def __init__(self):
# Variables to hold the description of the experiment
self.description = "Training configuration file for the RGB version of the ResNet50 network."
# System dependent variable
self._workers = 10
self._multiprocessing = True
# Variables for comet.ml
self._project_name = "jpeg-deep"
self._workspace = "classification_resnet50"
# Network variables
self._weights = None
self._network = ResNet50()
# Training variables
self._epochs = 90
self._batch_size = 32
self._steps_per_epoch = 1281167 // self.batch_size
self._validation_steps = 50000 // self._batch_size
self.optimizer_parameters = {"lr": 0.0125, "momentum": 0.9}
self._optimizer = SGD(**self.optimizer_parameters)
self._loss = categorical_crossentropy
self._metrics = ['accuracy', 'top_k_categorical_accuracy']
self.train_directory = join(environ["DATASET_PATH_TRAIN"], "train")
self.validation_directory = join(environ["DATASET_PATH_VAL"],
"validation")
self.test_directory = join(environ["DATASET_PATH_TEST"], "validation")
self.index_file = "data/imagenet_class_index.json"
# Defining the transformations that will be applied to the inputs.
self.train_transformations = [
SmallestMaxSize(256),
RandomCrop(224, 224),
HorizontalFlip()
]
self.validation_transformations = [
SmallestMaxSize(256), CenterCrop(224, 224)
]
self.test_transformations = [SmallestMaxSize(256)]
# Keras stuff
self._callbacks = []
self._train_generator = None
self._validation_generator = None
self._test_generator = None
# Stuff for display
self._displayer = ImageNetDisplayer(self.index_file)
def __init__(self):
# Variables to hold the description of the experiment
self.description = "Training configuration file for the VGG deconvolution network."
# System dependent variable
self._workers = 5
self._multiprocessing = True
# Variables for comet.ml
self._project_name = "jpeg_deep"
self._workspace = "classification_dct_deconv"
# Network variables
self._weights = None
self._network = VGG16_dct_deconv()
# Training variables
self._epochs = 180
self._batch_size = 64
self._steps_per_epoch = 1281167 // self._batch_size
self._validation_steps = 50000 // self._batch_size
self.optimizer_parameters = {
"lr": 0.0025, "momentum": 0.9}
self._optimizer = SGD(**self.optimizer_parameters)
self._loss = categorical_crossentropy
self._metrics = ['accuracy', 'top_k_categorical_accuracy']
self.train_directory = join(
environ["DATASET_PATH_TRAIN"], "train")
self.validation_directory = join(
environ["DATASET_PATH_VAL"], "validation")
self.test_directory = join(
environ["DATASET_PATH_TEST"], "validation")
self.index_file = "data/imagenet_class_index.json"
# Defining the transformations that will be applied to the inputs.
self.train_transformations = [
SmallestMaxSize(256),
RandomCrop(224, 224),
HorizontalFlip()
]
self.validation_transformations = [
SmallestMaxSize(256), CenterCrop(224, 224)]
self.test_transformations = [SmallestMaxSize(256)]
# Keras stuff
self.reduce_lr_on_plateau = ReduceLROnPlateau(patience=5, verbose=1)
self.terminate_on_nan = TerminateOnNaN()
self.early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=11)
self._callbacks = [self.reduce_lr_on_plateau,
self.terminate_on_nan, self.early_stopping]
# Creating the training and validation generator
self._train_generator = None
self._validation_generator = None
self._test_generator = None
self._displayer = ImageNetDisplayer(self.index_file)