catvsdog.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "catvsdog.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM4B5xvudLLp4mTdljsQZP1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KVSSetty/DLtutorials/blob/master/catvsdog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yi--_jAaSOe7",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "cd58cc64-bb94-46ee-b665-9d30d9bef3f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-66d9e847-131d-4707-94a7-ad0728450db3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-66d9e847-131d-4707-94a7-ad0728450db3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"kvssetty\",\"key\":\"fc8062f9debcf6b9bf2d77098df3b1ac\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n"
      ],
      "metadata": {
        "id": "c1Zi7dHuS4am"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c dogs-vs-cats\n",
        "\n",
        " "
      ],
      "metadata": {
        "id": "w-KlFBrVTTsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c95158dc-4695-42f5-c16e-10b2430644ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading dogs-vs-cats.zip to /content\n",
            " 98% 795M/812M [00:03<00:00, 217MB/s]\n",
            "100% 812M/812M [00:03<00:00, 221MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq dogs-vs-cats.zip\n",
        "\n"
      ],
      "metadata": {
        "id": "CDe9UsSDTsco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -qq train.zip\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DL4_0Wc992ZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os, shutil, pathlib\n",
        "  \n",
        "original_dir = pathlib.Path(\"train\")                          \n",
        "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")             \n",
        " \n",
        "def make_subset(subset_name, start_index, end_index):         \n",
        "    for category in (\"cat\", \"dog\"):\n",
        "        dir = new_base_dir / subset_name / category\n",
        "        os.makedirs(dir, exist_ok=True)\n",
        "        fnames = [f\"{category}.{i}.jpg\" \n",
        "                  for i in range(start_index, end_index)]\n",
        "        for fname in fnames:\n",
        "            shutil.copyfile(src=original_dir / fname,\n",
        "                            dst=dir / fname)\n",
        "make_subset(\"train\", start_index=0, end_index=1000)            \n",
        "make_subset(\"validation\", start_index=1000, end_index=1500)    \n",
        "make_subset(\"test\", start_index=1500, end_index=2500)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3hqpIlTlUxAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Let us build a CNN"
      ],
      "metadata": {
        "id": "3PRl8h093-PO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras \n",
        "from tensorflow.keras import layers\n",
        "  \n",
        "inputs = keras.Input(shape=(180, 180, 3))                            \n",
        "x = layers.Rescaling(1./255)(inputs)                                 \n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        " \n",
        "\n"
      ],
      "metadata": {
