real_world_example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "real_world_example.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_v_6vNOSZfip",
        "colab_type": "text"
      },
      "source": [
        "## Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vvh8CJ_7f_BW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install -q tensorflow tensorflow-datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTMs24Y1gEEG",
        "colab_type": "text"
      },
      "source": [
        "## Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTC_pP87gIkG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "from tensorflow import keras\n",
        "tfds.disable_progress_bar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhJUhH-vge82",
        "colab_type": "text"
      },
      "source": [
        "## Find Datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKqBKcLthj7A",
        "colab_type": "code",
        "outputId": "868394e2-da81-4365-ddac-b2173eba4b0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# https://www.tensorflow.org/datasets/overview\n",
        "# https://www.tensorflow.org/datasets/catalog/overview\n",
        "tfds.list_builders()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['abstract_reasoning',\n",
              " 'aeslc',\n",
              " 'aflw2k3d',\n",
              " 'amazon_us_reviews',\n",
              " 'arc',\n",
              " 'bair_robot_pushing_small',\n",
              " 'beans',\n",
              " 'big_patent',\n",
              " 'bigearthnet',\n",
              " 'billsum',\n",
              " 'binarized_mnist',\n",
              " 'binary_alpha_digits',\n",
              " 'c4',\n",
              " 'caltech101',\n",
              " 'caltech_birds2010',\n",
              " 'caltech_birds2011',\n",
              " 'cars196',\n",
              " 'cassava',\n",
              " 'cats_vs_dogs',\n",
              " 'celeb_a',\n",
              " 'celeb_a_hq',\n",
              " 'cfq',\n",
              " 'chexpert',\n",
              " 'cifar10',\n",
              " 'cifar100',\n",
              " 'cifar10_1',\n",
              " 'cifar10_corrupted',\n",
              " 'citrus_leaves',\n",
              " 'cityscapes',\n",
              " 'civil_comments',\n",
              " 'clevr',\n",
              " 'cmaterdb',\n",
              " 'cnn_dailymail',\n",
              " 'coco',\n",
              " 'coil100',\n",
              " 'colorectal_histology',\n",
              " 'colorectal_histology_large',\n",
              " 'cos_e',\n",
              " 'curated_breast_imaging_ddsm',\n",
              " 'cycle_gan',\n",
              " 'deep_weeds',\n",
              " 'definite_pronoun_resolution',\n",
              " 'diabetic_retinopathy_detection',\n",
              " 'div2k',\n",
              " 'dmlab',\n",
              " 'downsampled_imagenet',\n",
              " 'dsprites',\n",
              " 'dtd',\n",
              " 'duke_ultrasound',\n",
              " 'dummy_dataset_shared_generator',\n",
              " 'dummy_mnist',\n",
              " 'emnist',\n",
              " 'eraser_multi_rc',\n",
              " 'esnli',\n",
              " 'eurosat',\n",
              " 'fashion_mnist',\n",
              " 'flic',\n",
              " 'flores',\n",
              " 'food101',\n",
              " 'gap',\n",
              " 'gigaword',\n",
              " 'glue',\n",
              " 'groove',\n",
              " 'higgs',\n",
              " 'horses_or_humans',\n",
              " 'i_naturalist2017',\n",
              " 'image_label_folder',\n",
              " 'imagenet2012',\n",
              " 'imagenet2012_corrupted',\n",
              " 'imagenet_resized',\n",
              " 'imagenette',\n",
              " 'imagewang',\n",
              " 'imdb_reviews',\n",
              " 'iris',\n",
              " 'kitti',\n",
              " 'kmnist',\n",
              " 'lfw',\n",
              " 'librispeech',\n",
              " 'librispeech_lm',\n",
              " 'libritts',\n",
              " 'lm1b',\n",
              " 'lost_and_found',\n",
              " 'lsun',\n",
              " 'malaria',\n",
              " 'math_dataset',\n",
              " 'mnist',\n",
              " 'mnist_corrupted',\n",
              " 'movie_rationales',\n",
              " 'moving_mnist',\n",
              " 'multi_news',\n",
              " 'multi_nli',\n",
              " 'multi_nli_mismatch',\n",
              " 'natural_questions',\n",
              " 'newsroom',\n",
              " 'nsynth',\n",
              " 'omniglot',\n",
              " 'open_images_v4',\n",
              " 'opinosis',\n",
              " 'oxford_flowers102',\n",
              " 'oxford_iiit_pet',\n",
              " 'para_crawl',\n",
              " 'patch_camelyon',\n",
              " 'pet_finder',\n",
              " 'places365_small',\n",
