Car_or_Truck.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Car or Truck.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP3wMSAWiLa7ztSs9GTJiPK"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fYjhR7BResKr"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "AD6p0G1OhrB8"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "DJc2h998h6CK"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d ryanholbrook/car-or-truck"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsvQujkzh9NA",
        "outputId": "c444753b-338b-422a-af91-a02c89e7238f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading car-or-truck.zip to /content\n",
            " 73% 57.0M/77.7M [00:00<00:00, 70.5MB/s]\n",
            "100% 77.7M/77.7M [00:00<00:00, 89.3MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip car-or-truck.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKerYspUiRrb",
        "outputId": "8e9e71fa-058d-444e-d4f2-7e7d1e76e9f4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: valid/Car/05205.jpeg    \n",
            "  inflating: valid/Car/05206.jpeg    \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports\n",
        "import os, warnings\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import gridspec\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory"
      ],
      "metadata": {
        "id": "zfLxd6ZDiZvN"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reproducability\n",
        "def set_seed(seed=31415):\n",
        "    np.random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
        "set_seed(31415)"
      ],
      "metadata": {
        "id": "5QhFRU3WijoE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Matplotlib defaults\n",
        "plt.rc('figure', autolayout=True)\n",
        "plt.rc('axes', labelweight='bold', labelsize='large',\n",
        "       titleweight='bold', titlesize=18, titlepad=10)\n",
        "plt.rc('image', cmap='magma')\n",
        "warnings.filterwarnings(\"ignore\") # to clean up output cells"
      ],
      "metadata": {
        "id": "3iq-FHjvimdC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load training and validation sets\n",
        "ds_train_ = image_dataset_from_directory(\n",
        "    '/content/train',\n",
        "    labels='inferred',\n",
        "    label_mode='binary',\n",
        "    image_size=[128, 128],\n",
        "    interpolation='nearest',\n",
        "    batch_size=64,\n",
        "    shuffle=True,\n",
        ")\n",
        "ds_valid_ = image_dataset_from_directory(\n",
        "    '/content/valid',\n",
        "    labels='inferred',\n",
        "    label_mode='binary',\n",
        "    image_size=[128, 128],\n",
        "    interpolation='nearest',\n",
        "    batch_size=64,\n",
        "    shuffle=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lnLkAjYioAU",
        "outputId": "41bb52da-e583-439a-b284-54439007b6d1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5117 files belonging to 2 classes.\n",
            "Found 5051 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Pipeline\n",
        "def convert_to_float(image, label):\n",
        "    image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n",
        "    return image, label\n",
        "\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "ds_train = (\n",
        "    ds_train_\n",
        "    .map(convert_to_float)\n",
        "    .cache()\n",
        "    .prefetch(buffer_size=AUTOTUNE)\n",
        ")\n",
        "ds_valid = (\n",
        "    ds_valid_\n",
        "    .map(convert_to_float)\n",
        "    .cache()\n",
        "    .prefetch(buffer_size=AUTOTUNE)\n",
        ")"
      ],
      "metadata": {
        "id": "DMsx6m-Xi4Cg"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define Pretrained Base"
      ],
      "metadata": {
        "id": "g5paH4wtjHxG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The pretrained model used is called VGG16."
      ],
      "metadata": {
        "id": "TVqXNeUsjMb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d ryanholbrook/cv-course-models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8saQzdf7j5ZG",
        "outputId": "c986d580-6069-4dd7-c001-ecff9887c496"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading cv-course-models.zip to /content\n",
            " 97% 468M/481M [00:03<00:00, 161MB/s]\n",
            "100% 481M/481M [00:03<00:00, 158MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip cv-course-models.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clXnoV_Pj9tX",
        "outputId": "3542e0d4-cf8b-4a88-bef5-447c7628483e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  cv-course-models.zip\n",
            "  inflating: cv-course-models/.ipynb_checkpoints/encyclopedia-of-course-models-checkpoint.ipynb  \n",
            "  inflating: cv-course-models/.ipynb_checkpoints/inceptionv1-checkpoint.ipynb  \n",
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            "  inflating: cv-course-models/.ipynb_checkpoints/vgg16-checkpoint.ipynb  \n",
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            "  inflating: cv-course-models/inceptionv1.ipynb  \n",
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            "  inflating: cv-course-models/inceptionv1/saved_model.pb  \n",
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            "  inflating: cv-course-models/inceptionv1/variables/variables.index  \n",
            "  inflating: cv-course-models/inceptionv3/saved_model.pb  \n",
