{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":21154,"databundleVersionId":1243559,"sourceType":"competition"},{"sourceId":37130068,"sourceType":"kernelVersion"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Classifying Flowers Using Machine Learning Models #\n\n#### ESOF4011 Project Code\nConnor McNally, Aric Duckert, Matthew Camire\n\nLakehead University (Department of Software Engineering)\n\nThunder Bay, Ontario Canada\n\n# Introduction\nWe live in an incredibly vast and diverse world full of many different lifeforms. There are over 5,000 species of mammals, 10,000 species of birds, 30,000 species of fish, and astonishingly, over 400,000 different types of flowers. Classifying flowers from such a great list is difficult for humans to accomplish, let alone machine learning models which are likely to be even less successful at the same task. This monumental task can be solved by using various machine learning, deep learning, and image processing techniques to classify a large dataset of images consisting of various different species of flowers. In the [**Petals to the Metal**](https://www.kaggle.com/c/tpu-getting-started) competition, you’re challenged to build a machine learning model to classify 104 types of flowers based on their images. This is what will be accomplished in the following notebook.","metadata":{}},{"cell_type":"markdown","source":"# Step 1: Imports #\n\nWe begin by importing several Python packages. Numpy was used for some functions, mainly for converting data into the format required for plotting graphs. Matplotlib was used for graphs and plots. Tensorflow was used to load the .tfrec files and contain them within a dataset. Tensorflow and keras were also used to build, train, and test certain models. Some other libraries used were Sklearn which provided some more advanced data visualization like plotting the confusion matrix for certain models and obtaining some metrics for the trained models.","metadata":{}},{"cell_type":"code","source":"import math, re, os\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport tensorflow as tf\n\nprint(\"Tensorflow version \" + tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:23.902874Z","iopub.execute_input":"2024-03-08T22:24:23.903230Z","iopub.status.idle":"2024-03-08T22:24:37.369839Z","shell.execute_reply.started":"2024-03-08T22:24:23.903197Z","shell.execute_reply":"2024-03-08T22:24:37.368706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 2: Distribution Strategy #\n\nA TPU has eight different *cores* and each of these cores acts as its own accelerator. (A TPU is sort of like having eight GPUs in one machine.) We tell TensorFlow how to make use of all these cores at once through a **distribution strategy**. Run the following cell to create the distribution strategy that we'll later apply to our model.\n\nNormally in this competition, you are meant to utilize *Tensor Processing Units (TPUs)* to accelerate the speed of image classification and training, but for our runs, we utilized the P100 GPU accelerator instead to train our models due to some issues with the TPU runtime. Thankfully, the P100 GPU runtime is is still fast enough for our use cases. Since we are using this accelerator, we will be using the default distribution strategy.","metadata":{}},{"cell_type":"code","source":"# Detect TPU, return appropriate distribution strategy\ntry:\n    resolver = tf.distribute.cluster_resolver.TPUClusterResolver()\n    tf.config.experimental_connect_to_cluster(resolver)\n    tf.tpu.experimental.initialize_tpu_system(resolver)\n    strategy = tf.distribute.experimental.TPUStrategy(resolver)\n    print('Running on TPU ', resolver.master())\nexcept ValueError:\n    strategy = tf.distribute.get_strategy()\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:37.371998Z","iopub.execute_input":"2024-03-08T22:24:37.372777Z","iopub.status.idle":"2024-03-08T22:24:37.382400Z","shell.execute_reply.started":"2024-03-08T22:24:37.372700Z","shell.execute_reply":"2024-03-08T22:24:37.381444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load Data ##\n\nAll of the data stored in this dataset are serialized within [TFRecords](https://www.kaggle.com/ryanholbrook/tfrecords-basics) files. This is a format that is convenient for distributing data to each of the TPUs cores. The next few cells are dedicated to loading the data from these TFRecord files.","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = [331, 331]\nGCS_PATH = \"/kaggle/input/tpu-getting-started\" + '/tfrecords-jpeg-331x331'\nAUTO = tf.data.experimental.AUTOTUNE\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') \n\nCLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09\n           'snapdragon',       \"colt's foot\",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19\n           