              " 'plant_leaves',\n",
              " 'plant_village',\n",
              " 'plantae_k',\n",
              " 'qa4mre',\n",
              " 'quickdraw_bitmap',\n",
              " 'reddit_tifu',\n",
              " 'resisc45',\n",
              " 'rock_paper_scissors',\n",
              " 'rock_you',\n",
              " 'scan',\n",
              " 'scene_parse150',\n",
              " 'scicite',\n",
              " 'scientific_papers',\n",
              " 'shapes3d',\n",
              " 'smallnorb',\n",
              " 'snli',\n",
              " 'so2sat',\n",
              " 'speech_commands',\n",
              " 'squad',\n",
              " 'stanford_dogs',\n",
              " 'stanford_online_products',\n",
              " 'starcraft_video',\n",
              " 'sun397',\n",
              " 'super_glue',\n",
              " 'svhn_cropped',\n",
              " 'ted_hrlr_translate',\n",
              " 'ted_multi_translate',\n",
              " 'tf_flowers',\n",
              " 'the300w_lp',\n",
              " 'tiny_shakespeare',\n",
              " 'titanic',\n",
              " 'trivia_qa',\n",
              " 'uc_merced',\n",
              " 'ucf101',\n",
              " 'vgg_face2',\n",
              " 'visual_domain_decathlon',\n",
              " 'voc',\n",
              " 'wider_face',\n",
              " 'wikihow',\n",
              " 'wikipedia',\n",
              " 'wmt14_translate',\n",
              " 'wmt15_translate',\n",
              " 'wmt16_translate',\n",
              " 'wmt17_translate',\n",
              " 'wmt18_translate',\n",
              " 'wmt19_translate',\n",
              " 'wmt_t2t_translate',\n",
              " 'wmt_translate',\n",
              " 'xnli',\n",
              " 'xsum',\n",
              " 'yelp_polarity_reviews']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mj-hCwENhrQ-",
        "colab_type": "text"
      },
      "source": [
        "## Get information on the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-DCEGhnh_4m",
        "colab_type": "code",
        "outputId": "e3842ec9-e145-44c2-d70d-1d03132f443e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "builder = tfds.builder('rock_paper_scissors')\n",
        "info = builder.info\n",
        "\n",
        "info"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='rock_paper_scissors',\n",
              "    version=3.0.0,\n",
              "    description='Images of hands playing rock, paper, scissor game.',\n",
              "    homepage='http://laurencemoroney.com/rock-paper-scissors-dataset',\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(300, 300, 3), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=3),\n",
              "    }),\n",
              "    total_num_examples=2892,\n",
              "    splits={\n",
              "        'test': 372,\n",
              "        'train': 2520,\n",
              "    },\n",
              "    supervised_keys=('image', 'label'),\n",
              "    citation=\"\"\"@ONLINE {rps,\n",
              "    author = \"Laurence Moroney\",\n",
              "    title = \"Rock, Paper, Scissors Dataset\",\n",
              "    month = \"feb\",\n",
              "    year = \"2019\",\n",
              "    url = \"http://laurencemoroney.com/rock-paper-scissors-dataset\"\n",
              "    }\"\"\",\n",
              "    redistribution_info=,\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxzo48KfiiGH",
        "colab_type": "text"
      },
      "source": [
        "## Prepare Rock, Paper, Scissors Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNfZOqrcioeH",
        "colab_type": "code",
        "outputId": "7d689e9e-d0aa-4158-8a74-c9c3bc0abec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "ds_train = tfds.load(name=\"rock_paper_scissors\", split=\"train\")\n",
        "ds_test = tfds.load(name=\"rock_paper_scissors\", split=\"test\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset rock_paper_scissors/3.0.0 (download: 219.53 MiB, generated: Unknown size, total: 219.53 MiB) to /root/tensorflow_datasets/rock_paper_scissors/3.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
            "  InsecureRequestWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
            "  InsecureRequestWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/rock_paper_scissors/3.0.0.incompleteVUSL9W/rock_paper_scissors-train.tfrecord\n",
            "Shuffling and writing examples to /root/tensorflow_datasets/rock_paper_scissors/3.0.0.incompleteVUSL9W/rock_paper_scissors-test.tfrecord\n",
            "\u001b[1mDataset rock_paper_scissors downloaded and prepared to /root/tensorflow_datasets/rock_paper_scissors/3.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrVXk6-ji7ez",
        "colab_type": "text"
      },
      "source": [
        "## Show Examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57ef-WlcjR7H",