            "  inflating: cv-course-models/inceptionv3/variables/variables.data-00000-of-00002  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained_base = tf.keras.models.load_model(\n",
        "    '/content/cv-course-models/vgg16-pretrained-base',\n",
        ")\n",
        "pretrained_base.trainable = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Qr6vcQIjLeD",
        "outputId": "472660a7-436e-4f17-a99b-b4d2e0e6a102"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Attach Head"
      ],
      "metadata": {
        "id": "jKyO9gGHkjxe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we'll use a layer of hidden units (the first Dense layer) followed by a layer to transform the outputs to a probability score for class 1, Truck.\n",
        " \n",
        "The Flatten layer transforms the two dimensional outputs of the base into the one dimensional inputs needed by the head."
      ],
      "metadata": {
        "id": "m9FdxqsHklnV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    pretrained_base,\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(6, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid'),\n",
        "])"
      ],
      "metadata": {
        "id": "IZGkiHbUki0T"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the model"
      ],
      "metadata": {
        "id": "4-rZh3vPk25_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since this is a two-class problem, we'll use the binary versions of crossentropy and accuracy. "
      ],
      "metadata": {
        "id": "E-11v73yk5O8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['binary_accuracy'],\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    ds_train,\n",
        "    validation_data=ds_valid,\n",
        "    epochs=30\n",
        ")\n",
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rn-kSmE4k4Ur",
        "outputId": "f17c5d31-4621-4004-c898-fea2bb5e093c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "80/80 [==============================] - 50s 458ms/step - loss: 0.4754 - binary_accuracy: 0.7723 - val_loss: 0.3734 - val_binary_accuracy: 0.8390\n",
            "Epoch 2/30\n",
            "80/80 [==============================] - 32s 398ms/step - loss: 0.3473 - binary_accuracy: 0.8525 - val_loss: 0.3397 - val_binary_accuracy: 0.8559\n",
            "Epoch 3/30\n",
            "80/80 [==============================] - 31s 390ms/step - loss: 0.2982 - binary_accuracy: 0.8800 - val_loss: 0.3264 - val_binary_accuracy: 0.8628\n",
            "Epoch 4/30\n",
            "80/80 [==============================] - 32s 396ms/step - loss: 0.2643 - binary_accuracy: 0.8970 - val_loss: 0.3207 - val_binary_accuracy: 0.8666\n",
            "Epoch 5/30\n",
            "80/80 [==============================] - 31s 394ms/step - loss: 0.2381 - binary_accuracy: 0.9105 - val_loss: 0.3174 - val_binary_accuracy: 0.8652\n",
            "Epoch 6/30\n",
            "80/80 [==============================] - 31s 395ms/step - loss: 0.2168 - binary_accuracy: 0.9222 - val_loss: 0.3138 - val_binary_accuracy: 0.8687\n",
            "Epoch 7/30\n",
            "80/80 [==============================] - 32s 397ms/step - loss: 0.1994 - binary_accuracy: 0.9324 - val_loss: 0.3085 - val_binary_accuracy: 0.8737\n",
            "Epoch 8/30\n",
            "80/80 [==============================] - 31s 396ms/step - loss: 0.1860 - binary_accuracy: 0.9365 - val_loss: 0.3055 - val_binary_accuracy: 0.8731\n",
            "Epoch 9/30\n",
            "80/80 [==============================] - 31s 391ms/step - loss: 0.1785 - binary_accuracy: 0.9388 - val_loss: 0.3187 - val_binary_accuracy: 0.8668\n",
            "Epoch 10/30\n",
            "80/80 [==============================] - 31s 395ms/step - loss: 0.1848 - binary_accuracy: 0.9304 - val_loss: 0.3743 - val_binary_accuracy: 0.8483\n",
            "Epoch 11/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.2156 - binary_accuracy: 0.9060 - val_loss: 0.3296 - val_binary_accuracy: 0.8676\n",
            "Epoch 12/30\n",
            "80/80 [==============================] - 31s 392ms/step - loss: 0.1935 - binary_accuracy: 0.9201 - val_loss: 0.3721 - val_binary_accuracy: 0.8503\n",
            "Epoch 13/30\n",
            "80/80 [==============================] - 31s 391ms/step - loss: 0.1781 - binary_accuracy: 0.9293 - val_loss: 0.3399 - val_binary_accuracy: 0.8660\n",
            "Epoch 14/30\n",
            "80/80 [==============================] - 31s 394ms/step - loss: 0.1665 - binary_accuracy: 0.9320 - val_loss: 0.3285 - val_binary_accuracy: 0.8745\n",
            "Epoch 15/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.1364 - binary_accuracy: 0.9474 - val_loss: 0.3659 - val_binary_accuracy: 0.8582\n",
            "Epoch 16/30\n",
            "80/80 [==============================] - 31s 396ms/step - loss: 0.1104 - binary_accuracy: 0.9652 - val_loss: 0.3790 - val_binary_accuracy: 0.8557\n",
            "Epoch 17/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.0940 - binary_accuracy: 0.9752 - val_loss: 0.3819 - val_binary_accuracy: 0.8541\n",
            "Epoch 18/30\n",
            "80/80 [==============================] - 31s 392ms/step - loss: 0.0835 - binary_accuracy: 0.9824 - val_loss: 0.3846 - val_binary_accuracy: 0.8555\n",
            "Epoch 19/30\n",
            "80/80 [==============================] - 31s 392ms/step - loss: 0.0757 - binary_accuracy: 0.9844 - val_loss: 0.3880 - val_binary_accuracy: 0.8565\n",
            "Epoch 20/30\n",
            "80/80 [==============================] - 32s 396ms/step - loss: 0.0692 - binary_accuracy: 0.9869 - val_loss: 0.3922 - val_binary_accuracy: 0.8565\n",
