目录
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)