        "colab_type": "code",
        "outputId": "07fa3f19-fbbe-497e-a725-5b16cb2cd151",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        }
      },
      "source": [
        "fig = tfds.show_examples(info, ds_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FC2jJLEdjbCx",
        "colab_type": "text"
      },
      "source": [
        "## Additional Data Prep"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOahag-mjuof",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = np.array([example['image'].numpy()[:,:,0] for example in ds_train])\n",
        "train_labels = np.array([example['label'].numpy() for example in ds_train])\n",
        "\n",
        "test_images = np.array([example['image'].numpy()[:,:,0] for example in ds_test])\n",
        "test_labels = np.array([example['label'].numpy() for example in ds_test])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg56AOko4qby",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape(2520, 300, 300, 1)\n",
        "test_images = test_images.reshape(372, 300, 300, 1)\n",
        "\n",
        "train_images = train_images.astype('float32')\n",
        "test_images = test_images.astype('float32')\n",
        "\n",
        "train_images /= 255\n",
        "test_images /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGgO23W14xB9",
        "colab_type": "code",
        "outputId": "1d626ac7-21f1-4245-b63c-299c90dc6a11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300, 300, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hM_SDKpFBpGs",
        "colab_type": "text"
      },
      "source": [
        "## Train a network (basic approach)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pULotR1OPZX",
        "colab_type": "code",
        "outputId": "07f9a278-b44a-4066-cf1f-0b793883bf08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model = keras.Sequential([\n",
        "  keras.layers.Flatten(),\n",
        "  keras.layers.Dense(512, activation='relu'),\n",
        "  keras.layers.Dense(256, activation='relu'),\n",
        "  keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "79/79 [==============================] - 24s 306ms/step - loss: 20.9577 - accuracy: 0.4563\n",
            "Epoch 2/5\n",
            "79/79 [==============================] - 24s 305ms/step - loss: 1.5840 - accuracy: 0.6619\n",
            "Epoch 3/5\n",
            "79/79 [==============================] - 24s 305ms/step - loss: 1.6209 - accuracy: 0.6706\n",
            "Epoch 4/5\n",
            "79/79 [==============================] - 23s 288ms/step - loss: 0.6419 - accuracy: 0.8310\n",
            "Epoch 5/5\n",
            "79/79 [==============================] - 24s 308ms/step - loss: 0.3114 - accuracy: 0.8917\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7eff367cdcf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkA2gAtmO88d",
        "colab_type": "code",
        "outputId": "e6e90419-6414-42d7-a0dd-8a935cc7dd3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12/12 [==============================] - 1s 74ms/step - loss: 2.1391 - accuracy: 0.5081\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.13912034034729, 0.5080645084381104]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twfV36ChP4Ce",
        "colab_type": "text"
      },
      "source": [
        "## Train a Network (convolutional approach)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcuC8NuAQPew",
        "colab_type": "code",
        "outputId": "f3471605-7252-4592-bcb5-85fb41f6d218",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model = keras.Sequential([\n",
        "   keras.layers.Conv2D(64, 3, activation='relu', input_shape=(300,300,1)),\n",
        "   keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "   keras.layers.Flatten(),\n",
        "   keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "79/79 [==============================] - 8s 103ms/step - loss: 4.3401 - accuracy: 0.7048\n",
            "Epoch 2/5\n",
            "79/79 [==============================] - 8s 99ms/step - loss: 0.4482 - accuracy: 0.9016\n",
            "Epoch 3/5\n",
            "79/79 [==============================] - 8s 98ms/step - loss: 0.2057 - accuracy: 0.9480\n",
            "Epoch 4/5\n",
            "79/79 [==============================] - 8s 99ms/step - loss: 0.0267 - accuracy: 0.9937\n",
            "Epoch 5/5\n",
            "79/79 [==============================] - 8s 99ms/step - loss: 0.0071 - accuracy: 0.9996\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6e0001fb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2cAP7RCfsvP",
        "colab_type": "code",
        "outputId": "2a9462b0-5784-41ac-826a-e4363ed0f8b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12/12 [==============================] - 1s 43ms/step - loss: 2.6121 - accuracy: 0.4758\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.612107038497925, 0.47580644488334656]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3J6Q2DJ6hX7c",