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29\n           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39\n           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49\n           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59\n           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69\n           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79\n           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89\n           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99\n           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102\n\n\ndef decode_image(image_data):\n    image = tf.image.decode_jpeg(image_data, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range\n    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU\n    return image\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"class\": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element\n    }\n    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    return image, label # returns a dataset of (image, label) pairs\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"id\": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element\n        # class is missing, this competitions's challenge is to predict flower classes for the test dataset\n    }\n    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    idnum = example['id']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files\n    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)\n    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False\n    return dataset","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-03-08T22:24:37.387811Z","iopub.execute_input":"2024-03-08T22:24:37.388105Z","iopub.status.idle":"2024-03-08T22:24:37.457579Z","shell.execute_reply.started":"2024-03-08T22:24:37.388080Z","shell.execute_reply":"2024-03-08T22:24:37.456752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create Data Pipelines ##\n\nIn this final step we'll use the `tf.data` API to define an efficient data pipeline for each of the training, validation, and test splits.","metadata":{}},{"cell_type":"code","source":"def get_training_dataset():\n    loaded_dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset = loaded_dataset\n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n\ndef get_test_dataset(ordered=False):\n    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n\ndef count_data_items(filenames):\n    # the number of data items is written in the name of the .tfrec\n    # files, i.e. flowers00-230.tfrec = 230 data items\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)\n\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-03-08T22:24:37.458718Z","iopub.execute_input":"2024-03-08T22:24:37.459000Z","iopub.status.idle":"2024-03-08T22:24:37.472174Z","shell.execute_reply.started":"2024-03-08T22:24:37.458976Z","shell.execute_reply":"2024-03-08T22:24:37.471076Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This next cell will create the datasets that we'll use with Keras during training and inference. Notice how we scale the size of the batches to the number of TPU cores.","metadata":{}},{"cell_type":"code","source":"# Define the batch size. This will be 16 with TPU off and 128 (=16*8) with TPU on\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\n\nds_train = get_training_dataset()\nds_valid = get_validation_dataset()\nds_test = get_test_dataset()\n\nprint(\"Training:\", ds_train)\nprint (\"Validation:\", ds_valid)\nprint(\"Test:\", ds_test)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:37.473334Z","iopub.execute_input":"2024-03-08T22:24:37.473609Z","iopub.status.idle":"2024-03-08T22:24:38.493767Z","shell.execute_reply.started":"2024-03-08T22:24:37.473584Z","shell.execute_reply":"2024-03-08T22:24:38.492559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"These datasets are `tf.data.Dataset` objects. You can think about a dataset in TensorFlow as a *stream* of data records. The training and validation sets are streams of `(image, label)` pairs.","metadata":{}},{"cell_type":"code","source":"np.set_printoptions(threshold=15, linewidth=80)\n\nprint(\"Training data shapes:\")\nfor image, label in ds_train.take(3):\n    print(image.numpy().shape, label.numpy().shape)\nprint(\"Training data label examples:\", label.numpy())","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:38.495521Z","iopub.execute_input":"2024-03-08T22:24:38.495929Z","iopub.status.idle":"2024-03-08T22:24:41.295409Z","shell.execute_reply.started":"2024-03-08T22:24:38.495892Z","shell.execute_reply":"2024-03-08T22:24:41.294357Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The test set is a stream of `(image, idnum)` pairs; `idnum` here is the unique identifier given to the image that we'll use later when