiPleUxLuWtXarMSYM+DMwHLgV6OyTyKTUiooI5fVr29EqvjxbDx5j8GuLOJDye6DDEhEREfGpoiTeR4wx1YDuwGprbXaBbnjxhyWlXWxkOG9d14Fzq8WyYW8KQ9/4kaTjWuFSRERESq+iJN4vAIuBqcBLnm1dgF+LOygpGypGR/D2jR2oV7kcq3Ye4fo3F3MsLSPQYYmIiIj4RKETb2vtU0AvoIu19l3P5h3Ajb4ITMqGs2MjmXpTR2pViGLplkPcNHkJqemZpz5QREREJMgUaTpBa+06a+1GcLOcADWstSt8EpmUGbUqRDHlxguoEnMW8zcc4PZpP5GemRXosERERESKVVGmE/zWGNPF8/xfwLvANGPM/b4KTsqO+lWimXrjBVQoF87cNXv5x/u/kJllAx2WiIiISLEpSo93C2Ch5/lNQA+gI3BLcQclZdO51WOZfH0HYs4KY+YvO7n/4xVkKfkWERGRUqIoiXcIYI0xDQFjrV1trd0GVPRNaFIWtYqvwBvXticyPIT3lmzj0dmrsVbJt4iIiAS/oiTe3wMvAmOBTwA8Sfh+H8QlZViH+pV4ZUg7IkJDmDR/M898uS7QIYmIiIicsaIk3tcCh4HlwCjPtibA88Ubkgh0b1yV/17dhtAQwwtfb2DCtxsDHZKIiIjIGSnKdIIHrLX3W2v/k714jrV2trX2Od+FJ2XZxS2qM+7/WmMMPDnnV95esDnQIYmIiIictqLMahJujHnYGLPJGJPq+fmwMSbClwFK2da/TS0e798SgJHTV/HR0u0BjkhERETk9BSl1GQMbgGdW4DWnp89gad8EJdIjkEX1OGBvzQF+P/27jw+qvre//jrMzPZQwiQEGSTXRDKGlDxgsGlaq87aq1eK7VVq9drr3pt7/21Xnu93vZ2tbW1ttbdtu5Y9bpiNQpolR0Jsi+yBEhYspFtZr6/P2YShhCWQDJnJryfj8c8MvM9Z04+k68H3/nme76Hu15cwpuflXpckYiIiEjbtSV4XwFc5Jx7xzm30jn3DnApcGXHlCayzw1TB/Gds4YSdnDbs4t4f+UOr0sSERERaZO2BG9rY7tIu/rXs4fyrX8YSGPI8e2nF/Dx2p1elyQiIiJyxNoSvF8AXjOzc81shJmdB/wVeL5jShPZn5nx/X8cwdcm9ac+GObrj33Cg++vIajby4uIiEgSaEvw/i7wLvAgsAD4DfA+0NABdYm0ysy475JRzJg8gMaQ42dvr2T67z9mzY5qr0sTEREROaS2LCfY4Jz7T+fcEOdcpnNuKPA/wJ0dV57Igfw+44cXjeTpb06id9d0lmzawz8+MJtHZq8jpFvMi4iISIJqy4h3axya4y0emTI0n7dun8qVhX2pD4a57/XPuerhj9m4s8br0kREREQOcKzBGyLhW8QTOekp/PTyMTw2o5D8LmnM27Cb8341m6c+3kBYo98iIiKSQAKH28HMzjzEZt08RxLCmcMLmHV7N+55tYRXFm/lP18p4a1l2/jp5aPp2y3T6/JEREREDh+8gUcPs/2L9ihE5FjlZqby66vGcf6oXnz/5WV8tHYn5/1qNj/4xxF8dWI/zDQrSkRERLxz2ODtnBsYj0JE2st5o06gcEB37v7rMt5cto1/n/kZb5Vs438vG02vrulelyciIiLHqfaY4y2ScPKy0/jdNeP59VVj6ZqRQvHKMr58/wfMXLgZ5zT3W0REROJPwVs6LTPj4rF9mHX7VM4a3pPKuiB3PL+EG59eQFlVvdfliYiIyHFGwVs6vZ456TxyXSE/vXw0XdICzFq+nS/f/wGvLy31ujQRERE5jsQteJtZdzN72cxqzGyjmV19kP3uMrNlZlZlZuvN7K4W2weY2ftmttfMVpjZ2fH5BJLMzIwrC/vx9u1TmTI0j917G/nnvyzk1r8sZFeNbr4qIiIiHS+eI94PErm9fAFwDfCQmY1sZT8Dvg50A84DbjWzq2K2PwMsAnoA3wdeNLP8jixcOo/euRk8df0k7rtkFJmpfv5vaSlfvv9D3inZ5nVpIiIi0snFJXibWRYwHbjbOVftnJsDvApc23Jf59xPnXMLnXNB59xK4BXg9OhxhgHjgXucc7XOuZeAz6LHFjkiZsY/nXoib31nKqcM7E55dT03Pr2AO55fTEVto9fliYiISCcVrxHvYUDQObcqpm0J0NqIdzOLLLw8BSiJNo0E1jnnqtpyHJHW9O+RyTM3nMp/XnAyaQEfMxdu4dz7P+SXs1bxyuItLN28h8o6BXERERFpHxaPpdXMbArwgnOuV0zbDcA1zrmiQ7zvv4BLgEnOuXozuxb4Z+fcqTH7/A/Qxzk3o5X33wjcCFBQUDDh2WefbadPJG1RXV1Ndna212Uc0raaMH9cWs/aivAB23JSoVeWj4JMHwVZRq9MH72yfPTMNFL9nfOmPMnQZ7I/9VlyUX8lH/VZ8vGqz6ZNm7bAOVfY2rYjuXNle6gGclq05QBVrewLgJndSmSu9xTnXNPab206jnPuYeBhgMLCQldUVNTmwuXYFRcXkww/+yvOd7y5rJTlWytZX17D+vIaNuysobIhTGVDmFW79w/lZtC7awYD87IOePTtlkHAn7yLBiVLn8k+6rPkov5KPuqz5JOIfRav4L0KCJjZUOfc6mjbGPZNIdmPmV0P/Dsw1Tm3OWZTCTDIzLrETDcZA/ylg+qW44jfZ1wwujcXjO7d3BYOO7ZV1jUH8djHpl172bKnli17apmzpny/YwV8Rv/umc1BfGhBNheO6U1marxOOREREUk0cUkBzrkaM5sJ3Gtm3wLGAhcDk1vua2bXAD8Cpjnn1rU4ziozWwzcY2Y/AM4HRqOLK6WD+HxG79wMeudmcPqQvP22NYbCbN5dy/ryataVRUbH15fXsL6shq0Vdawrr2FdeU3z/g8Vr+VnV4xh4oDu8f4YIiIikgDiOfx2C/AYsAPYCdzsnCuJzv9+0znXNAnnPiJLBc6LXFsJwJ+cc9+OPr8KeALYDXwBXO6cK4vPRxDZJ8Xvax7RPnP4/ttqG0Js3BUJ4evKa3htyVZWbKviyj98zDdPH8i/nXsS6Sl+bwoXERERT8QteDvndhG5ULJl+2wgO+b1wMMcZwNQ1M7libSrjFQ/w3vlMLxX5JKEG6YM4oG/reahD9byyJz1vL9yBz+/Ygzj+nfzuFIRERGJl+S9+kskiaQGfPzbuScx8+bJDOmZzdqyGqY/9BE/eWsF9cGQ1+WJiIhIHCh4i8TRmH65/N+//AM3TR2EIzLv+6LfzGXZlgqvSxMREZEOpuAtEmfpKX7+4ysjePHbpzEwL4uV26u45MG53D9rFQ3BA9cRFxERkc5BwVvEIxNO7M4bt01hxuQBBMOOX/9tNZc8OJcV2yq9Lk1EREQ6gIK3iIcyUv388KKRPHvjqfTrnsHy0kou/M0cHnx/DcGQRr9FREQ6EwVvkQRw6qAevPWdqVxzSn8aQ46fvb2S6b//mDU7DnpzVxEREUkyCt4iCSIrLcD/XPolnv7mJHp3TWfJpj185YE5PPzhWkJh53V5IiIicowUvEUSzJSh+bx1+1SuLOxLQzDMj95YwVf/8DHrY+6CKSIiIslHwVskAeWkp/DTy8fw+IyJFOSkMX/jbs7/9Yc8Pnc9YY1+i4iIJCUFb5EENm14T9751zO4dFwf6hrD/Ndry7n6kb+zadder0sTERGRNlLwFklwXTNTuP+rY/nDtRPIy07l7+t2cd6vPuTPn2zEubaPfofDjvpgiJr6IBV7Gymrqqe0opZNu/ayrqyaVdurqA9pVF1ERKS9BbwuQESOzLkjezFxQHfufmUZry8t5fsvL+PpjzeSk55CYzhMYyhMMORoDIVpDDmCoTCN4ejXaHsw7I7oQs0+2cY50xw+n8Xhk4mIiBwfFLxFkkj3rFQevHo854/ayt1/XcaKbUe33GCK3wj4fAT8Rqo/8jXg85HiN7ZX1rOlOsQn63dx2uAe7fwJREREjl8K3iJJ6ILRvZkyNJ+SLRX4fUbAHwnNKdGvTaE68jr63NcUsA2zg49k//ztlfz2/TW8tHCzgreIiEg7UvAWSVJdM1KYPCSv3Y976fg+/Pb9Nbz5WSn3XjySzFT9MyEiItIedHGliOxncH42g7v6qGkI8XbJNq/LERER6TQUvEXkAKf3iYxyv7Rgi8eViIiIdB4K3iJygFNOCJAa8DF3bTlb99R6XY6IiEinoOAtIgfISjHOGVGAc/DyIo16i4iItAcFbxFp1fQJfQB4aeHmo7pRj4iIiOxPwVtEWjVlaD552amsK6th8aY9XpcjIiKS9BS8RaRVKX4fF4+NjHrPXKjpJiIiIsdKwVtEDmr6+L4AvLpkK/XBkMfViIiIJDcFbxE5qJN75zDihBwqaht57/MdXpcjIiKS1BS8ReSQpo/fd5GliIiIHD0FbxE5pIvH9sHvM4pXllFeXe91OSIiIklLwVtEDim/SxpnDMsnGHa8unir1+WIiIgkLQVvETmspossNd1ERETk6Cl4i8hhnTWiJznpAUq2VrJiW6XX5YiIiCQlBW8ROaz0FD8XjukNwEsLNOotIiJyNBS8ReSIXBadbvLyoq0EQ2GPqxEREUk+Ct4ickTG989lYF4W5dX1zF5d7nU5IiIiSUfBW0SOiJlpTW8REZFjoOAtIkfs0vF9MYN3lm+norbR63JERESSioK3iByxPrkZnDaoBw3BMK8vLfW6HBERkaSi4C0ibaI1vUVERI6OgreItMl5o3qRmepnwcbdrC+v8bocERGRpKHgLSJtkpUW4LxRvQCYqVFvERGRIxa34G1m3c3sZTOrMbONZnb1QfabZmbvm1mFmW1oZfsGM6s1s+ro450OL15E9nN5dLrJzIVbCIedx9WIiIgkh3iOeD8INAAFwDXAQ2Y2spX9aoDHgLsOcawLnXPZ0ceX279UETmUUwf1oE9uBlv21PLJ+l1elyMiIpIU4hK8zSwLmA7c7Zyrds7NAV4Frm25r3PuU+fc08C6eNQmIm3n8xmXjtOa3iIiIm0RrxHvYUDQObcqpm0J0NqI95H4s5mVmdk7Zjbm2MsTkba6NHoznTc/K2VvQ9DjakRERBJfIE7fJxuobNFWAXQ5imNdAywEDPgO8LaZDXfO7Wm5o5ndCNwIUFBQQHFx8VF8OzlW1dXV+tknmSPts8FdfaytCHH/C+9zep+Uji9MDkrnWXJRfyUf9VnyScQ+i1fwrgZyWrTlAFVtPZBzbm7Myx+b2XXAFOC1VvZ9GHgYoLCw0BUVFbX120k7KC4uRj/75HKkfbY5fSM/+OsyPq/N4ftFp3R8YXJQOs+Si/or+ajPkk8i9lm8ppqsAgJmNjSmbQxQ0g7HdkRGv0Ukzi4c3ZvUgI+5a8vZuqfW63JEREQSWlyCt3OuBpgJ3GtmWWZ2OnAx8HTLfc3MZ2bpQErkpaWbWWp0W38zO93MUqPtdwF5wNyWxxGRjtc1M4VzRhTgHLy8aIvX5YiIiCS0eC4neAuQAewAngFuds6VmNkUM6uO2W8qUAu8AfSPPm9aq7sL8BCwG9gCnAec75zbGZ+PICItXTZ+3+omzmlNbxERkYOJ1xxvnHO7gEtaaZ9N5OLLptfFHGTqiHOuBBjdQSWKyFGYOiyfvOxU1pXVsHjTHsb17+Z1SSIiIglJt4wXkWOS4vdx8Vit6S0iInI4Ct4icsymR28h/9qSUuqDIY+rERERSUwK3iJyzE7uncOIE3KoqG3kvc93eF2OiIhIQlLwFpF2MX28ppuIiIgcioK3iLSLi8f2we8zileWUV5d73U5IiIiCUfBW0TaRX6XNM4Ylk8w7Hhl8VavyxEREUk4Ct4i0m6aLrJ8aYGmm4iIiLSk4C0i7easET3JSQ+wvLSSz0srvS5HREQkoSh4i0i7SU/xc+GY3gDM1EWWIiIi+1HwFpF2dVl0usnLi7YSDIU9rkZERCRxKHiLSLsa3z+XgXlZlFfXM3t1udfliIiIJAwFbxFpV2bWvKb3i5puIiIi0kzBW0Ta3aXj+2IGs5Zvp6K20etyREREEoKCt4i0uz65GZw2qAcNwTCvLy31uhwREZGEoOAtIh2i6SJL3UJeREQkQsFbRDrE+aN6kZnqZ8HG3awvr/G6HBEREc8peItIh8hKC3DeqF6A1vQWEREBBW8R6UCXR6ebzFy4hXDYeVyNiIiItxS8RaTDnDqoB727prNlTy2frN/ldTkiIiKeUvAWkQ7j8xmXRtf01kWWIiJyvFPwFpEO1bS6yZuflbK3IehxNSIiIt5R8BaRDjU4P5tx/XOpaQjx1rJtXpcjIiLiGQVvEelwl0+IjHr/7O2VbN1T63E1IiIi3lDwFpEON318XyYO6EZpRR3XPfYpFXt1G3kRETn+KHiLSIdLT/HzyNcnMqwgm9U7qvnWU/Ooawx5XZaIiEhcKXiLSFx0zUzhiW9M4oSu6czbsJvbnllESGt7i4jIcUTBW0TipnduBk9eP4mc9ADvLN/O3a8swzmFbxEROT4oeItIXA0r6MKjMyaSFvDxl0++4IG/rfG6JBERkbhQ8BaRuJs4oDsPfG0cPoP7313FM59+4XVJIiIiHU7BW0Q8ce7IXtx78SgAvv/yZ8xavt3jikRERDqWgreIeOafTj2R284aStjBrX9ZyIKNu7wuSUREpMMoeIuIp24/eyhXTexHfTDM9U/MZ82OKq9LEhER6RAK3iLiKTPjvktGcfaInlTUNvL1Rz9lW0Wd12WJiIi0OwVvEfFcwO/jN18bz/j+uWxturtlre5uKSIinYuCt4gkhIxUP49eN5HB+Vms3F7FDU/N190tRUSkU1HwFpGE0S0rlae+eQoFOWl8un4Xtz+3WHe3FBGRTkPBW0QSSp/o3S27pAd4c9k2/uu1Et3dUkREOgUFbxFJOMN75fDHrxeSGvDx1Mcb+V3xWq9LEhEROWYK3iKSkE4d1INff3UsZvCzt1fy/PxNXpckIiJyTOIWvM2su5m9bGY1ZrbRzK4+yH7TzOx9M6swsw2tbB8Q3b7XzFaY2dkdXryIeOL8L53AvReNBOA/Zn7Geyt0d0sREUle8RzxfhBoAAqAa4CHzGxkK/vVAI8Bdx3kOM8Ai4AewPeBF80sv/3LFZFEcO1pA7h12hBCYcctf17Ioi92e12SiIjIUYlL8DazLGA6cLdzrto5Nwd4Fbi25b7OuU+dc08D61o5zjBgPHCPc67WOfcS8Fn02CLSSd355WFcWdiXusYw1z8xj7Vl1V6XJCIi0mYWj9UCzGwcMNc5lxnT9m/AGc65Cw/ynrOBR5xzA2LaLgV+5JwbEdP2W8A55/6llWPcCNwIUFBQMOHZZ59tp08kbVFdXU12drbXZUgbJGKfhcKOBxbVs6QsRI904wenptMtXZepNEnEPpODU38lH/VZ8vGqz6ZNm7bAOVfY2rZAnGrIBipbtFUAXY7iOBWtHKdPazs75x4GHgYoLCx0RUVFbfx20h6Ki4vRzz65JGqfnfYPQa7+4ycs3rSHh1em8NxNp5KTnuJ1WQkhUftMWqf+Sj7qs+STiH0Wr+GiaiCnRVsOUOXRcUQkCWWmBnhsxkQG5WXxeWklNz21gPqg7m4pIiLJIV7BexUQMLOhMW1jgJI2HqcEGGRmsSPlR3McEUlS3bNSefL6SeR3SePjdTu54/klVNU1el2WiIjIYcUleDvnaoCZwL1mlmVmpwMXA0+33NfMfGaWDqREXlq6maVGj7MKWAzcE22/FBgNvBSPzyEiiaFf90ye/MYkuqQFeH1pKWPvncWlv5vLz99eyUdry6lr1Ci4iIgknnjN8Qa4hcgygTuAncDNzrkSM5sCvOmca5r9PhV4P+Z9tcAHQFH09VXAE8Bu4AvgcudcWYdXLyIJ5eTeOTxx/UR+9MYKFm/aw6IvIo/fvr+GtICPwgHdmDw4j8mDe/ClPl0J+HUhpoiIeCtuwds5twu4pJX22UQummx6XQzYIY6zgX0hXESOYxNO7M5LN0+muj7Ip+t3MnfNTj5au5PPSyuZuybyGqBLWoBTBvVg8uAenD4kj2EF2Zgd9J8ZERGRDhHPEW8RkQ6RnRbgzOEFnDm8AICd1fV8vC4Swj9aU86GnXt59/PtvPt55M6XedmpnDY4j9OjQbxf98xDHV5ERKRdKHiLSKfTIzuNC0b35oLRvQHYsqeWj9aU89HancxdU86OqnpeW7KV15ZsBaBvtwxOH5zH5CE9OG1wD3p2SfeyfBER6aQUvEWk0+uTm8EVhf24orAfzjnWltXw0dpy5q4p5+O1O9m8u5bn5m/iufmbABhWkM15o07gtjOHaG64iIi0GwVvETmumBlDemYzpGc2Xz9tAKGwY/nWSuaujYyIz1u/i1Xbq1m1fTXl1fX8zyWjNB9cRETahYK3iBzX/D7jS3278qW+Xfn2GYOpD4YoXlnGbc8s4i+ffEGf3Az+edoQr8sUEZFOQH9DFRGJkRbwc+7IXvz6qrGYwc/eXslLCzZ7XZaIiHQCCt4iIq04b9QJ3HPByQB876WlzF6t2wWIiMixUfAWETmIGacP5KapgwiGHTf/aSElWyu8LklERJKYgreIyCF877zhXDimN9X1Qb7x+Dw2797rdUkiIpKkFLxFRA7B5zN+fsVoTh3UnR1V9cx4fB4Vexu9LktERJKQgreIyGGkBfz84dpChhVks2ZHNTc8PZ+6xpDXZYmISJJR8BYROQJdM1J44huT6JWTzqfrd3HnC0sIh53XZYmISBJR8BYROUK9czN44vqJdEkL8PrSUn70xudelyQiIklEwVtEpA2G98rhD9dOIMVvPDJnPY/OWe91SSIikiQUvEVE2mjykDx+dvkYAO57fTmvLy31uCIREUkGCt4iIkfhknF9+N55w3EObn9+MZ+u3+V1SSIikuAUvEVEjtK3zxjEtaeeSEMwzLeenMfq7VVelyQiIglMwVtE5CiZGT+8aCTnnFxAZV2QGY/PY3tlnddliYhIglLwFhE5Bn6f8cBV4xjXP5cte2r5xuPzqKrTDXZERORACt4iIscoI9XPo9dNZGBeFstLK7nlzwtpDIW9LktERBKMgreISDvonpXKk9+YRF52KrNXl/O9l5binG6wIyIi+yh4i4i0k/49Mnn0uolkpPiZuXALv5y1yuuSREQkgSh4i4i0ozH9cvndNePx+4zfvLeGv3zyhdcliYhIglDwFhFpZ9OG9+S+S0YB8IO/fsbfPt/ucUUiIpIIFLxFRDrA1yb157YzhxB2cOtfFrF40x6vSxIREY8peIuIdJDbzxnG5RP6UtsY4ptPzGNDeY3XJYmIiIcUvEVEOoiZ8ePLvsTUYfnsrGlgxuOfsrO63uuyRETEIwreIiIdKMXv43fXjGdk7xw27NzLZQ99xF8XbSEU1lKDIiLHGwVvEZEOlp0W4PEZExnSM5uNO/fyr88t5pz7P+DlRZsJ6kY7IiLHDQVvEZE46JmTzpvfmcJPp4+mf/dM1pXVcPtzS/jy/R8yc6ECuIjI8UDBW0QkTlL8Pq6c2I+/3XkGP708GsDLa7jj+SWcowAuItLpKXiLiMRZit/HlYWRAP6zy0dzYo9M1kcD+Nm//ICXFiiAi4h0RgreIiIeSfH7uKKwH3+74wx+fsUYTuyRyYade7nzhUgAf1EBXESkU1HwFhHxWMDv4/IJffnbHWfwiyvGMCAawP/thSWc9csPeGH+JgVwEZFOQMFbRCRBBPw+pk/oy7t3nMEvrxzDwLwsNu7cy10vLuXMX3zA8/M30agALiKStBS8RUQSTMDv47LxfZl1+9TmAP7Frr1898WlnPWLD3h+ngK4iEgyUvAWEUlQsQH8/q+OYVBTAH9pKWf+opjn5n2hAC4ikkQCXhcgIiKHFvD7uHRcXy4a04fXlmzlgfdWs66shu+99Bm/eW8Nt04bQp7uhCkikvDiNuJtZt3N7GUzqzGzjWZ29UH2MzP7iZntjD5+YmYWs91Fj1EdfTwSr88gIuIlv8+4ZFwfZt1+Br++aiyD8rPYvLuWf5/5Gf8xu5aZCzfrVvQiIgksnlNNHgQagALgGuAhMxvZyn43ApcAY4DRwIXATS32GeOcy44+vtWBNYuIJBy/z7h47P4BvKzWccfzSzj3Vx/yxmelhBXARUQSTlyCt5llAdOBu51z1c65OcCrwLWt7H4d8Avn3Gbn3BbgF8CMeNQpIpJMmgL4O/86lW+OSqVvtwzW7Kjmlj8v5MLfzuH9FTtwTgFcRCRRxGvEexgQdM6timlbArQ24j0yuu1Q+31oZtvMbKaZDWjPQkVEkk3A72NK3xTeu7OI/754JD27pFGytZJvPDGPK37/MX9ft9PrEkVEBLB4jIaY2RTgBedcr5i2G4BrnHNFLfYNASOdcyuir4cCqwCfc86Z2VTg70AmcB9QBIx1zgVb+b43Epm6QkFBwYRnn322Az6dHE51dTXZ2dlelyFtoD5LPrF9Vh9yvPdFkNfXNVDdGNk+soeP6UNTGZTr97BKaaJzLPmoz5KPV302bdq0Bc65wta2xSt4jwPmOucyY9ruBIqccxe22LcCOMc592n09QSg2DnXpZXj+oFK4FTn3GeHqqGwsNDNnz//2D+MtFlxcTFFRUVelyFtoD5LPq31WVVdI4/N2cAjs9dRVR8Zmzjn5ALuOGcYI07I8aBKaaJzLPmoz5KPV31O0X8MAAATAElEQVRmZgcN3vGaarIKCERHr5uMAUpa2bckuu1w+zVxgB1iu4jIcalLegrfOXsos783jZuLBpOR4mfW8u185YHZ/Mszi1hXVu11iSIix5W4BG/nXA0wE7jXzLLM7HTgYuDpVnZ/CrjDzPqYWW/gTuAJADMbaWZjzcxvZtlELrzcAnwej88hIpKMcjNT+d55w/ngu0XMmDyAFJ+P15Zs5Zz7P+S7Ly5h8+69XpcoInJciOdygrcAGcAO4BngZudciZlNMbPYYZc/AK8BnwHLgNejbRBZivA5ItNL1gEDgAucc41x+QQiIkmsZ5d0fnjRSN6/q4irJvYD4Pn5mznz5x9wzyvL2FFV53GFIiKdW9zuXOmc20Vkfe6W7bOB7JjXDvhu9NFy3/eAkzqwTBGRTq9Pbgb/O300N50xmF+9u4pXl2zlyY838tz8TVw3eQDfnjqYblmpXpcpItLpxHPEW0REEsjAvCx+fdU43vzOFL58cgF1jWH+8ME6pv70fX717iqq6vTHRBGR9hS3EW8REUlMw3vl8PDXC1myaQ8/f2cls1eX86t3V/PonPWM69+Nkb1zoo+unNg9E59P17OLiBwNBW8REQFgTL9cnv7mKXyybic/f2cl8zbs5sNVZXy4qqx5n+y0ACNO6MLI3l05ORrIh/bsQmpAf0AVETkcBW8REdnPKYN68PxNp7F5dy0lWyso2VoZfVSwvbKeeRt2M2/D7ub9U/0+hhZkN4+Kj+ydw4gTcshK0/9iRERi6V9FERE5gJnRr3sm/bpnct6oE5rby6rqWV5a2RzIl2+tZH15TXM4h83R98PAHlnRUfGuzdNVemSnefSJRES8p+AtIiJHLL9LGmd0yeeMYfnNbdX1QT4vraRky77R8dU7qlhXXsO68hr+b2lp8769ctIZ2y+XqcPyKTopn965GV58DBERTyh4i4jIMclOCzBxQHcmDuje3FYfDLF6ezXLt8aMjpdWsq2yjrdKtvFWyTYAhhVkU3RST4qG5VM4oLvmiotIp6bgLSIi7S4t4GdUn66M6tMViNysJxx2rN9ZwyfrdlG8cgdz15Szans1q7ZX8/CH68hK9TN5SB5FJ+VTdFJP+mg0XEQ6GQVvERGJC5/PGJyfzeD8bK4+pT8NwTDzN+7ig5VlFK8sY+X2KmYt386s5dsBGNozm6KT8jljWE8mDuxGWsDv8ScQETk2Ct4iIuKJ1ICPyYPzmDw4j//4ygi27qnlw1WRED5nTTmrd1Szekc1f5y9nsxUP5MH9+CM6LSUft0zvS5fRKTNFLxFRCQh9M7N4KpJ/blqUn8aQ2EWbNxN8coyilfuYMW2Kt79fAfvfr4DgMH5WZG54SflM2lgd42Gi0hSUPAWEZGEk+L3ceqgHpw6qAf/fv5wtlXU8cGqHZHR8NXlrC2rYW3Zeh6ds56MFD+nDe7BhBO7MaRnZCrLiT0ySfHrQk0RSSwK3iIikvB6dU3nqxP789WJkdHwRV/soXhlJIgvL63kvRU7eG/Fjub9Az6jf4/M5jnlg/OzGBwN5V0zUjz8JCJyPFPwFhGRpJLi9zFpYHcmDezOd88bzo7KOj5cXc7npZWsLatmbVk1m3fXsq6shnVlNcxi+37vz++SFgniTaG8ZySY9+6agc9nHn0qETkeKHiLiEhS65mTzuUT+u7XVtcYYn15TSSI76hpDuRry6opq6qnrKqev6/btd970lN8DMrbF8QH52czMC+L7LQAAb+R4veR4vdFnvsiXwM+w0xhXUSOjIK3iIh0OukpfkackMOIE3L2aw+HHaWVdazdEQnha3Y0BfIayqrqWV4audFPWwR81hzGUwI+Aj5rDuj7P/eRGn1eW1XH4uAqxvTLZWzfXLplpbbnxxeRBKXgLSIixw2fz+iTm0Gf3Aymxtz2HqCitpF10RAeGSmvZuPOvdQ2hgiGwjSGHcFQmGDI0RiOfA2G9z3qCEP9kdey6N3Vzc9P7JHJmL65kSDerysje3clPUUrtYh0NgreIiIiQNeMFMb178a4/t2O+D3OORpDjmA4HPkaChMMOxqbAnoo3Or22fMWE8rpzZJNFXy2pYKNO/eycedeXl2yFYiMog8/oUtMGM9lcH42fs1BF0lqCt4iIiJHycxIDRiptG3pwsbNAYqKTgYgGAqzekc1izftYcmmPSzetIdV26tYtqWSZVsq+fMnXwCQnRbgS326No+Kj+mXS6+cdM0xF0kiCt4iIiIeCvh9zfPRvzapPwB7G4Is21LJ4k27WbKpgsWb9rBlTy0fr9vJx+t2Nr+3Z5e05hHxsf1yGZiXRXqKn9RAZD55il8Xf4okEgVvERGRBJOZGmheMrFJWVU9SzdHRsUXRUfHd1TVM2v5dmYt397qccwg1e8jNeAjLRrG01L8zW3N7dFtkdf+/drTAj58ZpgR+QrNyy7u1xbdh5jnTfsakb8ORNoMX/R9aSk+MlL8ZKT6yUjxkx7zPDM18jot4NMvD9JpKHiLiIgkgfwuaZw1ooCzRhQAkfnlG3bubZ6esnjTHkoramkIhqkPhmkIRuaT10dfV3lc/9EyIxLOWwTz5q/R5+nR52kpvuZgHxv4fRYJ/C1/AWjaBvvv44u8OfIaY+3mRmqWlpKZ6o8+AmQ0P4/UkOrvPL8kNATDVNY1UlUXpLK2kcq6RmobQvh9ht8XWaXHH13RJ/J6//b922K2+fe99psdd2vnK3iLiIgkITNjYF4WA/OyuGRcn1b3CUUv9KxvDFMfCu0XyhuCYRqi2xpitrW2PeQcOEfYgcPhHM3PcRB2+7dFdnc4iLY3PW/aBiEX+aWgtiFEbWMw+jVMXWMo+jzyaAiG2dsQYm9DKK4/39Y8umzhIbcHfBYTxgPNI/eZaQEyU/YF9MjXAFmpkb8upESnBUWWnvSRGvM8JWYN+djnAZ+R2rR8ZfQvFk2B1syoawxRWddIZW0w+rWRyrogVa20VdY2RtpjQnZdYzguP1MzDgjtsYHd10qobwr7frOY174Dgn5qbQNFRXH5GEdMwVtERKSTiow8+qNLE6Z4Xc5RCYVdJIQ3hCKhvEUwr4s+3xvdXh8MEw5Hgn7Y7f9LQLjpl4fYtlb2bfoFInbfL7aUktMtj72NIWobguxtiNQR+aUg8joYdlTVBamqC9KmtSXbkRn4zQiG3TEdx+8zctID5GSkkJOeQk5G5BeJsINg2BGKLqkZii6nue9r5C8t4QPaI6v6tNzfOWgMRVYHgvYN+yf3aNtFz/Gg4C0iIiIJy+8zstMCZKd5G1mKi3dRVDThkPs0REfw9za2HswjbTHPoyP6DaFwZK34kNvveWQ5yn1LUTaEmpaqPHDfhui+zkHQOVL8RteMFLqkpxwQoCNfI+1dDmjbF7I7etqMc5EAHnKOcBiC4QODeSi6HGfYRdfMPyDshw/63s1rPu/Q+o+GgreIiIhIO2i6YLWrh39daAqjyTDf3CwyTWRfGG3fm0YV717VrsdrDwreIiIiIp1E0/QiSUyJN/lFRERERKQTUvAWEREREYkDBW8RERERkThQ8BYRERERiQMFbxERERGROFDwFhERERGJAwVvEREREZE4UPAWEREREYkDBW8RERERkThQ8BYRERERiQMFbxERERGROIhb8Daz7mb2spnVmNlGM7v6IPuZmf3EzHZGHz8xM4vZPtbMFpjZ3ujXsfH6DCIiIiIiRyueI94PAg1AAXAN8JCZjWxlvxuBS4AxwGjgQuAmADNLBV4B/gR0A54EXom2i4iIiIgkrLgEbzPLAqYDdzvnqp1zc4BXgWtb2f064BfOuc3OuS3AL4AZ0W1FQAD4lXOu3jn3AGDAmR38EUREREREjkm8RryHAUHn3KqYtiVAayPeI6PbWttvJLDUOediti89yHFERERERBJGIE7fJxuobNFWAXQ5yL4VLfbLjs7zbrntUMfBzG4kMnWFgoICiouL21y4HLvq6mr97JOM+iz5qM+Si/or+ajPkk8i9lm8gnc1kNOiLQeoOoJ9c4Bq55wzs7YcB+fcw8DDAIWFha6oqKjtlcsxKy4uRj/75KI+Sz7qs+Si/ko+6rPkk4h9Fq/gvQoImNlQ59zqaNsYoKSVfUui2z5tZb8S4E4zs5jpJqOJXLh5SAsWLCg3s41H+wHkmOQB5V4XIW2iPks+6rPkov5KPuqz5ONVn514sA1xCd7OuRozmwnca2bfAsYCFwOTW9n9KeAOM3sDcMCdwG+i24qBEHCbmf0euCHa/t4R1JB/TB9CjpqZzXfOFXpdhxw59VnyUZ8lF/VX8lGfJZ9E7LN4Lid4C5AB7ACeAW52zpWY2ZToFJImfwBeAz4DlgGvR9twzjUQWWrw68Ae4Hrgkmi7iIiIiEjCitdUE5xzu4iE5pbts4lcNNn02gHfjT5aO84iYEIHlSkiIiIi0iF0y3iJh4e9LkDaTH2WfNRnyUX9lXzUZ8kn4frM9l8SW0REREREOoJGvEVERERE4kDBW0REREQkDhS8pcOYWbGZ1ZlZdfSx0uuaZH9mdquZzTezejN7osW2s8xshZntNbP3zeyg65JKfBysv8xsgJm5mHOt2szu9rBUiTKzNDN71Mw2mlmVmS02s/Njtus8SyCH6i+dZ4nLzP5kZqVmVmlmq6JLVzdtS6hzTMFbOtqtzrns6OMkr4uRA2wF7gMei200szxgJnA30B2YDzwX9+qkpVb7K0ZuzPn233GsSw4uAGwCzgC6Aj8Ano+GOJ1nieeg/RWzj86zxPNjYIBzLge4CLjPzCYk4jkWt+UERSTxOOdmAphZIdA3ZtNlQIlz7oXo9h8C5WY23Dm3Iu6FCnDI/pIE5ZyrAX4Y0/R/ZraeyLK4PdB5llAO018LPClKDss5F3sndBd9DCbSbwl1jmnEWzraj82s3MzmmlmR18XIERsJLGl6Ef2f0dpouySujWa22cwej470SIIxswJgGFCCzrOE16K/mug8S0Bm9jsz2wusAEqBN0jAc0zBWzrS94BBQB8ia2m+ZmaDvS1JjlA2UNGirQLo4kEtcnjlwETgRCIjPF2AP3takRzAzFKI9MuT0dE2nWcJrJX+0nmWwJxztxDpkylEppfUk4DnmIK3dBjn3CfOuSrnXL1z7klgLvAVr+uSI1IN5LRoywGqPKhFDsM5V+2cm++cCzrntgO3Al82MwW4BGFmPuBpoIFI/4DOs4TVWn/pPEt8zrmQc24Okal4N5OA55iCt8STA8zrIuSIlABjml6YWRaR+XIlB32HJJKmO6Pp3/gEYGYGPAoUANOdc43RTTrPEtAh+qslnWeJK8C+cymhzjH9xyIdwsxyzexcM0s3s4CZXQNMBd7yujbZJ9o36YAf8Df1F/AyMMrMpke3/yewVBd8eetg/WVmp5jZSWbmM7MewANAsXOu5Z9YxRsPASOAC51ztTHtOs8SU6v9pfMsMZlZTzO7ysyyzcxvZucCXwP+RgKeY7plvHQIM8sncmHDcCBE5GKHu51zszwtTPYTvcL7nhbN/+Wc+6GZnQ38lsh8xk+AGc65DfGtUGIdrL+AlcCPgJ5AJTAL+K5zbltcC5QDRNcM3kBkvmkwZtNNzrk/6zxLLIfqLyCMzrOEE80bLxIZ2fYBG4EHnHN/jG5PqHNMwVtEREREJA401UREREREJA4UvEVERERE4kDBW0REREQkDhS8RURERETiQMFbRERERCQOFLxFREREROJAwVtERI6ZmTkzG+J1HSIiiUzBW0SkEzKzDWZWa2bVMY/fel2XiMjxLOB1ASIi0mEudM6963URIiISoRFvEZHjiJnNMLO5ZvZbM6swsxVmdlbM9t5m9qqZ7TKzNWZ2Q8w2v5n9PzNba2ZVZrbAzPrFHP5sM1ttZnvM7EEzs7h+OBGRBKcRbxGR488pwItAHnAZMNPMBjrndgHPAsuA3sBwYJaZrXXOvQfcAXwN+AqwChgN7I057gXARCAHWAC8BrwVl08kIpIEzDnndQ0iItLOzGwDkWAdjGm+C2gEfgT0cdH/AZjZp8BvgGJgA5DrnKuKbvsxcIJzboaZrQS+65x7pZXv54Apzrk50dfPAwudc//bIR9QRCQJaaqJiEjndYlzLjfm8cdo+xa3/6jLRiIj3L2BXU2hO2Zbn+jzfsDaQ3y/bTHP9wLZx1a+iEjnouAtInL86dNi/nV/YGv00d3MurTYtiX6fBMwOD4lioh0PgreIiLHn57AbWaWYmZXACOAN5xzm4CPgB+bWbqZjQa+Cfwp+r5HgP82s6EWMdrMenjyCUREkpAurhQR6bxeM7NQzOtZwCvAJ8BQoBzYDlzunNsZ3edrwO+JjH7vBu6JWZLwl0Aa8A6R+eMrgEs7+kOIiHQWurhSROQ4YmYzgG855/7B61pERI43mmoiIiIiIhIHCt4iIiIiInGgqSYiIiIiInGgEW8RERERkThQ8BYRERERiQMFbxERERGROFDwFhERERGJAwVvEREREZE4UPAWEREREYmD/w8WH3/80iJHPwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier4.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier4.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier4.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('overall-accuracy-4-9542.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "c65aa1f9-dccd-4f8d-aaec-37daadf59403",
        "id": "H-VzXzLy-U-h"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 5**"
      ],
      "metadata": {
        "id": "6rWl-CC5AZxV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model5 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv1\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv2\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv4\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool1\"),\n",
