hyperparameter_tuning.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will optimize our model's hyperparameters using Keras to find their optimal values. As in our previous notebooks, we'll use the `tf_flowers` dataset."
      ],
      "metadata": {
        "id": "KtWMu0SzUeqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start by importing the required libraries and retrieving our dataset."
      ],
      "metadata": {
        "id": "SCN2FQuvUzo-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QKZtMiuqJ-F1"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras import layers\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = tfds.load('tf_flowers', split=['train'], as_supervised=True)[0]\n",
        "print(\"Number of samples in dataset:\", dataset.cardinality().numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "8c98294e-b01a-4341-c4b9-159b46eb6098"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 218.21 MiB (download: 218.21 MiB, generated: 221.83 MiB, total: 440.05 MiB) to /root/tensorflow_datasets/tf_flowers/3.0.1...\n"
          ]
        },
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          "text": [
            "Dataset tf_flowers downloaded and prepared to /root/tensorflow_datasets/tf_flowers/3.0.1. Subsequent calls will reuse this data.\n",
            "Number of samples in dataset: 3670\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we split the dataset into training, validation and test sets (70-15-15)."
      ],
      "metadata": {
        "id": "6VjBJmb9VDVO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = dataset.take(2569)\n",
        "remaining_ds = dataset.skip(2569)\n",
        "val_ds = remaining_ds.take(550)\n",
        "test_ds = remaining_ds.skip(550)\n",
        "print(\"Number of samples in training set:\", train_ds.cardinality().numpy())\n",
        "print(\"Number of samples in validation set:\", val_ds.cardinality().numpy())\n",
        "print(\"Number of samples in test set:\", test_ds.cardinality().numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GD8NgIeg5ESV",
        "outputId": "a912acf4-fc6b-43de-d7bc-cea968b2ac55"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in training set: 2569\n",
            "Number of samples in validation set: 550\n",
            "Number of samples in test set: 551\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's apply data augmentation layers on our training set."
      ],
      "metadata": {
        "id": "Hgq_AsEfV_1M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation_layers = [\n",
        "    layers.RandomFlip(\"horizontal\"),\n",
        "    layers.RandomZoom(height_factor=0.4),\n",
        "    layers.RandomBrightness(factor=0.2),\n",
        "    layers.RandomTranslation(height_factor=0.2, width_factor=0.2)\n",
        "]\n",
        "\n",
        "def data_augmentation(images):\n",
        "    for layer in data_augmentation_layers:\n",
        "        images = layer(images)\n",
        "    return images"
      ],
      "metadata": {
        "id": "05wkQJo8R3Dg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.map(lambda x, y: (data_augmentation(x), y))"
      ],
      "metadata": {
        "id": "gdMWlYecS9m-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we resize the images to 180x180 pixels and create batches of 64 images."
      ],
      "metadata": {
        "id": "v8qVdayJWM7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.map(lambda x, y: (layers.Resizing(180,180)(x), y))\n",
        "val_ds = val_ds.map(lambda x, y: (layers.Resizing(180,180)(x), y))\n",
        "test_ds = test_ds.map(lambda x, y: (layers.Resizing(180,180)(x), y))"
      ],
      "metadata": {
        "id": "RPOfattKQH9q"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.batch(64)\n",
        "val_ds = val_ds.batch(64)\n",
        "test_ds = test_ds.batch(64)"
      ],
      "metadata": {
        "id": "SK5JXys0ZP99"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we will experiment with learning rates of 1e-3 and 1e-4. In practice, you may need to try random learning rates within a range."
      ],
      "metadata": {
        "id": "3YMLQjDG5k78"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our training process involves randomness, each call to `model.fit` may converge at different epochs. To address this variability, we will train the models for a fixed duration of 15 epochs and select the model with the highest validation accuracy. We will use `keras.callbacks.ModelCheckpoint` with `monitor=\"val_sparse_categorical_accuracy\"` to save the model achieving the highest accuracy on the validation set."