we make our submission as a `csv` file.","metadata":{}},{"cell_type":"code","source":"print(\"Test data shapes:\")\nfor image, idnum in ds_test.take(3):\n    print(image.numpy().shape, idnum.numpy().shape)\nprint(\"Test data IDs:\", idnum.numpy().astype('U')) # U=unicode string","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:41.296661Z","iopub.execute_input":"2024-03-08T22:24:41.297029Z","iopub.status.idle":"2024-03-08T22:24:41.679548Z","shell.execute_reply.started":"2024-03-08T22:24:41.297002Z","shell.execute_reply":"2024-03-08T22:24:41.675891Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 4: Explore Data #\n\nLet's take a moment to look at some of the images in the dataset.","metadata":{}},{"cell_type":"code","source":"def batch_to_numpy_images_and_labels(data):\n    images, labels = data\n    numpy_images = images.numpy()\n    numpy_labels = labels.numpy()\n    if numpy_labels.dtype == object: # binary string in this case,\n                                     # these are image ID strings\n        numpy_labels = [None for _ in enumerate(numpy_images)]\n    # If no labels, only image IDs, return None for labels (this is\n    # the case for test data)\n    return numpy_images, numpy_labels\n\ndef title_from_label_and_target(label, correct_label):\n    if correct_label is None:\n        return CLASSES[label], True\n    correct = (label == correct_label)\n    return \"{} [{}{}{}]\".format(CLASSES[label], 'OK' if correct else 'NO', u\"\\u2192\" if not correct else '',\n                                CLASSES[correct_label] if not correct else ''), correct\n\ndef display_one_flower(image, title, subplot, red=False, titlesize=16):\n    plt.subplot(*subplot)\n    plt.axis('off')\n    plt.imshow(image)\n    if len(title) > 0:\n        plt.title(title, fontsize=int(titlesize) if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))\n    return (subplot[0], subplot[1], subplot[2]+1)\n    \ndef display_batch_of_images(databatch, predictions=None):\n    \"\"\"This will work with:\n    display_batch_of_images(images)\n    display_batch_of_images(images, predictions)\n    display_batch_of_images((images, labels))\n    display_batch_of_images((images, labels), predictions)\n    \"\"\"\n    # data\n    images, labels = batch_to_numpy_images_and_labels(databatch)\n    if labels is None:\n        labels = [None for _ in enumerate(images)]\n        \n    # auto-squaring: this will drop data that does not fit into square\n    # or square-ish rectangle\n    rows = int(math.sqrt(len(images)))\n    cols = len(images)//rows\n        \n    # size and spacing\n    FIGSIZE = 13.0\n    SPACING = 0.1\n    subplot=(rows,cols,1)\n    if rows < cols:\n        plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))\n    else:\n        plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))\n    \n    # display\n    for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):\n        title = '' if label is None else CLASSES[label]\n        correct = True\n        if predictions is not None:\n            title, correct = title_from_label_and_target(predictions[i], label)\n        dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images\n        subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)\n    \n    #layout\n    plt.tight_layout()\n    if label is None and predictions is None:\n        plt.subplots_adjust(wspace=0, hspace=0)\n    else:\n        plt.subplots_adjust(wspace=SPACING, hspace=SPACING)\n    plt.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-03-08T22:24:41.683212Z","iopub.execute_input":"2024-03-08T22:24:41.683628Z","iopub.status.idle":"2024-03-08T22:24:41.708043Z","shell.execute_reply.started":"2024-03-08T22:24:41.683586Z","shell.execute_reply":"2024-03-08T22:24:41.706757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You can display a single batch of images from a dataset with another of our helper functions. The next cell will turn the dataset into an iterator of batches of 20 images.","metadata":{}},{"cell_type":"code","source":"ds_iter = iter(ds_train.unbatch().batch(12))","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:41.713477Z","iopub.execute_input":"2024-03-08T22:24:41.713832Z","iopub.status.idle":"2024-03-08T22:24:41.749684Z","shell.execute_reply.started":"2024-03-08T22:24:41.713804Z","shell.execute_reply":"2024-03-08T22:24:41.748860Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Use the Python `next` function to pop out the next batch in the stream and display it with the helper function.","metadata":{}},{"cell_type":"code","source":"one_batch = next(ds_iter)\ndisplay_batch_of_images(one_batch)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:41.751152Z","iopub.execute_input":"2024-03-08T22:24:41.751976Z","iopub.status.idle":"2024-03-08T22:24:46.093802Z","shell.execute_reply.started":"2024-03-08T22:24:41.751937Z","shell.execute_reply":"2024-03-08T22:24:46.092638Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"By