            "Epoch 21/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.0638 - binary_accuracy: 0.9881 - val_loss: 0.3967 - val_binary_accuracy: 0.8559\n",
            "Epoch 22/30\n",
            "80/80 [==============================] - 31s 392ms/step - loss: 0.0590 - binary_accuracy: 0.9896 - val_loss: 0.4021 - val_binary_accuracy: 0.8553\n",
            "Epoch 23/30\n",
            "80/80 [==============================] - 32s 395ms/step - loss: 0.0548 - binary_accuracy: 0.9922 - val_loss: 0.4074 - val_binary_accuracy: 0.8551\n",
            "Epoch 24/30\n",
            "80/80 [==============================] - 31s 394ms/step - loss: 0.0510 - binary_accuracy: 0.9936 - val_loss: 0.4135 - val_binary_accuracy: 0.8549\n",
            "Epoch 25/30\n",
            "80/80 [==============================] - 32s 395ms/step - loss: 0.0476 - binary_accuracy: 0.9943 - val_loss: 0.4195 - val_binary_accuracy: 0.8551\n",
            "Epoch 26/30\n",
            "80/80 [==============================] - 31s 394ms/step - loss: 0.0446 - binary_accuracy: 0.9953 - val_loss: 0.4263 - val_binary_accuracy: 0.8551\n",
            "Epoch 27/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.0420 - binary_accuracy: 0.9959 - val_loss: 0.4342 - val_binary_accuracy: 0.8549\n",
            "Epoch 28/30\n",
            "80/80 [==============================] - 31s 390ms/step - loss: 0.0399 - binary_accuracy: 0.9967 - val_loss: 0.4430 - val_binary_accuracy: 0.8545\n",
            "Epoch 29/30\n",
            "80/80 [==============================] - 31s 393ms/step - loss: 0.0382 - binary_accuracy: 0.9969 - val_loss: 0.4535 - val_binary_accuracy: 0.8541\n",
            "Epoch 30/30\n",
            "80/80 [==============================] - 32s 396ms/step - loss: 0.0369 - binary_accuracy: 0.9973 - val_loss: 0.4653 - val_binary_accuracy: 0.8543\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f065a461b50>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "history_frame = pd.DataFrame(history.history)\n",
        "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
        "history_frame.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "ClqYCK6EyfzJ",
        "outputId": "191d9b10-3e15-44b6-bd31-88604637a874"
      },
      "execution_count": 17,
      "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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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing inceptionv1 pretrained model"
      ],
      "metadata": {
        "id": "QHvhDq3hylFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained_base = tf.keras.models.load_model(\n",
        "    '/content/cv-course-models/inceptionv1',\n",
        ")\n",
        "pretrained_base.trainable = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4KMKYAzytBI",
        "outputId": "5337ddae-c4da-4210-da27-3f4791a5f530"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    pretrained_base,\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(6, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid'),\n",
        "])"
      ],
      "metadata": {
        "id": "QF_H6JrfzAE6"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['binary_accuracy'],\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    ds_train,\n",
        "    validation_data=ds_valid,\n",
        "    epochs=30\n",
        ")\n",
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQXpPMyQzB7s",
        "outputId": "22777e72-6b12-4d9d-e312-5c7b5ab2a3cd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "80/80 [==============================] - 13s 93ms/step - loss: 0.4912 - binary_accuracy: 0.7571 - val_loss: 0.4131 - val_binary_accuracy: 0.8190\n",
            "Epoch 2/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.3909 - binary_accuracy: 0.8231 - val_loss: 0.3836 - val_binary_accuracy: 0.8349\n",
            "Epoch 3/30\n",
            "80/80 [==============================] - 5s 67ms/step - loss: 0.3629 - binary_accuracy: 0.8407 - val_loss: 0.3683 - val_binary_accuracy: 0.8390\n",
            "Epoch 4/30\n",
            "80/80 [==============================] - 5s 65ms/step - loss: 0.3436 - binary_accuracy: 0.8513 - val_loss: 0.3591 - val_binary_accuracy: 0.8452\n",
            "Epoch 5/30\n",
            "80/80 [==============================] - 5s 65ms/step - loss: 0.3289 - binary_accuracy: 0.8626 - val_loss: 0.3512 - val_binary_accuracy: 0.8483\n",
            "Epoch 6/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.3171 - binary_accuracy: 0.8691 - val_loss: 0.3447 - val_binary_accuracy: 0.8505\n",
            "Epoch 7/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.3070 - binary_accuracy: 0.8716 - val_loss: 0.3388 - val_binary_accuracy: 0.8557\n",
            "Epoch 8/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2982 - binary_accuracy: 0.8741 - val_loss: 0.3353 - val_binary_accuracy: 0.8578\n",
            "Epoch 9/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2906 - binary_accuracy: 0.8782 - val_loss: 0.3334 - val_binary_accuracy: 0.8586\n",
            "Epoch 10/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2838 - binary_accuracy: 0.8829 - val_loss: 0.3313 - val_binary_accuracy: 0.8594\n",
            "Epoch 11/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2775 - binary_accuracy: 0.8884 - val_loss: 0.3296 - val_binary_accuracy: 0.8604\n",
            "Epoch 12/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2717 - binary_accuracy: 0.8906 - val_loss: 0.3283 - val_binary_accuracy: 0.8624\n",