        "colab_type": "text"
      },
      "source": [
        "## A Better Convolutional Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDAtTVtahbIj",
        "colab_type": "code",
        "outputId": "39d244b7-79e3-4719-ec09-d16629434911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "model = keras.Sequential([\n",
        "   keras.layers.AveragePooling2D(6,3, input_shape=(300,300,1)),\n",
        "   keras.layers.Conv2D(64, 3, activation='relu'),\n",
        "   keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "   keras.layers.MaxPool2D(2,2),\n",
        "   keras.layers.Dropout(0.5),\n",
        "   keras.layers.Flatten(),\n",
        "   keras.layers.Dense(128, activation='relu'),\n",
        "   keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "79/79 [==============================] - 1s 17ms/step - loss: 0.7607 - accuracy: 0.7353\n",
            "Epoch 2/5\n",
            "79/79 [==============================] - 1s 17ms/step - loss: 0.0931 - accuracy: 0.9786\n",
            "Epoch 3/5\n",
            "79/79 [==============================] - 1s 17ms/step - loss: 0.0104 - accuracy: 0.9972\n",
            "Epoch 4/5\n",
            "79/79 [==============================] - 1s 17ms/step - loss: 0.0026 - accuracy: 0.9996\n",
            "Epoch 5/5\n",
            "79/79 [==============================] - 1s 17ms/step - loss: 0.0011 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f6d9c8b4908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjTdABs_ibEO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.evaluate(test_images,test_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nhGv_IdwOpBF",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NLw5d4VPsBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install -U keras-tuner"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0YZ-MS8Pyfj",
        "colab_type": "code",
        "outputId": "024292a0-a034-4f34-9405-f86bf52c9c17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from kerastuner.tuners import RandomSearch\n",
        "\n",
        "def build_model(hp):\n",
        "  model = keras.Sequential()\n",
        "\n",
        "  model.add(keras.layers.AveragePooling2D(6,3,input_shape=(300,300,1)))\n",
        "\n",
        "  for i in range(hp.Int(\"Conv Layers\", min_value=0, max_value=3)):\n",
        "    model.add(keras.layers.Conv2D(hp.Choice(f\"layer_{i}_filters\", [16,32,64]), 3, activation='relu'))\n",
        "  \n",
        "  model.add(keras.layers.MaxPool2D(2,2))\n",
        "  model.add(keras.layers.Dropout(0.5))\n",
        "  model.add(keras.layers.Flatten())\n",
        "\n",
        "  model.add(keras.layers.Dense(hp.Choice(\"Dense layer\", [64, 128, 256, 512, 1024]), activation='relu'))\n",
        "\n",
        "  model.add(keras.layers.Dense(3, activation='softmax'))\n",
        "\n",
        "  model.compile(optimizer='adam',\n",
        "              loss=keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])\n",
        "  \n",
        "  return model\n",
        "\n",
        "tuner = RandomSearch(\n",
        "    build_model,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=32,\n",
        ")\n",
        "\n",
        "tuner.search(train_images, train_labels, validation_data=(test_images, test_labels), epochs=10, batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "79/79 [==============================] - 3s 43ms/step - loss: 0.9874 - accuracy: 0.6770 - val_loss: 1.0167 - val_accuracy: 0.5296\n",
            "Epoch 2/10\n",
            "79/79 [==============================] - 3s 38ms/step - loss: 0.1578 - accuracy: 0.9591 - val_loss: 1.0066 - val_accuracy: 0.6747\n",
            "Epoch 3/10\n",
            "79/79 [==============================] - 3s 39ms/step - loss: 0.0580 - accuracy: 0.9869 - val_loss: 1.1765 - val_accuracy: 0.6909\n",
            "Epoch 4/10\n",
            "79/79 [==============================] - 2s 24ms/step - loss: 0.0631 - accuracy: 0.9825 - val_loss: 1.0420 - val_accuracy: 0.4624\n",
            "Epoch 5/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.3522 - accuracy: 0.8500 - val_loss: 1.0036 - val_accuracy: 0.5699\n",
            "Epoch 6/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.0900 - accuracy: 0.9730 - val_loss: 1.2060 - val_accuracy: 0.6720\n",
            "Epoch 7/10\n",
            "79/79 [==============================] - 3s 37ms/step - loss: 0.0484 - accuracy: 0.9853 - val_loss: 1.2661 - val_accuracy: 0.7016\n",
            "Epoch 8/10\n",
            "79/79 [==============================] - 2s 24ms/step - loss: 0.0360 - accuracy: 0.9865 - val_loss: 1.3964 - val_accuracy: 0.6801\n",
            "Epoch 9/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.0314 - accuracy: 0.9905 - val_loss: 1.3423 - val_accuracy: 0.6962\n",