defining `ds_iter` and `one_batch` in separate cells, you only need to rerun the cell above to see a new batch of images.","metadata":{}},{"cell_type":"markdown","source":"## Data Analysis\n\nThe following cells look at the overall distribution of the dataset using various graphs and charts. The class distribution of all of the datasets are not very balanced at all. There are a few classes of flowers that occur very often and a lot of other classes that do not occur very often at all. The pie charts show not only the class distribution of the datasets but also which specific classes of flowers occur the most often.\n\nDue to the shown imbalance of the dataset, our models might have a harder time classifying some of the flower types that occur infrequently in the dataset but might excel at the more common flower types.\n\nSince generating these graphs take up a lot of memory on the runtime, it is best to not run the three cells below during training/testing and comment them out. Instead, have the following pre-generated graphs instead.\n\n### Training Dataset\n![image.png](attachment:ff2631d7-dc76-4da6-ab27-56f7c4041cb7.png)\n![image.png](attachment:5dc133f3-6639-49f4-8492-3b4121847413.png)\n\n### Validation Dataset\n![image.png](attachment:5935f8c4-6ecd-4cbb-85c7-e127fe2ff7d4.png)\n![image.png](attachment:5724bd9b-507e-4e68-9a7e-e94fb89e46d9.png)","metadata":{},"attachments":{"ff2631d7-dc76-4da6-ab27-56f7c4041cb7.png":{"image/png":"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"},"5dc133f3-6639-49f4-8492-3b4121847413.png":{"image/png":"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"},"5724bd9b-507e-4e68-9a7e-e94fb89e46d9.png":{"image/png":"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"}}},{"cell_type":"code","source":"'''\nfrom collections import Counter\n\n# display the distribution of the dataset\ndef display_distribution(dataset, dataset_length, num_common_classes):\n    labels = dataset.map(lambda image, label: label).unbatch()\n    labels = next(iter(labels.batch(dataset_length))).numpy()\n    \n    # display histogram\n    plt.hist(labels, bins=len(np.unique(labels)))\n    plt.title(\"Distribution of dataset\")\n    plt.ylabel(\"Frequency\")\n    plt.xlabel(\"Class ID\")\n    plt.show()\n\n    # get the most common classes in the labels list\n    counter = Counter(labels)\n    common_classes = counter.most_common(num_common_classes)\n    \n    # display a pie graph containing the frequency of each class\n    pie_labels = [f\"%s (%.2f%%)\" % (CLASSES[common_classes[x][0]], common_classes[x][1]/(len(labels)/100.0)) for x in range(num_common_classes)]\n    pie_sizes = [common_classes[x][1] for x in range(num_common_classes)]\n    pie_labels.append(f\"other (%.2f%%)\" % float((len(labels) - sum(pie_sizes))/(len(labels)/100.0)))\n    pie_sizes.append(len(labels) - sum(pie_sizes))\n    \n    plt.rc('font', size=8)\n    plt.pie(pie_sizes, labels=pie_labels, startangle=25)\n    plt.show()\n'''","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:46.095565Z","iopub.execute_input":"2024-03-08T22:24:46.096729Z","iopub.status.idle":"2024-03-08T22:24:46.105955Z","shell.execute_reply.started":"2024-03-08T22:24:46.096662Z","shell.execute_reply":"2024-03-08T22:24:46.104748Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# display the distribution graphs for the training dataset\n#display_distribution(ds_train, NUM_TRAINING_IMAGES, 19)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:46.107431Z","iopub.execute_input":"2024-03-08T22:24:46.107815Z","iopub.status.idle":"2024-03-08T22:24:46.125923Z","shell.execute_reply.started":"2024-03-08T22:24:46.107781Z","shell.execute_reply":"2024-03-08T22:24:46.124880Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# display the distribution graphs for the validation dataset\n#display_distribution(ds_valid, NUM_VALIDATION_IMAGES, 19)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:46.127296Z","iopub.execute_input":"2024-03-08T22:24:46.127681Z","iopub.status.idle":"2024-03-08T22:24:46.139382Z","shell.execute_reply.started":"2024-03-08T22:24:46.127638Z","shell.execute_reply":"2024-03-08T22:24:46.138161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 6: Define Model #\n\nNow we're ready to create a neural network for classifying images! We'll use what's known as **transfer learning**. With transfer learning, you reuse part of a pretrained model to get a head-start on a new dataset.\n\nFor our case, we'll be using four different models (Xception, InceptionV3, ~~DenseNet201~~ DenseNet121, and VGG16), all of which are pretrained on [ImageNet](http://image-net.org/).