            "Epoch 13/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2665 - binary_accuracy: 0.8913 - val_loss: 0.3275 - val_binary_accuracy: 0.8636\n",
            "Epoch 14/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2616 - binary_accuracy: 0.8941 - val_loss: 0.3273 - val_binary_accuracy: 0.8636\n",
            "Epoch 15/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2571 - binary_accuracy: 0.8953 - val_loss: 0.3272 - val_binary_accuracy: 0.8642\n",
            "Epoch 16/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2529 - binary_accuracy: 0.8978 - val_loss: 0.3272 - val_binary_accuracy: 0.8650\n",
            "Epoch 17/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2490 - binary_accuracy: 0.8996 - val_loss: 0.3275 - val_binary_accuracy: 0.8652\n",
            "Epoch 18/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2454 - binary_accuracy: 0.9027 - val_loss: 0.3271 - val_binary_accuracy: 0.8656\n",
            "Epoch 19/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2415 - binary_accuracy: 0.9037 - val_loss: 0.3269 - val_binary_accuracy: 0.8656\n",
            "Epoch 20/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2378 - binary_accuracy: 0.9062 - val_loss: 0.3272 - val_binary_accuracy: 0.8664\n",
            "Epoch 21/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2346 - binary_accuracy: 0.9080 - val_loss: 0.3277 - val_binary_accuracy: 0.8668\n",
            "Epoch 22/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2311 - binary_accuracy: 0.9078 - val_loss: 0.3279 - val_binary_accuracy: 0.8672\n",
            "Epoch 23/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2278 - binary_accuracy: 0.9099 - val_loss: 0.3287 - val_binary_accuracy: 0.8658\n",
            "Epoch 24/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2246 - binary_accuracy: 0.9123 - val_loss: 0.3281 - val_binary_accuracy: 0.8654\n",
            "Epoch 25/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2216 - binary_accuracy: 0.9142 - val_loss: 0.3285 - val_binary_accuracy: 0.8650\n",
            "Epoch 26/30\n",
            "80/80 [==============================] - 5s 65ms/step - loss: 0.2184 - binary_accuracy: 0.9152 - val_loss: 0.3299 - val_binary_accuracy: 0.8644\n",
            "Epoch 27/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2156 - binary_accuracy: 0.9177 - val_loss: 0.3305 - val_binary_accuracy: 0.8634\n",
            "Epoch 28/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2128 - binary_accuracy: 0.9187 - val_loss: 0.3312 - val_binary_accuracy: 0.8644\n",
            "Epoch 29/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2103 - binary_accuracy: 0.9201 - val_loss: 0.3320 - val_binary_accuracy: 0.8650\n",
            "Epoch 30/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.2079 - binary_accuracy: 0.9209 - val_loss: 0.3334 - val_binary_accuracy: 0.8638\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f063ac1b5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "history_frame = pd.DataFrame(history.history)\n",
        "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
        "history_frame.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "uEP256qAz0fc",
        "outputId": "849fc43e-4901-43d8-d1fd-9f1e315b4fd4"
      },
      "execution_count": 22,
      "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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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see difference between the learning curves from inceptionv1 and learing curves from VGG16.\n",
        "\n",
        "Explanation:\n",
        "\n",
        "The training loss and validation loss stay fairly close is evidence that the model isn't just memorizing the training data, but rather learning general properties of the two classes."
      ],
      "metadata": {
        "id": "d_608S_R0sR3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Designing a custom Convnet to classify Cars and Trucks"
      ],
      "metadata": {
        "id": "qbDP8RrCTlZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "\n",
        "    # First Convolutional Block\n",
        "    layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\", padding='same',\n",
        "                  # give the input dimensions in the first layer\n",
        "                  # [height, width, color channels(RGB)]\n",
        "                  input_shape=[128, 128, 3]),\n",
        "    layers.MaxPool2D(),\n",
        "\n",
        "    # Second Convolutional Block\n",
        "    layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\", padding='same'),\n",
        "    layers.MaxPool2D(),\n",
        "    # Third Convolutional Block\n",
        "    layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\", padding='same'),\n",
        "    layers.MaxPool2D(),\n",
        "\n",
        "    # Classifier Head\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(units=6, activation=\"relu\"),\n",
        "    layers.Dense(units=1, activation=\"sigmoid\"),\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHtIrY1tV0AJ",
        "outputId": "5c006e57-b154-40d2-ba6b-1ef06ccece0b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 64, 64, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 16, 16, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 32768)             0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 6)                 196614    \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 289,869\n",
