def __init__(self, training_name, data_path, training_results_path):
super().__init__(training_name, data_path, training_results_path)
# Config of the data
self.data_dataset = FashionMNISTDataset
# Config of the model
self.model_model = ImageClassifierSimple
# Config for training
self.training_loss = SparseCrossEntropyLossFromLogits
self.training_optimizer = smart_optimizer(SGD)
self.training_trainer = SupervisedTrainer
self.training_epochs = 10
self.training_batch_size = 32
def __init__(self, training_name, data_path, training_results_path):
super().__init__(training_name, data_path, training_results_path)
# Config of the data
self.data_dataset = FashionMNISTDataset
# Config of the model
self.model_model = lambda config: Module.create_from_file(
"deeptech/examples/mnist_model.json",
"MNISTModel",
num_classes=10,
logits=True)
# Config for training
self.training_loss = SparseCrossEntropyLossFromLogits
self.training_optimizer = smart_optimizer(SGD)
self.training_trainer = SupervisedTrainer
self.training_epochs = 10
self.training_batch_size = 32
def __init__(self, training_name, data_path, training_results_path):
super().__init__(training_name, data_path, training_results_path)
# Config of the data
self.data_dataset = lambda split: COCODataset(
split, COCODataset.InputType, FasterRCNNOutput)
self.data_version = 2014
self.data_image_size = (800, 600)
# Config of the model
self.model_categories = [] # Fill from dataset.
self.model_log_delta_preds = False
self.model_model = lambda: Module.create(
"FasterRCNN",
num_classes=len(self.model_categories),
log_deltas=self.model_log_delta_preds)
# Config for training
self.training_loss = self.create_loss
self.training_optimizer = smart_optimizer(SGD, momentum=0.9)
self.training_trainer = SupervisedTrainer
self.training_epochs = 10
self.training_batch_size = 1
self.training_initial_lr = 0.001