        "id": "vwgkwRKlDt0z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "w1LN9B2KErGy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6bd709e-e6e4-4f56-b19a-e43264e4c14f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 180, 180, 3)]     0         \n",
            "                                                                 \n",
            " rescaling (Rescaling)       (None, 180, 180, 3)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 178, 178, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 89, 89, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 87, 87, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 43, 43, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 41, 41, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 20, 20, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 18, 18, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 9, 9, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 7, 7, 256)         590080    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 12544)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 12545     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 991,041\n",
            "Trainable params: 991,041\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "CSAK0P9OEyym"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation\n"
      ],
      "metadata": {
        "id": "Lf4M3hnEHBid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "\n"
      ],
      "metadata": {
        "id": "jVt19hfhHIn_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"train\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "validation_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"validation\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "test_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"test\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "\n"
      ],
      "metadata": {
        "id": "EDMWF4mNHQRO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2abac457-c9a9-4255-9447-5a7009a9cdcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 files belonging to 2 classes.\n",
            "Found 1000 files belonging to 2 classes.\n",
            "Found 2000 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Displaying the shapes of the data and labels yielded by the Dataset\n",
        "\n",
        "  \n"
      ],
      "metadata": {
        "id": "i7PutV6PJR-C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for data_batch, labels_batch in train_dataset:\n",
        "     print(\"data batch shape:\", data_batch.shape)\n",
        "     print(\"labels batch shape:\", labels_batch.shape)\n",
        "     break\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "VZZomRCuJfvj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9588054-a8da-458a-c194-16aa80424e93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data batch shape: (32, 180, 180, 3)\n",
            "labels batch shape: (32,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fitting the model using a Dataset\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1QsEePDPLXHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"convnet_from_scratch.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=30,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)\n",