            "Trainable params: 289,869\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['binary_accuracy']\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    ds_train,\n",
        "    validation_data=ds_valid,\n",
        "    epochs=30,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHQCf1OVaUqw",
        "outputId": "32a45860-1b9d-404b-b0f7-70637b9dbd03"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "80/80 [==============================] - 8s 73ms/step - loss: 0.6796 - binary_accuracy: 0.5722 - val_loss: 0.6713 - val_binary_accuracy: 0.5785\n",
            "Epoch 2/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.6648 - binary_accuracy: 0.6029 - val_loss: 0.6573 - val_binary_accuracy: 0.6129\n",
            "Epoch 3/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.6568 - binary_accuracy: 0.6142 - val_loss: 0.6560 - val_binary_accuracy: 0.5969\n",
            "Epoch 4/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.6489 - binary_accuracy: 0.6238 - val_loss: 0.6554 - val_binary_accuracy: 0.5959\n",
            "Epoch 5/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.6391 - binary_accuracy: 0.6353 - val_loss: 0.6488 - val_binary_accuracy: 0.6009\n",
            "Epoch 6/30\n",
            "80/80 [==============================] - 5s 64ms/step - loss: 0.6270 - binary_accuracy: 0.6478 - val_loss: 0.6386 - val_binary_accuracy: 0.6145\n",
            "Epoch 7/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.6135 - binary_accuracy: 0.6602 - val_loss: 0.6255 - val_binary_accuracy: 0.6331\n",
            "Epoch 8/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.6000 - binary_accuracy: 0.6811 - val_loss: 0.6127 - val_binary_accuracy: 0.6545\n",
            "Epoch 9/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.5864 - binary_accuracy: 0.6942 - val_loss: 0.5959 - val_binary_accuracy: 0.6741\n",
            "Epoch 10/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.5722 - binary_accuracy: 0.7102 - val_loss: 0.5777 - val_binary_accuracy: 0.6991\n",
            "Epoch 11/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.5561 - binary_accuracy: 0.7235 - val_loss: 0.5693 - val_binary_accuracy: 0.7094\n",
            "Epoch 12/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.5419 - binary_accuracy: 0.7342 - val_loss: 0.5625 - val_binary_accuracy: 0.7145\n",
            "Epoch 13/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.5259 - binary_accuracy: 0.7495 - val_loss: 0.5504 - val_binary_accuracy: 0.7276\n",
            "Epoch 14/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.5047 - binary_accuracy: 0.7633 - val_loss: 0.5346 - val_binary_accuracy: 0.7367\n",
            "Epoch 15/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.4827 - binary_accuracy: 0.7786 - val_loss: 0.5186 - val_binary_accuracy: 0.7500\n",
            "Epoch 16/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.4619 - binary_accuracy: 0.7919 - val_loss: 0.5302 - val_binary_accuracy: 0.7371\n",
            "Epoch 17/30\n",
            "80/80 [==============================] - 5s 62ms/step - loss: 0.4369 - binary_accuracy: 0.8097 - val_loss: 0.5158 - val_binary_accuracy: 0.7434\n",
            "Epoch 18/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.4122 - binary_accuracy: 0.8186 - val_loss: 0.5238 - val_binary_accuracy: 0.7357\n",
            "Epoch 19/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.3871 - binary_accuracy: 0.8358 - val_loss: 0.4974 - val_binary_accuracy: 0.7581\n",
            "Epoch 20/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.3586 - binary_accuracy: 0.8546 - val_loss: 0.4583 - val_binary_accuracy: 0.7852\n",
            "Epoch 21/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.3313 - binary_accuracy: 0.8698 - val_loss: 0.4449 - val_binary_accuracy: 0.7996\n",
            "Epoch 22/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.3058 - binary_accuracy: 0.8788 - val_loss: 0.4389 - val_binary_accuracy: 0.8026\n",
            "Epoch 23/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2777 - binary_accuracy: 0.8906 - val_loss: 0.4400 - val_binary_accuracy: 0.8052\n",
            "Epoch 24/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2503 - binary_accuracy: 0.9021 - val_loss: 0.4487 - val_binary_accuracy: 0.8086\n",
            "Epoch 25/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2196 - binary_accuracy: 0.9207 - val_loss: 0.4683 - val_binary_accuracy: 0.8115\n",
            "Epoch 26/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.1979 - binary_accuracy: 0.9320 - val_loss: 0.4706 - val_binary_accuracy: 0.8125\n",
            "Epoch 27/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.1879 - binary_accuracy: 0.9316 - val_loss: 0.4767 - val_binary_accuracy: 0.8101\n",
            "Epoch 28/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.1865 - binary_accuracy: 0.9281 - val_loss: 0.7651 - val_binary_accuracy: 0.7104\n",
            "Epoch 29/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.2126 - binary_accuracy: 0.9134 - val_loss: 0.6100 - val_binary_accuracy: 0.7575\n",
            "Epoch 30/30\n",
            "80/80 [==============================] - 5s 63ms/step - loss: 0.1816 - binary_accuracy: 0.9328 - val_loss: 0.5514 - val_binary_accuracy: 0.7909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "history_frame = pd.DataFrame(history.history)\n",
        "history_frame.loc[:, ['loss', 'val_loss']].plot()\n",
        "history_frame.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "AI6cyyA2a_x9",