\n\nThe distribution strategy we created earlier contains a [context manager](https://docs.python.org/3/reference/compound_stmts.html#with), `strategy.scope`. This context manager tells TensorFlow how to divide the work of training among the eight TPU cores. When using TensorFlow with a TPU, it's important to define your model in a `strategy.scope()` context.","metadata":{}},{"cell_type":"markdown","source":"## Data Augmentation\n\nFor our data augmentation, it will be included as part of our model. The main upshot of this approach is that with each epoch, our model will be trained on different variants of each image. This should help our model be a lot more generalized and to hopefully reduce overfitting.","metadata":{}},{"cell_type":"code","source":"# data augmentation model\ndata_augmentation = tf.keras.Sequential([\n    tf.keras.layers.RandomFlip(\"horizontal\"),                                 # Randomly flip the image horizontally and/or vertically\n    tf.keras.layers.RandomRotation(0.2),                                      # Randomly rotate the image by 20%\n    tf.keras.layers.RandomTranslation(height_factor=0.2, width_factor=0.2),   # Randomly translate the image by up to 20%\n    tf.keras.layers.RandomZoom(0.2),                                          # Randomly zoom the image in or out by up to 20%\n    tf.keras.layers.RandomContrast(0.2),                                      # Randomly change the contrast of the image by up to 20%\n])","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:46.140628Z","iopub.execute_input":"2024-03-08T22:24:46.141011Z","iopub.status.idle":"2024-03-08T22:24:46.171148Z","shell.execute_reply.started":"2024-03-08T22:24:46.140983Z","shell.execute_reply":"2024-03-08T22:24:46.170131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Creating the Model","metadata":{}},{"cell_type":"code","source":"EPOCHS = 25\nLEARNING_RATE = 0.0001\n\nwith strategy.scope():\n    # DenseNet121 model pre-trained on the imagenet dataset\n    pretrained_model = tf.keras.applications.DenseNet121(\n        weights='imagenet',\n        include_top=False,\n        input_shape=[*IMAGE_SIZE, 3]\n    ) \n    pretrained_model.trainable = True\n    \n    model = tf.keras.Sequential([\n        data_augmentation,\n        pretrained_model,\n        # ... attach a new head to act as a classifier\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(units=len(CLASSES), activation='softmax'),\n    ])\n    \n    # use the adam optimizer with a defined learning rate\n    optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n    \n    # compile model\n    model.compile(\n        optimizer=optimizer,\n        loss = 'sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy'],\n    )\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:46.172381Z","iopub.execute_input":"2024-03-08T22:24:46.173545Z","iopub.status.idle":"2024-03-08T22:24:49.577886Z","shell.execute_reply.started":"2024-03-08T22:24:46.173514Z","shell.execute_reply":"2024-03-08T22:24:49.576696Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The `'sparse_categorical'` versions of the loss and metrics are appropriate for a classification task with more than two labels, like this one.","metadata":{}},{"cell_type":"markdown","source":"# Step 7: Training #\n\n## Learning Rate Schedule ##\n\nWe'll train this network using a callback that reduces the learning rate depending on the validation loss. We use this callback to ensure that our model doesn't massively overfit on the training dataset.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ReduceLROnPlateau\n\n# reduce the learning rate when the validation loss plateaus\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), patience=4)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:49.579645Z","iopub.execute_input":"2024-03-08T22:24:49.580384Z","iopub.status.idle":"2024-03-08T22:24:49.589510Z","shell.execute_reply.started":"2024-03-08T22:24:49.580347Z","shell.execute_reply":"2024-03-08T22:24:49.588374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fit Model ##\n\nAnd now we're ready to train the model. After defining a few parameters, we're good to go!","metadata":{}},{"cell_type":"code","source":"# Define training epochs\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n\n# fit the model to the training set\nhistory = model.fit(\n    ds_train,\n    validation_data=ds_valid,\n    epochs=EPOCHS,\n    steps_per_epoch=STEPS_PER_EPOCH,\n    callbacks=[reduce_lr],\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:24:49.591027Z","iopub.execute_input":"2024-03-08T22:24:49.591560Z","iopub.status.idle":"2024-03-08T22:26:42.771639Z","shell.execute_reply.started":"2024-03-08T22:24:49.591522Z","shell.execute_reply":"2024-03-08T22:26:42.769980Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# save the model to a file for