前言

张量处理单元（TPU）现在可在Kaggle上使用

张量处理单元（TPU）配额现在可以在Kaggle上使用，无需您付费！

TPU是专门用于深度学习任务的强大硬件加速器。它们由Google开发（并首次使用）来处理大型图像数据库，例如从街景视图中提取所有文本。这项比赛是为您尝试TPU而设计的。

最新的Tensorflow版本（TF 2.1）专注于TPU，现在在使用自定义训练循环的模型中，它们都通过Keras高级API和较低级别得到支持。

我们迫不及待地想看看TPU如何加速您的解决方案！

挑战

很难理解我们自然世界的广阔和多样性。

这里有5,000多种哺乳动物，10,000多种鸟类，30,000多种鱼类，而且惊人地有400,000多种不同类型的花。

在这场比赛中，您面临的挑战是建立一个机器学习模型，该模型可识别图像数据集中的花朵类型（为简单起见，我们坚持使用100多种类型的花朵）。

Getting started with 100+ flowers on TPU

import math, re, os import tensorflow as tf import numpy as np from matplotlib import pyplot as plt from kaggle_datasets import KaggleDatasets from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix print("Tensorflow version " + tf.__version__) AUTO = tf.data.experimental.AUTOTUNE 

TPU or GPU detection

# Detect hardware, return appropriate distribution strategy try:     tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case. print('Running on TPU ', tpu.master()) except ValueError:     tpu = None if tpu:     tf.config.experimental_connect_to_cluster(tpu)     tf.tpu.experimental.initialize_tpu_system(tpu)     strategy = tf.distribute.experimental.TPUStrategy(tpu) else:     strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU. print("REPLICAS: ", strategy.num_replicas_in_sync) 

Competition data access

TPUs read data directly from Google Cloud Storage (GCS). This Kaggle utility will copy the dataset to a GCS bucket co-located with the TPU. If you have multiple datasets attached to the notebook, you can pass the name of a specific dataset to the get_gcs_path function. The name of the dataset is the name of the directory it is mounted in. Use !ls /kaggle/input/ to list attached datasets.

GCS_DS_PATH = KaggleDatasets().get_gcs_path() # you can list the bucket with "!gsutil ls $GCS_DS_PATH" 

Configuration

IMAGE_SIZE = [512, 512] # At this size, a GPU will run out of memory. Use the TPU. # For GPU training, please select 224 x 224 px image size. EPOCHS = 12 BATCH_SIZE = 16 * strategy.num_replicas_in_sync  GCS_PATH_SELECT = { # available image sizes 192: GCS_DS_PATH + '/tfrecords-jpeg-192x192', 224: GCS_DS_PATH + '/tfrecords-jpeg-224x224', 331: GCS_DS_PATH + '/tfrecords-jpeg-331x331', 512: GCS_DS_PATH + '/tfrecords-jpeg-512x512' } GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]  TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec') VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec') TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition  CLASSES = ['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea', 'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood', 'globe thistle', # 00 - 09 'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily', # 10 - 19 'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william', # 20 - 29 'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose', # 30 - 39 'barberton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy', 'common dandelion', # 40 - 49 'petunia', 'wild pansy', 'primula', 'sunflower', 'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia', # 50 - 59 'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris', 'windflower', 'tree poppy', # 60 - 69 'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium', # 70 - 79 'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily', # 80 - 89 'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower', # 90 - 99 'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose'] # 100 - 102 

Visualization utilities

data -> pixels, nothing of much interest for the machine learning practitioner in this section.

