Kaggle competition: Petals to the Metal - Flower Classification on TPU
<SOS> metric: macrofscore Kaggle data source: petals-to-the-metal-flower-classification-on-tpu<train_model>
!pip install -q efficientnet
X_train = train_data.drop("Survived", axis=1)
 Y_train = train_data["Survived"]
X_test = test_data.drop("PassengerId", axis=1 ).copy()
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, Y_train)
 decision_tree.score(X_train, Y_train )<save_to_csv>
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.keras.applications.densenet import DenseNet121, DenseNet169, DenseNet201
from tensorflow.keras.applications.xception import Xception
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet_v2 import ResNet50V2, ResNet101V2, ResNet152V2
from tensorflow.keras.applications.nasnet import NASNetLarge
from efficientnet.tfkeras import EfficientNetB7, EfficientNetL2, EfficientNetB0, EfficientNetB1
prediction = decision_tree.predict(X_test)
 output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': prediction})
 output.to_csv('my_submission.csv', index=False)
 print("Your submission was saved." )<set_options>
%matplotlib inline
print("Tensorflow version " + tf.__version__ )
warnings.filterwarnings("ignore" )<load_from_csv>
GCS_DS_PATH = KaggleDatasets().get_gcs_path("tpu-getting-started")
 MORE_IMAGES_GCS_DS_PATH = KaggleDatasets().get_gcs_path('tf-flower-photo-tfrec' )
df_train=pd.read_csv(".. /input/covid19-global-forecasting-week-3/train.csv")
 df_test=pd.read_csv(".. /input/covid19-global-forecasting-week-3/test.csv")
 df_sub=pd.read_csv(".. /input/covid19-global-forecasting-week-3/submission.csv" )<load_from_csv>
IMAGE_SIZE = [512, 512]
EPOCHS = 30
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
GCS_PATH_SELECT = {
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]]
MOREIMAGES_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')
 IMAGENET_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/imagenet' + MOREIMAGES_PATH + '/*.tfrec')
 INATURELIST_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/inaturalist' + MOREIMAGES_PATH + '/*.tfrec')
 OPENIMAGE_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/openimage' + MOREIMAGES_PATH + '/*.tfrec')
 TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES + IMAGENET_FILES + INATURELIST_FILES + OPENIMAGE_FILES
SEED = 2020
print(df_train.shape)
 print(df_test.shape)
 print(df_sub.shape )<count_unique_values>
def decode_image(image_data):

 image = tf.image.decode_jpeg(image_data, channels=3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
}
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
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def load_dataset(filenames, labeled=True, ordered=False):

 ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def data_augment(image, label):
flag = random.randint(1,3)
 coef_1 = random.randint(70, 90)* 0.01
coef_2 = random.randint(70, 90)* 0.01
if flag == 1:
image = tf.image.random_flip_left_right(image, seed=SEED)
 elif flag == 2:
image = tf.image.random_flip_up_down(image, seed=SEED)
 else:
image = tf.image.random_crop(image, [int(IMAGE_SIZE[0]*coef_1), int(IMAGE_SIZE[0]*coef_2), 3],seed=SEED)
 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()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset() :
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=False)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 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)
 return dataset
def count_data_items(filenames):
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))
print(f"Unique Countries: {len(df_train.Country_Region.unique())}")
 train_dates=list(df_train.Date.unique())
 print(f"Period : {len(df_train.Date.unique())} days")
 print(f"From : {df_train.Date.min() } To : {df_train.Date.max() }" )<count_values>
LR_START = 0.00001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 8
LR_SUSTAIN_EPOCHS = 0
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr =(LR_MAX - LR_START)/ LR_RAMPUP_EPOCHS * epoch + LR_START
elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:
lr = LR_MAX
else:
lr =(LR_MAX - LR_MIN)* LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)+ LR_MIN
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)
 rng = [i for i in range(EPOCHS)]
y = [lrfn(x)for x in rng]
plt.plot(rng, y)
 print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1]))
print(f"Unique Regions: {df_train.shape[0]/75}")
 df_train.Country_Region.value_counts()<feature_engineering>
def get_model(use_model):
base_model = use_model(weights='noisy-student',
include_top=False, pooling='avg',
input_shape=(*IMAGE_SIZE, 3))
x = base_model.output
predictions = Dense(104, activation='softmax' )(x)
 return Model(inputs=base_model.input, outputs=predictions)
 with strategy.scope() :
model = get_model(EfficientNetB7)
 model.compile(
 optimizer='nadam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
print(f"Number of rows without Country_Region : {df_train.Country_Region.isna().sum() }")
 df_train["UniqueRegion"]=df_train.Country_Region
df_train.UniqueRegion[df_train.Province_State.isna() ==False]=df_train.Province_State+" , "+df_train.Country_Region
df_train[df_train.Province_State.isna() ==False]<drop_column>
history = model.fit(get_training_dataset() ,
steps_per_epoch=STEPS_PER_EPOCH,
epochs=EPOCHS,
callbacks=[lr_callback, ModelCheckpoint(filepath='my_ef_net_b7.h5', monitor='val_loss',
save_best_only=True)],
validation_data=get_validation_dataset() ,
workers = 3 )
df_train.drop(labels=["Id","Province_State","Country_Region"], axis=1, inplace=True)
 df_train<define_variables>
model = tf.keras.models.load_model('my_ef_net_b7.h5' )
<feature_engineering><EOS> 
test_ds = get_test_dataset(ordered=True)
 print('Вычисляем предсказания...')
 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('Создание файла submission.csv...')
 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')
 np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='' )