safe-keeping\nmodel.save(\"densenet121.keras\")\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.772923Z","iopub.status.idle":"2024-03-08T22:26:42.773441Z","shell.execute_reply.started":"2024-03-08T22:26:42.773161Z","shell.execute_reply":"2024-03-08T22:26:42.773182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This next cell shows how the loss and metrics progressed during training. Thankfully, it converges!","metadata":{}},{"cell_type":"code","source":"# Visualize the training history\nacc = history.history['sparse_categorical_accuracy']\nval_acc = history.history['val_sparse_categorical_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\n#Plotting Train vs validation set to check for overfitting issues\nplt.figure(figsize=(8, 8))\nplt.subplot(1, 2, 1)\nplt.plot(range(EPOCHS), acc, label='Training Accuracy')\nplt.plot(range(EPOCHS), val_acc, label='Validation Accuracy')\nplt.legend(loc='lower right')\nplt.title('Training and Validation Accuracy')\nplt.subplot(1, 2, 2)\nplt.plot(range(EPOCHS), loss, label='Training Loss')\nplt.plot(range(EPOCHS), val_loss, label='Validation Loss')\nplt.legend(loc='upper right')\nplt.title('Training and Validation Loss')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.775029Z","iopub.status.idle":"2024-03-08T22:26:42.775485Z","shell.execute_reply.started":"2024-03-08T22:26:42.775251Z","shell.execute_reply":"2024-03-08T22:26:42.775270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 7: Evaluate Predictions #\n\nBefore making our final predictions on the test set, it's a good idea to evaluate your model's predictions on the validation set. This can help you diagnose problems in training or suggest ways your model could be improved. We'll look at two common ways of validation: plotting the **confusion matrix** and **visual validation**.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\n\ndef display_confusion_matrix(cmat, score, precision, recall):\n    plt.figure(figsize=(15,15))\n    ax = plt.gca()\n    ax.matshow(cmat, cmap='Reds')\n    ax.set_xticks(range(len(CLASSES)))\n    ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})\n    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"left\", rotation_mode=\"anchor\")\n    ax.set_yticks(range(len(CLASSES)))\n    ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})\n    plt.setp(ax.get_yticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n    titlestring = \"\"\n    if score is not None:\n        titlestring += 'f1 = {:.3f} '.format(score)\n    if precision is not None:\n        titlestring += '\\nprecision = {:.3f} '.format(precision)\n    if recall is not None:\n        titlestring += '\\nrecall = {:.3f} '.format(recall)\n    if len(titlestring) > 0:\n        ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'#804040'})\n    plt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-03-08T22:26:42.777836Z","iopub.status.idle":"2024-03-08T22:26:42.778367Z","shell.execute_reply.started":"2024-03-08T22:26:42.778094Z","shell.execute_reply":"2024-03-08T22:26:42.778115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix ##\n\nA [confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) shows the actual class of an image tabulated against its predicted class. It is one of the best tools you have for evaluating the performance of a classifier.\n\nThe following cell does some processing on the validation data and then creates the matrix with the `confusion_matrix` function included in [`scikit-learn`](https://scikit-learn.org/stable/index.html).","metadata":{}},{"cell_type":"code","source":"cmdataset = get_validation_dataset(ordered=True)\n\nimages_ds = cmdataset.map(lambda image, label: image)\nlabels_ds = cmdataset.map(lambda image, label: label).unbatch()\n\ncm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy()\n\ncm_probabilities = model.predict(cmdataset)\nprint(cm_probabilities)\nprint(len(cm_probabilities))\nprint(tf.shape(cm_probabilities))\ncm_predictions = np.argmax(cm_probabilities, axis=-1)\nprint(cm_predictions)\n\nlabels = range(len(CLASSES))\ncmat = confusion_matrix(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n)\ncmat = (cmat.T / cmat.sum(axis=1)).T # normalize","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.780535Z","iopub.status.idle":"2024-03-08T22:26:42.781155Z","shell.execute_reply.started":"2024-03-08T22:26:42.780883Z","shell.execute_reply":"2024-03-08T22:26:42.780905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You might be familiar with metrics like [F1-score](https://en.wikipedia.org/wiki/F1_score) or [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall). This cell will compute these metrics and display them with a plot of the confusion matrix. (These metrics are defined in the Scikit-learn module `sklearn.metrics`; we've imported them in the helper