            "Epoch 10/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.0263 - accuracy: 0.9929 - val_loss: 1.7462 - val_accuracy: 0.6586\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
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              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial summary</h1></span>"
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              "<IPython.core.display.HTML object>"
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        },
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Trial ID: f256e1ee5c8c163842f34922eee5bf52</span>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 0.7016128897666931</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Best step: 0</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          },
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Dense layer: 512</span>"
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          "metadata": {
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        {
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          "text": [
            "Epoch 1/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.7669 - accuracy: 0.7421 - val_loss: 0.8337 - val_accuracy: 0.6774\n",
            "Epoch 2/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.0984 - accuracy: 0.9770 - val_loss: 0.9933 - val_accuracy: 0.6801\n",
            "Epoch 3/10\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 0.0160 - accuracy: 0.9976 - val_loss: 1.2007 - val_accuracy: 0.6747\n",
            "Epoch 4/10\n",
            "79/79 [==============================] - 2s 23ms/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 1.2049 - val_accuracy: 0.6909\n",
            "Epoch 5/10\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.4265 - val_accuracy: 0.6747\n",
            "Epoch 6/10\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.7626 - val_accuracy: 0.6667\n",
            "Epoch 7/10\n",
            "79/79 [==============================] - 2s 20ms/step - loss: 6.6260e-04 - accuracy: 1.0000 - val_loss: 1.7962 - val_accuracy: 0.6720\n",
            "Epoch 8/10\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 5.2689e-04 - accuracy: 1.0000 - val_loss: 1.7830 - val_accuracy: 0.6747\n",
            "Epoch 9/10\n",
            "79/79 [==============================] - 2s 20ms/step - loss: 4.4145e-04 - accuracy: 1.0000 - val_loss: 2.0912 - val_accuracy: 0.6640\n",
            "Epoch 10/10\n",
            "79/79 [==============================] - 2s 21ms/step - loss: 3.4399e-04 - accuracy: 1.0000 - val_loss: 2.0889 - val_accuracy: 0.6667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial complete</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
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          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Trial summary</h1></span>"
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          }
        },
        {
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            "text/html": [
              "<span style=\"color:cyan\"> |-Trial ID: bfca3f2ccd903968fcb79115c9109f81</span>"
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Score: 0.6908602118492126</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Best step: 0</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">Hyperparameters:</h2></span>"
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              "<IPython.core.display.HTML object>"
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          },
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Dense layer: 128</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "79/79 [==============================] - 6s 70ms/step - loss: 1.0356 - accuracy: 0.7115 - val_loss: 0.8544 - val_accuracy: 0.6962\n",
            "Epoch 2/10\n",
            "79/79 [==============================] - 22s 273ms/step - loss: 0.0434 - accuracy: 0.9940 - val_loss: 1.0690 - val_accuracy: 0.7285\n",
            "Epoch 3/10\n",
            "79/79 [==============================] - 2s 29ms/step - loss: 0.0083 - accuracy: 0.9984 - val_loss: 1.4497 - val_accuracy: 0.6747\n",
            "Epoch 4/10\n",
            "79/79 [==============================] - 2s 29ms/step - loss: 0.0054 - accuracy: 0.9996 - val_loss: 1.4929 - val_accuracy: 0.6909\n",
            "Epoch 5/10\n",
            "79/79 [==============================] - 15s 191ms/step - loss: 0.0063 - accuracy: 0.9992 - val_loss: 1.3729 - val_accuracy: 0.7339\n",
            "Epoch 6/10\n",
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            "Epoch 4/10\n",
            "79/79 [==============================] - 2s 20ms/step - loss: 0.0061 - accuracy: 0.9996 - val_loss: 1.1458 - val_accuracy: 0.7043\n",
            "Epoch 5/10\n",
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            "Epoch 6/10\n",