        "outputId": "ddfc5a8a-4d65-45c1-ee49-1e0ec23bbaaf"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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vBWWrmncwQgj7NjnCWEl3wJyCvW7HbPhpAozfCeVrFk1t+SABZaJzqen8uv8sK6PPsvlYEpnZGn9fD+5rYLSsWtcoj7sl1/XSiYfhwI+wfykk7DEeCwy3hlVv42JkIYQA4wvum5Wh2Qjo+lbBXntmD3zeHvp+CWH9iqa+fJCAshMp6ZmsPniOldEJrDmUSFpGNn7ebjwQXpmBzUOoH1j6ry84f9zaslpqdAcCVAqD5o9A2ADwKFX8ByGEsB/pl+DtKtD5dWgzvmCvzc40wq3laOP1JpGAskPpmdlsOJLEkqg4fo0+S0Z2Do2CyzAgIoRe4UF/v0D4Uiwc+Ami5kHCXvAoDeEPQfNR0qoSoqQ6dwA+a3XnraAvOoGbN4z82fa15ZMElJ07fyWDH3bFsTDyNAcTUvF0c6F7aCADmofQsno5Y3DFdVrD6W3GVE7RP0BOJtTsZFznVbuzDFsXoiQ5ugrm9oGHV0DV1gV//c/Pwt7v4MWTxpRxJrhVQMkwcztQzsedR9pV5+G21dgTe4n520/z0+54vt8VR/UKPvSPCKZf02AqlvY0RttUaWncOr8BO2dD5Ffw7SDwqwIRjxoT6HqXM/uwhBBF7U5mkcgtqAlEfmmMJLbDnhhpQdmptIwslu9NYOH202w7YVxjdXddfwY2r0KnehVxdcnVqsrOhIPLYPsMOLEeXD2M5n7zUVC5qXkHIYQoWmvegTVvwr8TjWubCiphL0xrZ+pACWlBOSBvdwv9mgXTr1kwMYmXWRgZy+Kdsfx2IJKQcl6MaF2N/hEhlPFyM+YAbPiAcTu73+j+273AOF9VOQKaPwoNH5SploRwNqnxxgwSdxJOAP71jKXi43eZOpLvZqQF5UAys3P4df9ZZm08wbYT5/Fyc6Vvs8qMbFONWhV9/7px+iWI+tYIq+Sj4FXWGFQR8Yip1zwIIWxoXn+4fBYeX3fn+/jiHiOkHl5mu7oKQAZJOKF9cZeYtekES6PiycjOoX3tCjzcthp31an450XAYAyqOL7O6Gc+uAxysqDGXca5qrrdjTkKhRCOaWpbKBMCQ+bf+T6W/QN2z4eXTpkyUEICyoklXb7G/G2n+HrLSc6mXKNaeW9GtKlGv2bB+N44VD01wVh6ZMcsSIk15u9qOsK4yE+mVhLC8bxT3ei+7/HBne9j11z4cSyMizQmsC5mtwoox17yXVChlAfjOtVmw4ud+GRwE8r5uPN/P+2n1ZurmLg0muNJV/7c2DcAOj4PT+2GQd8ak0WufQc+DIX5DxnzAubkYykRIYT5Mq/C1fOF/3L5x9Ib9rfCrvTvOAk3Vxd6NQ6iV+Mgdp++yKxNJ5i39SSzNp2gU72KPNy2Gu1qVTCuqXK1QL3uxu38cdjxlfEt6uDPUK6mschjWH+5pkoIe/bHEPMCrAOVlz8GSkRBowGFr8uGpAXlhBqH+PHhwHA2vtSJCffUZk/sRYZ9uY0uH63jm62nuJqR/efG5arDfa/Cswegzwxj+qQlj8PnHeDwL8Y5LCGE/fkjoAILtx9XCwSE/Tmdmh2RgHJiFX09efa+Omx8qRPv9W+MxcWFfy3ZS+u3V/HOioOcuXT1z40tHtCoPzy2BvrNNFYb/qY/zOoBsfb3D1eIEi/1jPGzsC0ogDpd4PRWYxkgO5KvgFJKdVVKHVJKHVVKvXSTbQYopfYrpaKVUt/YtkxRGB4WV/o1C2bZhHYsGN2KVtXL8/naY7R7ZzXjvtnJzlMX/tzYxQVC+8LYbdD9PUg6BDPugQXDIOmIeQchhPir60u9+xayBQXQ8gnwrgC/TbSrXpPbnoNSSrkCU4D7gFhgu1JqqdZ6f65tagP/BNpqrS8opSoWVcHizimlaFmjPC1rlOf0+TTmbD7B/O2n+XnPGRqH+PFI22p0Cw00lv+wuEOLx6DxYNg8BTZ9YgxTbzoMOr5U+G4FIUThpMSDRxnbrGrg4QsdnocVL0LMamN+Tztw22HmSqnWwEStdRfr/X8CaK3fyrXNu8BhrfWM/L6xDDO3D1euZbF4ZyxfbTzB8aQrVCrtwbBWVRncogrlS3n8ueHlRFj/Hmz/Elws0HoMtH0KPMuYV7wQJdn8hyD5GIzdYpv9ZV0zFj/0LgePrS62a6IKO8y8MnA61/1Y62O51QHqKKU2KqW2KKW63qSQ0UqpSKVUZGJiYn5qF0XMx8PC8NbVWPVsR74a2Zw6lXx575fDtH77d15YtJsDZ1KMDUv5Q7d3YNx2Y0n69e/Dx41h06fGP2whRPFKibft9YsWD7j7X3Amylg01Q7YKiItQG3gLmAw8IVSyu/GjbTW07XWEVrrCH9/fxu9tbAFFxfF3fUq8vWjLfn1mQ70axbM0t3xdPt4PYOmb2ZldALZOdoY9dd3hjG1SlBT+OVlmNISDq2wq75rIZxeSrztu9obDYCKDWDVa8Yk1CbLT0DFASG57gdbH8stFliqtc7UWh8HDmMElnBAtSv58uaDYWz55z281K0ep5LTePzrHdz13mpmrI/h0tVMCGwMw76HYUvA1R2+HWjMC5Z01OzyhXB+2ZnGHHy2GMGXm4sr3PNfOH/MuDbSZPkJqO1AbaVUdaWUOzAIWHrDNj9gtJ5QSlXA6PKLsWGdwgR+3u480bEm6164m88eakpAaU9eX3aA1m+t4r8/7iMm8bJxMvXJjdDlTWOY6met4Jf/QHqK2eUL4bwunwV00UxRVqcrhLQ0ZpnJSLP9/gvgtgGltc4CxgErgQPAQq11tFLqVaVUL+tmK4FkpdR+YDXwvNY6uaiKFsXL4upC97BAvnuiDT+Pb0fX0ADmbztNp/fXMvKrbWyIuYRuNQbG74DGA2HTZPg0wphNXaZOEsL2rl+k61sEAaUU3DvRuM5q23Tb778gpchkseJOJKZeY97Wk8zdcoqky9doGFSa0R1q0D0sELeEXbD8BePK9ODm0O1dWThRCFuK/gG+GwFPbISA0KJ5j3kD4PQWY+5Or7JF8x7IZLGiCPj7evD0vXXY8OLdvNM3jPTMbJ6aH8Vdk9bw5fFyXBn2P3hgKlw4CV90gh/HGUPVhRCFV9il3vPjnv8aXfUbPy6697gNCShRKJ5urgxsXoVfn+nIjOERVPbz4rWf99P67dW8m9CUxJGboM042P0tTG4Gmz+DzHSzyxbCsaXEgcWrSFs2BIQak0ZvmQYpZ4rufW5BuviEze06dYEv1sewYl8CFhcXHmgSxNiwHKpuew2OrTJmTq7S2lg4sebdUCnMlIXShHBYix4xlseYUMRLZJw/Dp82N2aQ6fFhkbyFLFgoTHEy+Qoz1h/nux2nSc/M4Z66/vyjzlnqpWxEHV8L56yzZXmVgxodocbdRmiVrWpm2ULYv5ldjRldRv5c9O+1/HljBplx26F8TZvvXgJKmOr8lQzmbD7BnM0nOX8lg+bVyvJc57q08s+EmLXG3F8xa/6cnblcDSOoatwN1dsXbTeGEI7oozCjF6JPMYyyu3wOPg43Zjzv/5XNdy8BJexCemY2CyNPM2X1Uc6mXKNdrQo827kOTauUNWahSDoMx6xhdWI9ZFwG5WKs/Bvc3LhVjoDytaRLUJRcOTnwRiVoNQbu+7/iec/fX4d1k2D0WggKt+muJaCEXUnPzGbulpNMXXOM5CsZ3FOvIs/cV4fQyrkmns3ONNamObb6z3Vqrlkv/vUsYwRVcHMIjoDKzYwJLoUoCS4nwnu1oNskaDm6eN4z/ZLRigpqYswgY0O3CihZ8l0UO083V0a1r8HgFlWYtekE09fF0GPyBrqFBvDMfXWoU8kXXN2gSivjBsa3xuQjELvdeouEde+Ctl4IXL6WtYXVzOgarFDLvAMUoihdXweqKIeY38izDLR/zph78/g6qN6hWN5WWlDCdCnpmXy5/jhfbjjOlYwsejcO4ql761C9gs+tX3gt1RjJFBtpvW2HK+eM56q0MUYeNegN7rfZjxCO5OBymD8YHvvd+EJWXDLTYXJT8A2AUauMGSdsQLr4hEO4cCWDz9fFMGvTcTKzNf2aBjP+nloEl/XO3w60hosnIXoJ7PzamPDSozSE9YMmw4zuCRv9TyWEabbPgGXPwXOHjLAoTju/hqXjYOBcqN/TJruUgBIO5VxqOlPXHGPellNoNP2ahfBY++rU8C/AyqFaw8lNsHMO7P8BstKN662aDodG/WVkoHBcq141Znf49zlj9vHilJ0FU1sDCp7cBK6FP0skASUc0plLV5my+igLI2PJzM7h3vqVeLxDDZpVLYsqSEvo6kXY+x3s+hrO7AZXD2jQywirqu1kRKBwLEuegOPr4dloc95//1JYOAx6T4EmQwu9Owko4dCSLl9jzqYTzNlykotpmTSp4sfjHWpwX4MAXF0K2GV3ZrfRTbFnIVy7BGWrQ8Qjxs2jAC00IcwyuxdkXoVRv5rz/lrDjHsg9ayxgoGbZ6F2JwElnEJaRhaLdsQyY/1xTp1Po1p5bx5tX4N+TYPxci9gV0fmVeOb4M7ZcHIjeFeAtk9B81Hgns9zXkKYYXKEcW3ggNnm1XBiI5zaBK3HgZtXoXYlASWcSnaOZmV0Ap+vi2H36YuU83FnWKuqDG9dlfKlPAq+w9PbYM1bcOx38KkI7Z42WlSF/B9PCJvTGt6sDM1GQtc3za7GJiSghFPSWrP9xAWmr4vhtwNn8bC40LdZMKPb16Da7Yao5+XUFlj9JhxfC6UCoN0zxh+CQnZhCGEz6Zfg7SrQ+XVoM97samxCLtQVTkkpRYvq5WhRvRxHz11mxvoYFkXGsmD7afo0qcyEe2oTUq4A3XVVWsGIpUb3xeo3YcWLxmip9s8aAyosd9A6E8KWimMdKDsiw5eEU6hVsRRv923EhpfuZnjrqvy4O55O76/h5SV7OXPpasF2Vq0tPLwMRvxkzKy+/B/wSVOInAlZGUVzAELkxx+zSFQ2t45iIgElnEpFX09e6dmQtc/fxcDmISyMPE3HSWuYuDSac6kFXCixegd4+H8w7AfjG+vPzxiLLu6YbcwVKERxu75woG+guXUUEwko4ZQCy3jx+gNh/P7cXTwYXpmvt5ykw7ureWv5Ac5fKUArSCljUcVHf4Ghi6GUP/w0AT6NgKhvISe76A5CiBtd7+KTgBLC8YWU8+adfo1Y9WxHuoUGMn19DO3f+Z33Vh7iUloBWkFKQa17jTnIBi8wplD64Qn4rBXsW2xMZltYaedh63RYONy4Tis7q/D7FM4lJc4YaWpxN7uSYiGj+ESJcvRcKh/+doRle87g62lhVLsaPNKuGr6ebgXbUU4OHPzZGEyReAAqNoS7/wX17i/YfH/ZmXD0N4iaB4dWQE6mscLw1fNQpgq0HmtMeisT3gqAef3h8ll4fJ3ZldiMDDMX4gYHzqTw4a+H+WX/WSqU8uDtPmHc26BSwXeUk21MTrv6TWNy2sBwuPtlqH3frYMqYR/s/hb2LIAricaFwo0GQvhgI+yOrIQNH8HpLUZgtRht3HzK3/lBgxGISYehXE0ZPu+IprYFvyow+FuzK7EZCSghbmL36Yu8uHgPBxNS6d8smP/2bFDw1hQY3XF7FsDat+HiKQhuAZ1ehuod/wyqK8nGnIBR8yBhD7i4Qd2uEP6Q0X3omsf7ntpiDHU/tBwsXkZrqvU4Y3RhfuTkwNl9xho+x9caE+hmXIaARjBkQYkZruw03qkGoX3h/vfNrsRmJKCEuIVrWdl8suoIU9ccI7CMF5P6NaJNrQp3trOsDIiaC+veM84XVG0HjQfB4RVweKXRhRfY2Ail0H75bxGdOwibPjHOTekcCO1jTM0UEPbX7bSG5KNGGMWshRMbjO5CMBZ1rN4RyteE1W8Zcw8Onm/zJbxFEcm8Cm8EQKf/QId/mF2NzUhACZEPO09d4B8LdxOTdIWRbarxYtd6BZ/j77rMdNgxC9a/byyi6ONvdOE1HgwBoXde5KU42PKZse+My1DzHmj5hNFNeHyt0VJKtQ5FLh0MNToaw+WrtYcyua6dORsN3wyEtGTo8wXU73HnNYnikXzMWDDwgWlGV7CTkIASIp+uZmTzzoqDzNp0ghoVfHhvQGOaVinE2lEZaXBuv9FqyqsL744LvQDbv4St04xwAuM8VvUOf97K1bj1ebDUswLs+BMAABzwSURBVMbKrHE74b5XjalzZEFH+3V8PczuAcOXGl88nIQElBAFtOloEs8v2sOZS1d5omNNnrq3Nh6WYl4cLj8y041JbstWhYoNCh4wmVfhhyeNgR5Nh8P9H9g2SIXt7F4AS0bDuB1QoZbZ1djMrQJKroMSIg9talXgf0+3p2/TYD5bc4zen25kf3yK2WX9nZsn1OtuLL9wJ60fNy/oOxM6PG+sPjy3j9E6E/Yn9fo8fCXjIl2QgBLipkp7ujGpf2NmDI8g6XIGvadsYMrqo2Rl2+CiXHvi4gKd/m2c2zi5GWbcZ5zvEPYlJR48y5Soa+LyFVBKqa5KqUNKqaNKqZfyeH6kUipRKRVlvY2yfalCmOPeBpX45ZkOdG4QwKSVh+j/+WZOJF0xuyzbCx9szOaelmysmHpyk9kVidxS4kvMJLHX3TaglFKuwBSgG9AAGKyUapDHpgu01uHW2wwb1ymEqcr5uPPpkCZ8PCicY+cu0/2T9SzYfgqzzuEWmaptYNRv4F3eWFo8ynkuCHV4KfElZg6+6/LTgmoBHNVax2itM4D5QO+iLUsI+6OUond4ZVY83YHGwX68uHgvT8zdUbDJZx1B+ZpGSFVtbcw3uOo128w1KAonJb7EXVidn4CqDJzOdT/W+tiN+iql9iilFimlQvLakVJqtFIqUikVmZiYeAflCmG+ID8v5o1qyb+61+P3g+fo8tE61h52sn/PXmVh6PfGyL7178Hy58yuqGTLzjTm4JMuvjvyE1BNa90I+BWYnddGWuvpWusIrXWEv7+/jd5aiOLn4qIY3aEmP45th5+XGyNmbmPi0mjSM51o+Q1XN+j5CbSZYCzWGPmV2RWVXJfPArpEjeCD/AVUHJC7RRRsfewPWutkrfU1690ZQDPblCeEfWsQVJqfxrdjZJtqzNp0gp6TNxAdf8nssmxHKbh3ojFX4PLn4dRWsysqmf5Y6l1aUDfaDtRWSlVXSrkDg4CluTdQSuWO9V7AAduVKIR983RzZWKvhsx+pAUXr2bywJSNTF93jJwcJxlA4eIKfWdAmWBYOOzPVV1F8fljqXc5B/UXWussYBywEiN4Fmqto5VSryqlelk3m6CUilZK7QYmACOLqmAh7FXHOv6sfLoDnepV5M3lB3loxlbiL141uyzb8CoLg76Ba5eNBRWzrt3+NcJ2SthS79fJVEdC2JjWmoWRp/m/n/ZjcVG88WAYPRs7yTff6CXw3Uho9jD0/MjsakqOlS8bcy++fMbp5kuUqY6EKEZKKQY2r8LyCe2p4V+K8d/uYuw3O0m+7AStjoYPQtunYcdXxozqonhcH2LuZOF0OxJQQhSRahV8WPREa57vUpdfohPo/OE6lu91gvM39/wXanYyBk2c3m52NSVDCbwGCiSghChSFlcXxt5di5/HtyfIz4sx83Y6fmvKxRX6fmn8wVwwFFITzK7IuWhtnHM6tALWvgvzH4IzUSUyoOQclBDFJCs7h8/XxfDRb4cp7enGaw+E0j3MgU96J+yDL+8zlo8f8RNY3M2uyPFoDRdOwJndxi1hj/Hz+hpfKGNmj4BGxnpdlZuaWW2RkPWghLAjhxJS+cd3u9kbd4n7GwXyaq+GlC/lYXZZd2bf97DoYYh4FHp8YHY19kNruJYKV89D2nljCZOrF6y/n4crSZB4EM7sgWvW6+ZcLOBfHwIbGQtcBjQyVl/28DX3WIrYrQLKUtzFCFHS1Q3wZcmYNn+0prYcS3bc1lRoH6P7aePHEBRuTI1U0iQegnXvwaXTfw2jnMybv8ajDFSoDWF9/wyjig2M9b3EH6QFJYSJnKI1lZMNc/vCyY3w8P8gOM8vw84nMx02fADrPwA3bwgIA++y4FUOvMv99adX2b/+7iptg+uki08IO+YU56bSzsP0u4xJTUevAd9KJhdUxI6vh5+fhuSjEDYAurwJpWR+0Tsh10EJYcfyGun37x/2kpHlQEtceJeDQfOMrq3vRkCWky1Bcl3aefhxLMzuYYTx0MXQ9wsJpyIiASWEnbh+burxjjWYu+UUQ77YwrnUdLPLyr+AMOj9KZzabAw/j9thdkW2ozXs+Q4+bW4s4tj2KRizxZhEVxQZ6eITwg79tDueFxbtobSXhWlDm9GkSlmzS8q/jZ/A2ncg4zIEt4CWj0OD3sbyHY7o/HFY9iwc+x0qN4OeHxthLGxCzkEJ4YD2x6fw+NxIzl66xmsPNGRg8ypml5R/6SkQ9Q1s+xzOxxiTnEY8Cs1GOk53WHYmbJ4Ca942Lk6+57/QfJTxu7AZCSghHNSFKxlMmL+L9UeSGNqqCv/t0RB3iwP1zOfkwNHfYOs0OLYKXN0htJ/RqgoKN7u6m4vdAT9NgLP7oO790P1dY7kRYXMSUEI4sKzsHCatPMTn62KIqFqWz4Y2paKvA14vk3gYtk03WlaZV6BKayOo6vUwv/svKwNObzVC9OgqY0YH30DoPgnq9zS3NicnASWEE3Do81K5Xb0IUfOMsLpwwlglttEACGpiXLBatlrxzNp9PsYIo6Or4MR645yZiwVCWhqDH5o/Cp5lir6OEk4CSggn4dDnpW6Ukw1HfjG6/46vB51tPO5R2hiEENDI+BnYCPzrFb6VdS3VeJ/rraQLx43H/aoagVTrHqjWHjxLF+59RIFIQAnhRC6mZTD+Wwc+L5WXzKtwbr8xN13CXqOL7Ww0ZKYZz7u6GyEV2AgCGhutrOwMyEo3bplXc/2e/vffU8/A6W3G9ENuPlC9PdS8xwilcjVK3DpL9kQCSggnk52jeXflQT5f6+DnpW4lJxuSjxlhlbDHGl57IC351q9TLmDxMua1s1hvXn5QrZ0RSlVagcXBppNyYhJQQjipn/fE8/x3e/D1tDB1aDOaVXXQ81L5pbXRGroUZ4SMxdMaRF7GfTcv4zyStIgchkx1JIST6tEoiO/HtMHTzZVB0zczb+tJzPrSWSyUMhbuC2luPTdVB/yqGNdWeZY2zlNJODkNCSghHFz9wNL8NK4dbWtV4OUl+3hp8V7SM7PNLkuIQpOAEsIJlPF248sRzRnfqRYLIk8z8PPNxF+8anZZQhSKBJQQTsLVRfFc57p8PqwZxxKv0HPyBjYfu82AAiHsmASUEE6mS8MAfhjbFj9vN4Z+uZUZ62Oc+7yUcFoSUEI4oVoVS/HD2LbcW78iry87wNMLoriaIeelhGORgBLCSfl6ujH1oWY836UuS3fH8+BnGzmVnGZ2WULkmwSUEE7MxUUx9u5afDWyOfEXr9Lz0w2sOXTO7LKEyBcJKCFKgLvqVuSn8e0ILOPJw7O2M2X1UTkvJeyeBJQQJUTV8j58P6YNPRoFMWnlIcZ+s5Mr17LMLkuIm8pXQCmluiqlDimljiqlXrrFdn2VUloplee0FUIIc3m7W/hkUDj/6l6PFfsS6PPZJk4mXzG7LCHydNuAUkq5AlOAbkADYLBSqkEe2/kCTwFbbV2kEMJ2lFKM7lCT2Y+0ICElnV6fbmTt4USzyxLib/LTgmoBHNVax2itM4D5QO88tnsNeAdIt2F9Qogi0r62Pz+Ns56X+mob09Yek/NSwq7kJ6AqA6dz3Y+1PvYHpVRTIERrvcyGtQkhiliV8t58P6YN3UIDeft/Bxn/7S7SMuS8lLAPhR4koZRyAT4AnsvHtqOVUpFKqcjEROlSEMIeeLtb+HRIE17oWpdle8/Q57NNnD4v10sJ8+UnoOKAkFz3g62PXecLhAJrlFIngFbA0rwGSmitp2utI7TWEf7+/ndetRDCppRSjLmrFjNzXS+14UiS2WWJEi4/AbUdqK2Uqq6UcgcGAUuvP6m1vqS1rqC1rqa1rgZsAXpprWU1QiEczN11K7J0XDsq+nowfOZWvlgn8/gJ89w2oLTWWcA4YCVwAFiotY5WSr2qlOpV1AUKIYpXtQo+fD+mLZ0bBPDGcpnHT5hHlnwXQuQpJ0fz2ZqjvP/rYRoH+/HVyOaU9XE3uyzhZGTJdyFEgbm4KMZ1qs3Uh5qx/0wK/WURRFHMJKCEELfUNTSA2Q+3IOFSOv2mbuLouctmlyRKCAkoIcRtta5ZnvmjW5GRnUP/aZuIOn3R7JJECSABJYTIl9DKZVj0RBtKeVoY8sUW1h+RaxlF0ZKAEkLkW7UKPix+og1VynnzyKzt/Lwn3uyShBOTgBJCFEjF0p4seLw14SF+jP92F19vOWl2ScJJSUAJIQqsjJcbcx5pSae6FfnPD/v46LfDckGvsDkJKCHEHfFyd2XasGb0bRrMR78dYeLSaHJyJKSE7VjMLkAI4bjcXF2Y1K8R5Xzc+GL9cc6nZfJ+/8a4W+S7ryg8CSghRKG4uChevr8B5Ut58Pb/DnIxLYNpQ5vh4yF/XkThyNccIYRNPNGxJu/2bcTGo0kM+WILianXzC5JODgJKCGEzQxoHsLnwyI4dDaVPlM3cixRZp0Qd04CSghhU/c1qMT80a25mpFN36mb2H7ivNklCQclASWEsLnwED++f7It5bzdeWjGVrmgV9wRCSghRJGoUt6bxU+2oXFwGcZ9s4vp647JtVKiQCSghBBFpqyPO18/2pL7GwXy5vKDvLI0mmy5Vkrkk4wDFUIUKU83VyYPakKwnxefr4sh/uJVPhncBG93+fMjbk1aUEKIIufiovhn9/q82rshvx88x+DpMgxd3J4ElBCi2AxvXU2GoYt8k4ASQhSr+xpUYoF1GHqfzzax7bgMQxd5k4ASQhS7xiF+LBnTlvKl3Bk6YytLd8swdPF3ElBCCFOElPPm+yfbEB7ix4Rvd/H+