      ],
      "metadata": {
        "id": "3MoBY9cxmOCH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### LEARNING RATE = 1e-3"
      ],
      "metadata": {
        "id": "_QM2RmED6-KF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We create our model using the `Sequential` class from `Keras`. It consists of four convolutional layers, each followed by a max-pooling layer. The output from the convolutional layers is flattened and passed to a fully connected layer with a softmax activation function for class prediction. (same architecture as previous notebook)"
      ],
      "metadata": {
        "id": "22pZrZlzrqkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = keras.Sequential()\n",
        "\n",
        "model_1.add(keras.Input(shape=(180,180,3)))\n",
        "\n",
        "model_1.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_1.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model_1.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_1.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model_1.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_1.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model_1.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_1.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model_1.add(layers.Flatten())\n",
        "model_1.add(layers.Dense(units=5, activation=\"softmax\"))\n",
        "\n",
        "model_1.summary()"
      ],
      "metadata": {
        "id": "D1xmtCo-ZQDf",
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          "height": 449
        },
        "outputId": "0fd3ef0a-ee4c-42ba-9a17-751711c5e705"
      },
      "execution_count": 8,
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              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
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            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │           \u001b[38;5;34m3,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20736\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)                   │         \u001b[38;5;34m103,685\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20736</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">103,685</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We compile our model using `sparse_categorical_crossentropy` loss and `sparse_categorical_accuracy` metric because our labels are encoded as integers. We train the model for 15 epochs using the `adam` optimizer with `learning_rate=1e-3`.\n",
        "\n",
        "_Note: Use categorical_crossentropy as the loss when your labels are one-hot encoded._"
      ],
      "metadata": {
        "id": "9g65sPbar3o8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.compile(loss=\"sparse_categorical_crossentropy\", optimizer=keras.optimizers.Adam(learning_rate=1e-3), metrics=[\"sparse_categorical_accuracy\"])\n",
        "\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    \"model_1.keras\",\n",
        "    monitor=\"val_sparse_categorical_accuracy\",\n",
        "    save_best_only=True,\n",
        ")\n",
        "\n",
        "history = model_1.fit(train_ds, epochs=15, callbacks=[model_checkpoint_callback], validation_data=val_ds)"
      ],
      "metadata": {
        "id": "jno7QxPVZQJP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75e32ba1-6bee-4209-fad6-3e0d69997d06"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 2s/step - loss: 32.0815 - sparse_categorical_accuracy: 0.2240 - val_loss: 1.5324 - val_sparse_categorical_accuracy: 0.2727\n",
            "Epoch 2/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 2s/step - loss: 1.4833 - sparse_categorical_accuracy: 0.3021 - val_loss: 1.2760 - val_sparse_categorical_accuracy: 0.4709\n",
            "Epoch 3/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 2s/step - loss: 1.2370 - sparse_categorical_accuracy: 0.4718 - val_loss: 1.2973 - val_sparse_categorical_accuracy: 0.5236\n",
            "Epoch 4/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 2s/step - loss: 1.2086 - sparse_categorical_accuracy: 0.5161 - val_loss: 1.1151 - val_sparse_categorical_accuracy: 0.5527\n",
            "Epoch 5/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 1.1313 - sparse_categorical_accuracy: 0.5518 - val_loss: 1.1593 - val_sparse_categorical_accuracy: 0.5927\n",
            "Epoch 6/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 2s/step - loss: 1.0539 - sparse_categorical_accuracy: 0.5946 - val_loss: 1.2190 - val_sparse_categorical_accuracy: 0.5782\n",
            "Epoch 7/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 2s/step - loss: 1.1203 - sparse_categorical_accuracy: 0.5602 - val_loss: 1.0710 - val_sparse_categorical_accuracy: 0.6218\n",