script for you.)","metadata":{}},{"cell_type":"code","source":"score = f1_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\nprecision = precision_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\nrecall = recall_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\ndisplay_confusion_matrix(cmat, score, precision, recall)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.782774Z","iopub.status.idle":"2024-03-08T22:26:42.783252Z","shell.execute_reply.started":"2024-03-08T22:26:42.783002Z","shell.execute_reply":"2024-03-08T22:26:42.783023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visual Validation ##\n\nIt can also be helpful to look at some examples from the validation set and see what class your model predicted. This can help reveal patterns in the kinds of images your model has trouble with.\n\nThis cell will set up the validation set to display 20 images at a time -- you can change this to display more or fewer, if you like.","metadata":{}},{"cell_type":"code","source":"dataset = get_validation_dataset()\ndataset = dataset.unbatch().batch(20)\nbatch = iter(dataset)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.785441Z","iopub.status.idle":"2024-03-08T22:26:42.785880Z","shell.execute_reply.started":"2024-03-08T22:26:42.785641Z","shell.execute_reply":"2024-03-08T22:26:42.785657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"And here is a set of flowers with their predicted species. Run the cell again to see another set.","metadata":{}},{"cell_type":"code","source":"images, labels = next(batch)\nprobabilities = model.predict(images)\npredictions = np.argmax(probabilities, axis=-1)\ndisplay_batch_of_images((images, labels), predictions)","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.787330Z","iopub.status.idle":"2024-03-08T22:26:42.787652Z","shell.execute_reply.started":"2024-03-08T22:26:42.787492Z","shell.execute_reply":"2024-03-08T22:26:42.787505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 8: Make Test Predictions #\n\nOnce we're satisfied with everything, you're ready to make predictions on the test set.","metadata":{}},{"cell_type":"code","source":"test_ds = get_test_dataset(ordered=True)\n\nprint('Computing predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\nprobabilities = model.predict(test_images_ds)\npredictions = np.argmax(probabilities, axis=-1)\nprint(predictions)\nprint(len(predictions))","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.789003Z","iopub.status.idle":"2024-03-08T22:26:42.789332Z","shell.execute_reply.started":"2024-03-08T22:26:42.789165Z","shell.execute_reply":"2024-03-08T22:26:42.789178Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll generate a file `submission.csv`. This file is what you'll submit to get your score on the leaderboard.","metadata":{}},{"cell_type":"code","source":"print('Generating submission.csv file...')\n\n# Get image ids from test set and convert to unicode\ntest_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\ntest_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U')\n\nprint(len(test_ids))\n\n# Write the submission file\nnp.savetxt(\n    'submission.csv',\n    np.rec.fromarrays([test_ids, predictions]),\n    fmt=['%s', '%d'],\n    delimiter=',',\n    header='id,label',\n    comments='',\n)\n\n# Look at the first few predictions\n!head submission.csv","metadata":{"execution":{"iopub.status.busy":"2024-03-08T22:26:42.790880Z","iopub.status.idle":"2024-03-08T22:26:42.791257Z","shell.execute_reply.started":"2024-03-08T22:26:42.791075Z","shell.execute_reply":"2024-03-08T22:26:42.791090Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 9: Make a submission #\n\nThe final step is to create a submission to the competition to get our model's performance on the testing dataset. We do this by doing the following:\n\n1. Begin by clicking on the blue **Save Version** button in the top right corner of the window.  This will generate a pop-up window.  \n2. Ensure that the **Save and Run All** option is selected, and then click on the blue **Save** button.\n3. This generates a window in the bottom left corner of the notebook.  After it has finished running, click on the number to the right of the **Save Version** button.  This pulls up a list of versions on the right of the screen.  Click on the ellipsis **(...)** to the right of the most recent version, and select **Open in Viewer**.  This brings you into view mode of the same page. You will need to scroll down to get back to these instructions.\n4. Click on the **Output** tab on the right of the screen.  Then, click on the file you would like to submit, and click on the blue **Submit** button to submit your results to the leaderboard.\n","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161321) to chat with other Learners.*","metadata":{}}]}