def train(trial: Trial):
context = Optuna.get_optuna_conext('minist_optuna', trial)
print("New trial ", trial.number, "++++++++++++++++++++++++++++", context)
ENABLE_SUSPEND_RESUME_TRAINING()
print(context)
Optuna.suggest_float(name='lr', low=1e-6, high=1e-2, log=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))
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop",
lr=Optuna.get_value(
'lr', default=0.1))
model = SimpleClassificationModel.get_model(input_shape=(IMAGE_SIZE,
IMAGE_SIZE, 1),
classes=CLASS_NUM)
callbacks = CallbackBuilder.get_callbacks(tensorboard=True,
reduce_lr_on_plateau=True,
reduce_patience=5,
reduce_factor=0.25,
early_stopping_patience=16)
history = TrainingExecutor.train_classification(
train_data=train,
train_size=train_len,
batch_size=BATCH_SIZE,
validation_data=validation,
validation_size=validation_len,
shuffle_size=SHUFFLE_SIZE,
model=model,
callbacks=callbacks,
optimizer=optimizer,
loss="categorical_crossentropy",
max_epoch=EPOCHS)
return history.history['val_loss'][-1]
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,
IMAGE_SIZE, 1),
classes=CLASS_NUM)
callbacks = CallbackBuilder.get_callbacks(tensorboard=False,
reduce_lr_on_plateau=True,
reduce_patience=3,
reduce_factor=0.25,
early_stopping_patience=5)
ImageTrain.train_image_classification(train_data=train,
train_size=train_len,
batch_size=BATCH_SIZE,
validation_data=validation,
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:
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,IMAGE_SIZE,1),classes=CLASS_NUM)
callbacks = CallbackBuilder.get_callbacks(tensorboard=False, reduce_lr_on_plateau=True,reduce_patience=5,reduce_factor=0.25,early_stopping_patience=16)
ImageTrain.train_image_classification(train_data=train,train_size=train_len,batch_size=BATCH_SIZE,validation_data=validation,validation_size=validation_len,shuffle_size=SHUFFLE_SIZE,model=model,callbacks=callbacks,optimizer=optimizer,loss="categorical_crossentropy",max_epoch=EPOCHS)
BATCH_SIZE = 10
# BATCH_SIZE = 48
CLASS_NUM = 2
IMAGE_SIZE = 150
EPOCHS = 100
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")
# dataset,dataset_len = CatsVsDogs.get_train_dataset()
# dataset = dataset.map(ImageDatasetUtil.resize_with_crop_or_pad(IMAGE_SIZE,IMAGE_SIZE))
# (train, train_len), (validation, validation_len) = ImageDatasetUtil.devide_train_validation(dataset,dataset_len,0.90)
train = train.map(ImageDatasetUtil.resize_with_crop_or_pad(IMAGE_SIZE,IMAGE_SIZE),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageAugument.randaugment_map(1,2))
train = train.map(ImageDatasetUtil.image_reguralization(),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.one_hot(CLASS_NUM),num_parallel_calls=tf.data.experimental.AUTOTUNE).apply(ImageAugument.mixup_apply(200,0.1))
validation = validation.map(ImageDatasetUtil.resize_with_crop_or_pad(IMAGE_SIZE,IMAGE_SIZE),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.image_reguralization(),num_parallel_calls=tf.data.experimental.AUTOTUNE).map(ImageDatasetUtil.one_hot(CLASS_NUM),num_parallel_calls=tf.data.experimental.AUTOTUNE)
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop")
model = ResNetD18.get_model(input_shape=(IMAGE_SIZE,IMAGE_SIZE,3),classes=CLASS_NUM,resnest=True,resnet_c=True,resnet_d=True,mish=True)
# model = SimpleClassificationModel.get_model(input_shape=(IMAGE_SIZE,IMAGE_SIZE,3),classes=CLASS_NUM)
callbacks = CallbackBuilder.get_callbacks(base_dir = "tmp" , tensorboard=True, save_weights=True, consine_annealing=False, reduce_lr_on_plateau=True,reduce_patience=5,reduce_factor=0.25,early_stopping_patience=8)
Trainer.train_classification(train_data=train,train_size=train_len,batch_size=BATCH_SIZE,validation_data=validation,validation_size=validation_len,shuffle_size=1000,model=model,callbacks=callbacks,optimizer=optimizer,loss="binary_crossentropy",max_epoch=EPOCHS)
"""
train, train_len = RockPaperScissors.get_train_dataset()
validation, validation_len = Place365Small.get_validation_dataset()
train = train.map(ImageDatasetUtil.resize_with_crop_or_pad(IMAGE_SIZE,IMAGE_SIZE))
validation = validation.map(ImageDatasetUtil.resize_with_crop_or_pad(IMAGE_SIZE,IMAGE_SIZE))
dataset, len = MVTecAd.get_train_dataset(type="bottle")
print(dataset)
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)
validation = validation.map(
ImageDatasetUtil.map_max_square_crop_and_resize(
IMAGE_SIZE, IMAGE_SIZE),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
validation = validation.map(
ImageDatasetUtil.image_reguralization(),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
validation = validation.map(
ImageDatasetUtil.one_hot(CLASS_NUM),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
optimizer = OptimizerBuilder.get_optimizer(name="rmsprop")
model = KerasResNet18.get_model(input_shape=(IMAGE_SIZE, IMAGE_SIZE,
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tftk
from tftk.image.dataset import Mnist
from tftk.image.dataset import ImageDatasetUtil
from tftk.image.augument import ImageAugument
from tftk.image.dataset import ImageLabelFolderDataset
if __name__ == '__main__':
BATCH_SIZE = 100
CLASS_NUM = 10
IMAGE_SIZE = 28
EPOCHS = 2
SHUFFLE_SIZE = 1000
train, train_len = ImageLabelFolderDataset.get_train_dataset(name="dogs-vs-cats", manual_dir="tmp")
train = train.map(ImageDatasetUtil.resize_with_crop_or_pad(224,224)).map(ImageDatasetUtil.one_hot(2))
train = train.map(ImageDatasetUtil.image_reguralization()).apply(ImageAugument.mixup_apply(mixup_size=100, alpha=0.2))
for d in train.take(1):
image = d["image"] * 255
image = tf.cast(image, tf.uint8)
print(image)
image = image.numpy()
y = d["label"]
print("y",d["label"].numpy())
im = Image.fromarray(image)
im.show()