            "Epoch 8/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 2s/step - loss: 1.0030 - sparse_categorical_accuracy: 0.6130 - val_loss: 1.1631 - val_sparse_categorical_accuracy: 0.6018\n",
            "Epoch 9/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - loss: 0.9916 - sparse_categorical_accuracy: 0.6151 - val_loss: 0.9712 - val_sparse_categorical_accuracy: 0.6491\n",
            "Epoch 10/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.9687 - sparse_categorical_accuracy: 0.6290 - val_loss: 0.9148 - val_sparse_categorical_accuracy: 0.6418\n",
            "Epoch 11/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.9862 - sparse_categorical_accuracy: 0.6188 - val_loss: 0.9646 - val_sparse_categorical_accuracy: 0.6436\n",
            "Epoch 12/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - loss: 0.9253 - sparse_categorical_accuracy: 0.6357 - val_loss: 1.0016 - val_sparse_categorical_accuracy: 0.6436\n",
            "Epoch 13/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.9002 - sparse_categorical_accuracy: 0.6507 - val_loss: 1.0275 - val_sparse_categorical_accuracy: 0.6455\n",
            "Epoch 14/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.8863 - sparse_categorical_accuracy: 0.6503 - val_loss: 0.9667 - val_sparse_categorical_accuracy: 0.6582\n",
            "Epoch 15/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.8761 - sparse_categorical_accuracy: 0.6710 - val_loss: 0.8882 - val_sparse_categorical_accuracy: 0.6582\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['sparse_categorical_accuracy'])\n",
        "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "8QrYc6uHZ9oT",
        "outputId": "1d7c6787-fe58-4afe-c47c-2eba4fd67af4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we evaluate our model on the test set and display the accuracy and loss."
      ],
      "metadata": {
        "id": "B7n8e7cSr5Kl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = keras.saving.load_model(\"model_1.keras\")\n",
        "val_loss, val_accuracy = model_1.evaluate(val_ds)\n",
        "print(\"Validation Loss:\", val_loss)\n",
        "print(\"Validation Accuracy:\", val_accuracy)"
      ],
      "metadata": {
        "id": "mC9HMdT7ZQNq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "571cb809-6db4-4661-e8c2-535cd493fabd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 164ms/step - loss: 0.9836 - sparse_categorical_accuracy: 0.6485\n",
            "Validation Loss: 0.9666903614997864\n",
            "Validation Accuracy: 0.6581818461418152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### LEARNING RATE = 1e-4\n",
        "\n",
        "Next, we create the same model but train it with `learning_rate=1e-4`."
      ],
      "metadata": {
        "id": "7U0sSNaMsd7d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = keras.Sequential()\n",
        "\n",
        "model_2.add(keras.Input(shape=(180,180,3)))\n",
        "\n",
        "model_2.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_2.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model_2.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_2.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model_2.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_2.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model_2.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model_2.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model_2.add(layers.Flatten())\n",
        "model_2.add(layers.Dense(units=5, activation=\"softmax\"))\n",
        "\n",
        "model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "_BoYCynVArPh",
        "outputId": "fe2f3ced-3fac-4fb6-9287-8dc11d1d6b44"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │           \u001b[38;5;34m3,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20736\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)                   │         \u001b[38;5;34m103,685\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20736</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">103,685</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.compile(loss=\"sparse_categorical_crossentropy\", optimizer=keras.optimizers.Adam(learning_rate=1e-4), metrics=[\"sparse_categorical_accuracy\"])\n",
        "\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    \"model_2.keras\",\n",
        "    monitor=\"val_sparse_categorical_accuracy\",\n",
        "    save_best_only=True,\n",
        ")\n",
        "\n",
        "history = model_2.fit(train_ds, epochs=15, callbacks=[model_checkpoint_callback], validation_data=val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BujabgBQ8jUB",