# numpy and matplotlib defaults np.set_printoptions(threshold=15, linewidth=80) def batch_to_numpy_images_and_labels(data):     images, labels = data     numpy_images = images.numpy()     numpy_labels = labels.numpy() if numpy_labels.dtype == object: # binary string in this case, these are image ID strings         numpy_labels = [None for _ in enumerate(numpy_images)] # If no labels, only image IDs, return None for labels (this is the case for test data) return numpy_images, numpy_labels  def title_from_label_and_target(label, correct_label): if correct_label is None: return CLASSES[label], True     correct = (label == correct_label) return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"u2192" if not correct else '',                                 CLASSES[correct_label] if not correct else ''), correct  def display_one_flower(image, title, subplot, red=False, titlesize=16):     plt.subplot(*subplot)     plt.axis('off')     plt.imshow(image) if len(title) > 0:         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)) return (subplot[0], subplot[1], subplot[2]+1) def display_batch_of_images(databatch, predictions=None): """This will work with:     display_batch_of_images(images)     display_batch_of_images(images, predictions)     display_batch_of_images((images, labels))     display_batch_of_images((images, labels), predictions)     """ # data     images, labels = batch_to_numpy_images_and_labels(databatch) if labels is None:         labels = [None for _ in enumerate(images)] # auto-squaring: this will drop data that does not fit into square or square-ish rectangle     rows = int(math.sqrt(len(images)))     cols = len(images)//rows              # size and spacing     FIGSIZE = 13.0     SPACING = 0.1     subplot=(rows,cols,1) if rows < cols:         plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows)) else:         plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE)) # display for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):         title = '' if label is None else CLASSES[label]         correct = True if predictions is not None:             title, correct = title_from_label_and_target(predictions[i], label)         dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images         subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize) #layout     plt.tight_layout() if label is None and predictions is None:         plt.subplots_adjust(wspace=0, hspace=0) else:         plt.subplots_adjust(wspace=SPACING, hspace=SPACING)     plt.show() def display_confusion_matrix(cmat, score, precision, recall):     plt.figure(figsize=(15,15))     ax = plt.gca()     ax.matshow(cmat, cmap='Reds')     ax.set_xticks(range(len(CLASSES)))     ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})     plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")     ax.set_yticks(range(len(CLASSES)))     ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})     plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor")     titlestring = "" if score is not None:         titlestring += 'f1 = {:.3f} '.format(score) if precision is not None:         titlestring += 'nprecision = {:.3f} '.format(precision) if recall is not None:         titlestring += 'nrecall = {:.3f} '.format(recall) if len(titlestring) > 0:         ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'#804040'})     plt.show() def display_training_curves(training, validation, title, subplot): if subplot%10==1: # set up the subplots on the first call         plt.subplots(figsize=(10,10), facecolor='#F0F0F0')         plt.tight_layout()     ax = plt.subplot(subplot)     ax.set_facecolor('#F8F8F8')     ax.plot(training)     ax.plot(validation)     ax.set_title('model '+ title)     ax.set_ylabel(title) #ax.set_ylim(0.28,1.05)     ax.set_xlabel('epoch')     ax.legend(['train', 'valid.']) 

Datasets

def decode_image(image_data):     image = tf.image.decode_jpeg(image_data, channels=3)     image = tf.cast(image, tf.float32) / 255.0 # convert image to floats in [0, 1] range     image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU return image  def read_labeled_tfrecord(example):     LABELED_TFREC_FORMAT = { "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring "class": tf.io.FixedLenFeature([], tf.int64), # shape [] means single element }     example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)     image = decode_image(example['image'])     label = tf.cast(example['class'], tf.int32) return image, label # returns a dataset of (image, label) pairs def read_unlabeled_tfrecord(example):     UNLABELED_TFREC_FORMAT = { "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring "id": tf.io.FixedLenFeature([], tf.string), # shape [] means single element # class is missing, this competitions's challenge is to predict flower classes for the test dataset }     example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)     image = decode_image(example['image'])     idnum = example['id'] return image, idnum # returns a dataset of image(s) def load_dataset(filenames, labeled=True, ordered=False): # Read from TFRecords. For optimal performance, reading from multiple files at once and # disregarding data order. Order does not matter since we will be shuffling the data anyway.      ignore_order = tf.data.Options() if not ordered:         ignore_order.experimental_deterministic = False # disable order, increase speed      dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files     dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order     dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO) # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False return dataset  def data_augment(image, label): # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below), # this happens essentially for free on TPU. Data pipeline code is executed on the "CPU" part # of the TPU while the TPU itself is computing gradients.     image = tf.image.random_flip_left_right(image) #image = tf.image.random_saturation(image, 0, 2) return image, label     def get_training_dataset():     dataset = load_dataset(TRAINING_FILENAMES, labeled=True)     dataset = dataset.map(data_augment, num_parallel_calls=AUTO)     dataset = dataset.repeat() # the training dataset must repeat for several epochs     dataset = dataset.shuffle(2048)     dataset = dataset.batch(BATCH_SIZE)     dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size) return dataset  def get_validation_dataset(ordered=False):     dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)     dataset = dataset.batch(BATCH_SIZE)     dataset = dataset.cache()     dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size) return dataset  def get_test_dataset(ordered=False):     dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)     dataset = dataset.batch(BATCH_SIZE)     dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size) return dataset  def count_data_items(filenames): # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items     n = [int(re.compile(r"-([0-9]*).").search(filename).group(1)) for filename in filenames] return np.sum(n)  NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES) NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES) NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES) STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES)) 