        "            layers.Dropout(0.2, input_shape=(2,)),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv5\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv7\"),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu', name=\"Conv8\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool2\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(name=\"Flatten1\"),\n",
        "            layers.Dense(128, activation = 'relu', name=\"Dense1\"),\n",
        "            layers.Dense(20, activation = 'softmax', name=\"Dense2\"),\n",
        "])"
      ],
      "metadata": {
        "id": "wQtV_UEWAZxX"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model5.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = keras.optimizers.Adam(lr = 1e-3),\n",
        "        metrics = ['acc'],\n",
        ")"
      ],
      "metadata": {
        "id": "p32JUdsbAZxY"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 2, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ],
      "metadata": {
        "id": "SFqNGZzVAZxY"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier5 = model5.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e32820a7-cb9f-4680-8d50-b4c0f16c0983",
        "id": "CGh2R9MSAZxZ"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 11s 8ms/step - loss: 1.5040 - acc: 0.5493 - val_loss: 0.5583 - val_acc: 0.8400 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.4532 - acc: 0.8797 - val_loss: 0.3490 - val_acc: 0.9104 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.3112 - acc: 0.9198 - val_loss: 0.3090 - val_acc: 0.9193 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.2503 - acc: 0.9319 - val_loss: 0.3352 - val_acc: 0.9156 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.2118 - acc: 0.9422 - val_loss: 0.3071 - val_acc: 0.9258 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.1818 - acc: 0.9480 - val_loss: 0.3482 - val_acc: 0.9246 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.1628 - acc: 0.9539\n",
            "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.1628 - acc: 0.9540 - val_loss: 0.3347 - val_acc: 0.9299 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 10s 7ms/step - loss: 0.0972 - acc: 0.9717 - val_loss: 0.2903 - val_acc: 0.9449 - lr: 5.0000e-04\n",
            "Epoch 9/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0690 - acc: 0.9795 - val_loss: 0.3454 - val_acc: 0.9378 - lr: 5.0000e-04\n",
            "Epoch 10/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0647 - acc: 0.9793\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0647 - acc: 0.9794 - val_loss: 0.3636 - val_acc: 0.9418 - lr: 5.0000e-04\n",
            "Epoch 11/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0391 - acc: 0.9881 - val_loss: 0.3808 - val_acc: 0.9418 - lr: 2.5000e-04\n",
            "Epoch 12/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0302 - acc: 0.9905\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0301 - acc: 0.9906 - val_loss: 0.3946 - val_acc: 0.9435 - lr: 2.5000e-04\n",
            "Epoch 13/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0207 - acc: 0.9935 - val_loss: 0.3963 - val_acc: 0.9453 - lr: 1.2500e-04\n",
            "Epoch 14/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0145 - acc: 0.9953\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0145 - acc: 0.9953 - val_loss: 0.4265 - val_acc: 0.9474 - lr: 1.2500e-04\n",
            "Epoch 15/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0133 - acc: 0.9955 - val_loss: 0.4065 - val_acc: 0.9501 - lr: 6.2500e-05\n",
            "Epoch 16/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9968\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0100 - acc: 0.9968 - val_loss: 0.4071 - val_acc: 0.9484 - lr: 6.2500e-05\n",
            "Epoch 17/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0095 - acc: 0.9968 - val_loss: 0.4161 - val_acc: 0.9484 - lr: 3.1250e-05\n",
            "Epoch 18/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0078 - acc: 0.9974\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0078 - acc: 0.9974 - val_loss: 0.4326 - val_acc: 0.9478 - lr: 3.1250e-05\n",
            "Epoch 19/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0078 - acc: 0.9972 - val_loss: 0.4327 - val_acc: 0.9474 - lr: 1.5625e-05\n",
            "Epoch 20/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0074 - acc: 0.9976\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0074 - acc: 0.9976 - val_loss: 0.4333 - val_acc: 0.9482 - lr: 1.5625e-05\n",
            "Epoch 21/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0070 - acc: 0.9977 - val_loss: 0.4353 - val_acc: 0.9484 - lr: 7.8125e-06\n",