        "outputId": "9b3b50a4-2a78-4bd0-ec62-4b0c4a89a33a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 2s/step - loss: 8.0515 - sparse_categorical_accuracy: 0.2659 - val_loss: 1.4724 - val_sparse_categorical_accuracy: 0.4491\n",
            "Epoch 2/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 2s/step - loss: 1.4491 - sparse_categorical_accuracy: 0.4139 - val_loss: 1.3729 - val_sparse_categorical_accuracy: 0.4745\n",
            "Epoch 3/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 2s/step - loss: 1.3426 - sparse_categorical_accuracy: 0.4612 - val_loss: 1.2700 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 4/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 2s/step - loss: 1.2350 - sparse_categorical_accuracy: 0.5101 - val_loss: 1.2005 - val_sparse_categorical_accuracy: 0.5527\n",
            "Epoch 5/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 2s/step - loss: 1.1656 - sparse_categorical_accuracy: 0.5311 - val_loss: 1.1507 - val_sparse_categorical_accuracy: 0.5600\n",
            "Epoch 6/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 2s/step - loss: 1.1584 - sparse_categorical_accuracy: 0.5423 - val_loss: 1.1362 - val_sparse_categorical_accuracy: 0.5855\n",
            "Epoch 7/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 1.1010 - sparse_categorical_accuracy: 0.5618 - val_loss: 1.1214 - val_sparse_categorical_accuracy: 0.5909\n",
            "Epoch 8/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 2s/step - loss: 1.1148 - sparse_categorical_accuracy: 0.5582 - val_loss: 1.0486 - val_sparse_categorical_accuracy: 0.6091\n",
            "Epoch 9/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 2s/step - loss: 1.0618 - sparse_categorical_accuracy: 0.5861 - val_loss: 1.0386 - val_sparse_categorical_accuracy: 0.6036\n",
            "Epoch 10/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 2s/step - loss: 1.0308 - sparse_categorical_accuracy: 0.5940 - val_loss: 1.0139 - val_sparse_categorical_accuracy: 0.6036\n",
            "Epoch 11/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 1.0172 - sparse_categorical_accuracy: 0.5838 - val_loss: 0.9736 - val_sparse_categorical_accuracy: 0.6164\n",
            "Epoch 12/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.9938 - sparse_categorical_accuracy: 0.6175 - val_loss: 1.0031 - val_sparse_categorical_accuracy: 0.6164\n",
            "Epoch 13/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 2s/step - loss: 0.9748 - sparse_categorical_accuracy: 0.6230 - val_loss: 0.9774 - val_sparse_categorical_accuracy: 0.6364\n",
            "Epoch 14/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 2s/step - loss: 0.9499 - sparse_categorical_accuracy: 0.6250 - val_loss: 0.9490 - val_sparse_categorical_accuracy: 0.6491\n",
            "Epoch 15/15\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.9213 - sparse_categorical_accuracy: 0.6478 - val_loss: 0.9486 - val_sparse_categorical_accuracy: 0.6455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['sparse_categorical_accuracy'])\n",
        "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "FjY2i1OfdcTI",
        "outputId": "fbe11e1a-7418-4006-b547-43dcae448fee"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = keras.saving.load_model(\"model_2.keras\")\n",
        "val_loss, val_accuracy = model_2.evaluate(val_ds)\n",
        "print(\"Validation Loss:\", val_loss)\n",
        "print(\"Validation Accuracy:\", val_accuracy)"
      ],
      "metadata": {
        "id": "b1tI_Ds78OxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96745bdc-5865-4b99-913a-b6d251eb2522"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 119ms/step - loss: 0.9759 - sparse_categorical_accuracy: 0.6452\n",
            "Validation Loss: 0.9489789605140686\n",
            "Validation Accuracy: 0.6490908861160278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Learning rate of 1e-3 performs better than learning rate of 1e-4 on the validation set.\n",
        "\n",
        "_Note: Additional learning rate values could be explored; however, we did not pursue this due to limited computational resources_"
      ],
      "metadata": {
        "id": "8-34O_4otFNf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is common practice to decrease the learning rate during training to help the model converge to its optimal minima. This can be done using a learning rate scheduler, which modifies the learning rate when the validation loss or metrics stop improving. In Keras, this can be achieved using the `ReduceLROnPlateau` callback."