Dataset visualizations

# data dump print("Training data shapes:") for image, label in get_training_dataset().take(3): print(image.numpy().shape, label.numpy().shape) print("Training data label examples:", label.numpy()) print("Validation data shapes:") for image, label in get_validation_dataset().take(3): print(image.numpy().shape, label.numpy().shape) print("Validation data label examples:", label.numpy()) print("Test data shapes:") for image, idnum in get_test_dataset().take(3): print(image.numpy().shape, idnum.numpy().shape) print("Test data IDs:", idnum.numpy().astype('U')) # U=unicode string 

# Peek at training data training_dataset = get_training_dataset() training_dataset = training_dataset.unbatch().batch(20) train_batch = iter(training_dataset) 

# run this cell again for next set of images display_batch_of_images(next(train_batch)) 

# peer at test data test_dataset = get_test_dataset() test_dataset = test_dataset.unbatch().batch(20) test_batch = iter(test_dataset) 

# run this cell again for next set of images display_batch_of_images(next(test_batch)) 

Model

Not the best but it converges …

with strategy.scope(): #pretrained_model = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3]) #pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])     pretrained_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])     pretrained_model.trainable = False # False = transfer learning, True = fine-tuning          model = tf.keras.Sequential([         pretrained_model,         tf.keras.layers.GlobalAveragePooling2D(),         tf.keras.layers.Dense(len(CLASSES), activation='softmax') ])          model.compile(     optimizer='adam',     loss = 'sparse_categorical_crossentropy',     metrics=['sparse_categorical_accuracy'] ) model.summary() 

Training

history = model.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset()) 

display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 211) display_training_curves(history.history['sparse_categorical_accuracy'], history.history['val_sparse_categorical_accuracy'], 'accuracy', 212) 

Confusion matrix

cmdataset = get_validation_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and labels, order matters. images_ds = cmdataset.map(lambda image, label: image) labels_ds = cmdataset.map(lambda image, label: label).unbatch() cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() # get everything as one batch cm_probabilities = model.predict(images_ds) cm_predictions = np.argmax(cm_probabilities, axis=-1) print("Correct   labels: ", cm_correct_labels.shape, cm_correct_labels) print("Predicted labels: ", cm_predictions.shape, cm_predictions) 

cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES))) score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro') precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro') recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro') cmat = (cmat.T / cmat.sum(axis=1)).T # normalized display_confusion_matrix(cmat, score, precision, recall) print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall)) 

Predictions

test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters. print('Computing predictions...') test_images_ds = test_ds.map(lambda image, idnum: image) probabilities = model.predict(test_images_ds) predictions = np.argmax(probabilities, axis=-1) print(predictions) print('Generating submission.csv file...') test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch() test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') # all in one batch np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='') !head submission.csv 

Visual validation

dataset = get_validation_dataset() dataset = dataset.unbatch().batch(20) batch = iter(dataset) 

# run this cell again for next set of images images, labels = next(batch) probabilities = model.predict(images) predictions = np.argmax(probabilities, axis=-1) display_batch_of_images((images, labels), predictions)