            "Epoch 22/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.9975\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0070 - acc: 0.9975 - val_loss: 0.4367 - val_acc: 0.9489 - lr: 7.8125e-06\n",
            "Epoch 23/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0070 - acc: 0.9976 - val_loss: 0.4371 - val_acc: 0.9487 - lr: 3.9063e-06\n",
            "Epoch 24/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9978\n",
            "Epoch 24: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0068 - acc: 0.9978 - val_loss: 0.4385 - val_acc: 0.9497 - lr: 3.9063e-06\n",
            "Epoch 25/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0068 - acc: 0.9977 - val_loss: 0.4387 - val_acc: 0.9493 - lr: 1.9531e-06\n",
            "Epoch 26/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0061 - acc: 0.9981\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0061 - acc: 0.9981 - val_loss: 0.4398 - val_acc: 0.9487 - lr: 1.9531e-06\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0065 - acc: 0.9978 - val_loss: 0.4402 - val_acc: 0.9487 - lr: 9.7656e-07\n",
            "Epoch 28/30\n",
            "1297/1299 [============================>.] - ETA: 0s - loss: 0.0063 - acc: 0.9980\n",
            "Epoch 28: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0063 - acc: 0.9980 - val_loss: 0.4406 - val_acc: 0.9485 - lr: 9.7656e-07\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0062 - acc: 0.9979 - val_loss: 0.4407 - val_acc: 0.9485 - lr: 4.8828e-07\n",
            "Epoch 30/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0057 - acc: 0.9982\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07.\n",
            "1299/1299 [==============================] - 10s 8ms/step - loss: 0.0057 - acc: 0.9982 - val_loss: 0.4408 - val_acc: 0.9485 - lr: 4.8828e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model5.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn6MatucZcRh",
        "outputId": "8928f303-2f6f-4d4f-8088-fc23636d6efe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 28ms/step - loss: 0.5222 - acc: 0.9523\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.522214412689209, 0.9522801637649536]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier5.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier5.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier5.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('overall-loss-5-05222.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "b6b05bf7-6736-4ca0-848d-fd38de189a7e",
        "id": "OGbioF98AZxa"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier5.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier5.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier5.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('overall-accuracy-5-9523.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "c06204ac-8479-4a4a-af9e-344630a59e96",
        "id": "pKEovRKYAZxb"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Proposed Solution 6**"
      ],
      "metadata": {
        "id": "JN1WS4J-tuSk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "piJRCwqKm_vm"
      },
      "outputs": [],
      "source": [
        "model6 = keras.Sequential([\n",
        "            keras.Input(shape = (32, 32, 1)),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv1\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv2\"),\n",
        "            layers.Conv2D(32, 3, padding = 'same', activation = 'relu', name=\"Conv3\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool1\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv4\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv5\"),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv6\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool2\"),\n",
        "            layers.Dropout(0.2, input_shape=(2,)),\n",
        "            layers.Conv2D(64, 3, padding = 'same', activation = 'relu', name=\"Conv7\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool3\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Conv2D(128, 3, padding = 'same', activation = 'relu', name=\"Conv8\"),\n",
        "            layers.MaxPooling2D(name=\"Maxpool4\"),\n",
        "            layers.Dropout(0.25, input_shape=(2,)),\n",
        "            layers.Flatten(name=\"Flatten1\"),\n",
        "            layers.Dense(128, activation = 'relu', name=\"Dense1\"),\n",
        "            layers.Dense(20, activation = 'softmax', name=\"Dense2\"),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "Q7vCTvZQ4XKY"
      },
      "outputs": [],
      "source": [