      ],
      "metadata": {
        "id": "LkkKezEFfzVS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's train our final model for 40 epochs with an initial learning rate of 1e-3. We'll utilize `ReduceLROnPlateau` and `ModelCheckpoint` callbacks to modify the learning rate and save the model with the highest validation accuracy."
      ],
      "metadata": {
        "id": "avXlTH0knZ8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "model.add(keras.Input(shape=(180,180,3)))\n",
        "\n",
        "model.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(layers.Conv2D(128, kernel_size=(3,3), activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(layers.Conv2D(256, kernel_size=(3,3), activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(units=5, activation=\"softmax\"))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "SwxVkIHcdhjS",
        "outputId": "eb45dd02-0bcb-4972-a620-b1a7130af09a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m178\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │           \u001b[38;5;34m3,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_20 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m89\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m87\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_21 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m43\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m41\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_22 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_23 (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m9\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20736\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)                   │         \u001b[38;5;34m103,685\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">178</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">89</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">87</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">43</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20736</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">103,685</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,140,101\u001b[0m (4.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,140,101</span> (4.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"sparse_categorical_accuracy\"])\n",
        "\n",
        "model_checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "    \"model.keras\",\n",
        "    monitor=\"val_sparse_categorical_accuracy\",\n",
        "    save_best_only=True,\n",
        ")\n",
        "\n",
        "reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor=\"val_loss\",\n",
        "    factor=0.1,\n",
        "    patience=3,\n",
        "    min_lr=0.00001,\n",
        ")\n",
        "\n",
        "history = model.fit(train_ds, epochs=40, callbacks=[model_checkpoint_callback, reduce_lr], validation_data=val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-cbhqX1dh7v",
        "outputId": "c8e2f183-32d2-481a-89eb-7ba379e565dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 2s/step - loss: 29.6583 - sparse_categorical_accuracy: 0.2691 - val_loss: 1.2694 - val_sparse_categorical_accuracy: 0.4564 - learning_rate: 0.0010\n",
            "Epoch 2/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - loss: 1.2959 - sparse_categorical_accuracy: 0.4309 - val_loss: 1.1635 - val_sparse_categorical_accuracy: 0.5109 - learning_rate: 0.0010\n",
            "Epoch 3/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - loss: 1.2206 - sparse_categorical_accuracy: 0.4916 - val_loss: 1.1123 - val_sparse_categorical_accuracy: 0.5764 - learning_rate: 0.0010\n",
            "Epoch 4/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 2s/step - loss: 1.1375 - sparse_categorical_accuracy: 0.5502 - val_loss: 1.0882 - val_sparse_categorical_accuracy: 0.5891 - learning_rate: 0.0010\n",
            "Epoch 5/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 2s/step - loss: 1.1060 - sparse_categorical_accuracy: 0.5472 - val_loss: 1.0786 - val_sparse_categorical_accuracy: 0.6218 - learning_rate: 0.0010\n",
            "Epoch 6/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 1.0392 - sparse_categorical_accuracy: 0.5977 - val_loss: 1.0304 - val_sparse_categorical_accuracy: 0.6218 - learning_rate: 0.0010\n",
            "Epoch 7/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.9890 - sparse_categorical_accuracy: 0.6146 - val_loss: 1.0199 - val_sparse_categorical_accuracy: 0.6455 - learning_rate: 0.0010\n",
            "Epoch 8/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - loss: 0.9431 - sparse_categorical_accuracy: 0.6431 - val_loss: 1.0047 - val_sparse_categorical_accuracy: 0.6509 - learning_rate: 0.0010\n",
            "Epoch 9/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.8891 - sparse_categorical_accuracy: 0.6480 - val_loss: 0.8980 - val_sparse_categorical_accuracy: 0.6909 - learning_rate: 0.0010\n",
            "Epoch 10/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.9326 - sparse_categorical_accuracy: 0.6515 - val_loss: 0.8529 - val_sparse_categorical_accuracy: 0.6709 - learning_rate: 0.0010\n",
            "Epoch 11/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.9393 - sparse_categorical_accuracy: 0.6588 - val_loss: 0.8407 - val_sparse_categorical_accuracy: 0.6909 - learning_rate: 0.0010\n",
            "Epoch 12/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.8633 - sparse_categorical_accuracy: 0.6735 - val_loss: 0.8442 - val_sparse_categorical_accuracy: 0.6982 - learning_rate: 0.0010\n",
            "Epoch 13/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.8046 - sparse_categorical_accuracy: 0.6984 - val_loss: 0.8221 - val_sparse_categorical_accuracy: 0.7073 - learning_rate: 0.0010\n",
            "Epoch 14/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.7904 - sparse_categorical_accuracy: 0.6952 - val_loss: 0.8370 - val_sparse_categorical_accuracy: 0.7127 - learning_rate: 0.0010\n",
            "Epoch 15/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.7825 - sparse_categorical_accuracy: 0.6813 - val_loss: 0.8331 - val_sparse_categorical_accuracy: 0.6964 - learning_rate: 0.0010\n",
            "Epoch 16/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.7724 - sparse_categorical_accuracy: 0.7036 - val_loss: 0.9381 - val_sparse_categorical_accuracy: 0.6800 - learning_rate: 0.0010\n",
            "Epoch 17/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.7098 - sparse_categorical_accuracy: 0.7279 - val_loss: 0.7939 - val_sparse_categorical_accuracy: 0.7291 - learning_rate: 1.0000e-04\n",
            "Epoch 18/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.6533 - sparse_categorical_accuracy: 0.7508 - val_loss: 0.7641 - val_sparse_categorical_accuracy: 0.7364 - learning_rate: 1.0000e-04\n",
            "Epoch 19/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - loss: 0.6382 - sparse_categorical_accuracy: 0.7496 - val_loss: 0.7506 - val_sparse_categorical_accuracy: 0.7418 - learning_rate: 1.0000e-04\n",
            "Epoch 20/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.6344 - sparse_categorical_accuracy: 0.7530 - val_loss: 0.7527 - val_sparse_categorical_accuracy: 0.7364 - learning_rate: 1.0000e-04\n",
            "Epoch 21/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.6306 - sparse_categorical_accuracy: 0.7537 - val_loss: 0.7405 - val_sparse_categorical_accuracy: 0.7436 - learning_rate: 1.0000e-04\n",
            "Epoch 22/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.6031 - sparse_categorical_accuracy: 0.7651 - val_loss: 0.7232 - val_sparse_categorical_accuracy: 0.7455 - learning_rate: 1.0000e-04\n",
            "Epoch 23/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5894 - sparse_categorical_accuracy: 0.7763 - val_loss: 0.7299 - val_sparse_categorical_accuracy: 0.7491 - learning_rate: 1.0000e-04\n",
            "Epoch 24/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5819 - sparse_categorical_accuracy: 0.7720 - val_loss: 0.7445 - val_sparse_categorical_accuracy: 0.7418 - learning_rate: 1.0000e-04\n",
            "Epoch 25/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m67s\u001b[0m 2s/step - loss: 0.5869 - sparse_categorical_accuracy: 0.7790 - val_loss: 0.7218 - val_sparse_categorical_accuracy: 0.7545 - learning_rate: 1.0000e-04\n",
            "Epoch 26/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5904 - sparse_categorical_accuracy: 0.7726 - val_loss: 0.7005 - val_sparse_categorical_accuracy: 0.7582 - learning_rate: 1.0000e-04\n",
            "Epoch 27/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5618 - sparse_categorical_accuracy: 0.7845 - val_loss: 0.7058 - val_sparse_categorical_accuracy: 0.7473 - learning_rate: 1.0000e-04\n",