        "\n"
      ],
      "metadata": {
        "id": "lV0MoYUgLiez",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cefc2d5a-592f-475a-f5f4-b7c70aa11d89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "63/63 [==============================] - 19s 83ms/step - loss: 0.7657 - accuracy: 0.5130 - val_loss: 0.6892 - val_accuracy: 0.5510\n",
            "Epoch 2/30\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.7037 - accuracy: 0.5405 - val_loss: 0.6850 - val_accuracy: 0.5320\n",
            "Epoch 3/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.6924 - accuracy: 0.6220 - val_loss: 0.6285 - val_accuracy: 0.6270\n",
            "Epoch 4/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.6182 - accuracy: 0.6695 - val_loss: 0.6290 - val_accuracy: 0.6570\n",
            "Epoch 5/30\n",
            "63/63 [==============================] - 5s 75ms/step - loss: 0.5954 - accuracy: 0.6915 - val_loss: 0.5821 - val_accuracy: 0.6940\n",
            "Epoch 6/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.5477 - accuracy: 0.7300 - val_loss: 0.8202 - val_accuracy: 0.6580\n",
            "Epoch 7/30\n",
            "63/63 [==============================] - 5s 74ms/step - loss: 0.4938 - accuracy: 0.7590 - val_loss: 0.9304 - val_accuracy: 0.6350\n",
            "Epoch 8/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.4624 - accuracy: 0.7890 - val_loss: 0.5982 - val_accuracy: 0.6940\n",
            "Epoch 9/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.3950 - accuracy: 0.8335 - val_loss: 0.7632 - val_accuracy: 0.6930\n",
            "Epoch 10/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.3361 - accuracy: 0.8545 - val_loss: 0.7742 - val_accuracy: 0.6950\n",
            "Epoch 11/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.2729 - accuracy: 0.8870 - val_loss: 1.0082 - val_accuracy: 0.6850\n",
            "Epoch 12/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.2343 - accuracy: 0.9030 - val_loss: 0.8707 - val_accuracy: 0.7080\n",
            "Epoch 13/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.1851 - accuracy: 0.9345 - val_loss: 0.9757 - val_accuracy: 0.6890\n",
            "Epoch 14/30\n",
            "63/63 [==============================] - 5s 78ms/step - loss: 0.1303 - accuracy: 0.9520 - val_loss: 1.4068 - val_accuracy: 0.6990\n",
            "Epoch 15/30\n",
            "63/63 [==============================] - 5s 78ms/step - loss: 0.1379 - accuracy: 0.9530 - val_loss: 1.1393 - val_accuracy: 0.7120\n",
            "Epoch 16/30\n",
            "63/63 [==============================] - 5s 78ms/step - loss: 0.0866 - accuracy: 0.9670 - val_loss: 1.5439 - val_accuracy: 0.6910\n",
            "Epoch 17/30\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.1003 - accuracy: 0.9710 - val_loss: 1.9477 - val_accuracy: 0.6480\n",
            "Epoch 18/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0710 - accuracy: 0.9780 - val_loss: 1.7904 - val_accuracy: 0.6820\n",
            "Epoch 19/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.0711 - accuracy: 0.9780 - val_loss: 1.9985 - val_accuracy: 0.6970\n",