        "adam =  keras.optimizers.Adam(lr=1e-3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "PNubZ416Sviu"
      },
      "outputs": [],
      "source": [
        "model6.compile(\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "        optimizer = adam,\n",
        "        metrics = ['acc'],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "MwlAQESPeIOd"
      },
      "outputs": [],
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 1, verbose = 1, factor = 0.5, min_lr = 1e-7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "Ob_iP5OgeIMD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfda3966-06ec-4af8-c0c7-57de4bed0732"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1299/1299 [==============================] - 10s 7ms/step - loss: 1.5674 - acc: 0.5197 - val_loss: 0.5690 - val_acc: 0.8464 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.4675 - acc: 0.8790 - val_loss: 0.3555 - val_acc: 0.9131 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.3544 - acc: 0.9116 - val_loss: 0.3360 - val_acc: 0.9177 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.3037 - acc: 0.9249 - val_loss: 0.2741 - val_acc: 0.9343 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2802 - acc: 0.9318 - val_loss: 0.2651 - val_acc: 0.9395 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2616 - acc: 0.9345 - val_loss: 0.2633 - val_acc: 0.9389 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1295/1299 [============================>.] - ETA: 0s - loss: 0.2413 - acc: 0.9411\n",
            "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.2412 - acc: 0.9412 - val_loss: 0.2646 - val_acc: 0.9381 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1761 - acc: 0.9562 - val_loss: 0.2049 - val_acc: 0.9534 - lr: 5.0000e-04\n",
            "Epoch 9/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.1524 - acc: 0.9626\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1527 - acc: 0.9626 - val_loss: 0.2206 - val_acc: 0.9514 - lr: 5.0000e-04\n",
            "Epoch 10/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.1227 - acc: 0.9685\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.1228 - acc: 0.9685 - val_loss: 0.2067 - val_acc: 0.9543 - lr: 2.5000e-04\n",
            "Epoch 11/30\n",
            "1293/1299 [============================>.] - ETA: 0s - loss: 0.0966 - acc: 0.9754\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0969 - acc: 0.9753 - val_loss: 0.2134 - val_acc: 0.9551 - lr: 1.2500e-04\n",
            "Epoch 12/30\n",
            "1299/1299 [==============================] - ETA: 0s - loss: 0.0840 - acc: 0.9785\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0840 - acc: 0.9785 - val_loss: 0.2118 - val_acc: 0.9555 - lr: 6.2500e-05\n",
            "Epoch 13/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0801 - acc: 0.9793\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0801 - acc: 0.9793 - val_loss: 0.2066 - val_acc: 0.9574 - lr: 3.1250e-05\n",
            "Epoch 14/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0765 - acc: 0.9802\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0766 - acc: 0.9802 - val_loss: 0.2053 - val_acc: 0.9580 - lr: 1.5625e-05\n",
            "Epoch 15/30\n",
            "1298/1299 [============================>.] - ETA: 0s - loss: 0.0746 - acc: 0.9805\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0745 - acc: 0.9805 - val_loss: 0.2049 - val_acc: 0.9588 - lr: 7.8125e-06\n",
            "Epoch 16/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0739 - acc: 0.9802 - val_loss: 0.2045 - val_acc: 0.9580 - lr: 3.9063e-06\n",
            "Epoch 17/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0733 - acc: 0.9809\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0735 - acc: 0.9809 - val_loss: 0.2055 - val_acc: 0.9572 - lr: 3.9063e-06\n",
            "Epoch 18/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0737 - acc: 0.9809\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0741 - acc: 0.9808 - val_loss: 0.2054 - val_acc: 0.9578 - lr: 1.9531e-06\n",
            "Epoch 19/30\n",
            "1296/1299 [============================>.] - ETA: 0s - loss: 0.0732 - acc: 0.9813\n",
            "Epoch 19: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0734 - acc: 0.9813 - val_loss: 0.2051 - val_acc: 0.9576 - lr: 9.7656e-07\n",
            "Epoch 20/30\n",
            "1292/1299 [============================>.] - ETA: 0s - loss: 0.0728 - acc: 0.9805\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 2.4414063659605745e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0733 - acc: 0.9804 - val_loss: 0.2051 - val_acc: 0.9578 - lr: 4.8828e-07\n",