            "Epoch 28/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5624 - sparse_categorical_accuracy: 0.7798 - val_loss: 0.6964 - val_sparse_categorical_accuracy: 0.7527 - learning_rate: 1.0000e-04\n",
            "Epoch 29/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5717 - sparse_categorical_accuracy: 0.7808 - val_loss: 0.6918 - val_sparse_categorical_accuracy: 0.7564 - learning_rate: 1.0000e-04\n",
            "Epoch 30/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5564 - sparse_categorical_accuracy: 0.7859 - val_loss: 0.7061 - val_sparse_categorical_accuracy: 0.7600 - learning_rate: 1.0000e-04\n",
            "Epoch 31/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5450 - sparse_categorical_accuracy: 0.7996 - val_loss: 0.6751 - val_sparse_categorical_accuracy: 0.7800 - learning_rate: 1.0000e-04\n",
            "Epoch 32/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5522 - sparse_categorical_accuracy: 0.7849 - val_loss: 0.6949 - val_sparse_categorical_accuracy: 0.7618 - learning_rate: 1.0000e-04\n",
            "Epoch 33/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5237 - sparse_categorical_accuracy: 0.7970 - val_loss: 0.6926 - val_sparse_categorical_accuracy: 0.7673 - learning_rate: 1.0000e-04\n",
            "Epoch 34/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5152 - sparse_categorical_accuracy: 0.8082 - val_loss: 0.7021 - val_sparse_categorical_accuracy: 0.7600 - learning_rate: 1.0000e-04\n",
            "Epoch 35/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5168 - sparse_categorical_accuracy: 0.8038 - val_loss: 0.6841 - val_sparse_categorical_accuracy: 0.7655 - learning_rate: 1.0000e-05\n",
            "Epoch 36/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 2s/step - loss: 0.5173 - sparse_categorical_accuracy: 0.8109 - val_loss: 0.6751 - val_sparse_categorical_accuracy: 0.7782 - learning_rate: 1.0000e-05\n",
            "Epoch 37/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5070 - sparse_categorical_accuracy: 0.8143 - val_loss: 0.6696 - val_sparse_categorical_accuracy: 0.7709 - learning_rate: 1.0000e-05\n",
            "Epoch 38/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - loss: 0.4975 - sparse_categorical_accuracy: 0.8102 - val_loss: 0.6720 - val_sparse_categorical_accuracy: 0.7727 - learning_rate: 1.0000e-05\n",
            "Epoch 39/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - loss: 0.5045 - sparse_categorical_accuracy: 0.8184 - val_loss: 0.6701 - val_sparse_categorical_accuracy: 0.7764 - learning_rate: 1.0000e-05\n",
            "Epoch 40/40\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - loss: 0.4872 - sparse_categorical_accuracy: 0.8076 - val_loss: 0.6797 - val_sparse_categorical_accuracy: 0.7727 - learning_rate: 1.0000e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['sparse_categorical_accuracy'])\n",
        "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rPg92f9JdkUK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "97b27327-941e-40d7-f7ce-d0f32842711e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.saving.load_model(\"model.keras\")\n",
        "test_loss, test_accuracy = model.evaluate(test_ds)\n",
        "print(\"Test Loss:\", test_loss)\n",
        "print(\"Test Accuracy:\", test_accuracy)"
      ],
      "metadata": {
        "id": "kOJQLYYkdrQ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "434ff1c3-4def-49dc-a228-77feca56ada5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 83ms/step - loss: 0.5283 - sparse_categorical_accuracy: 0.8086\n",
            "Test Loss: 0.4754926264286041\n",
            "Test Accuracy: 0.8221415877342224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook, we completed our exploration of the `tf_flowers` dataset. Starting with a [baseline architecture](https://github.com/shaivimalik/cv/blob/main/base_architecture.ipynb), we enhanced our model through [data augmentation](https://github.com/shaivimalik/cv/blob/main/data_augmentation.ipynb) to learn more generalizable features. We then optimized the model's performance by fine-tuning hyperparameters. In the upcoming notebooks, we will explore more advanced computer vision tasks using Keras."
      ],
      "metadata": {
        "id": "S0K-07iYuYzE"
      }
    }
  ]
}