def get_optuna_conext(cls, training_name: str, trial: Trial) -> Context:
"""トライアルの開始準備をする。Objective関数の最初に呼び出してください。
Parameters:
trial : 開始するトライアルオブジェクト
"""
print("get_context", trial)
cls.trial = trial
context = Context.init_context(training_name)
context[Context.OPTUNA] = True
context[Context.OPTUNA_TRIAL] = trial
return context
def pretext_dataset(dataset:tf.data.Dataset, start_label:int)->tf.data.Dataset:
filtered = dataset.filter(lambda data:data['label'] >= start_label)
def supervised_transform(data):
image = data['image']
image = tf.cast(image, tf.float32)
image = image / 255.0
def random_transform(image):
pass
if __name__ == '__main__':
context = Context.init_context(TRAINING_NAME='')
# ENABLE_SUSPEND_RESUME_TRAINING()
BATCH_SIZE = 500
CLASS_NUM = 10
IMAGE_SIZE = 28
EPOCHS = 2
SHUFFLE_SIZE = 1000
# if IS_SUSPEND_RESUME_TRAIN() == True and IS_ON_COLABOLATORY_WITH_GOOGLE_DRIVE()== True:
from tftk.image.dataset import Mnist
from tftk.image.dataset import ImageDatasetUtil
from tftk.image.model.classification import SimpleClassificationModel
from tftk.callback import CallbackBuilder
from tftk.optimizer import OptimizerBuilder
from tftk import Context
from tftk.train.image import ImageTrain
from tftk import ENABLE_SUSPEND_RESUME_TRAIN, IS_SUSPEND_RESUME_TRAIN, ResumeExecutor
if __name__ == '__main__':
context = Context.init_context(
TRAINING_NAME="20200519141141") # .TRAINING_NAME:})
ENABLE_SUSPEND_RESUME_TRAIN()
BATCH_SIZE = 500
CLASS_NUM = 10
IMAGE_SIZE = 28
EPOCHS = 20
SHUFFLE_SIZE = 1000
train, train_len = Mnist.get_train_dataset()
validation, validation_len = Mnist.get_test_dataset()
train = train.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
validation = validation.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop")
model = SimpleClassificationModel.get_model(input_shape=(IMAGE_SIZE,
from tftk.image.dataset import Mnist
from tftk.image.dataset import ImageDatasetUtil
from tftk.image.model.classification import SimpleClassificationModel
from tftk.callback import CallbackBuilder
from tftk.optimizer import OptimizerBuilder
from tftk import Context
from tftk.train.image import ImageTrain
from tftk import ENABLE_SUSPEND_RESUME_TRAINING, ResumeExecutor
if __name__ == '__main__':
context = Context.init_context(TRAINING_NAME='mnist_y')
# ENABLE_SUSPEND_RESUME_TRAINING()
BATCH_SIZE = 500
CLASS_NUM = 10
IMAGE_SIZE = 28
EPOCHS = 2
SHUFFLE_SIZE = 1000
# if IS_SUSPEND_RESUME_TRAIN() == True and IS_ON_COLABOLATORY_WITH_GOOGLE_DRIVE()== True:
train, train_len = Mnist.get_train_dataset()
validation, validation_len = Mnist.get_test_dataset()
train = train.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
validation = validation.map(ImageDatasetUtil.image_reguralization()).map(
ImageDatasetUtil.one_hot(CLASS_NUM))
from tftk.image.dataset import Mnist
from tftk.image.dataset import ImageDatasetUtil
from tftk.image.model.classification import SimpleClassificationModel
from tftk.callback import CallbackBuilder
from tftk.optimizer import OptimizerBuilder
from tftk import Context
from tftk.train.image import ImageTrain
from tftk import ENABLE_SUSPEND_RESUME_TRAIN, IS_SUSPEND_RESUME_TRAIN, ResumeExecutor
if __name__ == '__main__':
context = Context.init_context(
TRAINING_BASE_DIR="tmp",
TRAINING_NAME="resume_test"
)
ENABLE_SUSPEND_RESUME_TRAIN()
BATCH_SIZE = 500
CLASS_NUM = 10
IMAGE_SIZE = 28
EPOCHS = 3
SHUFFLE_SIZE = 1000
BASEDIR = "./tmp"
TRAIN_NAME = "resume_test"
# if IS_SUSPEND_RESUME_TRAIN() == True and IS_ON_COLABOLATORY_WITH_GOOGLE_DRIVE()== True:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tftk
from tftk.image.dataset import Food1o1
from tftk.image.dataset import ImageDatasetUtil
from tftk.image.model import KerasResNet50V2
from tftk.train.image import ImageTrain
from tftk.image.augument import ImageAugument
from tftk.callback import CallbackBuilder
from tftk.optimizer import OptimizerBuilder
from tftk import Context
if __name__ == '__main__':
context = Context.init_context(TRAINING_BASE_DIR="tmp",
TRAINING_NAME="food101")
tftk.USE_MIXED_PRECISION()
BATCH_SIZE = 64
CLASS_NUM = 101
IMAGE_SIZE = 224
CHANNELS = 3
EPOCHS = 100
SHUFFLE_SIZE = 1000
train, train_len = Food1o1.get_train_dataset()
validation, validation_len = Food1o1.get_validation_dataset()
train = train.map(ImageDatasetUtil.resize_with_crop_or_pad(
IMAGE_SIZE, IMAGE_SIZE),
from tftk.image.model import SimpleClassificationModel
from tftk.train.image import ImageTrain
from tftk.callback import CallbackBuilder
from tftk.optimizer import OptimizerBuilder
if __name__ == '__main__':
CLASS_NUM = 10
IMAGE_SIZE = 150
IMAGE_CHANNELS = 3
EPOCHS = 100
BATCH_SIZE = 100
tftk.ENABLE_MIXED_PRECISION()
context = Context.init_context(TRAINING_NAME='DogsVsCats')
train, train_len = ImageLabelFolderDataset.get_train_dataset(
name="dogs-vs-cats", manual_dir="tmp")
validation, validation_len = ImageLabelFolderDataset.get_validation_dataset(
name="dogs-vs-cats", manual_dir="tmp")
train = train.map(ImageDatasetUtil.map_max_square_crop_and_resize(
IMAGE_SIZE, IMAGE_SIZE),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train = train.map(ImageAugument.randaugment_map(2, 4),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train = train.map(ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train = train.map(ImageDatasetUtil.one_hot(CLASS_NUM),
num_parallel_calls=tf.data.experimental.AUTOTUNE)