L4dkNnTxFxJQQgjT+Hm78/WoFgyMCGHy70d5fO4OLl/LMrssYSckoIQQpvKwuPJ23zAm9mzA7wfP0eezjZxMvmJ2WcIOSEAJIUynlGJk2+rMeaQF51Kv0evTjWw8mmR2WcJkElBCCLvRtlYFfhzblkqlPRg+cxtfbTwu0yOVYBJQQgi7UrW8D9+PaUunehX5v5/28+LiPVzLyja7LGECCSghhN0p5WHh86HNGN+pFgsjYxnyxVbOpaabXZYoZhJQQgi75OKieK5zXaYMacr++BR6f7qRvbGXzC5LFCMJKCGEXbu/USCLnmyNi1L0m7aJH6PizC5JFJN8BZRSqqtS6pBS6qhS6qU8nn9CKbVXKRWllNqglGpg+1KFECVVw6Ay/DiuLY2D/XhqfhTvrDgoF/WWALcNKKWUKzAF6AY0AAbnEUDfaK3DtNbhwLvABzavVAhRolUo5cHcUS0Z3KIKU9cc45mFUWRk5ZhdlihC+VmQpQVwVGsdA6CUmg/0BvZf30BrnZJrex9AvtoIIWzO3eLCmw+GElzWi0krD3H+SgZThzajlIesLeWM8tPFVxk4net+rPWxv1BKjVVKHcNoQU3Ia0dKqdFKqUilVGRiYuKd1CuEKOGUUoy9uxaT+jVi07FkBk3fLGtLOSmbDZLQWk/RWtcEXgT+fZNtpmutI7TWEf7+/rZ6ayFECdQ/IoQvhjfj6LnL9J26iRNJMj2Ss8lPQMUBIbnuB1sfu5n5wAOFKUoIIfKjU71KfPtYK1LTM+k3bZMMQ3cy+Qmo7UBtpVR1pZQ7MAhYmnsDpVTtXHfvB47YrkQhhLi5JlXKsujJNnhYXBk4fTPrDsvpA2dx24DSWmcB44CVwAFgodY6Win1qlKql3WzcUqpaKVUFPAsMKLIKhZCiBvU9C/F92PaULW8D4/M2s4Pu+RaKWegzJqIMSIiQkdGRpry3kII55SSnsnoOZFsiTnPy93r81iHGmaXJG5DKbVDax2R13Myk4QQwmmU9nRj9iMtuD8skDeWH+D1n/fLBb0OTC4eEEI4FQ+LK5MHN8Hf14MZG46TePkak/o1xt0i38cdjQSUEMLpuLgoXunZgIqlPXh3xSGSL2cwdWhTfD3dzC5NFIB8pRBCOCWlFGPuMi7o3RyTzIDPt5BwSZbscCQSUEIIp9Y/IoSZI5tzKvkKfT7byOGzqWaXJPJJAkoI4fQ61vFn4ROtycrR9J26iU3HkswuSeSDBJQQokRoGFSGJWPbElDakxEzt8m1Ug5AAkoIUWJU9vNi0RNtaFa1LE8viGLK6qOYdS2ouD0JKCFEiVLG27hWqnd4EJNWHuLfP+wjK1vWlbJHMsxcCFHieFhc+XBAOEF+Xkxdc4yES+lMHtIEb3f5k2hPpAUlhCiRXFwUL3atx2sPhLL60DkGTd8i60rZGQkoIUSJNqxVVaYPi+DI2cv0mbqRY4mXzS5JWElACSFKvHsbVGL+6FakXcum79RNRJ44b3ZJAgkoIYQAoHGIH0vGtKWctztDZmxl1YGzZpdU4klACSGEVZXy3ix+sg11K/nyxNwd/LpfQspMElBCCJFLWR935o5qSYOgMjw5dwcr9iWYXVKJJQElhBA3KOPlxtePtiAsuAxjv9nJsj1nzC6pRJKAEkKIPJT2dGPOIy1oEuLHhPm7WLo73uySShwJKCGEuAlf6wq9zaqW5en5u2T+vmImASWEELfg42Fh1sPNaVm9PM8sjGLRjlizSyoxJKCEEOI2vN0tzBzZnLY1K/D8ot0s3H7a7JJKBAkoIYTIBy93V2aMiKB9bX9eWLyHb7aeMrskpycBJYQQ+eTp5sr0Yc24u64//1qyl6+3nDS7JKcmASWEEAXg6ebKtGHNuLd+Rf7zwz5mbTxudklOSwJKCCEKyMPiymcPNaNzg0pM/Gk/M9bHmF2SU5KAEkKIO+BucWHKQ03pFhrA68sO8N8f93E1I9vsspyKBJQQQtwhN1cXJg9uwiNtqzNn80l6TF7P3thLZpflNCSghBCiECyuLvy3ZwPmPtqSK9eyefCzjUxedcShlpE/l5Jul+tgSUAJIYQNtKtdgZVPd6BbWCDv/3qYAZ9v5kTSFbPLuq0tMcl0/mgd3T9ez/ojiWaX8xcSUEIIYSNlvN2YPLgJHw8K5+i5y3T/ZD3fbjuF1trs0vK0YPsphs7YSnkfd6pX8OHR2ZGsO2w/IZWvgFJKdVVKHVJKHVVKvZTH888qpfYrpfYopVYpparavlQhhHAMvcMrs+LpDoSH+PHP7/cyanYkianXzC7rD9k5mtd/3s+Li/fSplYFloxty7ePtaKWfylGzYlkrZ2E1G0DSinlCkwBugENgMFKqQY3bLYLiNBaNwIWAe/aulAhhHAkQX5ezH20Jf/p0YD1R5Po+tE6u1gAMTU9k1GztzNjw3FGtqnGzBERlPZ0o6yPO/NGtaR2xVI8NieS1YfOmV1qvlpQLYCjWusYrXUGMB/onXsDrfVqrXWa9e4WINi2ZQohhONxcVE82q46P49vR6XSnjw2J5IXF+3h8rUsU+o5fT6NvlM3se5IEq8/EMrEXg2xuP4ZA9dDqk6lUjw+ZwerD5obUvkJqMpA7pkRY62P3cyjwP/yekIpNVopFamUikxMtI8mpBBCFLU6lXz5YWxbnryrJgt3nKbbx+tYuP00569kFFsN20+cp/eUjZxNucacR1owtFXeZ2L8vN2Z92gr6gb48vjXO1h1wLxWn00HSSilhgIRwKS8ntdaT9daR2itI/z9/W351kIIYdfcLS682LUeCx9vjZuLCy8s3kPE678yaPpmZm08TvzFq0X23t9FnmbIF1vw83JjyZg2tK1V4Zbbl/F2Y+6jLakX6MsTc3fwm0ldk+p2o0uUUq2BiVrrLtb7/wTQWr91w3b3ApOBjlrr27YLIyIidGRk5J3WLYQQDktrTXR8CiujE1ixL4Ej54xrkBoHl6FLaABdGwZQw79Uod8nO0fz7sqDfL42hra1yvPZkGaU8XbL9+svXc1k+Jdb2X8mhSlDmtK5YUCha7qRUmqH1joiz+fyEVAW4DBwDxAHbAeGaK2jc23TBGNwRFet9ZH8FCUBJYQQhqPnLrMyOoFfohPYbZ2JonbFUnQNDaBLwwAaBpVGKVWgfV65lsVT86P47cBZhraqwis9G+LmWvBOs5T0TIZ/uY3o+Et8OqQpXWwcUoUKKOsOugMfAa7ATK31G0qpV4FIrfVSpdRvQBhwxvqSU1rrXrfapwSUEEL8XfzFq/wSncCK6AS2HT9Pjobgsl7UreSLl7sr3u6ueLtbrD9d8XK34OPuan3OeFwpePWn/Rw5d5lXejZgeOtqhaopJT2TETO3sTfWCKmuobYLqUIHVFGQgBJCiFtLvnyNVQfO8cv+s8RfvMrVzGzSMrJIy8gmLSOb7Jyb//329bQwZUhTOtSxzfn+VGtI7Ym9xOTBTegWFmiT/UpACSGEk9Fak5Gdw1VrWBk3I7yuZmRTP7A0AWU8bfqeqemZjPxqO1GnL/LJoCbc36jwIXWrgLIUeu9CCCGKnVIKD4srHhZX/LyL5z19Pd2Y/UgLRs7cxguLdtO6ZnnK+bgX2ftJQAkhhMi3Uh4WZj3SgkMJKUUaTiCTxQohhCigUh4WmlUtV+TvIwElhBDCLklACSGEsEsSUEIIIeySBJQQQgi7JAElhBDCLklACSGEsEsSUEIIIeySBJQQQgi7JAElhBDCLpk2WaxSKhE4aYNdVQCSbLAfR1HSjhdK3jGXtOMFOeaS4GbHW1VrneeU66YFlK0opSJvNhOuMyppxwsl75hL2vGCHHNJcCfHK118Qggh7JIElBBCCLvkDAE13ewCillJO14oecdc0o4X5JhLggIfr8OfgxJCCOGcnKEFJYQQwglJQAkhhLBLDhtQSqmuSqlDSqmjSqmXzK6nOCilTiil9iqlopRSkWbXUxSUUjOVUueUUvtyPVZOKfWrUuqI9WdZM2u0pZsc70SlVJz1c45SSnU3s0ZbU0qFKKVWK6X2K6WilVJPWR93ys/5FsfrtJ+zUspTKbVNKbXbesz/Z328ulJqq/Xv9gKl1C3XjHfIc1BKKVfgMHAfEAtsBwZrrfebWlgRU0qdACK01k57cZ9SqgNwGZijtQ61PvYucF5r/bb1y0hZrfWLZtZpKzc53onAZa31e2bWVlSUUoFAoNZ6p1LKF9gBPACMxAk/51sc7wCc9HNWSinAR2t9WSnlBmwAngKeBb7XWs9XSk0Ddmutp95sP47agmoBHNVax2itM4D5QG+TaxI2oLVeB5y/4eHewGzr77Mx/ud2Cjc5XqemtT6jtd5p/T0VOABUxkk/51scr9PShsvWu27WmwY6AYusj9/2M3bUgKoMnM51PxYn/8CtNPCLUmqHUmq02cUUo0pa6zPW3xOASmYWU0zGKaX2WLsAnaKrKy9KqWpAE2ArJeBzvuF4wYk/Z6WUq1IqCjgH/AocAy5qrbOsm9z277ajBlRJ1U5r3RToBoy1dg+VKNrok3a8fumCmQrUBMKBM8D75pZTNJRSpYDFwNNa65Tczznj55zH8Tr156y1ztZahwPBGL1e9Qq6D0cNqDggJNf9YOtjTk1rHWf9eQ5YgvGhlwRnrf341/vzz5lcT5HSWp+1/s+dA3yBE37O1vMSi4F5WuvvrQ877eec1/GWhM8ZQGt9EVgNtAb8lFIW61O3/bvtqAG1HahtHRHiDgwClppcU5FSSvlYT7CilPIBOgP7bv0qp7EUGGH9fQTwo4m1FLnrf6StHsTJPmfrCfQvgQNa6w9yPeWUn/PNjteZP2ellL9Sys/6uxfGgLYDGEHVz7rZbT9jhxzFB2AdkvkR4ArM1Fq/YXJJRUopVQOj1QRgAb5xxmNWSn0L3IUxNf9Z4BXgB2AhUAVjiZYBWmunGFhwk+O9C6PbRwMngMdznZtxeEqpdsB6YC+QY334XxjnZZzuc77F8Q7GST9npVQjjEEQrhgNoYVa61etf8fmA+WAXcBQrfW1m+7HUQNKCCGEc3PULj4hhBBOTgJKCCGEXZKAEkIIYZckoIQQQtglCSghhBB2SQJKCCGEXZKAEkIIYZf+H895SIHV7BazAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This model is much smaller than the VGG16 model only 3 convolutional layers versus the 16 of VGG16. It was nevertheless able to fit this dataset fairly well. We might still be able to improve this simple model by adding more convolutional layers, hoping to create features better adapted to the dataset. "
      ],
      "metadata": {
        "id": "RPPRC4w3bB4L"
      }
    }
  ]
}