            "Epoch 20/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.0573 - accuracy: 0.9825 - val_loss: 2.1166 - val_accuracy: 0.6840\n",
            "Epoch 21/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.0902 - accuracy: 0.9740 - val_loss: 2.0605 - val_accuracy: 0.7030\n",
            "Epoch 22/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0376 - accuracy: 0.9865 - val_loss: 2.0914 - val_accuracy: 0.6850\n",
            "Epoch 23/30\n",
            "63/63 [==============================] - 5s 74ms/step - loss: 0.0449 - accuracy: 0.9825 - val_loss: 2.2532 - val_accuracy: 0.7000\n",
            "Epoch 24/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.0692 - accuracy: 0.9825 - val_loss: 1.8151 - val_accuracy: 0.6740\n",
            "Epoch 25/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0480 - accuracy: 0.9870 - val_loss: 2.2630 - val_accuracy: 0.7000\n",
            "Epoch 26/30\n",
            "63/63 [==============================] - 5s 74ms/step - loss: 0.0397 - accuracy: 0.9865 - val_loss: 2.2237 - val_accuracy: 0.7210\n",
            "Epoch 27/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0442 - accuracy: 0.9880 - val_loss: 2.3971 - val_accuracy: 0.7040\n",
            "Epoch 28/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0512 - accuracy: 0.9865 - val_loss: 2.4833 - val_accuracy: 0.6990\n",
            "Epoch 29/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.0523 - accuracy: 0.9810 - val_loss: 2.9013 - val_accuracy: 0.7060\n",
            "Epoch 30/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.0431 - accuracy: 0.9910 - val_loss: 3.0375 - val_accuracy: 0.6910\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Displaying curves of loss and accuracy during training\n",
        "\n"
      ],
      "metadata": {
        "id": "EqnCMgd4MEbl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "accuracy = history.history[\"accuracy\"]\n",
        "val_accuracy = history.history[\"val_accuracy\"]\n",
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(accuracy) + 1)\n",
        "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "V_gYUTprL6wC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "f2f1563b-6608-4c5f-ba40-dd1dd68eb8a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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33ngDgPfff5/09HSaNGnC7bffzuHDh4/vb+jQoTRv3pwmTZqwbl3eERlOZsPsGhM548ZBtWpwzTWxjuSEuO2H/sc/gjfXRMSkpcFzz+X/fOXKlWnVqhVz5syhS5cuTJ48mZtuugkRYfjw4VSuXJmjR49y2WWX8cUXX9C0aVO/21mxYgWTJ09m1apV5OTk0Lx5c1q0aAFA165d6d+/PwB//vOfGTNmDPfccw+dO3fm2muvpVu3bidt69ChQ/Tt25f333+f+vXr06dPH1566SX++Mc/AlC1alVWrlzJiy++yIgRIxg9enS+x2fD7BoTGT//DDNmwD33FP3l/QWxFnoevmUX33LL1KlTad68Oenp6axevfqk8kheS5Ys4YYbbqBs2bJUrFiRzp07H3/uq6++4uKLL6ZJkyZkZWXlO/xurvXr11OvXj3q168PwK233srixYuPP9/VuyytRYsWxwf0yo8Ns2tMZGRlwZEjrndLPInbFnpBLelo6tKlC/fffz8rV67kwIEDtGjRgu+++44RI0bw6aefctZZZ9G3b998h80NpG/fvkyfPp1mzZoxfvx4Fi5cGFa8uUPwhjP87uDBg7nmmmuYPXs2bdq0Ye7cuceH2Z01axZ9+/blgQceoE+fPmHFakwyUIUxY9xVoY0LNTNE9FgLPY/y5cvToUMHbr/99uOt83379lGuXDkqVarEjh07mDNnToHbaNeuHdOnT+fgwYPs37+fmTNnHn9u//791KxZkyNHjhwf8hagQoUK7N+//5RtNWjQgE2bNrFhwwYAXn31VS655JJCHZsNs2tM+FauhC+/jK+TobnitoUeSz179uSGG244XnrJHW62YcOG1K5dmzZt2hT4+ubNm3PzzTfTrFkzzj77bFr6DPDw5JNP0rp1a6pVq0br1q2PJ/EePXrQv39/Ro4cefxkKECZMmUYN24c3bt3Jycnh5YtWzJw4MBCHVfuXKdNmzalbNmyJw2zu2DBAkqUKEGjRo3o2LEjkydP5tlnn6V06dKUL1/eJsIwxjNuHJQpAz16xDqSU9nwuabQ7PMyxc2hQ25KuU6dIj8hfbBs+FxjjImAt9+GPXvi72RoLkvoxhgTpLFj3bzFl14a60j8i7uEHqsSkAmNfU6muPnhB3jvPejbF0rEXeZ04iqsMmXKsGvXLksWcU5V2bVrF2XKlIl1KMYUmVdecV0W+/aNdST5i6teLikpKWzZsoWdO3fGOhQTQJkyZUhJSYl1GMYUiWPHXO+WDh2gXr1YR5O/uEropUuXpl48v1vGmGJpyRI3EfRf/xrrSAoWVyUXY4yJR+PGQcWKsZ0AOhiW0I0xpgC5k0D36AFly8Y6moJZQjfGmALE0yTQgQRM6CJSW0QWiMgaEVktIvf5Wae9iOwVkVXe7fHohGuMMUUrdxLoVq1iHUlgwZwUzQEeVNWVIlIBWCEi76lq3vFjl6iqzXVmjEka69a5SaCffTY+JoEOJGALXVW3qepK7/5+YC1QK9qBGWNMrI0f7yaB/t//jXUkwQmphi4idYF04GM/T18oIp+LyBwRaZTP6weIyHIRWW59zY0x8SonBzIz4eWX3RRz8TIJdCBBJ3QRKQ9MA/6oqvvyPL0SSFXVZsC/gen+tqGqo1Q1Q1UzqlWrVtiYjTEmKlRh+nRo0gQGDIBGjVy5JVEEldBFpDQumWep6pt5n1fVfaqa7d2fDZQWkaoRjdQYY6Loww+hbVu44Qb3+K234IMPwJv9MSEE08tFgDHAWlX9Zz7r1PDWQ0RaedvdFclAjTEmGtasgS5dXDL/7jsYNcrNSHT99YlxItRXML1c2gC9gS9FZJW37DGgDoCqvgx0AwaJSA5wEOihNsKWMSaObdkCw4a5q0DLl4fhw+G++6BcuVhHVngBE7qqfgAU+D2lqs8Dz0cqKGOMiZbsbJe8n3sOjh6Fe++FIUOgahIUieNqcC5jjImmQ4fg2mth0SLo1QuefDK+R08MlSV0Y0yxkJPjxmNZvNjNB3rLLbGOKPIsoRtjkp4q3HmnmxP03/9OzmQONjiXMaYYGDzYjcny+ONw992xjiZ6LKEbY5Las8/CM8/AH/7gerUkM0voxpikNW4cPPww3HwzjByZeP3KQ2UJ3RiTlN5+G/r1gyuugAkT3CBbyc4SujEm6Sxa5FrlGRnw5ptw2mmxjqhoWEI3xsStn35yIx5++qnrdhiMzz6Dzp3hnHNg9mx3FWhxYQndGBMR06a5VvHhw5Hb5pNPwqBBbragypWhUyf4v/+DZcvgyJFT19+wAa6+GipVgrlzoUqVyMWSCKwfujEmbG+84S7aOXoUbroJbrwx/G0eOQJTprgk3ru3K6MsWgRz5rjny5WDiy6C9u3hkksgJQWuvNLF8O67ULt2+DEkGkvoxpiwvPUW9OwJF1wA334Lr74amYQ+bx7s3An9+7uRD3v0cMt37HBXe+Ym+CFDTrymXDlYsAAaNgx//4nIEroxptBmzHAt8pYtXct52DB3JeauXeGXO7Ky4MwzoWPHk5dXrw7du7sbwM8/w5Ilrs7epYuLpbiyGroxplBmzYJu3aB5c5fMK1RwpZHcUkk4srNdy797dzj99ILXrVrVTUrx1FPQunV4+010ltCNMSGbOxe6doWmTd39SpXc8mbN3PRtr74a3vbffhsOHEicyZnjhSV0Y0xI5s1zpY3zz3cnH88888RzIq6VvmwZfPNN4feRleVOarZtG368xYkldGNM0ObPh+uugwYNXGKvXPnUdW65xSX2iRMLt4+ffnJfFLfcAiUsQ4XE3i5jTFAWLXLJ/Pe/d8k8v5OetWrBZZe5hF6YiSinTnVdD3v1Ci/e4sgSujFJYutW132wTx/473+Dv7IyGB98ANdcA6mp8P77UK1awev37g0bN8LSpaHvKyvL1eGbNClcrMWZJXRjksDs2e6E5MyZ7taxI/zud27s76VLC9dSBve6RYvc9lJSXMmlevXAr+vaFcqWDf3k6Lffuvq7tc4LxxK6MQnst9/gT39yredatWDFCti+HaZPhw4dYMwYaNPGjWsyZAisXp3/to4dg6+/hsmT3ZCzl1/uyirt20PNmi6Z16gRXFzly7uuhFOnhjYUwKRJ7m/PnsG/xpwgWtiv7jBlZGTo8uXLY7JvY5LBpk3u6smPP3aTN/zjH1CmzMnr7N/vkvukSfDee6423bSpO+F4ySWwfj2sXOluq1a5/t/gRids0sT1MU9Pd/3Bq1YNLb65c924KtOmuRZ7IKpw3nnuF8CiRaHtqzgRkRWqmuH3OUvoxiSeadPgjjtcEhwzxl3gE8iOHfD66y65f/TRieVly0Jamkveubfzzgt/yNmcHNf18IIL3EVCgaxY4Ya7/c9/YMCA8PadzApK6AEv/ReR2sAEoDqgwChV/VeedQT4F9AJOAD0VdWV4QZujDnZoUPw0EPwwgvuEvfJk105JRjVq7ua+t13uxOWn30GjRrBuedGZ/KHUqXcL4FghwLIyoLSpYP7cjL+BVNDzwEeVNXzgQuAu0Tk/DzrdATO9W4DgJciGqUxhq+/hgsvdMn8wQddz5Ngk3le55zjBtBq2DC6M/nkDgUwdWrB6x096r6cOnXy37fdBCdgQlfVbbmtbVXdD6wFauVZrQswQZ1lwJkiUjPi0RpTTGVluVLI99+7XiwjRiTGLDzNmkHjxm4KuIIsWADbtlnvlnCF1MtFROoC6cDHeZ6qBfzg83gLpyZ9Y0wh/POfbkyT9HT4/HO49tpYRxS8YIcCyMpyg3sl0rHFo6ATuoiUB6YBf1TVfYXZmYgMEJHlIrJ8586dhdmEMcXKkiWuC2HXrq4Vm5IS64hCF2gogIMH3UneG2+EM84o2tiSTVAJXURK45J5lqq+6WeVrYDv/CAp3rKTqOooVc1Q1YxqgS41M6aY27HDTel2zjkwbpw7yZiIUlLg0kvzHwrgnXdc90ort4QvYEL3erCMAdaq6j/zWW0G0EecC4C9qrotgnEakxC++Qbuuw9++SW87