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            "79/79 [==============================] - 2s 27ms/step - loss: 0.7525 - accuracy: 0.7202 - val_loss: 0.7620 - val_accuracy: 0.6720\n",
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            "79/79 [==============================] - 2s 22ms/step - loss: 0.0113 - accuracy: 0.9984 - val_loss: 1.5284 - val_accuracy: 0.5323\n",
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            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "average_pooling2d (AveragePo (None, 99, 99, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 97, 97, 64)        640       \n",
            "_________________________________________________________________\n",
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            "_________________________________________________________________\n",
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            "_________________________________________________________________\n",
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            "_________________________________________________________________\n",
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            "_________________________________________________________________\n",
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            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 3075      \n",
            "=================================================================\n",
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            "_________________________________________________________________\n"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-0N12CPSmJz",
        "colab_type": "text"
      },
      "source": [
        "# Miscellaneous Useful Techniques\n",
        "## Save & Load our Models\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5bkiowmUBhK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "best_model.save('./my_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vnFWB58gUFCr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loaded_model = keras.models.load_model('./my_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvaGztASUN9i",
        "colab_type": "code",
        "outputId": "85727f2c-4a79-454f-8a18-91bf018c00df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "loaded_model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12/12 [==============================] - 0s 12ms/step - loss: 1.3729 - accuracy: 0.7339\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3729289770126343, 0.7338709831237793]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_tDBoCCUTZS",
        "colab_type": "text"
      },
      "source": [
        "## Plot Image from Numpy Array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7xk18u_UZN7",
        "colab_type": "code",
        "outputId": "eac50823-ee7c-4834-8437-6b925630b8d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "rgb_images = np.array([example['image'].numpy() for example in ds_train.take(1)])\n",
        "rgb_image = rgb_images[0]\n",
        "\n",
        "image = train_images[0].reshape(300,300)\n",
        "\n",
        "plt.imshow(rgb_image)\n",
        "\n",
        "rgb_image.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(300, 300, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zUOTuQLOhUN",
        "colab_type": "text"
      },
      "source": [
        "## Use Model to Predict Result for Single Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51RnZwR5WoBc",
        "colab_type": "code",
        "outputId": "f37a5b82-9ab8-4904-dc3b-9c9d9d9fb32e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "result = best_model.predict(np.array([train_images[0]]))\n",
        "print(result)\n",
        "\n",
        "predicted_value = np.argmax(result)\n",
        "print(predicted_value)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.5396062e-15, 1.5290578e-12, 1.0000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEmWzmWZUgrr",
        "colab_type": "text"
      },
      "source": [
        "## Convert PNG/JPG images to Numpy Format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JS13XdUOVRbE",
        "colab_type": "code",
        "outputId": "12e3a0da-3ec0-496b-9f99-d59c39b91437",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import imageio\n",
        "\n",
        "im = imageio.imread('https://upload.wikimedia.org/wikipedia/commons/thumb/8/84/Massachusetts_State_House_-_panoramio_%281%29.jpg/280px-Massachusetts_State_House_-_panoramio_%281%29.jpg')\n",
        "\n",
        "print(type(im))\n",
        "\n",
        "im_np = np.asarray(im)\n",
        "\n",
        "print(im_np.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'imageio.core.util.Array'>\n",
            "(186, 280, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}