            "Epoch 21/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0735 - acc: 0.9805\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 1.2207031829802872e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0737 - acc: 0.9804 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 2.4414e-07\n",
            "Epoch 22/30\n",
            "1294/1299 [============================>.] - ETA: 0s - loss: 0.0714 - acc: 0.9810\n",
            "Epoch 22: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0716 - acc: 0.9809 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.2207e-07\n",
            "Epoch 23/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0715 - acc: 0.9818 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.0000e-07\n",
            "Epoch 24/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0723 - acc: 0.9808 - val_loss: 0.2050 - val_acc: 0.9580 - lr: 1.0000e-07\n",
            "Epoch 25/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0722 - acc: 0.9808 - val_loss: 0.2050 - val_acc: 0.9580 - lr: 1.0000e-07\n",
            "Epoch 26/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0730 - acc: 0.9807 - val_loss: 0.2050 - val_acc: 0.9580 - lr: 1.0000e-07\n",
            "Epoch 27/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0718 - acc: 0.9812 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.0000e-07\n",
            "Epoch 28/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0732 - acc: 0.9809 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.0000e-07\n",
            "Epoch 29/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0747 - acc: 0.9807 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.0000e-07\n",
            "Epoch 30/30\n",
            "1299/1299 [==============================] - 9s 7ms/step - loss: 0.0734 - acc: 0.9810 - val_loss: 0.2050 - val_acc: 0.9578 - lr: 1.0000e-07\n"
          ]
        }
      ],
      "source": [
        "classifier6 = model6.fit(train_ds, batch_size = batch_size, \n",
        "                                 epochs = 30,\n",
        "                                 callbacks=[reduce_lr],\n",
        "                                 validation_data = val_ds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model6.evaluate(test_ds, batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gB-oxr5bwkB",
        "outputId": "aa5dd298-e3af-4e2f-a846-5cc09bd21da5"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "163/163 [==============================] - 5s 28ms/step - loss: 0.1986 - acc: 0.9590\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.19860713183879852, 0.9590148329734802]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier6.history['val_loss'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier6.history['loss'][1:], linewidth=2)\n",
        "\n",
        "d = classifier6.history['val_loss']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5))\n",
        "\n",
        "plt.legend(['Validation Loss', \"Training Loss\"], loc='upper right')\n",
        "plt.title('loss vs epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig.savefig('best-overall-loss-6-01986.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "rISVhZxGQADW",
        "outputId": "1e31d402-a1f5-4840-8bbc-31667eb27e53"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig2 = plt.figure(figsize=(12,8))\n",
        "plt.plot(classifier6.history['val_acc'][1:], 'r--', linewidth=2)\n",
        "plt.plot(classifier6.history['acc'][1:], linewidth=2)\n",
        "\n",
        "d = classifier6.history['val_acc']\n",
        "plt.xticks(np.arange(3, len(d), step=5), np.arange(5, len(d)+1, step=5), fontsize=12)\n",
        "\n",
        "plt.legend(['Validation Accuracy', \"Training Accuracy\"], loc='lower right')\n",
        "plt.title('accuracy vs epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.grid()\n",
        "\n",
        "fig2.savefig('best-overall-accuracy-6-9590.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "nIuZY98-QIzD",
        "outputId": "d7b20e12-424f-4109-cc1b-581a3af58199"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nkgAR220Wk8j"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "4CvYsgCsuSa7",
        "ceCPcifFah1-",
        "sV3b0cetmmDq",
        "6bII9AAru-Bv",
        "FINgvX2a46UV",
        "iUn28buH7bWW"
      ],
      "name": "Handwritten Amharic Digit Recognition Using Deep Learning CNN 2.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}