Rw96i6u2bPHtV4rVoxMfLHSp0/+QwFkZbkLlzp0KPq4kk0wLfQ2QG/gUhFZ5d06ichAERnorTMb2AhsADKBP0QnXGPi27BhMHIkXHEF7N5d+O0MHepKLC+9lBxjmuQ3FMDu3W7Ygh49otvbprgI+CNOVT8AJMA6CtwVqaCMSUS7d7vWdNu2bjq0yy93V2eGOhXbrFkwfDj06we33hqdWIua71AA//rXiVmI3njDdWu0iSwiw8ZyMSZCXn3VjVvy/PNuxp01a9wwsj//HPw2Nm92vULS0lxLP5n07u1KUbNmnViWleX6wqenxy6uZGIJ3ZgIUIXMTHf1ZrNmcNVVrr/4+vXuhGAwnboOH3Zjphw75lquydbj47LL3OBeuWWX77+HxYvdyVApsAZggmUJ3ZgIWLbMjWTYv/+JZVdc4XpwbNjgTvjt2FHwNh580JVqxo93k0gkm9yhAGbNckMBvPaaW37LLbGNK5lYQjcmAjIzoVw5d3LP12WXuQT23XcuqW/f7v/1r73mLul/6CG4/vroxxsrvkMBZGW5oQwKO3yBOZUldGPCtG8fTJniuhlWqHDq8x06uJ4c33/vxhb/8ceTn1+71rXs27aFp54qkpBjJncogKefhi+/tL7nkWYJ3ZgwTZoEBw6cXG7J65JL3LRwW7e6pL7Vu+wuO9tdIVmunPtSKF26SEKOmdyhAH74wXVTvOmmWEeUXCyhGxOmzEw3aUTLlgWv17atm/Rh+3aX4H/4AQYOdCdOJ01yU8YVB7lDAVx1VeC5SU1oEvRiYmPiQ+5sP//+d3A9NS66CN591yWzZs1cN76//c3V2ouLlBT3BdasWawjST7WQjcmDJmZbtq3UGrBF1zgLjhSdXOBPvpo9OKLVz16uFmRTGRZC92YQvr1V9dTo1s3OOus0F7bqpW7iKhcOShhzSoTIZbQjSmkqVPdKIEFnQwtSKIPuGXij7UNjCmkzExo0AAuvjjWkRjjWEI3phBWr4aPPnIDaNll6yZeWEI3phAyM12f8WQZDdEkB0voxoTo0CE3wNT111s/ahNfLKEbE6I333Rjnxf2ZKgx0WIJ3ZgQZWZCvXrF62IgkxgsoRsTgm++gYUL4Y47rP+4iT/2T9KYEIwe7QaVuu22WEdizKksoRsTpN9+c5NPXHNN8RlIyyQWS+jGBGnmTPjpJzsZauKXJXRjgjR6NNSqBVdfHetIjPHPEroxQdi82Y1lfvvtbm5MY+KRJXRjgjB2rPt7xx2xjcOYggRM6CIyVkR+EpGv8nm+vYjsFZFV3u3xyIdpTOwcPeoS+pVXQmpqrKMxJn/BtNDHA4GqhktUNc27PRF+WMbEj//+F7ZssZOhJv4FTOiquhjYXQSxGBOXMjPh7LPhuutiHYkxBYtUDf1CEflcROaISKMIbdOYmNu2Dd55x42qeNppsY7GmIJF4nz9SiBVVbNFpBMwHTjX34oiMgAYAFCnTp0I7NqY6Bo/3tXQ+/WLdSTGBBZ2C11V96lqtnd/NlBaRKrms+4oVc1Q1YxqNu6oiXPHjrm+55dcAvXrxzoaYwILO6GLSA0RN2eLiLTytrkr3O0aE2sLFsDGjXYy1CSOgCUXEXkNaA9UFZEtwFCgNICqvgx0AwaJSA5wEOihqhq1iI0pIpmZcNZZcOONsY7EmOAETOiq2jPA888Dz0csImPClNucCGeuz59/hrfegjvvhDJlIhOXMdFmV4qapHLsGKSlwd13h7edV191oytaucUkEkvoJqksWQJffAEvvggff1y4bai6ckvr1tCkSWTjMyaaLKGbpDJhApQv78Yrv+su1+UwVEuXwtq11jo3iccSukkaBw7A669D9+4wYgSsWAFjxoS+ndGj3ZfCzTdHPkZjoskSukka06fD/v3Qpw/06OH6jz/6KOwKoRPt3r0wZQr07OmSujGJxBK6SRqvvOJGQ2zXzvVw+fe/XYL+85+D38akSXDwoJVbTGKyhG6SwtatMG8e9O4NJbx/1U2auN4u//kPrFwZ3HYyM6FZM8jIiF6sxkSLJXSTFLKyXJfFPn1OXv7Xv0K1au4E6bFjBW9j5Ur47DPXOg+nD7sxsWIJ3SQ8VVduuegiOMyhFHYAABKwSURBVDfPsHCVKsEzz8CyZa4HTEEyM91FRLfcEr1YjYkmS+gm4a1cCWvWnNo6z9W7t0v2Dz8Me/b4X+fXX10rv3t3d7m/MYnIErpJeK+8AqefDjfd5P/5EiXg+efd5fxDh/pfZ+pU10PGToaaRGYJ3SS0336D116Dzp0Lblmnp8PAgS6xf/HFqc+PHg0NG0LbttGL1Zhos4RuEtqcOa7lfeutgdf9299c0r/77hMDeAGsXu2uDu3Xz06GmsRmCd0ktAkT3HyfV14ZeN3KleHpp914L6+9dmL56NFQunT+NXhjEoUldJOwdu2CmTOhVy+XkINxxx3QsiU89BDs2weHDrkvheuvd90bjUlkkZhT1JiYmDwZjhwJrWWde4K0dWt48klo3hx277aToSY5WEI3CWvCBGja1I1/HopWrVxL/bnn3FyhdevCZZdFJURjipSVXExCWrcOPvkkuJOh/jz9tBt8a80al9xL2P8EkwTsn7FJSBMmQMmShb+qs1o1ePZZqFgRbrstsrEZEyuW0E3COXrUTRF31VVQo0bht9Ovn6uf16oVudiMiSVL6CbhLFwIW7ZEppthyZLhb8OYeGEJ3SScV15xg2517hzrSIyJL5bQTULZvx+mTXPjtpxxRqyjMSa+WEI3CeXNN93coYXt3WJMMguY0EVkrIj8JCJf5fO8iMhIEdkgIl+ISPPIh2mM88or8Pvfu+FwjTEnC6aFPh64uoDnOwLnercBwEvhh2XMqTZvhgUL3MlQG0TLmFMFTOiquhjYXcAqXYAJ6iwDzhSRmpEK0CSeTz6Brl1hw4bIbnfiRPe3d+/IbteYZBGJGnot4Aefx1u8ZacQkQEislxElu/cuTMCuzbxJjsbevaEt96CCy90w9JGQu40c+3aQb16kdmmMcmmSE+KquooVc1Q1YxqNrRdUvrTn+C772D8eDf2+KWXutmAwjVzJnzzjZ0MNaYgkUjoW4HaPo9TvGWmmJk7F15+GR54wCXejz5yQ9XefDP8/e8nTyoRrOxsuOce6NLFTQDdrVvk4zYmWUQioc8A+ni9XS4A9qrqtghs1ySQPXvcIFfnnedmBgKoUgXee8+VYB59FAYMcMPdBmv+fDea4gsvwL33usmgK1aMTvzGJIOAw+eKyGtAe6CqiGwBhgKlAVT1ZWA20AnYABwAbKijKFm6FKZPhyeegDJlYh3Nye67D7Zvd/H5xlamDGRlua6Gf/sbfP+9K8FUqpT/tvbtc6WbUaNcq3zxYpvr05igqGpMbi1atFATnMOHVR99VLVECVVQff75WEd0srfecnE9/njB640dq1qqlGrjxqqbN/tfZ84c1dq13bE+9JDqgQORj9eYRAYs13zyqmhhCpsRkJGRocuXL4/JvhPJV1+5bnqrVsHtt7vxu3/80XUJDHbatWjauRMaNYKUFFi2DE47reD133/fdWksV86d6GzRwi3/5RdXex8/3pVtxo1zswoZY04mIitUNcPfc3bpf5w6dgz++U/IyICtW10pY8wY+MtfXNkit092LKnCwIGwd6/rUhgomYObGWjpUvdl1K6dS+ozZ7ovhVdfhcceg88+s2RuTGFYCz0Obd4Mffu6YWK7dHG15LPPds+pulZtdjasXRvb4V8nTXITNP/97/DII6G9dvt2uO46WLHCHVPTpjB27IkWuzHGP2uhJ4jci2eaNnWJbuxYd4FObjIHd8n7Y4+5PtnTpsUu1h9/hLvuchcPPfRQ6K+vUcN9YfXv7yZr/vRTS+bGhMta6HFi5064806XwNu1c4m9bl3/6x496koUZcq48kRRj2uiCp06waJF8PnnrieKMaZoWAs9zv33v9CkCcya5ea5nD8//2QOrszy6KMumc6aVWRhHjd6tIv5mWcsmRsTT6yFHmPr10NamkuMWVkusQfjyBGoX9+VLpYuLbpW+nffuZJQq1buoqES1iQwpkglTQs9K8u1XEuUcH+zsmIdUXiOHnUzzp9xhrtsPthkDq6XyMMPu66CCxdGLcSTHDvm4hVx9X1L5sbEl4BXisaLrCx36fiBA+7x5s3uMbieFono//0/N97JxIlQsxADDt92m7tqdPhw6NAhsrEdPeou5//lF9i9293mzXN187FjITU1svszxoQvYUoudeu6JJ5Xaips2hSxsIrMunWu1HL11e5EaGFLJiNGuMvkly0rXN/ttWvhqadgx44TifuXX1wy9+eGG1zvGptgwpjYKKjkkjAJvUQJ/6P1ibhSQCI5ehTatHFdD1evdnXwwsrOhjp14OKL4e23Q3vt11+7HjWHDrmrMytXdkPeVq6c//369a3UYkwsFZTQE6bkUqeO/xZ6nTr+18/KgiFD3FWVdeq4skS8lGb+8Q/4+GN3YU44yRygfHk3MNawYfDll8HX4b/7zl21eeyYK/ucd154cRhjYi9h2lrDh0PZsicvK1vWLc8rt96+ebNr1efW2/2dRC3qE61r1sDjj7vSRY8ekdnmPfe4xP7UU8Gt/8MPbuKJX391PVUsmRuTJPIbtSvat8KMtjhxompqqqqI+ztxov/1UlPd6H95b6mpp26vbNmT1ylbNv/thuvIEdWWLVWrVFHdvj2y2374YTdC4ddfF7zejz+qnnuuasWKqp9+GtkYjDHRRzKNtjh/Pvz5z24gqNNP93976SX/r81bby/qE61//7u7IGjyZDeLTyTt2OGOp1cvd+GPPzt3Qvv27pjffRcuuiiyMRhjoi8paui5SpZ0Q68ePuxG+Tt8+NSbiP8TqNWqQU4OlPKO+vvv/e8jv+XhWL0ahg6FG2+Em26K/ParV3czBo0a5fZTu/bJz//yC1x5JWzcCHPmWDI3JhklXAs9GHn7rPuqXt3Vrnv1cvNT+kvekW6h5+S4Qaw2bXKJ3XewrUj6/ns3M9CgQTBy5Inl+/bBFVe4MdVnzICrrorO/o0x0Zc0V4oGq1cv11JNTXWt9dRUdzHM66+7lulLL7lL13/77URrPVd+J1qh8CdQn3kGli93c2NGK5mD683TuzdkZsJPP7llv/4K11zj5uN84w1L5sYks6RsoQfyyy/u4pisLHflY+5bcNpprlRRubJ77Hu6dNcu1wL2fbvOOMMlz7zdIX27TNao4ZJr165uLs1oW7/e9Vp55BHXm+baa93QAJMnQ/fu0d+/MSa6kqqGHglnnQX9+rnbli0u2b3/vrvgR8T/be3aU+vyBw+6aeE2bnSt4PR017fct9yzbZv7G+lL8/PToIFL3C+84MZUX7AAJkywZG5McVAsW+iFkd+VqnDiJGzNmrB/v7t6M6+iHKLg88/dsALgfkH061c0+zXGRF+xq6FHQ35XpKamuunUxo93l/P7S+aQf8+ZaFzY1KwZPP20m6PTkrkxxYe10IPkr+dM2bLu5KtvDT01NfieM8Fu0xhjclkLPQL89Zzxl3ifeir4IQqGDDm1a+WBA265McaEKqiELiJXi8h6EdkgIoP9PN9XRHaKyCrvlpQ/9Hv1cq3sY8fcX3+t6GATPxTthU3GmOQXMKGLSEngBaAjcD7QU0TO97PqFFVN8275XHxePAST+CH/ury/5ck2W5MxJvKCaaG3Ajao6kZV/Q2YDHSJbljFQ7AjSIYyeqQxpvgKJqHXAn7webzFW5bXjSLyhYi8ISK1/TyPiAwQkeUisnznzp2FCDe5BFueCbXWbq15Y4qngL1cRKQbcLWq9vMe9wZaq+rdPutUAbJV9bCI3AncrKqXFrTdROvlEkuhzNZkPWeMSW7h9nLZCvi2uFO8Zcep6i5VPew9HA20KEygxr9Qau3Wc8aY4iuYhP4pcK6I1BOR04AewAzfFUTEd876zsDayIVoQpmtyXrOGFN8BUzoqpoD3A3MxSXqqaq6WkSeEJHO3mr3ishqEfkcuBfoG62Ai6NQukJazxljii+7UjTJBFtDt1q7MYnJrhQtRqLVc8YYE/+shV5MhdJzxhgTP6yFbk4RSq0drN5uTCKwhF5MhdJzxq5UNSYxWEIvpkLpOWP1dmMSg9XQTUBWbzcmflgN3YTF+rYbkxgsoZuAbFRIYxKDJXQTkPVtNyYxWEI3QQlm0o5Qx5Gx8owxkWUJ3URMqLV2K88YE1mW0E3EhNK33cozxkSeJXQTMdGaINtKM8YEp1SsAzDJpVev4EZrrFPHlVn8LfeVd1TI3NJM7r6MMSdYC93ERLDlGSvNGBM8S+gmJoItz0RrBiYr45hkZAndxEwwXSGjMSqk9bAxycoSuolr0RgV0so4JllZQjdxLRqjQhb3Mk6ixGkKQVVjcmvRooUaE0kiqq5tfvJN5OT1UlP9r5ea6n+7Eye650Tc34kT/a9TtuzJ2ytb1v+6sZQocZr8Acs1n7xqLXSTNIKtt8dDGSfYVnKkW9PxUG6yXwhRlF+mj/bNWugm0kJpfQbT6lYNvjUf7K+DUOIMtTUdzDGFEmcogn0/o/EZRSPOUNaNRpwFoYAWuiV0k1Qi/Z8rGmWcYNcNZZvBJspYl5uC3X80vsxC/TKJ1ZduIJbQjSmkaCSgYL8kQmlNRyPOaHxJROMLMhpxxvJLN5CwEzpwNbAe2AAM9vP86cAU7/mPgbqBtmkJ3SSCWJZxopEooxFnNL54Yr3NWH7pBhJWQgdKAt8C5wCnAZ8D5+dZ5w/Ay979HsCUQNu1hG4SRaTLONH4OR+pZOErUVrTiVIWi9T5i3AT+oXAXJ/HjwKP5llnLnChd78U8DPeBNT53Syhm+Is0ifcotEdMVHq3bEsN8XiSzfchN4NGO3zuDfwfJ51vgJSfB5/C1T1s60BwHJgeZ06dUI7CmNMgWL1SyIa+w5lm9GKM16/dOMmofverIVuTPwr6i55hVWc4iwooYt7Pn8iciEwTFWv8h4/6vVff9pnnbneOh+JSClgO1BNC9h4RkaGLl++vMB9G2OMOZmIrFDVDH/PBXOl6KfAuSJST0ROw530nJFnnRnArd79bsD8gpK5McaYyAs4Y5Gq5ojI3bgTnyWBsaq6WkSewDX9ZwBjgFdFZAOwG5f0jTHGFKGgpqBT1dnA7DzLHve5fwjoHtnQjDHGhMIG5zLGmCRhCd0YY5JEwF4uUduxyE4g77zvVXEXJSWLZDseSL5jSrbjgeQ7pmQ7HgjvmFJVtZq/J2KW0P0RkeX5dcdJRMl2PJB8x5RsxwPJd0zJdjwQvWOykosxxiQJS+jGGJMk4i2hj4p1ABGWbMcDyXdMyXY8kHzHlGzHA1E6priqoRtjjCm8eGuhG2OMKSRL6MYYkyTiIqGLyNUisl5ENojI4FjHEwkisklEvhSRVSKSkMNKishYEflJRL7yWVZZRN4TkW+8v2fFMsZQ5HM8w0Rkq/c5rRKRTrGMMRQiUltEFojIGhFZLSL3ecsT+TPK75gS8nMSkTIi8omIfO4dz1+95fVE5GMv503xBj4Mf3+xrqGLSEnga+AKYAtudMeeqrompoGFSUQ2ARmqmrAXRIhIOyAbmKCqjb1lzwC7VfXv3pfvWar6SCzjDFY+xzMMyFbVEbGMrTBEpCZQU1VXikgFYAVwPdCXxP2M8jumm0jAz0lEBCinqtkiUhr4ALgPeAB4U1Uni8jLwOeq+lK4+4uHFnorYIOqblTV34DJQJcYx2QAVV2MGz3TVxfgFe/+K7j/bAkhn+NJWKq6TVVXevf3A2uBWiT2Z5TfMSUkb06KbO9hae+mwKXAG97yiH1G8ZDQawE/+DzeQgJ/gD4UeFdEVojIgFgHE0HVVXWbd387UD2WwUTI3SLyhVeSSZjyhC8RqQukAx+TJJ9RnmOCBP2cRKSkiKwCfgLew83otkdVc7xVIpbz4iGhJ6u2qtoc6Ajc5f3cTyreJCaJ3u/1JeD3QBqwDfhHbMMJnYiUB6YBf1TVfb7PJepn5OeYEvZzUtWjqpoGpOAqEg2jta94SOhbgdo+j1O8ZQlNVbd6f38C3sJ9kMlgh1fnzK13/hTjeMKiqju8/3DHgEwS7HPy6rLTgCxVfdNbnNCfkb9jSvTPCUBV9wALgAuBM73pOiGCOS8eEnowU9wlFBEp553QQUTKAVfiJtJOBr7TDd4KvB3DWMKWm/g8N5BAn5N3wm0MsFZV/+nzVMJ+RvkdU6J+TiJSTUTO9O6fgev8sRaX2Lt5q0XsM4p5LxcArwvSc5yY4m54jEMKi4icg2uVg5sValIiHpOIvAa0xw31uQMYCkwHpgJ1cMMf36SqCXGiMZ/jaY/7Ga/AJuBOn/pzXBORtsAS4EvgmLf4MVzNOVE/o/yOqScJ+DmJSFPcSc+SuAb0VFV9wssRk4HKwGfA/6rq4bD3Fw8J3RhjTPjioeRijDEmAiyhG2NMkrCEbowxScISujHGJAlL6MYYkyQsoRtjTJKwhG6MMUni/wMqXd1uR3JE4wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating the model on the test set\n",
        "\n"
      ],
      "metadata": {
        "id": "vMaEgyvXNotD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_model = keras.models.load_model(\"convnet_from_scratch.keras\")\n",
        "test_loss, test_acc = test_model.evaluate(test_dataset) \n",
        "print(f\"Test accuracy: {test_acc:.3f}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "oQpt_UavMb_n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb42198c-8fa9-440d-a513-234501666074"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 3s 38ms/step - loss: 0.5833 - accuracy: 0.7015\n",
            "Test accuracy: 0.701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google DRive in Google Coloab\n",
        "from google.colab import drive\n",
        "drive.mount (\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVbgweQ4jl0_",
        "outputId": "8c6d613a-d68e-4e79-bf41-320e18e8f7d8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    }
  ]
}